PREDICTION OF HETEROSIS AND OTHER TRAITS BY TRANSCRIPTOME ANALYSIS

This invention relates to methods of producing hybrid plants and hybrid non-human animals having high levels of hybrid vigour or heterosis and/or producing plants and non-human animals (e.g. hybrid, inbred or recombinant plants) having other traits such as desired flowering time, seed oil content and/or seed fatty acid ratios, and plants and non-human animals produced by these methods.

The invention relates to selection of suitable organisms, preferably plants or non-human animals, for use in producing hybrids and/or for use in breeding programmes, e.g. screening of germplasm collections for plants that may be suitable for inclusion in breeding programmes.

Many animal and plant species exhibit increased growth rates, reach larger sizes and, in the cases of crops [1,2] and farm animals [3,4], have higher yields and productivity when bred as hybrids, produced by crossing genetically dissimilar parents, a phenomenon known as hybrid vigour or heterosis [5]. The term heterosis can be applied to almost any aspect of biology in which a hybrid can be described as outperforming its parents.

The degree of heterosis observed varies a lot between different hybrids. The magnitude of heterosis can be described relative to the mean value of the parents (Mid-Parent Heterosis, MPH) or relative to the “better” of the parents (Best-Parent Heterosis, BPH).

Heterosis is of great importance in many agricultural crops and in plant and animal breeding, where it is clearly desirable to produce hybrids with high levels of heterosis. However, despite extensive genetic analysis in this area, the molecular mechanisms underlying heterosis remain poorly understood. Some progress has been made towards understanding the heterosis observed in simple traits controlled by single genes [6], but the mechanisms controlling more complex forms of heterosis, such as the vegetative vigour of hybrids, remain unknown [7, 8, 9].

Genetic analyses of heterosis have led to three, non-exclusive, genetic mechanisms being hypothesised to explain heterosis:

the “dominance” model, in which heterotic interactions are considered to be the cumulative effect of the phenotypic expression of dispersed dominant alleles, whereby deleterious alleles that are homozygous in the respective parents are complemented in the hybrids [2, 10];

the “overdominance” model, in which heterotic interactions are considered to be the result of heterozygous loci resulting in a phenotypic expression in excess of either parent, so that the heterozygosity per se produces heterosis [5, 11, 12];

the “epistatic” model, which includes other types of specific interactions between combinations of alleles at separate loci [13, 14].

Hypothetical models based on gene regulatory networks have been proposed to explain these types of interaction [15].

Whilst the hypothesised models attempt to explain in genetic terms at least a proportion of heterosis observed in hybrids, they do not provide a practical indicator that would enable breeders to predict quantitatively the level of heterosis for a given hybrid or to know which hybrid crosses are likely to perform well.

In allogamous crops, such as maize, heterotic groups have been established that enable the selection of inbreds that will show good heterosis when crossed. For example, Iowa Stiff Stalk vs. Non-Stiff Stalk lines [16]. Inter-group hybrids have greater genetic distance and heterosis than hybrids produced by crossing within an individual heterotic group [17] and it has been proposed that the level of genetic diversity may be a predictor of heterosis and yield [18]. However, this has not proven to be a reliable approach for the prediction of heterosis in crops [17]. Heterosis shows an inconsistent relationship with the degree of relatedness of the two parents, with an absence of correlation reported between heterosis and genetic distance in Arabidopsis thaliana [7, 19] and other species [20, 21, 22]. Thus, in general the level of heterosis observed in a hybrid does not depend solely upon the genetic distance between the two parents from which the hybrid was produced, nor does this variable, genetic distance, necessarily provide a good indicator of likely heterosis of hybrids.

At the gene transcript level, expression of alleles in a hybrid may represent the cumulative level of expression of the alleles inherited from each parent, or expression may be non-additive. Non-additive patterns of gene expression are believed to contribute to hybrid effects and therefore several studies have investigated non-additive gene expression in hybrids compared with their parents. Characteristics of the transcriptome (the contribution to the mRNA pool of each gene in the genome) have been analysed in heterotic hybrids of crop plants, and extensive differences in gene expression in the hybrids relative to the parents have been reported [23, 24, 25, 26, 27]. Hybrid transcriptomes were shown to be different from the transcriptomes of the parents. Quantitative changes were seen in the contribution to the mRNA pool of a subset of genes, when the transcriptomes of the hybrids were compared with the transcriptomes of their parents. These experiments were conducted with the expectation that differences in the transcriptomes of the hybrids, compared with their parents, contribute to the basis of heterosis.

Using differential display, Sun et al [24] identified differences in gene expression, of approximately 965 genes, between wheat seedling hybrids and their parents. The hybrids were generated from two single direction crosses, and represented one heterotic and one non-heterotic sample. Differences in gene expression were found between the hybrids and the parents, with some evidence provided of differences in response between the hybrids. In later experiments, Sun et al [28] used differential display techniques to identify changes in transcriptional remodelling for 2800 genes, between nine parental and 20 wheat hybrids. They found that around 30% of these genes showed some degree of remodelling. Broad trends in gene expression were assessed by random amplification. Gene expression differences were observed between the hybrid and both parents, between the hybrid and one parent only, and genes expressed only in the hybrid. The total number of non-additively expressed genes was found to correlate with some traits. The authors concluded that these differences in gene expression must be involved in developing a heterotic phenotype.

Guo et al. [29] reported allele-specific variation in transcript abundance in hybrids. Transcript abundance of 15 genes was analysed in maize hybrids, and transcript levels for the two alleles of each gene were compared. In 11 genes, the two alleles were found to be expressed unequally (bi-allelic expression), and in 4 genes just one allele was expressed (mono-allelic expression). Allele-specific differences in expression were observed between genetically different hybrids. Additionally, the two alleles in each hybrid were shown to respond differently to abiotic stress. Allele-specific differences may indicate different functions for the two parental alleles in hybrids, and this functional diversity of the two parental alleles in the hybrid was suggested to have an impact on heterosis.

Auger et al. [27] examined differences in transcript abundance between hybrids relative to their inbred parents. Several genes were found to be expressed at non-additive levels in the hybrids, but relevance to heterosis was not demonstrated.

Vuylsteke et al. [30] measured variations in transcript abundance between three inbred lines and two pairs of reciprocal F₁hybrids of Arabidopsis. Non-additive levels of gene expression in the hybrids were used to estimate the proportion of genes expressed in a “dominance” fashion according to a genetic model of heterosis.

Microarray technology has also been used to study differences in transcript abundance across plant populations. For example, Kliebenstein et al. [31] used microarrays to quantify gene expression in seven Arabidopsis accessions, and found an average of 2234 genes to be significantly differentially expressed between any pair of accessions. The differences in gene expression were found to be related to sequence diversity in the accessions. Kirst et al. [32] examined transcript abundance in a pseudobackcross population of eucalyptus in order to compare transcript regulation in different genetic backgrounds of eucalyptus, and concluded that the genetic control of transcript levels was modulated by variation at different regulatory loci in different genetic backgrounds. Paux et al. [33] also conducted transcript profiling of eucalyptus genes, to examine gene expression during tension wood formation.

Another mechanism that has been proposed to explain heterosis is complementation of bottlenecks in metabolic systems [34]. It is possible that several different mechanisms are involved in heterosis, so that any one specific mechanism may only explain a proportion of heterosis observed.

Heterosis has been the subject of intense genetic analysis for almost a century, but no reliable and accurate basis for determining, predicting or influencing the degree of heterosis in a given hybrid has yet been identified. Thus, there has been a long-felt need to identify some basis on which parents may be selected in order to produce hybrids of increased vigour.

Attempts to produce hybrids with high levels of heterosis must currently be undertaken on the basis of trial and error, by experimentally crossing different parents and then waiting for the progeny to grow until it can be seen which of the new hybrids exhibit the most vigour. Breeding for new heterotic hybrids thus necessarily results in the co-production of significant numbers of under-performing hybrids with low hybrid vigour. The desired hybrids may not be obtained, or may only represent a fraction of the total number of hybrids produced overall. Additionally, hybrids must normally reach a certain age before their level of heterosis can be determined, which increases still further the time, cost and resources that must be invested in a breeding program, since it is necessary to continue to grow large numbers of hybrids even though many, or perhaps all, will not have the desired characteristics.

A method that could provide at least some measure of prediction of the level of heterosis likely to be exhibited by a given hybrid could result in significantly more effective breeding programs.

There are comparable needs to determine a basis on which plants or animals may be selected as parents for producing hybrids with further desirable multigenic traits, and for predicting which hybrid, inbred or recombinant plants or animals are likely to exhibit desired traits.

The invention disclosed herein is based on the unexpected finding that transcript abundance of certain genes is predictive of the degree of heterosis in a hybrid. Transcriptome analysis may be used to identify genes whose transcript abundance in hybrids correlates with heterosis. The abundance of those gene transcripts in a new hybrid can then be used to predict the degree of heterosis of the new hybrid. Moreover, transcriptome analysis may be used to identify genes whose transcript abundance in plants or animals correlates with heterosis in hybrids produced by crossing those plants or animals. Thus, transcriptome data from parents can be used to predict the magnitude of heterosis in hybrids which have yet to be produced.

We show herein that changes in transcript abundance in the transcriptome represent the majority of the basis of heterosis. Importantly, this means that predictions based on transcript abundance are close to the observed magnitude of heterosis, i.e. the invention allows quantitative prediction of the degree of heterosis in a hybrid. Transcriptome characteristics alone may thus be used to predict heterosis in hybrids and as a basis for selection of parents.

Thus, remarkably, we have solved a problem that has been unanswered for almost a century. By demonstrating that the basis of heterosis resides primarily at the level of the regulation of transcript abundance, we have provided a means of predicting heterosis in hybrids and thus selecting which hybrids to maintain. Furthermore, we were able to identify characteristics of parental transcriptomes that could be used successfully as markers to predict the magnitude of heterosis in untested hybrids, and we have thus also provided basis for identifying parents which can be crossed to produce heterotic hybrids.

This invention differs from previous studies involving transcriptome analysis of hybrids, since those earlier studies did not identify any relationship between the transcriptomes of hybrids and the degree of heterosis observed in those hybrids. As discussed above, earlier studies showed that transcript levels of some genes differ in hybrids compared with the parents from which those hybrids were derived, and differences between hybrid and parent transcriptome were suggested to contribute to phenotypic differences including heterosis. However, the previous investigators did not compare transcriptome remodelling in a range of non-heterotic hybrids and heterotic hybrids, and did not show whether transcriptome remodelling correlates with heterosis.

We have recognised that most differences in the hybrid transcriptome are due to hybrid formation, not heterosis. We found that, in fact, transcriptome remodelling involving transcript abundance fold-changes of 2 or more occurs to a similar extent in all hybrids relative to their parents, regardless of the degree of heterosis observed in the hybrids. Accordingly, the overall degree of transcriptome remodelling in a hybrid is not an indicator of the degree of heterosis in that hybrid.

Therefore, earlier studies involving limited numbers of hybrids were not able to identify genes whose transcript abundance correlated with heterosis. The vast majority of differences in transcript abundance observed in earlier studies would have been due only to hybrid formation itself, and would not show any correlation with heterosis. Nor was any such correlation even looked for in the prior art, since it was not recognised that a correlation might exist.

However, despite showing that the overall degree of transcriptome remodelling in a hybrid is not related to heterosis, we found that transcriptome analysis can nevertheless be used to reveal features of the hybrid transcriptome that are predictive of the degree of heterosis in a hybrid. Through transcriptome analysis of a wide range of hybrids we have unexpectedly shown that transcript abundance of a proportion of genes correlates with heterosis. As described herein, we studied 13 different heterotic hybrids of Arabidopsis thaliana, and identified features of the hybrid transcriptome that are characteristic of heterotic interactions. We identified 70 genes whose transcript abundance in the hybrid transcriptome correlated with the degree of heterosis in the Arabidopsis hybrids. We then successfully used the transcript abundance of that defined set of 70 genes to quantitatively predict the magnitude of heterosis observed in 3 untested hybrid combinations. Transcript abundance of two additional genes, At1g67500 and At5g45500, was also shown to have a significant negative correlation with heterosis. Transcript abundance of each of these genes successfully predicted heterosis in further hybrids.

Further, we identified a larger set of genes whose transcript abundance in the transcriptome of Arabidopsis inbred lines correlated with the degree of heterosis in hybrid progeny produced by crossing those lines. We successfully used the transcript abundance of that set of genes to quantitatively predict the magnitude of heterosis in 3 hybrids produced from those lines. Transcript abundance of At3g11220 was found to be negatively correlated with heterosis in a highly significant manner and transcript abundance of this gene in the parental transcriptome was found to be predictive of heterosis in hybrid offspring.

Heterosis in hybrids of Arabidopsis thaliana may be predicted on the basis of the transcript abundance of these identified Arabidopsis genes. Moreover, since heterosis is a widely observed phenomenon, and is not restricted to Arabidopsis or even to plants, but is also observed in animals, it is to be expected that many of the same genes whose transcript abundance correlates with heterosis in Arabidopsis will also correlate with heterosis in other organisms. Transcript abundance of orthologues of those genes in other species may thus correlate with heterosis.

However, prediction of heterosis need not be based on genes selected from the sets of genes disclosed herein, since one aspect of the invention is use of transcriptome analysis to identify the particular genes whose transcript abundance correlates with heterosis in any population of hybrids that is of interest. Once identified, those genes may then be used for prediction of heterosis or other trait in the particular hybrids of interest. Whilst the identified genes may include at least some genes, or orthologues thereof, from the set of genes identified in Arabidopsis, they need not do so.

The invention enables hybrids likely to exhibit high levels of heterosis to be identified and selected, while hybrids likely to exhibit lower degrees of heterosis may be discarded. Notably, the invention may be used to predict the level of heterosis in a hybrid at an early stage in the life of the hybrid, for example in a seedling, before it would be possible to directly observe differences between heterotic and non-heterotic hybrids. Thus, the invention may be used in a hybrid whose degree of heterosis is not yet determinable from its phenotype. The invention thus provides significant benefits to a breeder, since it allows a breeder to determine which particular hybrids in a potentially vast array of different hybrids should be retained and grown. For example, a breeder may use transcript abundance data from seedlings to decide which plant hybrids to grow or test in yield/performance trials.

Furthermore, we have shown that regulation of transcript abundance underlies not only heterosis but also other traits. These may include all genetically complex traits in hybrid, inbred or recombinant plants and animals, e.g. flowering time or seed composition in plants. Accordingly, the invention also relates to determining features of plant or non-human animal transcriptomes (e.g. transcriptomes of hybrids and/or inbred or recombinant plants or animals) for prediction of other traits in the plant or animal or offspring thereof. Where the invention relates to traits other than heterosis, the plant or animal may be a hybrid or alternatively it may be inbred or recombinant. Examples of traits that may be predicted using the invention are yield, flowering time, seed oil content and seed fatty acid ratios in plants, especially plant hybrids, e.g. accessions of A. thaliana. These and other traits may also be predicted in the plant or non-human animal (e.g. hybrid, inbred or recombinant plant or animal) before those traits are manifested in the phenotype. Thus, for example, we demonstrate herein that the invention allows seed oil content of inbred plants to be accurately predicted by analysis of plants that have not yet flowered. The invention thus confers significant predictive, cost and workload reductive advantages, particularly for traits manifested at a relatively late stage, since it means that it is not necessary to wait until a plant or animal reaches a particular (often late) stage of development before being able to know the magnitude or properties of the trait that will be exhibited by a given plant or animal.

Other aspects of the invention allow prediction of traits in plants or animals based on characteristics of their parents, and thus traits of plants or animals may be predicted and selected for even before those plants or animals are produced. As noted above, the trait may be heterosis in a plant or animal hybrid. Therefore, in accordance with the invention, features of plant or animal transcriptomes may be identified that allow the degree of heterosis of plants or animals produced by crossing those plants or animals to be predicted. The invention can be used to predict one or more traits, such as the degree of heterosis observed in plants or animals produced by crossing different combinations of parental germplasms. This is potentially as valuable or even more valuable than being able to predict heterosis and other traits in plants and animals that have already been produced, since it avoids producing under-performing plants or animals and therefore allows significant savings in logistics, costs and time. Particular plants or animals may thus be selected for breeding, with an increased chance that their progeny will be heterotic hybrids, or possess other traits, compared with if the parents were selected at random. Thus, the methods of the invention allow prediction in terms of the level of heterosis or of other traits produced by any particular cross between different parents, and allow particular parents to be selected accordingly. For example in agricultural crop plant breeding the invention reduces the need to make large numbers of different crosses in order to obtain new heterotic hybrids, since the invention can be used to identify in advance which particular crosses will be most productive.

Remarkably, methods of the invention may be used to predict traits based on transcript abundance in tissues in which the trait is not exhibited or which have no apparent relevance to the trait. For example, traits such as flowering time or seed composition may be predicted in plants based on transcript abundance data from non-flowering tissue, such as leaf tissue. Thus, the invention allows generation of statistical correlations between one or more traits and abundance of one or more gene transcripts. There is no requirement for the tissue sampled for transcriptome analysis to be the same as that used for trait measurement. It may be preferable that the tissue sampled for transcriptome analysis is, in terms of evolution, be a more ancient origin—hence the transcriptome in leaves can be used to predict more recently evolved characteristics of plants, such as flowering time or seed composition.

Based on the extensive transcriptome remodelling in hybrids of Arabidopsis thaliana disclosed herein, including some combinations that are heterotic for vegetative biomass and some combinations that are non-heterotic, it is evident that the methods of the invention may be applied to advantage in crops of economic importance.

Maize is currently bred as a hybrid crop, with its cultivation in the UK being for silage from the whole plant. Biomass yield is therefore paramount, and heterosis underpins this yield. In the USA maize is primarily grown for corn production, for which kernel weight represents the productive yield, and this yield is also dependent on heterosis. The ability to efficiently select for hybrid performance at an early stage of the hybrid parent breeding process provided by the method of this invention greatly accelerates the development of hybrid plant lines to increase yields and introduce a range of “sustainability” traits from exotic germplasm without loss of yield. Oilseed rape hybrids hold much potential, but their exploitation is limited as heterosis is often restricted to vegetative vigour, with little improvement in seed dry weight yield. The ability to select for specific performance traits at early stages of growth similarly accelerates the development of more productive and sustainable varieties. There is great potential for hybrid breeding of bread wheat (already a hexaploid, so benefits from some “fixed” heterosis) which, like oilseed rape, is supported by a breeding community based in the UK. In addition, hybrid varieties are important for a large number of vegetable species cultivated in the UK (such as cabbages, onions, carrots, peppers, tomatoes, melons), which are grown for enhancement of crop uniformity, appearance and general quality. Use of the invention to define a predictive marker for heterosis and other performance traits thus has the potential to revolutionise both the breeding process and the performance of crops for the farmer.

As demonstrated in the Examples, we identified relationships between gene expression in glasshouse-grown seedlings of maize inbreds and phenotypes (grain yield) in related plants at a later developmental stage and after growth under different environmental conditions.

In summary, the invention involves use of transcriptome analysis of plants or animals, e.g. hybrids and/or inbred or recombinant plants or animals, for:

(i) identifying genes involved in the manifestation of heterosis and other traits; and/or

(ii) predicting and producing plants or animals of improved heterosis and other traits by selecting plants or animals for breeding, wherein the plants or animals which exhibit enhanced transcriptome characteristics with respect to a selected set of genes relevant to the transcriptional regulatory networks present in potential parental breeding partners; and/or

(iii) predicting a range of trait characteristics for plants and animals based on transcriptome characteristics.

The invention also relates to plant and animal hybrids of improved heterosis, and to hybrids, inbreds or recombinants with improved traits as produced or predicted by the methods of the invention.

The results disclosed herein provide evidence for a link between heterosis and growth repression that is a consequence of stress tolerance mechanisms. We identified a number of genes which are highly predictive of heterosis, and which showed a significant negative correlation between gene expression and heterotic performance. As discussed in the Examples herein, these genes may represent key genetic loci that are downregulated in heterotic hybrids, leading to decreased expression of stress-avoidance genes and thus allowing better hybrid performance under favourable conditions. This raises the possibility that heterosis, at least for vegetative biomass, is at least partly a consequence of genetic interactions that lead to a reduction in repression of growth, rather than direct promotion of growth. However, whatever the molecular mechanism underlying heterosis, we have established that certain genes and sets of genes predictive of heterosis may be identified and successfully used in accordance with the present invention for predicting heterosis.

A hybrid is offspring of two parents of differing genetic composition. Thus, a hybrid is a cross between two differing parental germplasms. The parents may be plants or animals. A hybrid is typically produced by crossing a maternal parent with a different paternal parent. In plants, the maternal parent is usually, though not necessarily, impaired in male fertility and the paternal parent is a male fertile pollen donor. Parents may for example be inbred or recombinant.

An inbred plant or animal typically lacks heterozygosity. Inbred plants may be produced by recurrent self-pollination. Inbred animals may be produced by breeding between animals of closely related pedigree.

Recombinant plants or animals are neither hybrid nor inbred. Recombinants are themselves derived by the crossing of genetically dissimilar progenitors and may contain extensive heterozygosity and novel combinations of alleles. Most samples in germplasm collections of plant breeding programmes are recombinant.

The invention may be used with plants or animals. In some embodiments the invention preferably relates to plants. For example, the plants may be crop plants. The crop plants may be cotton, sugar beet, cereal plants (e.g. maize, wheat, barley, rice), oil-seed crops (e.g. soybeans, oilseed rape, sunflowers), fruit or vegetable crop plants (e.g. cabbages, onions, carrots, peppers, tomatoes, melons, legumes, leeks, brassicas e.g. broccoli) or salad crop plants e.g. lettuce [35]. The invention may be applied to hardwood timber trees or alder trees [36]. All species grown as crops could benefit from the invention, irrespective of whether they are currently cultivated extensively as hybrids.

Other embodiments relate to non-human animals e.g. mammals, birds and fish, including farm animals for example cattle, pigs, sheep, birds or poultry (e.g. chickens), goats, and farmed fish e.g. salmon, and other animals such as sports animals e.g. racehorses, racing pigeons, greyhounds or camels. Heterosis has been described in a variety of different animals including for example pigs [37], sheep [38, 39], goats [39], alpaca [39], Japanese quail [40] and salmon [41], and the invention may be applied to these and to other animals.

The invention can most conveniently be used in relation to organisms for which the genome sequence or extensive collections of Expressed Sequence Tags are available and in which microarrays are preferably also available and/or resources for transcriptome analysis have been developed.

In one aspect, the invention is a method comprising:

analysing the transcriptomes of plants or animals in a population of plants or animals;

measuring a trait of the plants or animals in the population; and

identifying a correlation between transcript abundance of one or more, preferably a set of, genes in the plant or animal transcriptomes and the trait in the plants or animals.

Thus the invention provides a method of identifying an indicator of a trait in a plant or animal.

The population may comprise e.g. at least 5, 10, 20, 30, 40, 50 or 100 plants or animals. Use of a large population to obtain trait measurements from many different plants or animals may allow increased accuracy of trait predictions based on correlations identified using the population.

The invention may thus be used to generate a model (e.g. a regression, as described in detail elsewhere herein) for predicting the trait based on transcript abundance of the one or more genes e.g. a set of genes.

One or more traits may be determined or measured, and thus correlations may be identified, and models may be generated, for a plurality of traits.

The plant or animal may be a hybrid, or it may be inbred or recombinant. In a preferred embodiment the plant or animal is a hybrid. A preferred trait is heterosis.

Plants or animals in a population may or may not be related to one another. The population may comprise plants or animals, e.g. hybrids, having different maternal and/or paternal parents. In some embodiments, all plants or animals, e.g. hybrids, in the population have the same maternal parent, but may have different paternal parents. In other embodiments, all plants or animals, e.g. hybrids, in the population have the same paternal parent, but may have different maternal parents. Parents may be inbred or recombinant, as explained elsewhere herein.

Methods for determining heterosis, for transcriptome analysis and for identifying statistical correlations are described in detail elsewhere herein.

Determining or measuring heterosis or other trait can be performed once the relevant phenotype is apparent e.g. once the heterosis can be calculated, or once the trait can be measured.

Transcriptome analysis may be performed at a time when the degree of heterosis or other trait of the plant or animal can be determined. Transcriptome analysis may be performed after, normally directly after, measurements are taken for determining or measuring heterosis or other trait in the plant or animal. This is suitable e.g. when measurements are taken for determining heterosis for fresh weight in hybrids.

However, we have demonstrated herein that it is possible to use transcriptome analysis of plants at a relatively early developmental stage, e.g. before flowering, to identify genes whose transcript abundance correlates with traits that only occur later in development, e.g. traits such as the time of flowering and aspects of the composition of seeds produced by plants. Accordingly, transcriptome analysis may be performed when the degree of heterosis or other trait is not yet determinable from the phenotype. This is suitable e.g. when measuring aspects of performance other than fresh weight, such as yield, for determining heterosis. For example, transcriptome analysis may be performed when plants are in vegetative phase or when animals are pre-adolescent, in order to predict heterosis for characteristics that are evident later in development, or to predict other traits that are evident later in development. For example, heterosis for seed or crop yields, or traits such as flowering time, seed or crop yields or seed composition, may be predicted using transcriptome data from vegetative phase plants.

Correlations between traits and transcript abundance represent models that may be used to predict traits in further plants or animals by determining transcript abundance in those plants or animals.

Thus, in another aspect, the invention is a method comprising:

determining transcript abundance of one or more, preferably a set of, genes in a plant or animal, wherein the transcript abundance of the one or more genes, or set of genes, in the transcriptome of the plant or animal correlates with a trait in the plant or animal; and

thereby predicting the trait in the plant or animal.

The analysis of transcript abundance is predictive of the trait in a plant or animal of the same genotype as the plant or animal in which transcript abundance was determined. Thus, in some embodiments the method may be used for the purpose of predicting a trait in the actual plant or animal whose transcript abundance is determined, and in other embodiments the method may be used for the purpose of predicting a trait in another plant or animal that is genetically identical to the plant or animal whose transcript abundance was sampled. For example the method may be used for predicting a trait in a genetically identical plant or animal that may be grown or produced subsequently, and indeed the decision whether to grow or produce the plant or animal may be informed by the trait prediction.

Methods of the invention may comprise determining transcript abundance of one or more genes, preferably a set of genes, in a plurality of plants or animals, and thus predicting one or more traits in the plurality of plants or animals. Thus, the invention may be used to predict a rank order for the trait in those plants or animals, which allows selection of plants or animals that are predicted to exhibit the highest or lowest trait (e.g. longest or shortest time to flowering, highest seed oil content, highest heterosis).

The plant or animal may be a hybrid, or it may be inbred or recombinant. In a preferred embodiment the plant or animal is a hybrid. A preferred trait is heterosis, and thus the method may be for predicting the magnitude of heterosis in a hybrid.

A method of the invention may comprise:

determining transcript abundance of one or more, preferably a set of, genes in a plant or animal, e.g. a hybrid, wherein transcript abundance of the one or more genes, or set of genes, correlates with a trait in a population of plants or animals, e.g. a population of hybrids; and

thereby predicting the trait in the plant or animal.

Plants or animals in the population may or may not be related to one another. The population typically comprises plants or animals, e.g. hybrids, having different maternal and/or paternal parents. In some embodiments, all plants or animals in the population have the same maternal parent, but may have different paternal parents. In other embodiments, all plants or animals in the population have the same paternal parent, but may have different maternal parents. Where plants or animals in the population share a common maternal parent or a common paternal parent, the plant or animal in which the trait is predicted may share the same common maternal or paternal parent, respectively.

The method may comprise, as an earlier step, a method of identifying an indicator of the trait in a plant or animal, as described above.

The plant or animal in which the indicator of the trait is identified may be the same genus and/or species as the plant or animal in which transcript abundance is determined for prediction of the trait. However, as discussed elsewhere herein, predictions of traits in one species may be performed based on correlations between transcript abundance and trait data obtained in other genus and/or species.

Thus, the invention may be used to predict one or more traits in a plant or animal, typically a previously untested plant or animal. As noted above, the method is useful for predicting heterosis or other trait in a plant or animal when heterosis or other trait is not yet determinable from the phenotype of the organism at the time, age or developmental stage at which the transcriptome is sampled. In a preferred embodiment the method comprises analysing the transcriptome of a plant prior to flowering.

Suitable methods of determining transcript abundance and of predicting heterosis or other traits based on transcript abundance are described in more detail elsewhere herein.

Once genes whose levels of transcript abundance are involved in heterosis or other traits have been identified for a given plant or animal species, further aspects of the invention may involve regulation of transcript abundance, regulation of expression of one or more of those genes, or regulation of one or more proteins encoded by those genes, in order to regulate, influence, increase or decrease heterosis or another trait in a plant or animal organism.

Thus, the invention may involve increasing or decreasing heterosis or other trait in an organism, by upregulating one or more genes or their encoded proteins, wherein transcript abundance of the one or more genes correlates positively with heterosis or other trait in the organism, or by down-regulating one or more genes or their encoded proteins in an organism, wherein transcript abundance of the one or more genes correlates negatively with heterosis or other trait in the organism. Thus, heterosis and other desirable traits in the organism may be increased using the invention. The invention also extends to plants and animals in which traits are up- or down-regulated using methods of the invention. The invention may comprise down-regulating one or more genes involved in stress avoidance or stress tolerance, wherein transcript abundance of the one or more genes is negatively correlated with heterosis, e.g. heterosis for biomass.

Examples of genes whose transcript abundance correlates positively with heterosis, and examples of genes whose transcript abundance correlates negatively with heterosis, are shown in Table 1 and Table 19. Additionally, transcript abundance of genes At1g67500 and At5g45500 correlates negatively with heterosis. In a preferred embodiment the one or more genes are selected from At1g67500 and At5g45500 and/or those shown in Table 1 and/or Table 19, or are orthologues of At1g67500 and/or At5g45500 and/or of one or more genes shown in Table 1 and/or Table 19.

The invention may involve increasing or decreasing a trait in an organism, by upregulating one or more genes whose transcript abundance correlates negatively with the trait in the organism, or by downregulating one or more genes whose transcript abundance correlates positively with the trait in hybrids. Thus, undesirable traits in organisms may be decreased using the invention.

Examples of genes whose transcript abundance correlates with particular traits are shown in Tables 3 to 17, Table 20 and Table 22. Preferred embodiments of the invention relate to one or more of those traits, and preferably to one or more of the listed genes for which transcript abundance is shown to correlate with those traits, as discussed elsewhere herein. Thus, the one or more genes may be selected from the genes shown in the relevant tables, or may be orthologues of those genes. For example, flowering time (e.g. as represented by leaf number at bolting) may be delayed (time to flowering increased, e.g. leaf number at bolting increased) by upregulating expression of one or more genes in Table 3A or Table 4A. Flowering time may be accelerated (time to flowering decreased, e.g. leaf number at bolting decreased) by downregulating expression of one or more genes in Table 3B or Table 4B.

A trait may be increased by upregulating a gene for which transcript abundance correlates positively with the trait or by downregulating a gene for which transcript abundance correlates negatively with the trait. A trait may be decreased by downregulating a gene for which transcript abundance correlates positively with the trait or by upregulating a gene for which transcript abundance correlates positively with the trait.

Upregulation of a gene involves increasing its level of transcription or expression, and thus increasing the transcript abundance of that gene. Upregulation of a gene may comprise expressing the gene from a strong and/or constitutive promoter such as 35S CaMV promoter. Upregulation may comprise increasing expression of an endogenous gene. Alternatively, upregulation may comprise expressing a heterologous gene in a plant or animal, e.g. from a strong and/or constitutive promoter. Heterologous genes may be introduced into plant or animal cells by any suitable method, and methods of transformation are well known in the art. A plant or animal cell may for example be transformed or transfected with an expression vector comprising the gene operably linked to a promoter e.g. a strong and/or constitutive promoter, for expression in the cell. The vector may integrate into the cell genome, or may remain extra-chromosomal.

By “promoter” is meant a sequence of nucleotides from which transcription may be initiated of DNA operably linked downstream (i.e. in the 3′ direction on the sense strand of double-stranded DNA).

“Operably linked” means joined as part of the same nucleic acid molecule, suitably positioned and oriented for transcription to be initiated from the promoter. DNA operably linked to a promoter is under transcriptional initiation regulation of the promoter.

Downregulation of a gene involves decreasing its level of transcription or expression, and thus decreasing the transcript abundance of that gene. Downregulation may be achieved for example by antisense or RNAi, using RNA complementary to messenger RNA (mRNA) transcribed from the gene.

Anti-sense oligonucleotides may be designed to hybridise to the complementary sequence of nucleic acid, pre-mRNA or mature mRNA, interfering with the production of polypeptide encoded by a given DNA sequence (e.g. either native polypeptide or a mutant form thereof), so that its expression is reduce or prevented altogether. Anti-sense techniques may be used to target a coding sequence, a control sequence of a gene, e.g. in the 5′ flanking sequence, whereby the antisense oligonucleotides can interfere with control sequences. Anti-sense oligonucleotides may be DNA or RNA and may be of around 14-23 nucleotides, particularly around 15-18 nucleotides, in length. The construction of antisense sequences and their use is described in refs. [42] and [43].

Small RNA molecules may be employed to regulate gene expression. These include targeted degradation of mRNAs by small interfering RNAs (siRNAs), post transcriptional gene silencing (PTGs), developmentally regulated sequence-specific translational repression of mRNA by micro-RNAs (miRNAs) and targeted transcriptional gene silencing.

A role for the RNAi machinery and small RNAs in targeting of heterochromatin complexes and epigenetic gene silencing at specific chromosomal loci has also been demonstrated. Double-stranded RNA (dsRNA)-dependent post transcriptional silencing, also known as RNA interference (RNAi), is a phenomenon in which dsRNA complexes can target specific genes of homology for silencing in a short period of time. It acts as a signal to promote degradation of mRNA with sequence identity. A 20-nt siRNA is generally long enough to induce gene-specific silencing, but short enough to evade host response. The decrease in expression of targeted gene products can be extensive with 90% silencing induced by a few molecules of siRNA.

In the art, these RNA sequences are termed “short or small interfering RNAs” (siRNAs) or “microRNAs” (miRNAs) depending in their origin. Both types of sequence may be used to down-regulate gene expression by binding to complimentary RNAs and either triggering mRNA elimination (RNAi) or arresting mRNA translation into protein. siRNA are derived by processing of long double stranded RNAs and when found in nature are typically of exogenous origin. Micro-interfering RNAs (miRNA) are endogenously encoded small non-coding RNAs, derived by processing of short hairpins. Both siRNA and miRNA can inhibit the translation of mRNAs bearing partially complimentary target sequences without RNA cleavage and degrade mRNAs bearing fully complementary sequences.

The siRNA ligands are typically double stranded and, in order to optimise the effectiveness of RNA mediated down-regulation of the function of a target gene, it is preferred that the length of the siRNA molecule is chosen to ensure correct recognition of the siRNA by the RISC complex that mediates the recognition by the siRNA of the mRNA target and so that the siRNA is short enough to reduce a host response.

miRNA ligands are typically single stranded and have regions that are partially complementary enabling the ligands to form a hairpin. miRNAs are RNA genes which are transcribed from DNA, but are not translated into protein. A DNA sequence that codes for a miRNA gene is longer than the miRNA. This DNA sequence includes the miRNA sequence and an approximate reverse complement. When this DNA sequence is transcribed into a single-stranded RNA molecule, the miRNA sequence and its reverse-complement base pair to form a partially double stranded RNA segment. The design of microRNA sequences is discussed in ref. [44].

Typically, the RNA ligands intended to mimic the effects of siRNA or miRNA have between 10 and 40 ribonucleotides (or synthetic analogues thereof), more preferably between 17 and 30 ribonucleotides, more preferably between 19 and 25 ribonucleotides and most preferably between 21 and 23 ribonucleotides. In some embodiments of the invention employing double-stranded siRNA, the molecule may have symmetric 3′ overhangs, e.g. of one or two (ribo)nucleotides, typically a UU of dTdT 3′ overhang. Based on the disclosure provided herein, the skilled person can readily design of suitable siRNA and miRNA sequences, for example using resources such as Ambion's siRNA finder, see http://www.ambion.com/techlib/misc/siRNA_finder.html. siRNA and miRNA sequences can be synthetically produced and added exogenously to cause gene downregulation or produced using expression systems (e.g. vectors). In a preferred embodiment the siRNA is synthesized synthetically.

Longer double stranded RNAs may be processed in the cell to produce siRNAs (see for example ref. [45]). The longer dsRNA molecule may have symmetric 3′ or 5′ overhangs, e.g. of one or two (ribo)nucleotides, or may have blunt ends. The longer dsRNA molecules may be 25 nucleotides or longer. Preferably, the longer dsRNA molecules are between 25 and 30 nucleotides long. More preferably, the longer dsRNA molecules are between 25 and 27 nucleotides long. Most preferably, the longer dsRNA molecules are 27 nucleotides in length. dsRNAs 30 nucleotides or more in length may be expressed using the vector pDECAP [46].

Another alternative is the expression of a short hairpin RNA molecule (shRNA) in the cell. shRNAs are more stable than synthetic siRNAs. A shRNA consists of short inverted repeats separated by a small loop sequence. One inverted repeat is complimentary to the gene target. In the cell the shRNA is processed by DICER into a siRNA which degrades the target gene mRNA and suppresses expression. In a preferred embodiment the shRNA is produced endogenously (within a cell) by transcription from a vector. shRNAs may be produced within a cell by transfecting the cell with a vector encoding the shRNA sequence under control of a RNA polymerase III promoter such as the human H1 or 7SK promoter or a RNA polymerase II promoter. Alternatively, the shRNA may be synthesised exogenously (in vitro) by transcription from a vector. The shRNA may then be introduced directly into the cell. Preferably, the shRNA molecule comprises a partial sequence of the gene to be down-regulated. Preferably, the shRNA sequence is between 40 and 100 bases in length, more preferably between 40 and 70 bases in length. The stem of the hairpin is preferably between 19 and 30 base pairs in length. The stem may contain G-U pairings to stabilise the hairpin structure.

siRNA molecules, longer dsRNA molecules or miRNA molecules may be made recombinantly by transcription of a nucleic acid sequence, preferably contained within a vector. Preferably, the siRNA molecule, longer dsRNA molecule or miRNA molecule comprises a partial sequence of the gene to be down-regulated.

In one embodiment, the siRNA, longer dsRNA or miRNA is produced endogenously (within a cell) by transcription from a vector. The vector may be introduced into the cell in any of the ways known in the art. Optionally, expression of the RNA sequence can be regulated using a tissue specific promoter. In a further embodiment, the siRNA, longer dsRNA or miRNA is produced exogenously (in vitro) by transcription from a vector.

In one embodiment, the vector may comprise a nucleic acid sequence according to the invention in both the sense and antisense orientation, such that when expressed as RNA the sense and antisense sections will associate to form a double stranded RNA. In another embodiment, the sense and antisense sequences are provided on different vectors.

Alternatively, siRNA molecules may be synthesized using standard solid or solution phase synthesis techniques which are known in the art. Linkages between nucleotides may be phosphodiester bonds or alternatives, for example, linking groups of the formula P(O)S, (thioate); P(S)S, (dithioate); P(O)NR′2; P(O)R′; P(O)OR6; CO; or CONR′2 wherein R is H (or a salt) or alkyl (1-12C) and R6 is alkyl (1-9C) is joined to adjacent nucleotides through —O— or —S—.

Modified nucleotide bases can be used in addition to the naturally occurring bases, and may confer advantageous properties on siRNA molecules containing them.

For example, modified bases may increase the stability of the siRNA molecule, thereby reducing the amount required for silencing. The provision of modified bases may also provide siRNA molecules which are more, or less, stable than unmodified siRNA.

The term ‘modified nucleotide base’ encompasses nucleotides with a covalently modified base and/or sugar. For example, modified nucleotides include nucleotides having sugars which are covalently attached to low molecular weight organic groups other than a hydroxyl group at the 3′position and other than a phosphate group at the 5′position. Thus modified nucleotides may also include 2′substituted sugars such as 2′-O-methyl-; 2-O-alkyl; 2-O-allyl; 2′-S-alkyl; 2′-S-allyl; 2′-fluoro-; 2′-halo or 2; azido-ribose, carbocyclic sugar analogues a-anomeric sugars; epimeric sugars such as arabinose, xyloses or lyxoses, pyranose sugars, furanose sugars, and sedoheptulose.

Modified nucleotides are known in the art and include alkylated purines and pyrimidines, acylated purines and pyrimidines, and other heterocycles. These classes of pyrimidines and purines are known in the art and include pseudoisocytosine, N4,N4-ethanocytosine, 8-hydroxy-N-6-methyladenine, 4-acetylcytosine, 5-(carboxyhydroxylmethyl) uracil, 5 fluorouracil, 5-bromouracil, 5-carboxymethylaminomethyl-2-thiouracil, 5-carboxymethylaminomethyl uracil, dihydrouracil, inosine, N6-isopentyl-adenine, 1-methyladenine, 1-methylpseudouracil, 1-methylguanine, 2,2-dimethylguanine, 2-methyladenine, 2-methylguanine, 3-methylcytosine, 5-methylcytosine, N6-methyladenine, 7-methylguanine, 5-methylaminomethyl uracil, 5-methoxy amino methyl-2-thiouracil, -D-mannosylqueosine, 5-methoxycarbonylmethyluracil, 5-methoxyuracil, 2 methylthio-N6-isopentenyladenine, uracil-5-oxyacetic acid methyl ester, psueouracil, 2-thiocytosine, 5-methyl-2 thiouracil, 2-thiouracil, 4-thiouracil, 5-methyluracil, N-uracil-5-oxyacetic acid methylester, uracil 5-oxyacetic acid, queosine, 2-thiocytosine, 5-propyluracil, 5-propylcytosine, 5-ethyluracil, 5-ethylcytosine, 5-butyluracil, 5-pentyluracil, 5-pentylcytosine, and 2,6,diaminopurine, methylpsuedouracil, 1-methylguanine, 1-methylcytosine.

Methods relating to the use of RNAi to silence genes in C. elegans, Drosophila, plants, and mammals are known in the art [47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59].

Other approaches to specific down-regulation of genes are well known, including the use of ribozymes designed to cleave specific nucleic acid sequences. Ribozymes are nucleic acid molecules, actually RNA, which specifically cleave single-stranded RNA, such as mRNA, at defined sequences, and their specificity can be engineered. Hammerhead ribozymes may be preferred because they recognise base sequences of about 11-18 bases in length, and so have greater specificity than ribozymes of the Tetrahymena type which recognise sequences of about 4 bases in length, though the latter type of ribozymes are useful in certain circumstances. References on the use of ribozymes include refs. [60] and [61].

The plant or animal in which the gene is upregulated or downregulated may be hybrid, recombinant or inbred. Thus, in some embodiments the invention may involve over-expressing genes correlated with one or more traits, in order to improve vigour or other characteristics of the transformed derivatives of inbred plants and animals.

In a further aspect, the invention is a method comprising:

analysing transcriptomes of parental plants or animals in a population of parental plants or animals;

measuring heterosis or other trait in a population of hybrids, wherein each hybrid in the population is a cross between a first plant or animal and a plant or animal selected from the population of parental plants or animals;

and

identifying a correlation between transcript abundance of one or more genes, preferably a set of genes, in the population of parental plants or animals and heterosis or other trait in the population of hybrids.

Thus, the invention provides a method of identifying an indicator of heterosis or other trait in a hybrid.

The plants or animals in the population whose transcriptomes are analysed are thus parents of the hybrids. These parents may be inbred or recombinant.

All hybrids in the population of hybrids used for developing each predictive model are the result of crossing one common parent with an array of different parents. Normally, all hybrids in the population share one common parent, which may be either the maternal parent or the paternal parent. Thus, the paternal parent of the all the hybrids in the population may be the “first parent plant or animal”, or the maternal parent of all the hybrids in the population may be the “first parent plant or animal”. For plants, a first female parent is normally crossed to a population of different male parents. For animals, a first male parent may preferably be crossed with a population of different females.

Suitable methods of determining or measuring heterosis in hybrids, of transcriptome analysis and of identifying correlations are discussed elsewhere herein.

Correlations between traits and transcript abundance represent models that may be used to predict traits in further plants or animals by determining transcript abundance in those plants or animals. The invention may thus be used to generate a model (e.g. a regression, as described in detail elsewhere herein) for predicting the trait based on transcript abundance of the one or more genes e.g. a set of genes.

Accordingly, in another aspect, the invention is a method of predicting heterosis or other trait in a hybrid, wherein the hybrid is a cross between a first plant or animal and a second plant or animal; comprising

determining the transcript abundance of one or more genes, preferably a set of genes, in the second plant or animal, wherein the transcript abundance of those one or more genes, or of the set of genes, in a population of parental plants or animals correlates with heterosis or other trait in a population of hybrids produced by crossing the first plant or animal with a plant or animal from the population of parental plants and animals; and

thereby predicting heterosis or other trait in the hybrid.

The invention may be used to predict one or more traits in hybrid offspring of parental plants or animals, based on transcript abundance in one of the parents. The parental plants or animals may be inbred or recombinant. Plants or animals may be referred to as “parents” or “parental plants or animals” even where they have not yet been crossed to produce a hybrid, since the invention may be used to predict traits in hybrids before those hybrids are produced. This is a particular advantage of the invention, in that methods of the invention may be used to predict heterosis or other trait in a potential hybrid, without needing to produce that hybrid in order to determine its heterosis or traits.

A plurality of plants or animals may be tested by determining transcript abundance using the method of the invention, each plant or animal representing the second parent for crossing to produce a hybrid, in order to identify a suitable plant or animal to use for breeding to produce a hybrid with a desired trait. A parent may then be selected for breeding based on the predicted trait for a hybrid produced by crossing that parent. Thus, in one example a germplasm collection, which may comprise a population of recombinants, may be screened for plants that may be suitable for inclusion in breeding programmes.

Following prediction of the trait in the hybrid, the inbred or recombinant plant or animal may be selected for breeding to produce a hybrid, e.g. as discussed further below. Alternatively, if the hybrid for which the trait is predicted has already been produced, that hybrid may be selected e.g. for further cultivation.

The method of predicting the trait may comprise, as an earlier step, a method of identifying an indicator of the trait in a hybrid, as described above.

When the method is used for predicting heterosis in hybrids based upon parental transcriptome data, for example data from inbred plants or animals, the one or more genes may comprise At3g112200 and/or one or more of the genes shown in Table 2, or one or more orthologues thereof.

When the method is used for predicting yield, e.g. grain yield, in hybrids based on parental transcriptome data, for example data from inbred plants or animals, e.g. maize, the one or more genes may comprise one or more of the genes shown in Table 22, or one or more orthologues thereof. For example, transcript abundance of one or more genes, e.g. a set of genes, from Table 22 may be determined in a maize plant and used for predicting yield in a hybrid cross between that maize line and B73.

Genes with transcript abundance correlating with other traits are shown in Tables 3 to 17 and Table 20, and transcript abundance of one or more of those genes in parental plants or animals may be used to predict those traits in accordance with hybrid offspring of those plants or animals, in accordance with this aspect of the invention. Alternatively, the invention may be used to identify other genes with transcript abundance in parental plants or animals correlating with those traits in their hybrid offspring.

By predicting heterosis and other traits in hybrids produced by crossing parental germplasm, whether they be inbred or recombinant, the invention allows selection of inbred or recombinant plants and animals that can be crossed to produce hybrids with high or improved levels of heterosis and desirable or improved levels of other traits.

Inbred or recombinant plants and animals may thus be selected on the basis of heterosis or other trait predicted in hybrids produced by crossing those plants and animals.

Accordingly, one aspect of the invention is a method comprising:

determining transcript abundance of one or more genes, preferably a set of genes, in parental plants or animals, wherein the transcript abundance of the one or more genes in a population of parental plants or animals correlates with heterosis or other trait in hybrid crosses between a first parental plant or animal and plants or animals from the population of parental plants or animals;

selecting one of the parental plants or animals; and

producing a hybrid by crossing the selected plant or animal and a different plant or animal, e.g. by crossing the selected plant or animal and the first plant or animal.

Thus, one or more traits may be predicted for hybrid crosses between the parental plants or animals, and then a parental plant or animal predicted to produce a hybrid with a desired trait e.g. late flowering, high heterosis, and/or high yield, and/or with a reduced undesirable trait, may be selected. Methods for predicting traits are discussed in more detail elsewhere herein.

Genes whose transcript abundance correlates with heterosis or other trait in hybrids produced by crossing a first plant or animal and other plants or animals are referred to elsewhere herein, and may be At3g112200 and/or one or more genes selected from the genes in Table 2, or orthologues thereof. Genes with transcript abundance correlating with other traits are shown in Tables 3 to 17 and Table 20, as described elsewhere herein.

Hybrids produced by methods of the invention may be raised or cultivated, e.g. to maturity or breeding age. The invention also extends to hybrids produced using methods of the invention.

The invention may be applied to any trait of interest. For example, traits to which the invention applies include, but are not limited to, heterosis, flowering time or time to flowering, seed oil content, seed fatty acid ratios, and yield. Examples genes whose transcript abundance correlates with certain traits are shown in the appended Tables. For animals, preferred traits are heterosis, yield and productivity. Traits such as yield may be underpinned by heterosis, and the invention may relate to modelling and/or predicting yield and other traits, and/or modelling and/or predicting heterosis for yield and other traits, based on transcript abundances of genes.

Genes in Tables shown herein are identified by AGI numbers, Affymetrix Probe identifier numbers and/or GenBank database accession numbers. AGI numbers can be used to identify the gene from TAIR (The Arabidopsis Information Resource), available on-line at http://www.arabidopsis.org/index.jsp, or findable by searching for “TAIR” and/or “Arabidopsis information resource” using an internet search engine. Affymetrix Probe identifier numbers can be used to identify sequences from Netaffx, available on-line at http://www.affymetrix.com/analysis/index.affx, or findable by searching for “netaffx” and/or “Affymetrix” using an internet search engine. It is now possible to convert between the two identifier formats using the converter, from Toronto university, currently available at http://bbc.botany.utoronto.ca/ntools/cgi-bin/ntools_—agi_converter.cgi, or findable by searching for “agi converter” using an internet search engine. GenBank accession numbers can be used to obtain the corresponding sequence from GenBank, available at http://www.ncbi.nlm.nih.gov/Genbank/index.html or findable using any internet search engine.

A set of genes may comprise a set of genes selected from the genes shown in a table herein.

In methods of the invention relating to heterosis, the one or more genes may comprise one or more of the 70 genes listed in Table 1 or one or more orthologues thereof, and/or may comprise one or more of the genes listed in Table 19 or one or more orthologues thereof.

In methods relating to traits other than heterosis, the trait may for example be a trait referred for Tables 3 to 17, Table 20 or Table 22, and the one or more genes may comprise one or more of the genes shown in the relevant tables, or one or more orthologues thereof. Preferably, the genes in Tables 3 to 17, 20 and/or 22 are used for predicting or influencing (increasing or decreasing) traits in inbred plants or animals. However, the genes may also be used for predicting, increasing or decreasing traits in recombinants and/or hybrids.

When the trait is flowering time, or time to flowering, in plants, e.g. as represented by leaf number at bolting, the one or more genes may comprise one or more genes shown in Table 3 or Table 4, or orthologues thereof. Table 3 shows genes for which transcript abundance was shown to correlate with flowering time in vernalised plants, and Table 4 shows genes for which transcript abundance was shown to correlate with flowering time in unvernalised plants. These may be used for predicting flowering time in vernalised or unvernalised plants, respectively. However, as discussed elsewhere herein, transcript abundance of genes which correlates with a trait in vernalised plants may also correlate (normally according to a different model or equation) with the trait in unvernalised plants. Thus, transcript abundance of genes in either Table 3 or Table 4 may be used to predict flowering time in either vernalised or unvernalised plants, using the appropriate correlation for vernalised or unvernalised plants respectively.

Whilst the transcript abundance data of the genes listed in many of the Tables herein were used in our example for predicting traits in vernalised plants, these data could also be used to predict traits in unvernalised plants. Thus, a first correlation may be identified between transcript abundance and the trait in vernalised plants, and a second correlation may be identified between transcript abundance and the trait in unvernalised plants. The appropriate model may then be used to predict the trait in vernalised or unvernalised plants respectively, based on transcript abundance of one or more of those genes, or orthologues thereof.

Oil content is a useful trait to measure in plants. This is one of the measures used to determine seed quality, e.g. in oilseed rape.

When the trait is oil content of seeds, e.g. as represented by % dry weight, the one or more genes may comprise one or more genes shown in Table 6, or orthologues thereof.

Seed quality may also be represented by the proportion, percentage weight or ratio of certain fatty acids.

Normally, seed traits are predicted for vernalised plants, e.g. oilseed rape in the UK is grown as a Winter crop and will therefore be vernalised at the time of trait expression (seed production in this example). However, predictions may be for either vernalised or unvernalised plants.

When the trait is ratio of 18:2/18:1 fatty acids in seed oil, the one or more genes may comprise one or more genes selected from the genes shown in Table 7, or orthologues thereof.

When the trait is ratio of 18:3/18:1 fatty acids in seed oil, the one or more genes may comprise one or more genes selected from the genes shown in Table 8, or orthologues thereof.

When the trait is ratio of 18:3/18:2 fatty acids in seed oil, the one or more genes may comprise one or more genes selected from the genes shown in Table 9, or orthologues thereof.

When the trait is ratio of 20C+22C/16C+18C fatty acids in seed oil, the one or more genes may comprise one or more genes selected from the genes shown in Table 10, or orthologues thereof.

When the trait is ratio of polyunsaturated/monounsaturated+saturated 18C fatty acids in seed oil, the one or more genes may comprise one or more genes selected from the genes shown in Table 12, or orthologues thereof.

When the trait is % 16:0 fatty acid in seed oil, the one or more genes may comprise one or more genes selected from the genes shown in Table 14, or orthologues thereof.

When the trait is % 18:1 fatty acid in seed oil, the one or more genes may comprise one or more genes selected from the genes shown in Table 15, or orthologues thereof.

When the trait is % 18:2 fatty acid in seed oil, the one or more genes may comprise one or more genes selected from the genes shown in Table 16, or orthologues thereof.

When the trait is % 18:3 fatty acid in seed oil, the one or more genes may comprise one or more genes selected from the genes shown in Table 17, or orthologues thereof.

It may be desirable to predict responsiveness of a plant trait to vernalisation, and this may be measured for example as the ratio of a trait measurement in vernalised plants to the trait measurement in unvernalised plants.

For example, responsiveness of flowering time to vernalisation may be measured as the ratio of leaf number at bolting in vernalised plants to leaf number at bolting in unvernalised plants. Genes whose transcript abundance correlates with this ratio are shown in Table 5. Thus, in embodiments of the invention where the trait is responsiveness of plant flowering time to vernalisation, the one or more genes may comprise one or more genes shown in Table 5, or orthologues thereof.

Responsiveness to vernalisation of the ratio of 20C+22C/16C+18C fatty acids in seed oil may be measured as the ratio of (ratio of 20C+22C/16C+18C fatty acids in seed oil in vernalised plants) to (ratio of 20C+22C/16C+18C fatty acids in seed oil in unvernalised plants). Genes whose transcript abundance correlates with this ratio are shown in Table 11. Thus, in embodiments of the invention where the trait is responsiveness of this ratio to vernalisation, the one or more genes may comprise one or more genes shown in Table 11, or orthologues thereof.

Responsiveness to vernalisation of the ratio of polyunsaturated/monounsaturated+saturated 18C fatty acids in seed oil may be measured as the ratio of (ratio of polyunsaturated/monounsaturated+saturated 18C fatty acids in seed oil in vernalised plants) to (ratio of polyunsaturated/monounsaturated+saturated 18C fatty acids in seed oil in unvernalised plants). Genes whose transcript abundance correlates with this ratio are shown in Table 13. Thus, in embodiments of the invention where the trait is responsiveness of this ratio to vernalisation, the one or more genes may comprise one or more genes shown in Table 13, or orthologues thereof.

When the trait is yield, the one or more genes may comprise one or more of the genes shown in Table 20 or Table 22, or orthologues thereof.

Genes in Tables 1 to 17 are from Arabidopsis thaliana, and may be used in embodiments of the invention relating to A. thaliana or to another organism, such as for predicting or increasing heterosis in a plant or animal (genes of Tables 1 and 2, or orthologues thereof), or for predicting, increasing or decreasing another trait in A. thaliana or other plant. Genes in Tables 19, and 22 are from maize, and may be used in embodiments of the invention relating to maize or to another organism, such as for predicting or increasing heterosis in a plant or animal (genes of Table 19 or orthologues thereof) or for predicting, increasing or decreasing another trait in maize or other plant.

We have demonstrated that transcript abundance in plants of genes shown in Tables 1, 3 to 17, 20 and 22 is predictive of the described traits in those plants. In some embodiments of the invention relating to use of parental transcriptome data for prediction of traits in hybrids, transcript abundance in plants of genes shown in Tables 1, 3 to 17, 20 and 22 or orthologues thereof may be used to predict the described traits in hybrid offspring of those plants.

Preferably, in embodiments of the invention relating to use of parental transcriptome data for prediction of heterosis in hybrids, transcript abundance in plants of At3g112200 and/or of genes shown in Table 2, or orthologues thereof, is used to predict the magnitude of heterosis in hybrid offspring of those plants.

In embodiments of the invention relating to use of parental transcriptome data for prediction of yield, e.g. grain yield, in hybrids, transcript abundance in plants of one or more genes shown in Table 22 is used to predict the yield in hybrid offspring of those plants.

Heterosis or other trait is normally determined quantitatively. As noted above, heterosis may be described relative to the mean value of the parents (Mid-Parent Heterosis, MPH) or relative to the “better” of the parents (Best-Parent Heterosis, BPH).

Heterosis may be determined on any suitable measurement, e.g. size, fresh or dry weight at a given age, or growth rate over a given time period, or in terms of some measure of yield or quality. Heterosis may be determined using historical data from the parental and/or hybrid lines.

Heterosis may be calculated based on size, for which size measurements may for example be taken of the maximum length and width of the plant or animal, or of a part of the plant or animal, e.g. using electronic callipers. For plants, heterosis may be calculated based on total aerial fresh weight of the plants, which may be determined by cutting off all above soil plant material, quickly removing any soil attached, and weighing.

In preferred embodiments, heterosis is heterosis for yield (e.g. in plants or animals, yield of harvestable product), or heterosis for fresh weight (e.g. fresh weight of aerial parts of a plant).

The magnitude of heterosis may thus be determined, and is normally expressed as a % value. For example, mid parent heterosis for fresh weight can be presented as a percentage figure calculated as (weight of the hybrid−mean weight of the parents)/mean weight of the parents. Best parent heterosis for fresh weight can be presented as a percentage figure calculated as (weight of the hybrid−weight of the heaviest parent)/weight of the heaviest parent.

For other traits, an appropriate measurement can be determined by the skilled person. Some traits can be directly recorded as a magnitude, e.g. seed oil content, weight of plant or animal, or yield. Other traits would be determined with reference to another indicator, e.g. flowering time may be represented by leaf number at bolting. The skilled person is able to select an appropriate way to quantify a particular trait, e.g. as a magnitude, ratio, degree, volume, time or rate, and to measure suitable factors representative of the relevant trait.

A transcript is messenger RNA transcribed from a gene. The transcriptome is the contribution of each gene in the genome to the mRNA pool. The transcriptome may be analysed and/or defined with reference to a particular tissue, as discussed elsewhere herein. Analysis of the transcriptome may thus be determination of transcript abundance of one or more genes, or a set of genes.

Transcriptome analysis or determination of transcript abundance is normally performed on tissue samples from the plants or animals. Any part of the plant or animal containing RNA transcripts may be used for transcriptome analysis. Where an organism is a plant, the tissue is preferably from one or more, preferably all, aerial parts of the plant, preferably when the plant is in the vegetative phase before flowering occurs. In some embodiments, transcriptome analysis may be performed on seeds. Methods of the invention may involve taking tissue samples from the plants or animals. In methods of predicting the heterosis or other trait, the sampled organism may remain viable after the tissue sample has been taken. Where prediction is to be performed for genetically identical plants or animals, which may be grown on a different occasion, tissues may include all parts or all aerial plants or a whole seed (for plants) or the whole embryo (for animals). Where prediction is to be performed for the exact plant sampled, a subset of the leaves of the plant may be sampled. However, there is no requirement for the organism to remain viable, since sampling of one or more individuals for transcriptome analysis that results in loss of viability may be used for the prediction of heterosis or other traits in hybrid, inbred or recombinant organisms of similar or identical genetic composition grown on either the same or a different occasion and under the same or different environmental conditions.

Typically, transcriptome analysis is performed on RNA extracted from the plant or animal. The invention may comprise extracting RNA from a tissue sample of the hybrid or inbred plant or animal. Any suitable methods of RNA extraction may be used, e.g. see the protocol set out in the Examples.

Transcriptome analysis comprises determining the abundance of an array of RNA transcripts in the transcriptome. Where oligonucleotide chips are used for transcriptome analysis, the numbers of genes potentially used for model development are the numbers of probes on the GeneChips—ca. 23,000 for Arabidopsis and ca. 18,000 for the present maize Chip. Thus, while in some embodiments, the transcript abundance of each gene in the genome is assessed, normally transcript abundance of a selected array of genes in the genome is assessed.

Various techniques are available for transcriptome analysis, and any suitable technique may be used in the invention. For example, transcriptome analysis may be performed by bringing an RNA sample into contact with an oligonucleotide array or oligonucleotide chip, and detecting hybridisation of RNA transcripts to oligonucleotides on the array or chip. The degree of hybridisation to each oligonucleotide on the chip may be detected. Suitable chips are available for various species, or may be produced. For example, Affymetrix GeneChip array hybridisation may be used, for example using protocols described in the Affymetrix Expression Analysis Technical Manual II (currently available at http://www.affymetrix.com/support/technical/manuals.affx. or findable using any internet search engine). For detailed examples of transcriptome analysis, please see the Examples below.

Transcript abundance of one or more genes, e.g. a set of genes, may be determined, and any of the techniques above may be employed. Alternatively, reverse transcriptase may be used to synthesise double stranded DNA from the RNA transcript, and quantitative polymerase chain reaction (PCR) may be used for determining abundance of the transcript.

Transcript abundance of a set of genes may be determined. A set of genes is a plurality of genes, e.g. at least 10, 20, 30, 40, 50, 60, 70, 80, 90 or 100 genes. The set may comprise genes correlating positively with a trait and/or genes correlating negatively with the trait. As noted below, preferably, the set of genes is one for which transcript abundance of that set of genes allows prediction of heterosis or other trait. The skilled person may use methods of the invention to determine which genes are most useful for predicting heterosis or other traits in hybrids, and therefore to determine which genes can most usefully be assessed for transcript abundance in accordance with the invention. Additionally, examples of sets of genes for prediction of heterosis and other traits are shown herein.

Preferably, analysis of transcript abundance is performed in the same way for the plants or animals used to generate a model or correlation with a trait “model organism” as for the plants or animals in which the trait is predicted based on that model “test organism”. Preferably, the model and test organisms are raised under identical conditions and transcriptome analysis is performed on both the model and test organisms at the same age, time of day and in the same environment, in order to maximise the predictive value of the model based on transcriptome data from the model organisms.

Accordingly, predicting a trait in a test plant or animal may comprise determining transcript abundance of one or more genes in the test plant or animal at a particular age, wherein transcript abundance of the one or more genes in the transcriptome of model plants or animals at that age conditions correlates with the trait. Thus, preferably transcript abundance in the organism (i.e. plant or non-human animal) is determined when the organism is at the same age as the organisms in the population on which the correlation between transcript abundance and heterosis or other trait was determined. Thus, predicting the degree of a trait in an organism may comprise determining the abundance of transcripts of one or more genes, preferably a set of genes, in the organism at a selected age, and determining the transcript abundance of one or more genes, preferably a set of genes, wherein the transcript abundance of those one or more genes or set of genes in the transcriptome of organisms at the said age correlates with heterosis or other trait in the organism.

As noted elsewhere herein, the age at which transcript abundance is determined may be earlier than the age at which the trait is expressed, e.g. where the trait is flowering time the transcriptome analysis may be performed when plants are in vegetative phase.

Preferably, transcriptome analysis and determination of transcript abundance is determined on plant or animal material sampled at a particular time of day. For example, plant tissue samples may be taken at the middle of the photoperiod (or as close as practicable). Thus, when predicting a trait by determining the transcript abundance of one or more genes (e.g. set of genes) whose transcript abundance correlates with that trait, the transcript abundance data for making the prediction are preferably determined at the same time of day as the transcript abundance data used to generate the correlation.

Some aspects of the invention relate to plants, such as cereals, that require vernalisation before flowering. Vernalisation is a period of exposure to cold, which promotes subsequent flowering. Plants requiring vernalisation do not flower the same year when sown in Spring, but continue to grow vegetatively. Such plants (“winter varieties”) require vernalisation over Winter, and so are planted in the Autumn to flower the following year. In the present invention, plants may be vernalised or unvernalised.

Transcriptome data may be obtained from plants when vernalised or unvernalised, and those data may be used to identify a correlation between transcript abundance and a trait measured in vernalised plants and/or a correlation between transcript abundance and the trait measured in unvernalised plants. Thus, surprisingly, we have shown that transcriptome data from vernalised plants can be used to develop a model for predicting traits in unvernalised plants, as well as being useful to develop a model for predicting traits in vernalised plants.

In methods of the invention, comparisons and predictions are preferably between plants or animals of the same genus and/or species. Thus, methods of predicting heterosis or other trait in a plant or animal may be based on correlations obtained in a population of hybrids, inbreds or recombinants of that species of plant or animal. However, as discussed elsewhere herein, correlations obtained in one species may be applied to other species, e.g. to other plants or other animals in general, or to both plants and animals, especially where the other species exhibit similar traits. Thus, the test organism in which the trait is predicted need not be of the same species as the model organisms in which the correlation for prediction of the trait was developed.

Determination of transcript abundance for prediction of a trait is normally performed on the same type of tissue as that in which the correlation between the trait and transcript abundance was determined. Thus, predicting the degree of heterosis in a hybrid may comprise determining transcript abundance in tissue in or from the hybrid, and determining the transcript abundance of one or more genes, preferably a set of genes, wherein the transcript abundance of those one or more genes in the transcriptome of the said tissue in hybrids correlates with heterosis or other trait in hybrids.

Data may be compiled, the data comprising:

(i) a value representing the magnitude of heterosis or other trait in each plant or animal;

(ii) transcriptome analysis data in each plant or animal, wherein the transcriptome analysis data represents the abundance of each of an array of gene transcripts.

For determination of a correlation, data should be obtained from a plurality of plants or animals. In methods of the invention it is thus preferable that transcriptome analyses are performed and traits are determined for at least three plants or animals, more preferably at least five, e.g. at least ten. Use of more plants or animals, e.g. in a population, can lead to more reliable correlations and thus increase the quantitative accuracy of predictions according to the invention.

Any suitable statistical analysis may be employed to identify a correlation between transcript abundance of one or more genes in the transcriptomes of the plants or animals and the magnitude of heterosis or other trait. The correlation may be positive or negative. For example, it may be found that some transcripts have an abundance correlating positively with heterosis or other trait, while other transcripts have an abundance correlating negatively with heterosis or other trait.

Data from each plant or animal may be recorded in relation to heterosis and/or multiple other traits. Accordingly, the invention may be used to identify which genes have a transcript abundance correlating with which traits in the organism. Thus, a detailed profile may be compiled for the relationship between transcript abundance and heterosis and other traits in the population of organisms.

Typically, an analysis is performed using linear regression to identify the relationship between transcript abundance and the magnitude of heterosis (MPH and/or BPH) or other trait. An F-value may then be calculated. The F value is a standard statistic for regression. It tests the overall significance of the regression model. Specifically, it tests the null hypothesis that all of the regression coefficients are equal to zero. The F value is the ratio of the mean regression sum of squares divided by the mean error sum of squares with values that range from zero upward. From this we get the F Prob (the probability that the null hypothesis that there is no relationship is true). A low value implies that at least some of the regression parameters are not zero and that the regression equation does have some validity in fitting the data, indicating that the variables (gene expression level) are not purely random with respect to the dependent variable (trait value at that point).

Preferably a correlation identified using the invention is a statistically significant correlation. Significance levels may be determined as F statistics from the regression Mean Square in the analysis of variance tables of the linear regression analysis. Statistical significance may be indicated for example by F<0.05, or <0.001.

Other potential relationships exist between gene expression and plant phenotype, besides simple linear relationships. For example, relationships may fall on a logistic curve. A computer model (e.g. GenStat) may be used to fit the data to a logistic curve.

Non-linear modelling covers those expression patterns that form any part of a sigmoidal curve, from exponential-type patterns, to threshold and plateau type patterns. Non-linear methods may also cover many linear patterns, and thus may preferentially be used in some embodiments of the invention.

Normally a computer program is used to identify the correlation or correlations. For example, as described in more detail in the Examples below, linear regression analysis may be performed using GenStat, e.g. Program 3 below is an example of a linear regression programme to identify linear regressions between the hybrid transcriptome and MPH.

More generally, each of the methods of the above aspects may be implemented in whole or in part by a computer program which, when executed by a computer, performs some or all of the method steps involved. The computer program may be capable of performing more than one of the methods of the above aspects.

Another aspect of the invention provides a computer program product containing one or more such computer programs, exemplified by a data carrier such as a compact disk, DVD, memory storage device or other non-volatile storage medium onto which the computer program(s) is/are recorded.

A further aspect of the invention is a computer system having a processor and a display, wherein the processor is operably configured to perform the whole or part of the method of one or more of the above aspects, for example by means of a suitable computer program, and to display one or more results of those methods on the display. Typically the computer will be a general purpose computer and the display will be a monitor. Other output devices may be used instead of or in addition to the display including, but not limited to, printers.

Preferably, a set of genes, e.g. less than 1000, 500, 250 or 100 genes, is identified for which transcript abundance correlates with heterosis or other trait, wherein transcript abundance of that set of genes allows prediction of heterosis or other trait. A smaller set of genes that remains predictive of the trait may then be identified by iterative testing of the precision of predictions by progressively reducing the numbers of genes in the models, preferentially retaining those with the best correlation of transcript abundance with heterosis or the other trait, e.g. genes with the most significant (e.g. p<0.001) correlations between transcript abundance and traits. Thus, methods of the invention may comprise identifying a correlation between a trait and transcript abundance of a set of genes in transcriptomes, and then identifying a smaller set or sub-set of genes from within that set, wherein transcript abundance of the smaller set of genes is predictive of the trait. Preferably the smaller set of genes retains most of the predictive power of the set of genes.

The magnitude of heterosis or other trait may be predicted from transcript abundance of one or more genes, preferably of a set of genes as noted above, based on a correlation of the transcript abundance with heterosis or other trait (e.g. a linear regression as described above).

Thus, the equation of the linear regression line (linear or non-linear) for each of the gene transcripts showing a correlation with magnitude of heterosis or other trait may be used to calculate the expected magnitude of heterosis or other trait from the transcript abundance of that gene. The aggregate of the predicted contributions for each gene is then used to calculate the trait value (e.g. as the sum of the contribution from each gene transcript, normalised by the coefficient of determination, r².

DRAWINGS

FIG. 1: Workflows for the analysis of expression data for the investigation of heterosis. a) Standard protocols; b) Recommended Prediction Protocol; c) Alternative ‘Basic’ Prediction Protocol; d) Transcription Remodelling Protocol

LIST OF TABLES

Table 1: Genes in Arabidopsis thaliana hybrids, transcripts of which correlate with magnitude of heterosis in the hybrids

Table 2: Genes in Arabidopsis thaliana inbred lines, transcripts of which correlate with magnitude of heterosis in hybrids produced by crossing those lines with Ler ms1. (A: positive correlation; B: negative correlation)

Table 3: Genes in Arabidopsis thaliana inbred lines, showing correlation in transcript abundance with leaf number at bolting in vernalised plants (A: positive correlation; B: negative correlation)

Table 4: Genes in Arabidopsis thaliana inbred lines showing correlation in transcript abundance with leaf number at bolting in unvernalised plants (A: positive correlation; B: negative correlation)

Table 5: Genes in Arabidopsis thaliana inbred lines showing correlation in transcript abundance with ratio of leaf number at bolting (vernalised plants)/leaf number at bolting (unvernalised plants). (A: positive correlation; B: negative correlation)

Table 6: Genes in Arabidopsis thaliana inbred lines showing correlation between transcript abundance and oil content of seeds, % dry weight in vernalised plants (A: positive correlation; B: negative correlation)

Table 7: Genes in Arabidopsis thaliana inbred lines showing correlation between transcript abundance and ratio of 18:2/18:1 fatty acids in seed oil in vernalised plants (A: positive correlation; B: negative correlation)

Table 8: Genes in Arabidopsis thaliana inbred lines showing correlation between transcript abundance and ratio of 18:3/18:1 fatty acids in seed oil in vernalised plants (A: positive correlation; B: negative correlation)

Table 9: Genes in Arabidopsis thaliana inbred lines showing correlation between transcript abundance and ratio of 18:3/18:2 fatty acids in seed oil in vernalised plants (A: positive correlation; B: negative correlation)

Table 10: Genes in Arabidopsis thaliana inbred lines showing correlation between transcript abundance and ratio of 20C+22C/16C+18C fatty acids in seed oil in vernalised plants (A: positive correlation; B: negative correlation)

Table 11: Genes in Arabidopsis thaliana inbred lines showing correlation between transcript abundance and ratio of (ratio of 20C+22C/16C+18C fatty acids in seed oil (vernalised plants))/(ratio of 20C+22C/16C+18C fatty acids in seed oil (unvernalised plants)) (A: positive correlation; B: negative correlation)

Table 12: Genes in Arabidopsis thaliana inbred lines showing correlation between transcript abundance and ratio of polyunsaturated/monounsaturated+saturated 18C fatty acids in seed oil in vernalised plants (A: positive correlation; B: negative correlation)

Table 13: Genes in Arabidopsis thaliana inbred lines showing correlation between transcript abundance and ratio of (ratio of polyunsaturated/monounsaturated+saturated 18C fatty acids in seed oil (vernalised plants))/(ratio of polyunsaturated/monounsaturated+saturated 18C fatty acids in seed oil (unvernalised plants)) (A: positive correlation; B: negative correlation)

Table 14: Genes in Arabidopsis thaliana inbred lines showing correlation between transcript abundance and % 16:0 fatty acid in seed oil in vernalised plants (A: positive correlation; B: negative correlation)

Table 15: Genes in Arabidopsis thaliana inbred lines showing correlation between transcript abundance and % 18:1 fatty acid in seed oil (vernalised plants)

(A: positive correlation; B: negative correlation)

Table 16: Genes in Arabidopsis thaliana Inbred Lines Showing correlation between transcript abundance and % 18:2 fatty acid in seed oil (vernalised plants) (A: positive correlation; B: negative correlation)

Table 17: Genes in Arabidopsis thaliana inbred lines showing correlation between transcript abundance and % 18:3 fatty acid in seed oil (vernalised plants) (A: positive correlation; B: negative correlation)

Table 18: Prediction of complex traits in inbred lines (accessions) using models based on accession transcriptome data

Table 19: Genes in maize for prediction of heterosis for plant height. Data were obtained in plants at CLY location only (model from 13 hybrids). Representative public ID shows GenBank accession numbers. (A: positive correlation; B: negative correlation)

Table 20: Genes in maize for prediction of average yield. Data were obtained in plants across 2 sites, MO and L (model from 12 hybrids to predict 3). Representative public ID shows GenBank accession numbers. (A: positive correlation; B: negative correlation)

Table 21: Pedigree and seedling growth characteristics of maize inbred lines used in Example 6a

Table 22: Maize genes for which transcript abundance in inbred lines of the training dataset is correlated (P<0.00001) with plot yield of hybrids with line B73. A negative value for the slope indicates a negative correlation between abundance of the transcript and yield, and a positive value indicates a positive correlation.

Table 23: Maize plot yield data for Example 6a.

EXAMPLES
Example 1
Transcriptome Remodelling in Arabidopsis Hybrids

Our initial studies employed Arabidopsis thaliana. We conducted all of our heterosis analyses in F1 hybrids between accessions of A. thaliana, which can be considered inbred lines due to their lack of heterozygosity. The genome sequence of A. thaliana is available [62] and resources for transcriptome analysis in this species are well developed [63]. A. thaliana also shows a wide range of magnitude of hybrid vigour [7, 64, 65].

The null hypothesis is that all parental alleles contribute to the transcriptome in an additive manner, i.e. if alleles differ in their contribution to transcript abundance, the observed value in the hybrid will be the mean of the parent values. There are six patterns of transcript abundance in hybrids that depart from this expected additive effect of contrasting parental alleles [28]:

(i) transcript abundance in the hybrid is higher than either parent;

(ii) transcript abundance in the hybrid is lower than either parent;

(iii) transcript abundance in the hybrid is similar to the maternal parent and both are higher than the paternal parent;

(iv) transcript abundance in the hybrid is similar to the paternal parent and both are higher than the maternal parent;

(v) transcript abundance in the hybrid is similar to the maternal parent and both are lower than the paternal parent;

(vi) transcript abundance in the hybrid is similar to the paternal parent and both are lower than the maternal parent.

When using quantitative analytical methods, the terms “higher than”, “lower than” and “similar to” can be defined by specific fold-difference criteria. Although differences in the contributions to the transcriptome of divergent alleles in maize hybrids has been reported as common [29, 66] the lack of absolute quantitative analysis of transcript abundance in parental inbred lines means that it is not possible to determine whether the observed effects are due to allelic interaction in the hybrid or simply the expected additive effects of alleles with differing transcript abundance characteristics. We would not consider such additive effects as components of transcriptome remodelling.

We produced reciprocal hybrids between A. thaliana accessions Kondara and Br-0, and between Landsberg er ms1 and Kondara, Mz-0, Ag-0, Ct-1 and Gy-0, with Landsberg er ms1 as the maternal parent. Hybrids and parents were grown under identical environmental conditions and heterosis calculated for the fresh weight of the aerial parts of the plants after 3 weeks growth (see Materials and Methods). The heterosis observed for each combination was recorded (BPH (%) and MPH (%))

RNA was extracted from the same material and the transcriptome was analysed using ATH1 GeneChips. Plants were grown in three replicates on three successive occasions. RNA was pooled from the three replicates for analysis of gene expression levels on each occasion.

Transcript abundance values in A. thaliana hybrids were compared over all experimental occasions and genes showing differences, at defined fold-levels from 1.5 to 3.0, corresponding to the six patterns indicative of transcriptome remodelling, were identified. Genes with transcript abundance differing between the parents by the same defined fold-level were also identified. The number of genes that appeared consistently in each of these 8 categories across all 3 experimental occasions was counted. To assess whether the number of genes classified into each category differed from that expected by chance, permutation analysis (bootstrapping) was used to calculate an expected value under the null hypothesis of no remodelling.

The significance of the experimental results was assessed, for each category independently, using Chi square tests. The results of the analysis, summarised in Table 1 for 2-fold differences, show that transcriptome remodelling occurred in all of the hybrids analysed, with most individual observations showing highly significant (p<0.001) divergence from the null hypothesis. Similar analyses were conducted for 1.5- and 3-fold differences, with extensive remodelling also being identified. Based on the analysis of gene ontology information, there were no obvious functional relationships of the remodelled genes in the hybrids.

Further analysis of selected genes from these categories were conducted using additional GeneChip hybridisation experiments and by quantitative RT-PCR, and confirmed the transcript abundance patterns. GeneChip hybridization was also performed using genomic DNA from accessions Kondara, Br-0 and Landsberg er ms1, to assess the proportion of differences between parental transcriptomes attributable to sequence polymorphisms that would prevent accurate reporting of transcript abundance by the arrays. We found that ca. 20% of the differences between parental transcriptomes may be attributable to sequence variation. However, this does not affect the remodelling analysis, as additivity of allelic contributions to the mRNA pool in hybrids where one parental allele failed to report accurately on the array would result in intermediate signal strength, so would not be assigned to any of the remodelled classes.

The relationship of transcriptome remodelling with hybrid vigour was assessed by carrying out linear regression of the number of genes remodelled in each hybrid combination, at the 1.5, 2 and 3-fold levels, on the magnitude of heterosis observed. This revealed a strong relationship between heterosis and the transcriptome remodelling at the 1.5-fold level (r+0.738, coefficient of determination r²=0.544 for MPH; r=+0.736, r²=0.542 for BPH). The correlation was more modest between heterosis and the transcriptome remodelling involving higher fold level changes (r²=0.213 and 0.270 for MPH and BPH, respectively, for 2-fold changes; r²=0.300 and 0.359 for MPH and BPH, respectively, for 3-fold changes). There was extensive remodelling, at all fold changes, even in the hybrid combinations showing the least heterosis. Consequently, the majority of remodelling events identified that result in transcript abundance changes of 2-fold or greater, even in strongly heterotic hybrids, are likely to be unrelated to heterosis. The most highly enriched class in heterotic hybrids is those genes showing 1.5-fold differential abundance, which is below the threshold usually set in transcriptome analysis experiments.

Heterosis shows an inconsistent relationship with the degree of relatedness of parental lines, with an absence of correlation reported between heterosis and genetic distance in A. thaliana [7]. We estimated the genetic distance between the accessions used in the hybrid combinations we have analysed, and these are shown in Table 1. To assess the relationship of transcriptome remodelling with genetic distance, we regressed the number of genes classified as having remodelled transcript abundance in each hybrid combination against genetic distance. We found that transcriptome remodelling is associated with genetic distance in the higher-fold remodelling classes (r²=0.351 and 0.281 for 2 and 3-fold changes respectively), but not for 1.5-fold remodelling (r²=0.030). We found no relationship between heterosis and genetic distance, in accordance with previous reports in A. thaliana (r²=0.024 and 0.005 for MPH and BPH, respectively, against relative genetic distance). We conclude that the formation of hybrids between divergent inbred lines results in transcriptome remodelling, with the extent of remodelling increasing with the degree of genetic divergence of those lines. This result is consistent with the expected effects of allelic variation on transcriptional regulatory networks. The relationship between transcriptome remodelling and heterosis can be interpreted as meaning that heterosis is likely to require transcriptome remodelling to occur, but that much of this involves low magnitude remodelling of the transcript abundance of a large number of genes.

The results of the above experiments indicate that the conventional approach to the analysis of the transcriptome in the hybrid, i.e. studying one or very few hybrid combinations, is unlikely to result in the identification of genes involved specifically in heterosis.

Example 2
Transcript Abundance in Hybrid Transcriptomes

We carried out an analysis using linear regression to identify the relationship between transcript abundance in a range of hybrids and the strength of heterosis (both MPH and BPH) shown by those hybrids. Significance levels were determined as F statistics from the regression Mean Square in the analysis of variance tables of the linear regression analysis. For this, we used the heterosis measurements and hybrid transcriptome data from the combinations described above with Landsberg er ms1 as the maternal parent, and from additional hybrids between Landsberg er ms1, as the maternal parent, and Columbia, Wt-1, Cvi-0, Sorbo, Br-0, Ts-5, Nok3 and Ga-0. Transcriptome data from 32 GeneChips, representing between 1 and 3 replicates from each of these 13 hybrid combinations of accessions, were used in this study. Nine genes were identified that showed highly significant (F<0.001) regressions (all positive) of transcript abundance in the hybrid on the magnitude of both MPH and BPH. Thirty-four genes showed highly significant regressions (F<0.001; 22 positive, 12 negative) of transcript abundance in the hybrid on MPH and significant regressions (F<0.05) on BPH. Twenty-seven genes showed highly significant regressions (F<0.001; 23 positive, 4 negative) of transcript abundance in the hybrid on magnitude of BPH and significant (F<0.05) regression on MPH. The genes are shown in Table 1 below. Based on gene ontology information, there are no obvious functional relationships between these 70 genes and no excess representation of genes involved in transcription.

The ability to identify a set of genes that show highly significant correlation of transcript abundance and magnitude of heterosis across 13 hybrids indicates that transcriptome-level events are predominant in the manifestation of heterosis. To confirm that this is correct, and that the genes we have identified are indicative of the transcript abundance characteristics that are important in heterosis, we utilized these discoveries to predict the strength of heterosis in new hybrid combinations based on the transcript abundance of the 70 defined genes. We built a mathematical model using the equations of the linear regression lines recalculated for each of the 70 genes against both MPH and BPH, to calculate the expected heterosis as the sum of the contribution from each gene, normalised by the coefficient of determination, r². The model operates as a Microsoft Excel spreadsheet, which is available as supplementary materials on Science Online. The spreadsheet also contained the normalised transcriptome data for the 70 genes from each of the hybrids studied. The model was validated by “predicting” the heterosis in the training set of 32 hybrids from which transcriptome data were used for its construction. It predicted heterosis across the full range of magnitude observed, for both MPH and BPH, with a very high correlation between predicted and observed values for individual samples (r²=0.768 for MPH, r²=0.738 for BPH). Three new hybrid combinations were produced, between the maternal parent Landsberg er ms1 and accessions Shakdara, Kas-1 and Ll-0. These were grown, in a “blind” experiment, under the same environmental conditions as the training set for the model, heterosis for fresh weight was measured and the transcriptomes analysed. The transcript abundance data for the 70 genes of the model were extracted for each of the new hybrids and entered into the heterosis prediction model. The results, as summarised below, confirmed that the model produced excellent quantitative predictions of heterosis, particularly MPH, confirming that transcriptome-level events were, indeed, predominant in the manifestation of heterosis.

Prediction of Heterosis Using a Model Based on Hybrid Transcriptome Data

Mid-Parent
Best-Parent

Heterosis %
Heterosis %

Hybrid
Predicted
Observed
Predicted
Observed

Landsberg er ms1 ×
43
34
15
22

Shakdara

Landsberg er ms1 ×
46
57
16
24

Kas-1

Landsberg er ms1 ×
66
69
33
67

Ll-0

Mid parent heterosis for fresh weight is presented as a percentage figure calculated as (weight of the hybrid−mean weight of the parents)/mean weight of the parents.

Best parent heterosis for fresh weight is presented as a percentage figure calculated as (weight of the hybrid−weight of the heaviest parent)/weight of the heaviest parent.

Example 2a
Highly Significant and Specific Correlation Between Heterosis and Transcript Abundance of At1g67500 and At5g45500 in Hybrids

In a further experiment to identify specific genes that show transcript abundance (gene expression) patterns in hybrids correlated with heterosis, we conducted an additional analysis based upon linear regression. For this we used a “training” dataset consisting of hybrid combinations between Landsberg er ms1 and Ct-1, Cvi-0, Ga-0, Gy-0, Kondara, Mz-0, Nok-3, Ts-5, Wt-5, Br-0, Col-0 and Sorbo. For each individual gene represented on the array, the transcript abundance in hybrids was regressed on the magnitude of heterosis exhibited by those hybrids. Twenty one genes showed highly significant (p<0.001) correlation, but this is no more than is expected by chance, as data for almost 23,000 genes were analysed. However, the exceptionally high significance for the two genes showing the greatest correlation (r²=0.457, P=6.0×10⁻⁶for gene At1g67500; r²=0.453, P=6.9×10⁻⁶for gene At5g45500) is highly unlikely to have occurred by chance. In both cases the correlation was negative, i.e. expression is lower in more strongly heterotic hybrids.

We tested whether the expression characteristics of these genes could be used for the prediction of heterosis. This was conducted by removing one hybrid from the dataset, formulating the regression line and using this relationship to predict the expected heterosis corresponding to the gene expression measured for the hybrid that had been removed. The analysis was repeated by the removal and prediction of heterosis in each of the 12 hybrids in turn. Three untested hybrids were developed (Landsberg er ms1 crossed with Ll-0, Kas-1 and Shakdara) as a “test” dataset, grown and assessed for heterosis as for the lines of the training dataset, and their transcriptomes analysed using ATH1 GeneChips. Using formulae derived by regression using all 12 hybrids in the training dataset, the expression data for genes At1g67500 and At5g45500 in the hybrids of the test dataset were used to predict the heterosis in these test hybrids. Both showed very high correlation between predicted and measured heterosis. Overall, predicted heterosis based on the expression of At1g67500 are better correlated with measured heterosis (r²=0.708) than those based on the expression of At5g45500 (r²=0.594). However, removal of one anomalous prediction in the training dataset (that of the heterosis shown by the hybrid Landsberg er ms1×Nok-3) improves the latter to r²=0.773. Nevertheless, the predictions of heterosis in all three hybrids of the test dataset based on the expression of At5g45500, in particular, are remarkably accurate.

Hybrids that show greater heterosis tend to be heavier than hybrids that show little heterosis. As expected, we identified such a correlation between the magnitude of heterosis we measured and weight for the 15 hybrids of our training and test datasets (r²=0.492). In order to assess whether the expression of genes At1g67500 and At5g45500 are specifically predicting heterosis, we assessed the possibility of correlation between gene expression and the weight of the plants in which expression is being measured. For this, we used the plant weight and gene expression data from the 12 parental lines in the training dataset. We found the expression of At1g67500 to show weak negative correlation with the weight of the plants (r²=0.321), but there was no correlation for At5g45500 (r²<0.001). We conclude that the transcript abundance of At5g45500 is indicative specifically of heterosis, but that of At1g67500 is likely to be influenced also by the weight of hybrid plants. This conclusion is consistent with the errors in prediction of heterosis in the test dataset using the expression of At1g67500: the prediction of heterosis in the hybrid Landsberg er ms1×Kas-1 (which is unusually heavy for the heterosis it shows) is over-estimated, whereas the prediction of heterosis in the hybrid Landsberg er ms1×Ll-0 (which is unusually light for the heterosis it shows) is underestimated.

Gene At5g45500 is annotated as encoding “unknown protein”, so its functions in the process of heterosis cannot be deduced based upon homology. The function of gene At1g67500 is known: it encodes the catalytic subunit of DNA polymerase zeta and the locus has been named AtREV3 due to the homology of the corresponding protein with that of yeast REV3 [67]. REV3 is important in resistance to UV-B and other stresses that result in DNA damage as its function is in translesion synthesis, which is required to repair forms of damage to DNA that blocks replication. Studies have shown no differential expression for At1g67500 in response to UV-B or other stresses [68]. However, the expression of At5g45500 is increased in aerial parts that were subjected to UV-B, genotoxic and osmotic stresses [68]. Thus both of the genes with expression correlated with heterosis in hybrid plants have potential roles in stress resistance. As the expressions of both are negatively correlated with heterosis, one hypothesis is that greater expression of these genes might be related to increased resilience to specific stresses, but this has a repressive effect on growth under favourable conditions. This resembles the situation where biomass and seed yield penalties were found to be associated with R-gene-mediated pathogen resistance to Pseudomonas syringae [69]. Heterosis, at least for vegetative biomass, may therefore be the consequence of genetic interactions that lead to a reduction in repression of growth, rather than direct promotion of growth.

Example 3
Transcript Abundance in Transcriptomes of Inbred Lines

We carried out separate analyses using linear regression to identify the relationship between transcript abundance in the parental lines and the strength of MPH shown by their respective hybrids with Landsberg er ms1. Significance levels were determined as F statistics from the regression Mean Square in the analysis of variance tables of the linear regression analysis.

In total, 272 genes were identified that showed highly significant (F<0.00) regressions of transcript abundance in the parent on the magnitude of MPH. See Table 2 below. Based on gene ontology information, there are no obvious functional relationships between these genes and no excess representation of genes involved in transcription.

The invention permits use of transcriptome characteristics of inbred lines as “markers” to predict the magnitude of heterosis in new hybrid combinations.

We built mathematical models, using the equations of the linear regression lines for each of the genes, to calculate the expected heterosis. These models operate as programmes within the Genstat statistical analysis package [70]. The results, as summarised in the table below, confirmed that the model successfully predicted the heterosis observed in the untested combinations using transcriptome characteristics of the inbred parents as markers.

Prediction of Heterosis Using a Model Based on Parental Transcriptome Data

Mid-Parent

Heterosis % (44)

Hybrid
Predicted
Observed

Landsberg er ms1 ×
34
34

Shakdara

Landsberg er ms1 × Kas-1
46
57

Landsberg er ms1 × Ll-0
50
69

Example 3a
Highly Significant Correlation Between Heterosis and Transcript Abundance of At3g11220 in Inbred Parents

We conducted an additional analysis based upon linear regression to identify genes that show expression patterns in inbred parents correlated with heterosis shown by the hybrids. For each individual gene represented on the array, transcript abundance in paternal parent lines was regressed on the magnitude of heterosis exhibited by the corresponding hybrids with accession Landsberg er ms1 in the training dataset.

The expression of one gene, At3g11220, showed an exceptionally high correlation (r²=0.649; P=2.7×10⁻⁸). The correlation was negative, i.e. expression is lower in parental lines that produce more strongly heterotic hybrids. We assessed the utility of using the expression of this gene in parental lines to predict the heterosis that would be shown by the corresponding hybrids with accession Landsberg er ms1. This was conducted for both training and test datasets, as for the predictions based on the expression of At1g67500 and At5g45500 in hybrids. The heterosis predicted was well correlated with the measured heterosis (r²=0.719) and the predicted values for two of the three hybrids in the test dataset were very accurate. However, heterosis was substantially overestimated for the hybrid Landsberg er ms1×Kas-1, despite there being no correlation between the expression of At3g11220 in parental accessions and the weight of those accessions (r²<0.001).

Gene At3g11220 is annotated as encoding “unknown protein”, so its function in the process of heterosis cannot be deduced based upon homology.

Example 4
Transcriptome Analysis for Prediction of Other Traits

We used the methodology as described for the prediction of heterosis using parental transcriptome data to develop models for the prediction of additional traits in accessions. The transcriptome data set used for the construction of the models was that obtained for 11 accessions: Br-0, Kondara, Mz-0, Ag-0, Ct-1, Gy-0, Columbia, Wt-1, Cvi-0, Ts-5 and Nok3, as previously described. Trait data had previously been obtained from these, and accessions Ga-0 and Sorbo. Transcriptome data from accessions Ga-0 and Sorbo were used for trait prediction in these accessions. The lists of genes incorporated into the models relating to the 15 measured traits are listed in Tables 3 to 17. The predicted trait values for Ga-0 and Sorbo were compared with measured trait values for these accessions, to assess the performance of the models.

As the models developed for the prediction of additional traits were developed using only 11 accessions, we expected them to contain some false components. These would tend to shift trait predictions towards the average value of the trait across the set of accessions used for the construction of the models. Therefore, our criterion for success of each model was whether or not it ranked the accessions Ga-0 and Sorbo correctly. The results, as summarised in Table 18, show that the models were able to successfully predict flowering time, seed oil content and seed fatty acid ratios. As expected, the values produced by the models were between the measured value for the trait in the respective accessions and the average value of the trait across all accessions. Only the models to predict the absolute seed content of a subset of specific fatty acids were unsuccessful. This lack of success in the experiment we conducted may have been due to the relative lack of precision of the data for these traits and/or insufficient numbers of genes with transcript abundance correlated with the trait to overcome the effects of false components in the models developed using the data sets available at the time. We believe that models based on more extensive data sets would be able to successfully predict these traits.

The ability to use transcriptome data from an early stage of plant growth under specific environmental conditions (i.e. aerial parts of vegetative-phase plants after 3 weeks growth in a controlled environment room under 8 hour photoperiod) to predict characteristics that appear later in the development of plants grown in different environmental conditions (flowering time, details of seed composition and vernalisation responses of plants grown in a glasshouse under 16 hour photoperiod) is remarkable. We interpret this as evidence of extensive interconnection and multiplicity of gene function, regulated, as for heterosis, largely at the level of transcript abundance. The results presented here indicate that our methodology will allow the use of specific characteristics of the transcriptomes of organisms, including both plants and animals, early in their life cycle as “markers” to predict many complex traits later in their life cycle, and to increase our understanding of the underlying biological processes.

Example 5
Methods and Materials
Accessions Used

The accessions used for the studies underlying this disclosure were obtained from the Nottingham Arabidopsis Stock Centre (NASC): Kondara, Cvi-0, Sorbo, Ag-0, Br-0, Col-0, Ct-1, Ga-0, Gy-0, Mz-0, Nok-3, Ts-5, Wt-5 (catalogue numbers N916, N902, N931, N936, N994, N1092, N194, N1180, N1216, N1382, N1404, N1558 and N1612, respectively). A male sterile mutant of Landsberg erecta (Ler ms1) was also obtained from NASC (catalogue number N75).

Growth Conditions

Seeds of parental accessions and hybrids were sown into pots containing A. thaliana soil mix (as described in O'Neill et al [71]) and Intercept (Intercept 5GR). The pot was then watered, and sealed to retain moisture, before being placed at 4° C. for 6 weeks to partially normalize flowering time. At the end of this time period the pot was placed in a controlled environment room (heated at 22° C. and lit for 8 hours per day). Gradually the seal was removed in order to acclimatise the plants to the reduced air moisture. When the first true leaves appeared the plants were transplanted to individual pots, which were again sealed and returned to the controlled environment rooms. Again the seal was gradually removed over the next few days. The positions of A. thaliana plants in controlled environment rooms was determined using a complete randomised block design, with the trays of plants being regularly rotated and moved in order to reduce environmental effects.

The Production of Hybrid Seeds

Hybrids were produced by crossing accessions Kondara and Br-0 by selecting a raceme of the maternal plant, removing all branches and siliques, leaving only the inflorescence. All immature and open buds were removed, along with the apical meristem, leaving 5-6 mature closed buds. From these buds the sepals, petals, and stamens were removed leaving only a complete pistil. For crosses involving Ler ms1 as the maternal parent, only enough tissue was removed, from unopened buds, to allow access to the stigma. Buds of all plants were then pollinated by removing a stamen from the pollen donor plant, and rubbing the anther against the stigma. This was repeated until the stigma was well coated with pollen when viewed under the microscope. The pollinated buds were then protected from additional pollination by being enclosed in a ‘bubble’ of Clingfilm, which was removed after 2-3 days.

Trait Measurements

The total aerial fresh weight of the plants was determined by cutting off all above soil plant material, quickly removing any soil attached, and weighing on electronic scales (Ohaus Corp. New. Jersey. USA). The plant material was then frozen in liquid nitrogen. All plant harvesting and weight measurements were taken as close as practicable to the middle of the photoperiod. Where trait data were combined for replicate sets of plants grown at different time, the data were weighted to correct for differences in absolute growth rates between the replicates caused by environmental effects. The mean weight for each of the 14 parent accessions and 13 hybrids was calculated for each of the three growth replicates. These were then normalised to the first replicate mean, to take account of any between-occasion variation in the growth conditions. This was done by dividing each replicate mean by the first replicate mean and then multiplying by itself (for example [a/b]*b) in order to obtain the adjusted mean.

RNA Extraction and Hybridisation

200 mg of plant tissue were ground to a fine powder using liquid nitrogen in a baked pre-cooled mortar, and using a chilled spatula, transferred to labelled chilled 1.5 ml tube. To these tubes 1 ml of TRI Reagent (Sigma-Aldrich, Saint Louis USA) was added, then shaken to suspend the tissue. After a 5 minute incubation at room temperature 0.2 ml of chloroform was added, and thoroughly mixed with the TRI Reagent by inverting the tubes for around 15 seconds, followed by 2-3 minutes incubation at room temperature. The tubes were centrifuged at 12000 rpm for 15 minutes and the upper aqueous phase transferred to a clean, labelled tube. 0.5 ml of isopropanol was then added to the tubes, which were inverted repeatedly for 30 seconds to precipitate the RNA, followed by a10 minutes incubation at room temperature. The tubes were then were centrifuged at 12000 rpm for 10 minutes at 4° C., revealing a white pellet on the side of the tube. The supernatant was poured off of the pellet, and the lip of the tube gently blotted with tissue paper. 1 ml 75% ethanol was added and the tubes shaken to detach the pellet from the side of the tube, followed by centrifugation at 7500 rpm for 5 minutes. Again the supernatant was poured off of the pellet, which was quickly spun down again and any remaining liquid removed using a pipette. The pellet was then dried in a laminar flow hood, before 50 μl DEPC treated water (Severn Biotech Ltd. Kidderminster, UK) was added to dissolve the pellet.

Sample concentrations were determined using an Eppendorf BioPhotometer (Eppendorf UK Limited. Cambridge. UK), and RNA quality was determined by running out 111 on a 1% agarose gel for 1 hour. RNA from replicated plants were then pooled according concentration in order to ensure an equal contribution of each replicate.

The pooled samples were then cleaned using Qiagen Rneasy columns (Qiagen Sciences. Maryland. USA) following the protocol on page 79 of the Rneasy Mini Handbook (06/2001), before again determining the concentrations using an Eppendorf BioPhotometer, and running out 111 on a 1% agarose gel.

Affymetrix GeneChip array hybridisation was carried out at the John Innes Genome Lab (http://www.jicgenomelab.co.uk). All protocols described can be found in the Affymetrix Expression Analysis Technical Manual II (Affymetrix Manual II http://www.affymetrix.com/support/technical/manual.affx.)

Following clean up, RNA samples, with a minimum concentration of 1 μg, μl-1, were assessed by running 1 μl of each RNA sample on Agilent RNA6000 nano LabChips® (Agilent Technology 2100 Bioanalyzer Version A.01.20 SI211). First strand cDNA synthesis was performed according to the Affymetrix Manual II, using 10 μg of total RNA. Second strand cDNA synthesis was performed according to the Affymetrix Manual II with the following minor modifications: cDNA termini were not blunt ended and the reaction was not terminated using EDTA. Instead Double-stranded cDNA products were immediately purified following the “Cleanup of Double-Stranded cDNA” protocol (Affymetrix Manual II). cDNA was resuspended in 22 μl of RNase free water.

cRNA production was performed according to the Affymetrix Manual II with the following modifications: 11 μl of cDNA was used as a template to produce biotinylated cRNA using half the recommended volumes of the ENZO BioArray High Yield RNA Transcript Labelling Kit. Labelled cRNAs were purified following the “Cleanup and Quantification of Biotin-Labelled cRNA” protocol (Affymetrix Manual II). cRNA quality was assessed by on Agilent RNA6000 nano LabChips® (Agilent Technology 2100 Bioanalyzer Version A.01.20 SI211). 20 μg of cRNA was fragmented according to the Affymetrix Manual II.

High-density oligonucleotide arrays (either Arabidopsis ATH1 arrays, or AT Genomel arrays, Affymetrix, Santa Clara, Calif.) were used for gene expression detection. Hybridisation overnight at 45° C. and 60 RPM (Hybridisation Oven 640), washing and staining (GeneChip® Fluidics Station 450, using the EukGEws2_—450 Antibody amplification protocol) and scanning (GeneArray® 2500) was carried out according to the Affymetrix Manual II.

Microarray suite 5.0 (Affymetrix) was used for image analysis and to determine probe signal levels. The average intensity of all probe sets was used for normalization and scaled to 100 in the absolute analysis for each probe array. Data from MAS 5.0 was analysed in GeneSpring® software version 5.1 (Silicon Genetics, Redwood City, Calif.).

Identification of Genes with Non-Additive Transcript Abundance in Hybrids

Analysis of the normalised transcript abundance data was performed using GenStat [70]. This was undertaken using a script of directives programmed in the GenStat command language (see below), and used to identify the set of defined patterns of transcript abundance. Briefly, each hybrid transcript abundance data set was compared to its appropriate parental data sets, for each gene, for each of the particular expression patterns of interest. Those genes showing a particular pattern in each data set were given a test value. Once completed all of these values were added together and only those data sets with a combined test value equal to a given a critical value (equivalent to the value if all data sets displayed that pattern) were counted. Once this had been completed for the experimental data, the results were checked by hand against the source data.

Program 1 below is an example of the pattern recognition programme. This example identifies patterns in the KoBr hybrid and its parents, for three replicates of each at the two-fold threshold criteria.

Permutation Analysis to Calculate Expected Values for Non-Additive Transcript Abundance in Hybrids

Due to the relatively limited replication within the experiment and the large number of genes assayed on the GeneChips it is expected that a proportion of the genes displaying defined patterns will have occurred by chance. It is therefore essential to use appropriate statistical analysis of the data to determine the significance of the results. In order to determine this, random permutation analysis (bootstrapping) was used to generate expected values for random occurrences of defined abundance patterns of the data. Pseudoreplicate data sets were generated by randomly sampling the original data within individual arrays, and using a rotating ‘seed number’ in order to create random data sets of the same size, and variance, as the original. The same pattern recognition directives were then used for this random data set as were used on the original data and the resulting numbers of probes were recorded.

In order to get a statistically significant number of randomized replicates, this randomization and analysis of the data was repeated 250 times. The average numbers of probes identified for each pattern were then used as the value that would be expected to arise by random chance for that pattern. It was determined that 250 cycles was a sufficiently large random data set, for this experiment by comparing the expected random averages of the defined patterns at 1.5 fold, at 50 cycles and at 250 cycles. Comparisons between higher numbers of cycles (500-1000 cycles) exhibited very little difference between the means except that the longer runs served to reduce the standard errors. A Wilcoxon matched-pairs two-tailed t-test on the means of the two repetition levels (50 cycles and 250 cycles) gave a P-value of 0.674, suggesting very strongly that the means are not statistically different from each other. Based on this it was assumed that the average random values will not change significantly with increased replication, and that 250 cycles is a significantly large number of replicates to generate this mean random value in this case.

Program 2 below is an example of the bootstrapping programme. This example bootstraps the KoBr hybrid at the two-fold threshold criteria, for 250 repetitions.

Chi2 Tests for Significance of Transcriptome Remodelling

Fold changes in themselves are not statistical tests, and cannot be used alone to designate a confidence level of the reported differences in expression. The average numbers of probes identified for each pattern after permutation analysis represent the number expected to arise by random chance for that pattern. Once this expected value has been determined it can be used in a maximum likelihood Chi square test, under the null hypothesis of no difference between observed and expected, in order to determine whether the observed patterns differ significantly from random chance. This was undertaken using the “Chi-Square goodness of fit” option of GenStat, and testing the difference between the mean number of genes observed fitting a given expression pattern, and the mean number of genes expected to fit that same pattern (as calculated above), with a single degree of freedom. Significant relationships, fitting the alternative hypotheses of significant differences between the two mean values, were considered to be those exhibiting P values of 0.05 or less.

Normalisation of Transcriptome Remodelling

Transcriptome remodelling was calculated, normalised for the divergence of the transcriptomes of the parental accessions, using the equation:

NT=R
_T/(R_p/R_pm)

Where NT=normalised level of transcriptome remodelling of a cross

R_T=total number of genes summed across all 6 classes indicative of remodelling for the specific hybrid, at the appropriate fold-level

R_p=total number of genes with transcript abundance differing between the parental accessions of the specific hybrid, at the appropriate fold-level.

R_pm=Mean number of genes with transcript abundance differing between the parental accessions across all combinations analysed, at the appropriate fold-level.

Estimation of Relative Genetic Distance

In order to develop a measure of the Relative Genetic Distance (RGD) between accession Ler and the 13 accessions crossed with it to produce hybrids the following method was used. A set of 216 loci were selected that were polymorphic for the 14 main accessions studied in this thesis. These were downloaded from the web site of the NSF 2010 project DEB-0115062 (http://walnut.usc.edu/2010/). Loci were selected to cover the genome by defining 500 kb intervals throughout the genome, starting at base pair 1 on each chromosome, and selecting the polymorphic locus with the lowest base pair coordinate that has a complete set of sequence data for all 14 accessions, if any, in each interval. The number of polymorphisms across these 216 loci between each accession and Ler were determined and normalised relative to the polymorphism rate observed between Ler and Columbia (with 45 polymorphisms, the most similar to Ler) to give the RGD.

Regression Analysis to Identify Genes with Transcript Abundance in Hybrid Lines Correlated with the Strength of Heterosis

In order to identify genes showing a significant linear relationship between strength of heterosis and transcript abundance in hybrid lines, regression analysis was undertaken using a script of directives programmed in the GenStat command language. This programme conducted a linear regression, for the transcript abundance of each probe, against the phenotypic value for 32 GeneChips. There were three replicate GeneChips for each of the hybrids LaAg, LaCt, LaCv, LaGy, LaKo, and LaMz, and two replicates each for LaBr, LaCo, LaGa, LaNo, LaSo, LaTs, and LaWt, each representing the pooled RNA of three individual hybrid plants. The results of these regressions were presented as F-values. Once this had been completed for the experimental data, significant results were checked by hand against the source data.

Program 3 below is an example of the linear regression programme. This example identifies linear regressions between the hybrid transcriptome and MPH.

Once this had been completed for the transcription data, permutation analysis was used to determine how often particular regression line would arise by random chance. The data was randomised within individual arrays, using a rotating ‘seed number’ and the regression analyses were repeated for this random data, using the same directives used for the original data. In order to get a statistically significant number of random replicates, this randomisation and analysis of the data was repeated 1000 times. Following this, the 1000 regression values for each gene were ranked according to the probability of a relationship between the phenotypic values and random expression values, and the F values of the first, tenth and fiftieth values (corresponding to the 0.1%, 1% and 5% significance values) were recorded. The probabilities of the actual and randomised samples were then compared and only those genes where the probability of occurring randomly is less than in the actual data at one of the three significance values were counted as showing a significant relationship.

Program 4 below is an example of the linear regression bootstrapping programme. This example randomises linear regressions between the hybrid transcriptome and MPH. Due to the size of the outputs, the files are saved into intermediary files that can be read by the computer but not opened visually.

Program 5 below is an example of the programme written to extract the significant values out of the bootstrapping intermediary data files, into a file that can be manipulated in excel. Again this example handles linear regression data between the hybrid transcriptome and MPH.

Regression Analysis to Identify Genes with Transcript Abundance in Parental Lines Correlated with the Strength Of Heterosis

In order to identify genes showing a significant linear relationship between strength of heterosis and transcript abundance in parental lines, regression analysis was undertaken as described for the identification of genes with transcript abundance in hybrids correlated with the strength of heterosis.

Example 6
A Transcriptomic Approach to Modelling and Prediction of Hybrid Vigour and Other Complex Traits in Maize
Modelling and Prediction of Heterosis in Maize

The experimental design uses a series of 15 different hybrid maize lines, all with line B73 as the maternal parent. The hybrids and parental lines were grown in replicated trials at three locations (two in North Carolina and one in Missouri) in 2005, and data were collected for heterosis and a range of other traits, as listed below. All 31 lines (15 hybrids and 16 parents) were grown for 3 weeks and aerial tissues cut, weighed and frozen in liquid nitrogen. RNA was prepared and Affymetrix maize GeneChips were used to analyse the transcriptome in 2 replicates of each. The methods successfully developed in Arabidopsis, as described above, were used to (i) identify genes with transcript abundance correlated with the magnitude of heterosis, (ii) develop predictive models using the transcriptome data from 12 or 13 hybrids and the corresponding parents and (iii) test the ability of the models to “predict” the performance of additional hybrids, based only upon their transcriptome characteristics.

Genes whose transcript abundance was shown to correlate with heterosis in maize are shown in Table 19. Heterosis was calculated for plant height, for plants at CLY location (Clayton, N.C.) only (model from 13 hybrids).

These data were used to develop a model for prediction of heterosis in two further hybrids. All of the genes used in producing the calibration line were have been used in the prediction, both for the model development and the further “test” plants.

Prediction of Heterosis for Plant Height, CLY Location Only (Model from 13 Hybrids to Predict 2):

MPH PH CLY

Location

Hybrids

CLY

B73 × Ki3
B73 × OH43

Actual
149.19
134.88

Value

Predicted
144.59
141.45

No. of correlated
370

genes:

The same procedures can be used to develop predictive models for each of the additional traits for which complete data sets are available. For maize, the data from 14 inbred lines (used as parents of the hybrids described above) can be used to develop models for prediction of traits in further inbred lines.

The following traits may be measured in maize: yield; grain moisture; plant height; flowering time; ear height; ear length; ear diameter; cob diameter; seed length; seed width; 50 kernel weight; 50 kernel volume.

Genes with transcript abundance correlating with yield, measured as harvestable product, are shown in Table 20. Average yield was calculated for 12 plants across 2 sites, MO and L.

These genes were used to develop a model for prediction of yield in three further hybrids. All of the genes used in producing the calibration line were have been used in the prediction, both for the model development and the further “test” plants.

Rank order of yield was successfully predicted in these hybrids, and the magnitude was accurate for 2 out of the 3 hybrids, shown below. With improved trait data, accurate predictions would be expected for all hybrids.

Prediction of Average Yield Across 2 Sites, MO and L (Model from 12 Hybrids to Predict 3)

Weight

Mo&L

Location

Hybrids

MO & L

B73 ×
B73 × CML247
B73 × Mo18W

M37W

Actual
9.70
11.87
11.81

Value

Predicted
9.63
11.38
10.90

No. of correlated
419

genes:

Example 6a
Prediction of Plot Yield in Maize Hybrids Using Parental Transcriptome Data

We used linear regression to identify genes for which expression levels in a training dataset of 20 genetically diverse inbred lines (B97, CML52, CML69, CML228, CML247, CML277, CML322, CML333, IL14H, Ki11, Ky21, M37W, Mo17, Mo18W, NC350, NC358, Oh43, P39, Tx303, Tzi8) was correlated with the plot yield of the corresponding hybrids with line B73. Pedigrees and phylogenetic grouping 72 of the maize lines used in our studies are summarised in Table 21.

Using a stringent cut-off for significance (P<0.00001), correlations (0.288<r²<0.648) were identified for 186 genes. These are listed in Table 22. In the majority of cases (129), gene expression in the inbred lines was negatively correlated with yield of the hybrids. We were able to discount the possibility that these correlations were artefacts of differing proportions of cell types in different sizes of plants, which may have arisen if the sizes of the inbred seedlings were indicative of the performance of the corresponding hybrids, as we found no correlation between plot yield and either the weight (r²=0.039) or the height (r²=0.001) of the sampled seedlings of the corresponding parental lines.

To assess whether gene expression characteristics may be used successfully for the prediction of yield, each hybrid in turn was removed from the training dataset and models developed based upon a regression conducted with the remaining lines. This was conducted as for A. thaliana, except that the mean of the predictions for all of the genes with highly significant correlation (P<0.00001) was used as the overall prediction of heterosis for the excluded line. The numbers of genes exceeding this significance threshold varied from 84 (with P39 excluded) to 262 (with NC350 excluded). Gene expression data for a test dataset of four additional inbred lines (CML103, Hp301, Ki3, OH7B) was then used to predict the heterosis that would be shown by the corresponding hybrids with B73, by averaging the predictions from each of the 186 genes identified by regression analysis using the complete training dataset. The results showed that the predicted plot yield is strongly correlated with the measured plot yield (r²=0.707), demonstrating that gene expression characteristics can, indeed, be used for the prediction of heterosis, as quantified by yield. Although the relationship was non-linear, with reduced ability to quantitatively predict yields at the higher end of the range studied, the method was able to correctly resolve the two highest yielding hybrids in the test dataset from the two lowest yielding hybrids. The poor yield performance of hybrids including the popcorn (HP301) and the two sweet corns (IL14H and P39) were correctly predicted, but the exceptionally high yield of the hybrid NC350×B73 was not predicted. We conclude that maternal effects are minor, as the analysis was based on a mixture of crosses with B73 as the maternal parent (15 hybrids) and as the paternal parent (9 hybrids).

Growth and Trait Analysis of Maize Plants

Plants used for transcriptome analysis were grown from seeds for 2 weeks. Maize seeds were first imbibed in distilled water for 2 days in glasshouse conditions to break dormancy, before transfer to peat and sand P7 pots. They were grown in long day glass house conditions (16 hours photoperiod) at 22° C. Aerial parts above the coleoptiles were excised, weighed and frozen in liquid nitrogen. All plant harvesting and weight measurements were taken as close as practicable to the middle of the photoperiod. Plants for yield trials were grown in field conditions in Clayton, N.C. in 2005. Forty plants of each hybrid were grown in duplicate 0.0007 hectare plots. Yield was calculated as pounds of grain harvested per plot, corrected to 15% moisture, as shown in Table 23.

Example 7
A Transcriptomic Approach to Modelling and Prediction of Hybrid Vigour and Other Complex Traits in Oilseed Rape
Modelling and Prediction of Heterosis in Oilseed Rape

The experimental design uses a series of 14 different hybrid oilseed rape restorer lines, all with line MSL 007 C (which is a male sterile winter line and has been used for commercial hybrid production) as the maternal parent. The hybrids and parental lines were grown in Hohenlieth and Hovedissen in Germany and Wuhan in China in 2004/5, and data for heterosis and a range of other traits, as listed below, were collected. All 29 lines (14 hybrids and 15 parents) are grown for 3 weeks and aerial tissues cut, weighed and frozen in liquid nitrogen. RNA is prepared and Affymetrix Brassica GeneChips are used to analyse the transcriptome in 3 replicates of each. The methods successfully developed in Arabidopsis are used to (i) identify genes with transcript abundance correlated with the magnitude of heterosis, (ii) predictive models are developed using the transcriptome data from 12 hybrids and the corresponding parents and (iii) the ability of the models to “predict” the performance of the 2 additional hybrids, based only upon their transcriptome characteristics, is demonstrated.

Traits measured in oilseed rape: Seed yield, seed weight, seed oil content, seed protein content; seed glucosinolates; establishment; Winter hardiness; Spring development; flowering time; plant height; standing ability.

Modelling and Prediction of Additional Traits

Upon completion of heterosis modelling, the same procedures are used to develop predictive models for each of the additional traits for which complete data sets are available. For oilseed rape, the data from 12 inbred lines (used as parents of the hybrids described above) is used to develop models, which is used to “predict” the traits in 2 further inbred lines. The performance of the models is validated.

Example 8
Further Data Modelling Techniques
Improvement of the Models

The models developed in Arabidopsis utilize linear regression approaches. However, non-linear approaches may enable the identification of more comprehensive gene sets and, hence, more precise models. Non-linear approaches are therefore incorporated into the model development protocols. Additional opportunities for refinement include weighting of the contribution of individual genes and data transformations.

Development of Reduced Representation Models

Although approaches based on the use of GeneChips or microarrays may continue to be the preferred analytical platform for commercialization, there are other methods available for the quantitative determination of transcript abundance. Quantitative PCR methods can be reliable and are amenable to some automation. However, when such approaches are to be used, it is desirable to identify a subset of genes (ideally under 10) that retain most of the predictive power of the sets of genes used to date in the models (70 for prediction of heterosis based on hybrid transcriptomes, typically >150 for prediction of heterosis or other traits based on inbred transcriptomes). Therefore, a limited set of genes is identified by iterative testing of the precision of predictions by progressively reducing the numbers of genes in the models, preferentially retaining those with the best correlation of transcript abundance with the trait.

Example 9
Standard Operating Instruction for the Analysis of Gene Expression Data

This section provides detailed guidance for development and use of predictive models using the program GenStat [70].

List of Programmes

The following GenStat programmes may be used in accordance with the invention and are suitable for analysing any Affymetrix based expression data.

GenStat Programme 1˜Basic Regression Programme˜Method 4
GenStat Programme 2˜Basic Prediction Regression Programme˜Method 5
GenStat Programme 3˜Prediction Extraction Programme˜Method 5
GenStat Programme 4˜Basic Best Predictor Programme˜Method 7
GenStat Programme 5˜Basic Linear Regression Bootstrapping Programme˜Method 9
GenStat Programme 6˜Basic Linear Regression Bootstrapping Data Extraction Programme˜Method 9
GenStat Programme 7˜Basic Transcriptome Remodelling Programme˜Method 10
GenStat Programme 8˜Dominance Pattern Programme˜Method 11
GenStat Programme 9˜Dominance Permutation Programme˜Method 11
GenStat Programme 10˜Transcriptome Remodelling Bootstrap Programme˜Method 12
Introduction

These standard operating procedures are designed to enable the undertaking of gene expression analysis studies, from RNA extraction through to advanced prediction.

The procedures are divided into 4 workflows, depending on the type of analyses you wish to undertake. See FIG. 1.

Workflow a) follows the basic first steps, common to all analyses (methods 1-3), to the stage of predicting traits based upon transcription profiles.

Workflow b) follows the recommended analysis procedure (based on the latest analysis developments). It culminates in the prediction of traits based on a subset of best predictor genes.

Workflow c) follows an alternative analysis procedure, used to generate the prediction reported in my thesis, and includes a bootstrapping step.

Workflow d) describes to methods for analysing the degree of transcriptome remodelling between hybrids and their parent lines.

All of these workflows are designed to be ‘worked through’ and contain step-by-step instruction on how to complete the analysis.

a) Standard Protocols
Method 1, Extract RNA

This stage results in the production of good quality total RNA at a concentration of between 0.2-1 μg μl⁻¹for hybridisation to Affymetrix GeneChips. These methods are the same for both Arabidopsis and Maize chips, for other species, contact Affymetrix for their recommended methods.

1.1 Trizol RNA Extraction

200 mg of plant tissue were ground to a fine powder using liquid nitrogen in a baked pre-cooled mortar, and using a chilled spatula, transferred to labelled chilled capped tube. To these tubes 1 ml of TRI REAGENT (Sigma-Aldrich, Saint-Louis USA) was added and shaken to suspend the tissue. After a 5 minute incubation at room temperature 0.2 ml of chloroform was added, and thoroughly mixed with the TRI REAGENT by inverting the tubes for around 15 seconds, followed by 2-3 minutes incubation at room temperature. The tubes were centrifuged at 12000 rpm for 15 minutes and the upper aqueous phase transferred to a clean, labelled tube.

0.5 ml of isopropanol was then added to the tubes, which were inverted repeatedly for 30 seconds to precipitate the RNA, followed by 10 minutes incubation at room temperature. The tubes were then centrifuged at 12000 rpm for 10 minutes at 4° C., revealing a white pellet on the side of the tube. The supernatant was poured off the pellet, and the lip of the tube gently blotted with tissue paper. 1 ml 75% ethanol was added and the tubes shaken to detach the pellet from the side of the tube, followed by centrifugation at 7500 rpm for 5 minutes. Again the supernatant was poured off the pellet, which was quickly spun down again and any remaining liquid removed using a pipette. The pellet was then dried in a laminar flow-hood; before 50 μl DEPC treated water (Severn Biotech Ltd. Kidderminster, UK) was added to dissolve the pellet.

1.2 RNA Clean-Up

RNA samples were cleaned up using RNeasy® mini columns (Qiagen Ltd, Crawly, UK), according to the protocol given in the RNeasy® Mini Handbook (3^rdedition 06/2001 pages 79-81). Due to the maximum binding capacity, no more than 100 μg of RNA could be loaded on to each column. In order to obtain as high a concentration as possible during the elution step, 40 μl was used and the elute run through the column twice. This was followed by a second 40 μl volume of DEPC treated water in order to remove any remaining RNA, which could be used to increase the amount of clean RNA available, should further concentration be required.

1.3 Concentration of RNA Samples

If the concentration of the clean RNA was less than 1 μg μl⁻¹a further precipitation and dissolution can be performed using an Affymetrix recommended method which can be found in the Affymetrix Expression Analysis Technical Manual II (http://www.affymetrix.com/support/technical/manuals.affx).

5 μl 3 M NaOAc, pH 5.2 (or one tenth of the volume of the RNA sample) was added to the RNA sample requiring concentrating, together with 250 μl of 100% ethanol (or two and a half volumes of the RNA sample). These were mixed and incubated at −20° C. for at least 1 hour. The samples were centrifuged at 12000 rpm in a micro-centrifuge (MSE, Montana, USA) for 20 minutes at 4° C., and the supernatant poured off leaving a white pellet. This pellet was washed twice with 80% ethanol (made up with DEPC treated water), and air-dried in a laminar flow hood. Finally the pellet was re-suspended in DEPC treated water, to a volume appropriate to the required concentration.

Method 2, RNA Hybridisation
2.1 Hybridisation to GeneChips

Following clean up, RNA samples, with a concentration of between 0.2-1 μg, μl⁻¹, were assessed by running 1 μl of each RNA sample on Agilent RNA6000 nano LabChips® (Agilent Technology 2100 Bioanalyzer Version A.01.20 SI211). First strand cDNA synthesis was performed according to the Affymetrix Manual II, using 10 μg of total RNA. Second strand cDNA synthesis was performed according to the Affymetrix Manual II with the following minor modifications:

cDNA termini were not blunt ended and the reaction was not terminated using EDTA. Instead Double-stranded cDNA products were immediately purified following the “Cleanup of Double-Stranded cDNA” protocol (Affymetrix Manual II). cDNA was re-suspended in 22 μl of RNase free water.

cRNA production was performed according to the Affymetrix Manual II with the following modifications:

11 μl of cDNA was used as a template to produce biotinylated cRNA using half the recommended volumes of the ENZO BioArray High Yield RNA Transcript Labelling Kit. Labelled cRNAs were purified following the “Cleanup and Quantification of Biotin-Labelled cRNA” protocol (Affymetrix Manual II). cRNA quality was assessed by on Agilent RNA6000 nano LabChips® (Agilent Technology 2100 Bioanalyzer Version A.01.20 SI211). 20 μg of cRNA was fragmented according to the Affymetrix Manual II.

High-density oligonucleotide arrays were used for gene expression detection. Hybridisation overnight at 45° C. and 60 RPM (Hybridisation Oven 640), washing and staining (GeneChip® Fluidics Station 450, using the EukGEws2_—450 Antibody amplification protocol) and scanning (GeneArray® 2500) was carried out according to the Affymetrix Manual II.

Files were saved as .txt files, for further analysis.

Method 3, Data Loading

This section describes the methods used to load the expression data into GeneSpring, how to normalise the data, and how to save it in excel for further analysis. These instructions are best followed while carrying out the analysis. A GeneSpring course is recommended if further analysis is required using this programme.

3.1 Loading Data into GeneSpring

Open GeneSpring, >File>Import data>select the first of the data files you wish to load>click Open

Choose file format—Affy pivot table

(Create new genome—if you don't want to go into an existing one)

Select genome—Arabidopsis, Maize, etc, or create a new genome following instructions on screen

Import data: selected files—select any remaining files you want to analyse

Import data: sample attributes—this is where you can enter the MIAME info

Import data: create experiment—yes. Save new experiment—give it a name, it will appear in the experiment folder in the navigator toolbar.

3.2 New Experiment Checklist

These 4 factors should be completed in turn, to ensure that the data is properly normalised. This will impact upon all of the subsequent analyses. Generally the defaults or recommended orders should be used.

Define Normalisations

Click on ‘use recommended order’ and check that the following is included:

Data transformation: measurements less than 0.01 to 0.01 Per chip: 50th %

Per gene: normalise to median, cut off=10 in raw signal

Define Parameters

Here we define the names of the expression data. Depending upon the labelling of the expression files, changes may not be required here. If changes are required:

Click on ‘New custom’ Type the name of each sample.

Delete other parameters to avoid confusion.

Save
Define Default Interpretation

No changes needed for this experiment

Define Error model

No changes needed for this experiment

3.3 Transfer Data in to Excel

Once the data is normalised it can be transferred into an excel spreadsheet.

To do this, click on the relevant data in the experiment tree (on the far left of the main GeneSpring screen)

Click View>view as spreadsheet

select all>copy all>paste into Excel spreadsheet.

Save.

This forms the master Excel chart.

Method 4, Regression Analysis

These instructions describe the basic regression method. This regression forms the basis of the subsequent prediction methods.

4.1 Create Data File

To create a data file for use in GenStat. Open the master Excel file (with normalised expression data from GeneSpring)>Copy the relevant data columns (the data for those accessions that will form the ‘training data set’ from which significant predictive genes will be selected) into a new chart>add a column of “:” at the far end>save chart as .txt file>close file

Open the text file in GenStat>Enclose any title names in speech marks (“ ”), this should have the effect of turning the titles green>Find and replace (ctrl R)* with blanks>Replace all>Save file again

4.2 Regression Programme

Open ‘basic regression programme’ (GenStat Programme 1˜Basic Regression Programme) in GenStat

Check that the input data filename is correct, and is opening to channel 2

Check that the output data file is going to the correct destination and is opening to channel 3. These input and output file names should be RED

Check that the phenotypic trait data are correct for the trait under investigation. Use “\” to go on to new lines, these backslashes will turn GREEN.

Check that the number of genes to be investigated is set to the correct value (usually 22810 for Arabidopsis, or 17734 for Maize).

If the R², Slope, and Intercept are required remove the “ ” from the appropriate analysis section, and from the print command, both will turn BLACK from green.

4.3 Running the Programme

To run the programme, ensure that both the programme window and output windows are open (to tile horizontally Alt+Shift+F4). Select the programme window and press Ctrl+W. This will set the programme running, check that the GenStat server icon (histogram symbol, in taskbar at bottom right-hand corner of the screen) has changed colour to red.

To cancel the programme right click on the server icon and choose interrupt

Once complete the GenStat icon will change colour back to green

4.4 Analysing the Output

To analyse the data, first open it in Excel, select “delimited”>next>tick the “Tab” and “Space”>Finish

Add a new row at the far left-hand side of the sheet, and label the appropriate columns “value” “Df” and “R square” “Slope” and “Intercept” if these were included in the analysis

Add a new column to the beginning and label it “ID”

Fill the remaining cells of the ID column with a series 1-22810 for Arabidopsis or 1-17734 for Maize (edit>fill>series>OK)

Delete the column “Df”

Select all of the data columns>Data>Sort>P value ascending

Select all of the rows where the P value are less than or equal to 0.05. Colour these cells using the “paint” option, and record the number in this list. These are the genes significant at the 5% level

Select all of the rows where the P value are less than or equal to 0.01. Colour these cells an alternative colour using the “paint” option, and record the number in this list. These are the genes significant at the 1% level

Select all of the rows where the P value are less than or equal to 0.001. Colour these cells a third colour using the “paint” option, and record the number in this list. These are the genes significant at the 0.1% level

These three values are the number of OBSERVED significant probes in the data set

These observed significant probes, can be used as ‘prediction probes’ for the prediction of traits in other accessions, or hybrid combinations.

Method 5, Prediction

These instructions describe the basic prediction method. All subsequent prediction methods are a variation on this.

5.1 Producing the Prediction Calibration Lines

Using the list of identified prediction probes; create a specific prediction sub-set gene list. This can be done by copying your ID and P-value columns (sorted by ID to return the data to its original order) in to a new excel sheet along with the expression data of your training line accessions. You can then sort by P-value and delete those genes that do not appear in the relevant significance (usually 0.1%) list. Remember to sort by ID again to return the file to its correct order, then delete the ID and Sig0.1% columns you added. Save this file under a new file name as a .txt file (for example trainingsetdata.txt).

Open the ‘Basic Prediction Regression Programmer’ (GenStat Programme 2)

Check that the input file is the one that you have just created

Check that the output file is named correctly (calibration output file)

Check that the number of genes is correct (for example the 0.1% significant genes)

Check that the bin values are appropriate for the trait data. These values should cover the range of the data and a little way either side.

Save the file and run the programme (Ctrl+W)

5.2 Making the Test Expression File

To make the predictions use the identified prediction probes, and the expression data of the ‘unknown lines’ for which we are making the prediction of heterosis. Using the list of identified prediction probes, create a specific prediction sub-set gene list, as was done when generating the file for the calibration curves (section 5.1). This can be done by copying your ID and P-value columns (sorted by ID to return the data to its original order) in to a new excel sheet along with the expression data of your training line accessions. You can then sort by P-value and delete those genes that do not appear in the relevant significance (usually 0.1%) list. Remember to sort by ID again to return the file to its correct order, then delete the ID and Sig0.1% columns you added. Save this file under a new file name as an Excel spread sheet.

In this file add two blank columns between each of the data columns. In the first column, next to the first unknown line's expression measurement, insert a number series from 1 to however long the list on gene measurements is. In the next column, list the identifier for those measurements (the best identifier would be the parent name, for instance Kas, B73 etc.).

In the first column next to the second data list type the command “=B2+0.0” Then copy this down the column. This will have the effect of giving a number series that is 0.01 greater than its equivalent for the first parent. In the next column, list the identifier for those measurements again

Repeat this process for any remaining parent data sets. Each number series should always be 0.01 greater than its equivalent in the previous series.

Starting with the second set of data columns, cut all of the genes, number series and identifies, and add them to the bottom of first set of data columns. Be sure to use Edit>Paste Special>Values so as not to upset your commands. Repeat this for the remaining columns. You should now have three long columns with all of the data in.

Select all of the data. Click Data>Sort>Column B (or whichever is the column with the number sequence in). After sorting, you should have all of your parental data mixed together, with all of the same genes next to each other (for example, with three parents your number sequence should read 1, 1.1, 1.2, 2, 2.1, 2.2 etc. and the identifier column should read Kas, Sha, Ll-0, Kas, Sha, Ll-0 etc. or equivalent) save the file. This is your identifier file.

Copy only the column with the expression data into a new work book. Delete all headings and add a column of colons “:”. Save the file as a .txt file. This is your ‘Tester’ data file. Ensure that you close this file, as GenStat will not recognise the file if open in Excel.

Open this file in GenStat press Ctrl+R and in the ‘Find What’ box type * leave the ‘Replace With’ box blank. Click ‘Replace All’ then save this file. This is your test expression file.

5.3 Running the Prediction File
Open the ‘Prediction Extraction Programmer’ (GenStat Programme 3

Check the variate “mpadv” these are the X-axis values for the calibration lines. Ensure that these are the same as the bin values entered earlier (section 5.1).

Check the first input file. This should be the expression data of your Tester lines (section 5.2).

Check the second input file. This should be the output file from your calibration line (calibration output file—section 5.1).

Check that the “ntimes” command is the number of test genes multiplied by the number of parents, therefore the total number of genes in your test expression file.

Check that the “calc Z=Z+3” command is correct for your number of Tester lines, for example, for four Tester lines this should read “calc Z=Z+4”.

Check that your “if (estimate)” commands are appropriate for the range of your trait data. This is for the ‘capped’ prediction. These should be set at 2 ‘bin sizes’ beyond and below the bin range, if appropriate.

Run the programme (Ctrl+W). This programme prints to the output window, which should be saved as an output (.out) file.

Note it is normal for there to be error messages, if all of the previous steps have been followed ignore these.

5.4 Analysing the Output

Open your saved output file in Excel. Choose Delimited>Next and tick the Tab and Space buttons.

Delete the writing found in the file until you reach the first data point. Usually the first 60 lines.

Name the columns “No.” “Cap” “Raw”

Scroll to the bottom and delete all of the messages you see there.

Select all and sort by “No” ascending.

Check that you have the correct number of rows remaining. This should equal the ntimes value from the Prediction Extraction Programme (the number of prediction genes you have generated, multiplied by the number of Tester lines you are predicting for). Scroll to the bottom and delete all of the non-relevant information you see there (for example “regvr=regms/resms” “code CA” etc)

Delete any remaining warning messages, to the left and right of the ‘useful data.’

Open the identifier .xls file you generated earlier. Copy the Number series and Identifier columns in to your output file.

Select all (Ctrl+A) and sort by Identifier, this should separate the data by parent name.

Cut and paste all of the parents into neighbouring columns (so that they are next to each other).

Scroll to the bottom of the list under the cap column enter the command “=AVERAGE(B2:B203)” (Note, this command is based on 202 predictive genes, you should adjust this command to cover the number of predictions for your gene set).

Copy this command to the bottom of all of your lists. You should now have two predictions for each of your Tester lines, the CAPPED and RAW prediction values.

These predictions can be used individually, or they can be averaged between replicates of the same accessions.

b) Recommended Prediction Protocol
Method 6, N-1 Model

These instructions describe the first steps of the recommended prediction protocol. The N-1 model is a modification to the basic regression method, and using the same GenStat programme, however this regression is repeated for each accession in the training set.

6.1 Running the N-1 Model

To undertake the N-1 model, prepare an expression file containing all of the accessions you wish to use in your training set.

Run a basic regression (GenStat Programme 1-Basic Regression Programme) using all but one of these accessions. If you have multiple replicates of the same accession, ensure that all are removed.

Using the genes identified from this experiment, undertake a prediction as described in Method 5, using the removed accession as the tester line. Record the ID list of the predictive genes (section 4.4), and the results of the RAW prediction for each gene (as listed in section 5.4) for each replicate.

Repeat this process for all of the accession in the training set, until you have predicted each accession against a training set containing all of the other accessions. These data can be used to assess the overall accuracy of these predictions by plotting the ACTUAL trait values against the predicted, or they can be used for the later ‘Best Predictor’ prediction method.

Method 7, Best Predictor

This programme calculates which genes consistently predict well over a wide range of accessions and phenotypes. You can also use the output to investigate the frequency of genes appearing in the predictive lists, and thereby identify many noise genes.

7.1 Creating the Data File

To create the data file first open a new Excel spreadsheet. In the first column, paste the list of predictive gene IDs (the numbers assigned at the regressions stage) from the first of the N-1 accessions (section 6.1). In the next column paste the list of predictions for these genes for this accession, as generated in the prediction stage for that accession in the N-1 model. In the third column at each stage paste the accession name, repeated next to each gene in the list. In the fourth column type the replicate number for that accession, if there is only one replicate type 1. In the fifth type the actual trait value for that accession.

7.2 Running the Prediction File

Open the ‘Basic Best Predictor Programme’ (GenStat Programme 4) Check that the names of the accessions are correctly listed.

Check that the number of replicates is correct (note these should be written [values=‘chip 1’,‘chip 2’] and so on for however many replicates there are).

Check that the Input file name is correct.

Run the programme (Ctrl+W). This programme prints to the output window, which should be saved as an output (.out) file.

7.3 Generating a Best Predictor File

Open your saved output file in Excel. Choose Delimited>Next and tick the Tab and Space buttons.

Delete the copy of the programme in the output (first 31 lines or so) at the top of the file, and the programme information at the bottom of the file (last 8 lines).

Only the first 4 columns (gene, number, Delta, and se_delta) are at the top of the file. Scroll half way down the sheet; there are 3 further columns (a repeat of gene, Ratio, and se_ratio) copy these columns next to the 4 columns at the top of the sheet.

Ensure that the column names are gene, number, Delta, and se_delta, gene, Ratio, se_ratio; respectively.

Delete the second ‘gene’ column.

Save the file. This file is your Best Predictor file

7.4 Using the Best Predictor File

The information in the Best Predictor file is:

Gene Gene is the gene ID list of the predictive genes (section 4.4).

Number The number of occasions that each gene occurs in the predictive gene lists of the N-1 model. Using this we can quickly understand the distribution of this gene between gene lists from the N-1 model (section 6.1). This information can be used to quickly identify ‘noise genes’ by their low frequency in gene lists.

Delta The Absolute Difference (AD) is the mean of the differences between actual trait values and the values predicted for each line in the model. The closer the AD to 0 the closer the predictions are, on average, to the actual value. This value gives a good ‘feel’ for how close a prediction is to the actual, in relation to the trait of interest. For example, an AD of 4 might seem good if the trait was height in cm, and seem a fair tolerance for a prediction, however if the trait was plot yield in Kg, this value might be rather large.

se_delta The standard error of the Absolute Difference (seAD). This value gives a measure of the variability of the prediction, the smaller this value is the smaller the variability of the AD. An ideal predictive gene will have a small AD and seAD.

Ratio Ratio of the Difference (RD). This is the mean of the Ratio between actual trait values and the values predicted for each line in the model. This value is a more universal measure of AD, as all values are normalised to 1 (1 being a perfect match between prediction and actual), and the closer to 1 a gene is the better the gene appears to be for prediction. In theory this should allow the predictive ability of a gene can be assigned, independently of the trait value. For example, a particular gene might have an AD of −0.12 for yield weight, but an RD of 0.98. Saying that the gene is on average a 98% accurate predictor is perhaps an easier concept to understand.

se_ratio The standard error of the Ratio of the Difference (seRD). This value gives a measure of the variability of the ratio of the prediction, the smaller this value is the smaller the variability of the RD. An ideal predictive gene will have an RD close to 1 and a small seRD.

Using these parameters it is possible to generate more accurate gene list for the prediction of heterosis. This is a trial and error process at present, experimenting with different combinations of parameters will identify the best combination of genes for that trait. At present the most consistent combination of parameters for a good analysis has been a gene frequency of ALL MODELS (the predictive gene must appear in all N-1 models), and a Ratio (or RD) of >0.98 and <1.02.

In order to the gene combination with the parameters of gene frequency of all models, and an RD of >0.98 and <1.02, firstly sort (data>sort) the Best Predictor file by ‘number’ with the data descending. Before pressing ‘OK’ use the ‘THEN BY’ function to sort the data by Ratio ascending. Press OK.

This will bring all of the most consistent genes to the top of the worksheet. Select all of the genes that display an RD of between 0.98 and 1.02.

To test whether this is a good predictor list, calculate the average prediction for each accession and replicate for this best predictor gene list, and plot these predictions against the actual values for that trait.

An R²value between 0.5 and 1 suggests that gene list contains genes that are good markers for predictions of that trait.

Method 8, Best Predictor-Prediction
8.1 Best Predictor Prediction

This method is a variation on the standard predictive method (method 5), and uses the same GenStat programmes.

The only variation of this programme is to use the best predictor gene list in place of the 0.1% P-valve list, for generating the training and tester files.

c) Alternative “Basic” Prediction Protocol
Method 9, Bootstrapping

These instructions describe the first steps of the alternative prediction protocol. These methods are an addition to the basic regression method, and using the same GenStat programmes for the early stages. This Bootstrapping follows on directly from the basic regression (method 4), but prior to the prediction, and acts as an alternative method for identifying significant ‘marker’ genes. It works by generating a ‘customised T-table’ that is specific for the experiment in question.

9.1 Regression Bootstrapping
Open the ‘Basic Linear Regression Bootstrapping Programme’(GenStat Programme 5) in GenStat

Check that the input data filename is correct, and is opening to channel 2. This input file will be the same expression data file used for the initial regression (section 4.1)

Check that the output data files are going to the correct destinations and are opening to channels 2, 3, 4, and 5

Check that the numbers of genes to be analysed are correct for each output file (for Arabidopsis ATH-1 GeneChips this will be three files with 6000 genes and one with 4810), and that the print directives are pointing to the correct channels

To run the programme, ensure that both the programme window and output windows are open. Select the programme window and press Ctrl+W. This will set the programme running, check that the GenStat server icon (bottom right-hand corner of the screen) has changed colour to red.

To cancel the programme right click on the server icon and choose interrupt.

Once complete the GenStat icon will change colour back to green. This programme can take many days to run due to the large number calculations, and produces output files totaling up to 430 Mb, so plenty of disk space would be required. Once generated, the data for this programme needs to be extracted.

9.2 Data Extraction Programme

Open the ‘Basic Linear Regression Bootstrapping Data Extraction Programme’ (GenStat Programme 6) in GenStat

Check that the input files are correct (the output files from the bootstrapping programme)

Run the programme (Ctrl-W)

This programme prints to the Output window. Save this window as an .out file.

9.3 Analysing the Output

To analyse the data, first open it in Excel, select “delimited”>next>tick the “Tab” and “Space”>Finish Delete the first 32 rows, all of the gaps (after 6000, 12000, and 18000 probes), and all the text at the end of the data file. The data should be the same length as the regression file (for Arabidopsis 22810 lines long).

Add a new row, and label the columns “boot@5%” “boot@1%” and “boot@0.1%”

Add a new column to the beginning and label it “ID”

Fill the remaining cells of the ID column with a series 1-22810 (edit>fill>series>OK)

Copy all of these columns into the same sheet as the Observed significant probes data set, generated from the initial regression (section 4.4) with a one column gap

Leaving another single column gap label three further columns “sig@5%” “sig@1%” and “sig@0.1%”. In the first cell in the column “sig@5%” type “=E2−$B2”. Copy this to all of the cells in the three new columns.

9.4 Calculating Significance

Select all of the data columns>Data>Sort>Sig@5% descending Select all of the cells in this row where the value is positive. Colour these cells using the “paint” option, and record the number in this list. These are the genes significant at the 5% level

Select all of the data columns>Data>Sort>Sig@1% descending

Select all of the cells in this row where the value is positive. Colour these cells using the “paint” option, and record the number in this list. These are the genes significant at the 1% level

Select all of the data columns>Data>Sort>Sig@0.1% descending

Select all of the cells in this row where the value is positive. Colour these cells using the “paint” option, and record the number in this list. These are the genes significant at the 0.1% level

These results indicate whether or not the OBSERVED values differ significantly from random chance. These lists of significant genes can be used as markers, for the prediction of this trait as described in Method 5.

d) Transcription Remodelling Protocol

These analyses are designed to investigate the degree of difference in the transcriptome profiles between the hybrid and parental lines. There are two methods, investigating the transcriptome remodelling, and investigating the degree of dominance.

Method 10, Transcriptome Remodelling Fold-Change Experiments

This analysis is designed to investigating the transcriptome remodelling between hybrid and parental transcriptomes.

10.1 Create Data File

To create a data file for use in GenStat. Open master normalised expression Excel file>Copy the relevant data columns (in the order 3 hybrid files, 3 paternal files, 3 maternal files) into a new chart>add a colon “:” at the very end of the last row>save chart as .txt file>close file

Open the text file in GenStat>Enclose any title names in speech marks (“ ”), this should have the effect of turning the titles green>Find and replace (Ctrl+R)* with blanks>Save file again

10.2 Fold Change Analysis Programme
Open the ‘Basic Transcriptome Remodelling Programme’ (GenStat Programme 7) in GenStat

Check that the input data filename is correct, and is opening to channel 2

Check that the output data file is going to the correct destination and is opening to channel 3

Check that the ratios are set correctly for the ratio comparison under investigation.

For example, for

“if ((elem(i;k).gt.0.5).and.(elem(i;k).lt.2))”

This is set for a 2-fold ratio

For 3 fold the values would be 0.33 and 3

For 1.5 fold the values would be 0.66 and 1.5

The values are entered 3 times in the programme

Check that the ratios are set correctly for the fold change comparison under investigation. This is undertaken for all of the sections and should be set simply to the relevant fold level

To run the programme, ensure that both the programme window and output windows are open. Select the programme window and press ctrl>W. This will set the programme running, check that the GenStat server icon (bottom right-hand corner of the screen) has changed colour to red.

To cancel the programme right click on the server icon and choose interrupt

Once complete the GenStat icon will change colour back to green

10.3 Analysing the Output

To analyse the data, first open it in Excel, select “delimited”>next>tick the “Tab” and “Space”>Finish

Delete the first 266 rows in Excel, until you reach the column headers. Then delete bottom line beyond the data output

At the bottom of each column calculate the total number of significant patterns in that list. This can be done by using the directive “=SUM(C2:C22811)” in the first column and copying this into the remaining columns, ensuring that the correct data is selected.

The initial analysis is now complete. These values represent the OBSERVED data in the further analysis, following bootstrapping to generate the expected values.

Method 11, Transcriptome Remodelling Dominance Experiments

This analysis is designed to investigating dominance type transcriptome remodelling between hybrid and parental transcriptomes. Significance is calculated by comparing observed values to the expected generated from random data. Note, this programme is in its early stages, and is not easy to modify.

11.1 Create Data File

This experiment compares the expression of the profile of the hybrid against the mean of it parents. To do this we must first calculate these mean values.

Open a new Excel worksheet. Paste in the parent expression data (both maternal and paternal) for the first replicate of the first accession.

Calculate the mean value for each gene. This can be done using typing the equation=AVERAGE(A2:B2) into the next cell along. Copy this equation all the way down this column.

Open another worksheet and paste in the expression data of the first hybrid, copy the newly generated mean parental expression value and Edit>Paste Special>Values in to the next column. Repeat this for all of the replicates and accessions. Note that this programme is designed to analyse 3 replicates of each hybrid, a total of 6 columns per accession.

Once this is complete, save the file as .txt. Open the file in GenStat>enclose the titles in “ ” which should change their colour to green. Save the file again. This is the input file.

11.2 Running the Dominance Pattern Recognition Programme
Open the ‘Dominance Pattern Programme’ (GenStat Programme 8) in GenStat

Check the accession names (first scalar command) are correct. If you are investigating less than 8 accessions, you will need to change the numbers of these identifiers throughout the programme. Should you not wish to do this, running ‘pseudo-data’ in the remaining columns will not affect the output and can be ignored at the analysis stage.

Check the number of columns (second scalar command) is correct. It should be a 6× the number of accessions used (default is 48). Check that the out put file is correctly named and addressed.

Check that the input file is correct.

Check that the fold level is correct for the analysis you wish to under take. These values a recorded for 2 fold as

if (ratio.ge.0.5).and.(ratio.le.2) “calculates flags”

- calc heqmp=1
- elsif (ratio.gt.2)
- calc hgtmp=1
- elsif (ratio.lt.0.5)
- calc hltmp=1

For other fold levels change the 0.5 and 2 values to the appropriate value for that fold level.

For 3 fold the values would be 0.33 and 3

For 1.5 fold the values would be 0.66 and 1.5

Run the file by pressing Ctrl+W.

11.3 Analysing the Pattern Recognition Output

To analyse the output file, first open it in Excel, select “delimited”>next>tick the “Tab” and “Space”>Finish

You will see a file filled with ‘1s’ and ‘0s.’ Scroll to the bottom of this file. Underneath the first filled column write the equation “=SUM(B1:B22810)” (ensuring that all of the data in that column is filled). Copy this equation to all of the columns.

Each set of three ‘sum values’ represent the data output for a single accession (3 replicates), in the order that the data was loaded into the programme. These values represent

Column 1=The number of genes who's hybrid expression falls within the fold level criterion of the mid-parent value, for ALL 3 replicates.

Column 2=The number of genes who's hybrid expression is greater than that of the mid-parent value, by at least the fold level criterion, for ALL 3 replicates.

Column 3=The number of genes who's hybrid expression is lower than that of the mid-parent value, by at least the fold level criterion, for ALL 3 replicates.

Record these values, as the OBSERVED for these data.

11.4 Generating the EXPECTED value.

The expected data set is generated using the ‘Dominance Permutation Programme’ (GenStat Programme 9)

Check the number of columns (second scalar command) is correct. It should be a 6× the number of accessions used (default is 48).

Check that the out put file is correctly named and addressed.

Check that the input file is correct. This is the same input file as generated previously.

Check that the fold level is correct for the analysis you wish to under take. These values a recorded for 2 fold as before (section 11.1)

Check the number in the permutation loop is correct for then number of permutations you require. A minimum of 100 is recommended (although 1000 is ideal).

Run the file by pressing Ctrl+W.

This programme may take a few days to run, depending upon how many permutations are added.

11.5 Analysing the Pattern Recognition Permutation Output

To analyse the output file, first open it in Excel, select “delimited”>next>tick the “Tab” and “Space”>Finish

You will see a file filled with numbers. Scroll to the bottom of this file. Underneath the first filled column write the equation “=SUM(B1:B123)” (ensuring that all of the data in that column is filled). Copy this equation to all of the columns.

Each set of three ‘sum values’ represent the permuted data output for a single accession (3 replicates), in the order that the data was loaded into the programme. The three values represent the ‘expected by random chance’ versions of the values calculated in section 11.3.

The calculated values at the bottom of the columns are the EXPECTED values required for this analysis. As these data are effectively random it is acceptable to combine these for comparison, if time is limiting.

11.6 Analysing the Significance

The level of significance is calculated by chi square analysis, using the observed and expected data generated previously, and 1 degree of freedom.

Method 12, Transcriptome Remodelling Fold-Change Bootstrapping

This analysis is designed to assess the significance of fold change experiments described in Method 10. Significance is calculated by comparing observed values to expected generated from random data

12.1 Fold Change Bootstrapping
Open ‘Transcriptome Remodelling Bootstrap Programme’ (GenStat Programme 10) in GenStat

Check that the input data filename is correct, and is opening to channel 2. This will be the same input file as created in section 10.1.

Check that the output data files is going to the correct destinations and is opening to channels 3

Check that the number of randomisations is set to the desired value. As few as 50 randomisations are sufficient to give valid estimates of random chance, however 1000 would be ideal, but this can take many days to obtain.

Check that the ratios are set correctly for the ratio comparison under investigation.

For example:

“if ((elem(i;k).gt.0.5).and.(elem(i;k).lt.2))”

This is set for a 2-fold ratio

For 3 fold the values would be 0.33 and 3

For 1.5 fold the values would be 0.66 and 1.

To run the programme, ensure that both the programme window and output windows are open. Select the programme window and press Ctrl>W. This will set the programme running, check that the GenStat server icon (bottom right-hand corner of the screen) has changed colour to red.

To cancel the programme right click on the server icon and choose interrupt

Once complete the GenStat icon will change colour back to green

12.2 Analysing the Output

To analyse the data, first open it in Excel, select “delimited”>next>tick the “Tab” and “Space”>Finish

Delete the first 281 rows in Excel, until you reach the first row of data. Then delete bottom line beyond the data output

Select the whole sheet and go to data>sort>sort by “Column B”. This will remove the empty rows from the data.

At the bottom of each column calculate the mean number of significant patterns in that list. This can be done by using the directive “=AVERAGE(B2:B22811)” in the first column and copying this into the remaining columns, ensuring that the correct data is selected.

This will give the EXPECTED mean value, expected by random chance in the data

12.3 Calculating Significance

Calculating the significance of the observed patterns requires the use of a maximum likelihood chi square test

Firstly open GenStat>Stats>Statistical Tests>Chi-Square Goodness of Fit

Click on “Observed data create table”>Spreadsheet

Name the table OBS>Change rows and columns to 1>OK and ignore the error message

In the new table cell type the number of the first OBSERVED column sum value

Click on “expected frequencies create table”>Spreadsheet Name the table EXP>leave rows and columns as 1>OK and ignore the error message

In the new table cell type the number of the first Expected mean column mean value

On the Chi-Square window put 1 into the degrees of freedom box and click Run

Record the Chi-Square and P value that appears in the Output window.

Type the next OBSERVED value into the OBS box and click onto the output window

Type the next EXPECTED value into the EXP box and click onto the output window

On the Chi-Square window click Run, and record the new Chi-Square and P value that appears in the Output window

This should then be undertaken for all of the remaining OBSERVED and EXPECTED values.

These results indicate whether or not the OBSERVED values differ significantly from random chance.

Troubleshooting

This section describes some of the most common problems that can occur while running these programmes. Many of these problems/solutions apply to most of the programmes and as a result this section has not been divided up along programme lines. This list is not exhaustive, but should cover the majority of problems encountered. It should be noted that the ‘fault codes’ given are only for illustration, often many fault codes can result from the same root problem.

General GenStat problems

One common method of solving general problems is to ensure that all of the input files are closed prior to running the programme. This is achieved by typing (to close channel 2) “close ch=2” and then running this directive. By repeating this for channels 3-5, you can ensure that all of the channels are closed before running your programme, and thus avoiding conflicts.

Fault 16, code VA 11, statement 4 in for loop Command: fit [print=*]mpadv Invalid or incompatible type(s) Structure mpadv is not of the required type.

Remove comma from the end of the variate list.

Fault 29, code VA 11, statement 4 in for loop Command: fit [print=*]mpadv Invalid or incompatible type(s) Structure mpadv is not of the required type

Problem with the trait-data identifier. Possibly a different or missing identifier following the trait data variates (X-axis data)

Failure to Run Problems
—Too Many Values

Fault # code VA 5, statement 2 in for loop Command: read [ch=2; print=*; serial=n]exp Too many values

1) Ensure that the width parameter is large enough, set to a large enough value (400 is standard)

2) Ensure that if titles are included in the data file, that they are ‘greened out’ and not being read as data

3) Ensure that the “Unit” number (at the beginning of the programme) and the number of trait “variate”s are the same

—Too Few values

Fault 13, code VA 6, statement 4 in for loop Command: fit [print=*]mpadv Too few values (including null subset from RESTRICT) Structure mpadv has 37 values, whereas it should have 38

Ensure that the “Unit” number (at the beginning of the programme) and the number of trait “variate” are the same

Warning 6, code VA 6, statement 2 in for loop Command: read [ch=2; print=*; serial=n]exp Too few values (including null subset from RESTRICT)

Ensure that the “ntimes=” number and the number of probes in the data file are the same

File Opening Failure

Fault #, code IO 25, statement 2 in for loop Command: read [ch=2; print=*; serial=n]exp Channel for input or output has not been opened, or has been terminated Input File on Channel 2

1) Input file name is incorrect

2) Input file address is incorrect

Fault 32, code IO 25, statement 12 in for loop Command: print [ch=3; iprint=*; clprint=*; rlprint=*]bin Channel for input or output has not been opened, or has been terminated Output File on Channel 3

Output file address is incorrect.

Very Slow Running of Bootstrapping

Check that the programme is not having conflicts with anti-virus software. This should be solved by the computing department, but results from anti-virus software scanning the file each time it makes a write-to-disk operation. This can often be easily changed by modifying the scanning settings.

If all Else Fails

Check that the file C:\Temp\Genstat is not filled. This can result from too many temp (.tmp) files being generated as a result of bootstrapping programmes. Deleting these files may improve the running of the programme.

Finally VSN (GenStat providers) can be contacted at ‘support@vsn-intl.com’

Data Analysis Problems
Missing or Very High F-Problems

Ensure that the data has not ‘shifted’ at very low f-probabilities. At the regression stage (section 4.4), before creating the ID column, add an extra column to the beginning of the file. Insert the ID column, and sort by DF, if the data has shifted, this should become apparent here.

TABLE 1

Genes showing correlation of transcript abundance in

hybrids with the magnitude of heterosis exhibited by those

hybrids

Affymetrix
AGI Code
Description

Genes with transcript abundance in hybrids correlated with

strength of heterosis F < 0.001 MPH and F < 0.001 BPH

Positive correlation

251222_at
AT3G62580
expressed protein

257635_at
AT3G26280
cytochrome P450 family protein

250900_at
AT5G03470
serine/threonine protein phosphatase 2A (PP2A) regulatory

252637_at
AT3G44530
transducin family protein/WD-40 repeat family protein

253415_at
AT4G33060
peptidyl-prolyl cis-trans isomerase cyclophilin-type family protein

265226_at
AT2G28430
expressed protein

259770_s_at
AT1G07780
phosphoribosylanthranilate isomerase 1 (PAI1)

261075_at
AT1G07280
expressed protein

252501_at
AT3G46880
expressed protein

Genes with transcript abundance in hybrids correlated with

strength of heterosis F < 0.001 MPH and F < 0.01 BPH

Positive correlation

265217_s_at
AT4G20720
dentin sialophosphoprotein-related

253236_at
AT4G34370
IBR domain-containing protein

246592_at
AT5G14890
NHL repeat-containing protein

266018_at
AT2G18710
preprotein translocase secY subunit, chloroplast (CpSecY)

250755_at
AT5G05750
DNAJ heat shock N-terminal domain-containing protein

261555_s_at
AT1G63230
pentatricopeptide (PPR) repeat-containing protein

262321_at
AT1G27570
phosphatidylinositol 3- and 4-kinase family protein

246649_at
AT5G35150
CACTA-like transposase family (Ptta/En/Spm)

264214_s_at
AT1G65330
MADS-box family protein

261326_s_at
AT1G44180
aminoacylase, putative/N-acyl-L-amino-acid amidohydrolase,

255007_at
AT4G10020
short-chain dehydrogenase/reductase (SDR) family protein

246450_at
AT5G16820
heat shock factor protein 3 (HSF3)/heat shock transcription factor

Negative correlation

251608_at
AT3G57860
expressed protein

260595_at
AT1G55890
pentatricopeptide (PPR) repeat-containing protein

248940_at
AT5G45400
replication protein, putative

254958_at
AT4G11010
nucleoside diphosphate kinase 3, mitochondrial (NDK3)

257020_at
AT3G19590
WD-40 repeat family protein/mitotic checkpoint protein, putative

Genes with transcript abundance in hybrids correlated with

strength of heterosis F < 0.001 MPH and F < 0.05 BPH

Positive correlation

254431_at
AT4G20840
FAD-binding domain-containing protein

248941_s_at
AT5G45460
expressed protein

256770_at
AT3G13710
prenylated rab acceptor (PRA1) family protein

247443_at
AT5G62720
integral membrane HPP family protein

258059_at
AT3G29035
no apical meristem (NAM) family protein

246259_at
AT1G31830
amino acid permease family protein

262844_at
AT1G14890
invertase/pectin methylesterase inhibitor family protein

246602_at
AT1G31710
copper amine oxidase, putative

247092_at
AT5G66380
mitochondrial substrate carrier family protein

264986_at
AT1G27130
glutathione S-transferase, putative

Negative correlation

258747_at
AT3G05810
expressed protein

266427_at
AT2G07170
expressed protein

263908_at
AT2G36480
zinc finger (C2H2-type) family protein

250924_at
AT5G03440
expressed protein

249690_at
AT5G36210
expressed protein

245447_at
AT4G16820
lipase class 3 family protein

260383_s_at
AT1G74060
60S ribosomal protein L6 (RPL6B)

Genes with transcript abundance in hybrids correlated with

strength of heterosis F < 0.001 BPH and F < 0.01 MPH

Positive correlation

260260_at
AT1G68540
oxidoreductase family protein

252502_at
AT3G46900
copper transporter, putative

256680_at
AT3G52230
expressed protein

254651_at
AT4G18160
outward rectifying potassium channel, putative (KCO6)

264973_at
AT1G27040
nitrate transporter, putative

256813_at
AT3G21360
expressed protein

248697_at
AT5G48370
thioesterase family protein

267071_at
AT2G40980
expressed protein

246835_at
AT5G26640
hypothetical protein

252205_at
AT3G50350
expressed protein

Genes with transcript abundance in hybrids correlated with

strength of heterosis F < 0.001 BPH and F < 0.05 MPH

Positive correlation

266879_at
AT2G44590
dynamin-like protein D (DL1D)

253999_at
AT4G26200
1-aminocyclopropane-1-carboxylate synthase, putative/ACC

266268_at
AT2G29510
expressed protein

264565_at
AT1G05280
fringe-related protein

255408_at
AT4G03490
ankyrin repeat family protein

261166_s_at
AT1G34570
expressed protein

252375_at
AT3G48040
Rac-like GTP-binding protein (ARAC8)

264192_at
AT1G54710
expressed protein

259886_at
AT1G76370
protein kinase, putative

251255_at
AT3G62280
GDSL-motif lipase/hydrolase family protein

260197_at
AT1G67623
F-box family protein

253645_at
AT4G29830
transducin family protein/WD-40 repeat family protein

245621_at
AT4G14070
AMP-binding protein, putative

Negative correlation

246053_at
AT5G08340
riboflavin biosynthesis protein-related

264341_at
At1G70270
unknown protein

250349_at
AT5G12000
protein kinase family protein

256412_at
AT3G11220
Paxneb protein-related

TABLE 2

List of genes showing a correlation between

transcript abundance in parents with the magnitude of MPH

exhibited by their hybrids with Landsberg er msl.

2A: Genes showing positive correlation between transcript

abundance and trait value

AT5G10140
AT2G32340
AT4G04960
AT3G58010

AT1G03710
AT2G07717
AT3G06640
AT5G65520

AT3G29035
AT1G03620
AT1G02180
AT3G03590

AT5G24480
AT2G41650
AT4G25280
AT5G46770

AT3G47750
AT1G13980
AT5G20410
AT1G68540

AT1G65370
AT1G22090
AT4G01897
AT2G26500

AT5G66310
AT1G65310
AT1G31360
AT5G53540

AT1G70890
AT2G39680
AT2G21195
AT5G18150

AT2G06460
AT3G28750
AT5G13730
AT5G54095

AT4G19470
AT2G47780
AT5G43720
AT1G54780

AT1G54923
AT4G11760
AT3G59680
AT5G55190

AT5G60610
AT3G51000
AT2G27490
AT1G80600

AT5G46750
AT1G09540
AT2G16860
AT3G57040

AT1G27030
AT5G63080
AT2G20350
AT5G59400

AT4G18330
AT4G14410
AT2G13610
AT5G58960

AT5G61290
AT1G51360
AT4G00530
AT2G41890

AT3G23760
AT1G44180
AT1G14150
AT1G78790

AT3G47220
AT3G51530
AT2G14520
AT1G70760

AT3G05540
AT4G20720
AT1G72650
AT2G32400

AT3G47250
AT3G27400
AT1G64810
AT2G36440

AT3G22940
AT5G48340
AT4G24660
AT5G16610

AT3G23570
AT1G34460
AT5G38360
AT5G05700

AT5G25220
AT5G38790
AT5G03010
AT2G31820

AT5G28560
AT1G15000
AT3G21360
AT1G05190

AT1G14890
AT1G58080
AT3G56140
AT5G64350

AT5G27270
AT3G26130
AT3G17880
AT2G35795

AT4G10380
AT1G67910
AT1G60830
AT4G00420

AT2G07671
AT1G80130
AT1G79880
AT1G04830

AT2G16980
AT4G16170
AT2G42450
AT5G04410

AT2G45830
AT2G44480
AT2G36350
AT1G68550

AT3G09160
orf107f
AT5G04900

AT2G29710
AT1G21770
AT4G15545

AT5G17790
AT5G58130
AT4G21280

AT4G20860
AT2G35690
AT2G22905

AT1G04660
AT2G24040
AT2G32650

AT5G66380
AT1G18990
AT4G16470

nad9
AT4G10030
AT1G70480

AT5G56870
AT3G20270
AT2G36370

AT5G24310
ycf9
AT5G64280

AT5G06530
AT4G20830
AT3G10750

AT1G29410
AT1G71480
AT3G61070

AT1G67600
AT3G14560
AT5G11840

AT3G44120
AT5G66960
AT5G40960

AT3G58350
AT1G26230
AT1G76080

AT4G10410
AT4G28100
AT3G23540

AT1G70870
AT3G50810
AT1G34620

psbI
AT5G37540
AT3G12010

AT1G33910
AT1G03300
AT1G45050

AT3G10450
AT1G65070
AT4G17740

2B: Genes showing negative correlation between transcript

abundance and trait value

AT1G50120
AT4G22753

AT4G30890
AT5G66750

AT5G11560
AT3G53170

AT3G07170
AT5G28460

AT3G50000
AT3G22310

AT5G26100
AT3G47530

AT1G12310
AT3G02230

AT3G03070
AT4G37870

AT5G63220
AT3G30867

AT2G14835
AT1G25230

AT1G61770
AT2G14890

AT1G74050
AT1G47210

AT1G42480
AT4G19040

AT5G50000
AT5G10390

AT1G13900
AT1G71880

AT2G40290
AT3G52500

AT2G03220
AT1G04040

AT5G57870
AT5G06265

AT2G26140
AT4G34710

AT4G04910
AT3G60450

AT1G48140
AT4G21480

AT2G38970
AT3G23560

AT5G63400
AT5G45270

AT2G42910
AT2G34840

AT4G03550
AT5G11580

AT2G41110
AT3G23080

AT2G33845
AT3G09270

AT2G30530
AT5G40370

AT3G55360
AT4G23570

AT3G45770
AT5G53940

AT5G20280
AT4G36680

AT3G51550
AT1G64450

AT4G00860
AT3G19590

AT5G27120
AT5G45550

AT3G49310
AT2G32190

AT4G27430
AT2G37340

AT5G19320
AT3G11220

AT1G21830
AT2G32190

AT2G17440
AT4G27590

AT5G54100
AT2G22470

AT2G15000
AT1G31550

AT4G13270
AT2G22200

AT1G55890
AT5G45510

AT5G40890
AT5G45500

AT3G62960
AT1G59930

AT3G58180
AT4G21650

AT4G31630

AT3G57550

AT4G24370

TABLE 3

Genes used for prediction of leaf number at bolting in

vernalised plants; Transcript ID (AGI code)

3A: Genes showing positive correlation

between transcript abundance and

trait value

At1g02620

At1g09575

At1g10740

At1g16460

At1g27210

At1g27590

At1g29440

At1g29610

At1g30970

At1g32150

At1g32740

At1g35660

At1g36160

At1g43730

At1g45474

At1g52870

At1g52990

At1g53170

At1g55130

At1g55300

At1g57760

At1g58470

At1g67690

At1967960

At1968330

At1g68840

At1g70730

At1g70830

At1g75490

At1g77490

At2g02750

At2g03330

At2g03760

At2g06220

At2g07050

At2g15810

At2g16650

At2g19010

At2g20550

At2g22440

At2g23180

At2g23480

At2g23560

At2g24660

At2g24790

At2g25850

At2g27190

At2g27220

At2g30990

At2g31800

At2g32020

At2g34020

At2g40420

At2g40940

At2g42380

At2g42590

At2g43320

At2g44800

At3g02180

At3g05750

At3g09470

At3g10810

At3g11100

At3g11750

At3g13120

At3g13222

At3g14000

At3g14250

At3g14440

At3g15190

At3g18050

At3g19170

At3g19850

At3g20020

At3g21210

At3g22710

At3g27020

At3g27325

At3g27770

At3g30220

At3g44410

At3g44720

At3g45580

At3g45780

At3g45840

At3g48730

At3g51560

At3g53680

At3g55560

At3g57780

At3g60260

At3g60290

At3g60430

At3g61530

At3g62430

At4g02610

At4g08680

At4g10550

At4g10925

At4g12510

At4g13800

At4g14920

At4g17240

At4g17260

At4g17560

At4g18460

At4g18820

At4g19140

At4g19240

At4g19985

At4g23290

At4g23300

At4g27050

At4g27990

At4g29420

At4g31030

At4g32000

At4g32250

At4g32410

At4g32810

At4g35760

At4g35930

At4g39390

At4g39560

At5g04190

At5g14340

At5g14800

At5g16010

At5g16800

At5g17210

At5g17570

At5g38310

At5g40290

At5g41870

At5g44860

At5g45320

At5g45390

At5g47390

At5g48900

At5g49730

At5g51080

At5g51230

At5g52780

At5g52900

At5g53130

At5g55750

At5g56520

At5g57345

At5g59650

At5g63360

At5g63800

At5g67430

ndhA

ndhH

psbM

rpl33

3B: Genes showing negative correlation

between transcript abundance and

trait value

At1g01230

At1g03710

At1g03820

At1g03960

At1g07070

At1g13090

At1g13680

At1g14930

At1g15200

At1g18250

At1g18850

At1g19340

At1g20070

At1g22340

At1g24070

At1g24100

At1g24260

At1g29050

At1g29310

At1g29850

At1g32770

At1g51380

At1g51460

At1g52040

At1g52760

At1g52930

At1g53160

At1g59670

At1g61570

At1g62560

At1g63540

At1g64900

At1g68990

At1g69440

At1g69750

At1g69760

At1g74660

At1g75390

At1g77540

At1g77600

At1g78050

At1g78780

At1g79520

At1g80170

At2g01520

At2g01610

At2g04740

At2g14120

At2g17670

At2g18040

At2g18600

At2g18740

At2g19480

At2g19750

At2g19850

At2g20450

At2g22240

At2g22920

At2g23700

At2g25670

At2g27360

At2g28450

At2g29070

At2g34570

At2g35150

At2g36170

At2g37020

At2g40435

At2g41140

At2g45660

At2g45930

At2g47640

At3g02310

At3g02800

At3g03610

At3g05230

At3g09310

At3g09720

At3g12520

At3g13570

At3g14120

At3g15270

At3g16080

At3g18280

At3g19370

At3g20100

At3g20430

At3g22370

At3g22540

At3g25220

At3g28500

At3g49600

At3g51780

At3g52590

At3g53140

At3g56900

At4g02290

At4g03156

At4g08150

At4g11160

At4g14010

At4g14350

At4g14850

At4g15910

At4g17770

At4g18470

At4g18780

At4g19850

At4g21090

At4g29230

At4g29550

At4g35940

At4g39320

At5g01730

At5g01890

At5g02030

At5g03840

At5g04850

At5g04950

At5g05280

At5g06190

At5g07370

At5g08370

At5g11630

At5g15800

At5g16040

At5g17370

At5g17420

At5g20740

At5g22460

At5g22630

At5g37260

At5g40380

At5g42180

At5g43860

At5g44620

At5g45010

At5g47540

At5g50110

At5g50350

At5g50915

At5g52040

At5g53770

At5g54250

At5g55560

At5g57920

At5g58710

At5g59305

At5g59310

At5g59460

At5g60490

At5g60690

At5g60910

At5g61310

At5g62290

TABLE 4

Genes used for prediction of leaf number at bolting in

unvernalised plants; Transcript ID (AGI code)

4A. Genes showing positive correlation

between transcript abundance and

trait value

At1g02813

At1g02910

At1g03840

At1g08750

At1g13810

At1g15530

At1g16280

At1g18530

At1g20370

At1g21070

At1g24390

At1g24735

At1g28430

At1g28610

At1g31500

At1g31660

At1g33265

At1g34480

At1g42690

At1g45616

At1g47230

At1g47980

At1g48040

At1g50230

At1g51340

At1g52290

At1g52600

At1g53500

At1g55370

At1g56500

At1g59510

At1g59720

At1g61280

At1g62630

At1g63150

At1g63680

At1g66070

At1g66850

At1g68600

At1g69680

At1g70870

At1g74700

At1g74800

At1g76380

At1g76880

At1g77140

At1g77870

At1g78070

At1g78720

At1g78930

At2g01860

At2g01890

At2g02050

At2g03420

At2g03460

At2g03480

At2g04840

At2g07734

At2g12400

At2g13690

At2g17250

At2g17870

At2g20200

At2g23610

At2g28620

At2g30390

At2g30460

At2g35400

At2g38650

At2g41770

At2g42120

At2g44820

At3g01040

At3g01110

At3g01250

At3g01440

At3g01790

At3g02350

At3g03230

At3g03780

At3g07040

At3g11980

At3g13280

At3g15400

At3g16100

At3g17170

At3g17710

At3g17840

At3g17990

At3g18000

At3g18130

At3g18700

At3g20140

At3g20320

At3g21950

At3g23310

At3g24150

At3g25140

At3g25805

At3g25960

At3g27240

At3g27360

At3g27780

At3g28007

At3g29660

At3g51680

At3g55510

At3g59780

At4g00640

At4g01970

At4g02820

At4g04790

At4g05640

At4g08140

At4g08250

At4g12460

At4g14605

At4g16120

At4g17615

At4g18030

At4g18070

At4g18720

At4g21890

At4g22040

At4g22800

At4g23740

At4g26310

At4g26360

At4g30720

At4g31590

At4g33070

At4g33770

At4g38050

At4g38760

At5g05450

At5g05840

At5g07630

At5g07720

At5g08180

At5g10020

At5g10250

At5g10950

At5g11240

At5g11270

At5g16690

At5g20680

At5g25070

At5g26780

At5g27330

At5g36120

At5g40830

At5g41480

At5g42700

At5g46330

At5g46690

At5g47435

At5g51050

At5g51100

At5g53070

At5g56280

At5g57310

At5g59350

At5g59530

At5g63040

At5g63150

At5g63440

At5g64480

accD

nad4L

orf121b

orf294

rps12.1

rps2

ycf4

4B. Genes showing negative correlation

between transcript abundance and

trait value

At1g02360

At1g04300

At1g04810

At1g04850

At1g06200

At1g08450

At1g10290

At1g12360

At1g15920

At1g18700

At1g18880

At1g21000

At1g22190

At1g22930

At1g23050

At1g23950

At1g24340

At1g30720

At1g33990

At1g34300

At1g34370

At1g48090

At1g50570

At1g54250

At1g54360

At1g59590

At1g59960

At1g60710

At1g60940

At1g61560

At1g65980

At1g66080

At1g68920

At1g70090

At1g70590

At1g72300

At1g72890

At1g75400

At1g78420

At1g78870

At1g78970

At1g79380

At1g79840

At1g80630

At2g01060

At2g02390

At2g05070

At2g15080

At2g21180

At2g22800

At2g25080

At2g26300

At2g28070

At2g29120

At2g30140

At2g31350

At2g32850

At2g35900

At2g41640

At2g41870

At2g42270

At2g43000

At2g44130

At2g45600

At2g47250

At2g47800

At2g48020

At3g01650

At3g01770

At3g04070

At3g06130

At3g07690

At3g08650

At3g09735

At3g09840

At3g10500

At3g11410

At3g12480

At3g13062

At3g15900

At3g17770

At3g18370

At3g20250

At3g21640

At3g23600

At3g26520

At3g29180

At3g43520

At3g44880

At3g46960

At3g48410

At3g48760

At3g51010

At3g51890

At3g52550

At3g55005

At3g56310

At3g59950

At3g60245

At3g60980

At3g62590

At4g02470

At4g07950

At4g09800

At4g15420

At4g15620

At4g16760

At4g16830

At4g16845

At4g16990

At4g17040

At4g17340

At4g17600

At4g18260

At4g20110

At4g22190

At4g23880

At4g28160

At4g29735

At4g29900

At4g31985

At4g33300

At4g35060

At5g01650

At5g03455

At5g05680

At5g06960

At5g12250

At5g14240

At5g15880

At5g18900

At5g21070

At5g22450

At5g24450

At5g25120

At5g25440

At5g25490

At5g25560

At5g25880

At5g38850

At5g39610

At5g39950

At5g40250

At5g40330

At5g42310

At5g42560

At5g43460

At5g44390

At5g45050

At5g45420

At5g45430

At5g45500

At5g45510

At5g48180

At5g49000

At5g49500

At5g52240

At5g57160

At5g57340

At5g58220

At5g58350

At5g59150

At5g66810

At5g67380

TABLE 5

Genes used for prediction of ratio of leaf number at

bolting (vernalised plants)/leaf number at bolting

(unvernalised plants); Transcript ID (AGI code)

5A. Genes showing positive correlation

between transcript abundance and trait value

At1g01550

At1g02360

At1g02390

At1g02740

At1g02930

At1g03210

At1g03430

At1g07000

At1g07090

At1g08050

At1g08450

At1g09560

At1g10340

At1g10660

At1g12360

At1g13100

At1g13340

At1g14070

At1g14870

At1g15520

At1g15790

At1g15880

At1g15890

At1g18570

At1g19250

At1g19960

At1g21240

At1g21570

At1g22890

At1g22930

At1g22985

At1g23780

At1g23830

At1g23840

At1g26380

At1g26390

At1g28130

At1g28280

At1g28340

At1g28670

At1g30900

At1g32700

At1g32740

At1g32940

At1g34300

At1g34540

At1g35230

At1g35320

At1g35560

At1g43910

At1g45145

At1g48320

At1g49050

At1g50420

At1g50430

At1g50570

At1g51280

At1g51890

At1g53170

At1g54320

At1g54360

At1g55730

At1g57650

At1g57790

At1g58470

At1g61740

At1g62763

At1g66090

At1g66100

At1g66240

At1g66880

At1g67330

At1g67850

At1g68300

At1g68920

At1g69930

At1g71070

At1g71090

At1g72060

At1g72280

At1g72900

At1g73260

At1g73805

At1g75130

At1g75400

At1g78410

At1g79840

At1g80460

At2g02390

At2g02930

At2g03070

At2g03870

At2g03980

At2g05520

At2g06470

At2g11520

At2g13810

At2g14560

At2g14610

At2g15390

At2g16790

At2g17040

At2g17120

At2g17650

At2g17790

At2g18680

At2g18690

At2g20145

At2g22170

At2g22690

At2g22800

At2g23810

At2g24160

At2g24850

At2g25625

At2g26240

At2g26400

At2g26600

At2g26630

At2g28210

At2g28940

At2g29350

At2g29470

At2g30500

At2g30520

At2g30550

At2g30750

At2g30770

At2g31880

At2g31945

At2g32140

At2g33220

At2g33770

At2g34500

At2g35980

At2g39210

At2g39310

At2g40410

At2g40600

At2g40610

At2g41100

At2g42390

At2g43000

At2g43570

At2g44380

At2g45760

At2g46020

At2g46150

At2g46330

At2g46400

At2g46450

At2g46600

At2g47710

At3g01080

At3g03560

At3g04070

At3g04210

At3g04720

At3g08650

At3g08690

At3g08940

At3g09020

At3g09735

At3g09940

At3g10640

At3g10720

At3g11010

At3g11820

At3g11840

At3g12040

At3g13100

At3g13270

At3g13370

At3g13610

At3g13772

At3g13950

At3g13980

At3g14210

At3g14470

At3g16990

At3g18250

At3g18490

At3g18860

At3g18870

At3g20250

At3g22060

At3g22231

At3g22240

At3g22600

At3g22970

At3g23050

At3g23080

At3g23110

At3g25070

At3g25610

At3g26170

At3g26210

At3g26220

At3g26230

At3g26450

At3g26470

At3g28180

At3g28450

At3g28510

At3g43210

At3g44630

At3g45240

At3g45780

At3g47050

At3g47480

At3g48090

At3g48640

At3g50290

At3g50770

At3g50930

At3g51010

At3g51330

At3g51430

At3g51440

At3g51890

At3g52240

At3g52400

At3g52430

At3g53410

At3g56310

At3g56400

At3g56710

At3g57260

At3g57330

At3g60420

At3g60980

At3g61010

At3g61540

At4g00330

At4g00355

At4g00700

At4g00955

At4g01010

At4g01700

At4g02380

At4g02420

At4g02540

At4g03450

At4g04220

At4g05040

At4g05050

At4g08480

At4g10500

At4g11890

At4g11960

At4g12010

At4g12510

At4g12720

At4g13560

At4g14365

At4g14610

At4g15420

At4g15620

At4g16260

At4g16750

At4g16845

At4g16850

At4g16870

At4g16880

At4g16890

At4g16950

At4g16990

At4g17250

At4g17270

At4g17900

At4g19660

At4g21830

At4g22560

At4g22670

At4g23140

At4g23150

At4g23180

At4g23220

At4g23260

At4g23310

At4g25900

At4g26070

At4g26410

At4g27280

At4g29050

At4g29740

At4g29900

At4g33300

At4g34135

At4g34215

At4g35750

At4g36990

At4g37010

At5g04720

At5g05460

At5g06330

At5g06960

At5g07150

At5g08240

At5g10380

At5g10740

At5g10760

At5g11910

At5g11920

At5g13320

At5g14430

At5g18060

At5g18780

At5g21070

At5g22570

At5g24530

At5g25260

At5g25440

At5g26920

At5g27420

At5g35200

At5g37070

At5g37930

At5g38850

At5g38900

At5g39030

At5g39520

At5g39670

At5g40170

At5g40780

At5g40910

At5g41150

At5g42050

At5g42090

At5g42250

At5g42560

At5g43440

At5g43460

At5g43750

At5g44570

At5g44980

At5g45050

At5g45110

At5g45420

At5g45500

At5g45510

At5g48810

At5g51640

At5g51740

At5g52240

At5g52760

At5g53050

At5g53130

At5g53870

At5g54290

At5g54610

At5g55450

At5g55640

At5g57220

At5g58220

At5g59420

At5g60280

At5g60950

At5g61900

At5g62150

At5g62950

At5g63180

At5g64000

At5g66590

At5g67340

At5g67590

5B. Genes showing negative correlation between

transcript abundance and trait value

At1g03820

At1g05480

At1g06020

At1g06470

At1g07370

At1g18100

At1g20750

At1g28610

At1g31660

At1g44790

At1g47230

At1g49740

At1g51340

At1g52290

At1g61280

At1g63130

At1g63680

At1g64100

At1g66140

At1g67720

At1g69420

At1g69700

At1g71920

At1g74800

At1g76270

At1g77680

At1g78720

At1g78930

At2g01890

At2g03480

At2g13920

At2g14530

At2g17280

At2g18890

At2g20470

At2g22870

At2g33330

At2g36230

At2g36930

At2g37860

At2g39220

At2g39830

At2g40160

At2g44310

At3g05030

At3g05940

At3g06200

At3g10450

At3g10840

At3g13560

At3g13640

At3g15400

At3g17990

At3g18000

At3g18070

At3g19790

At3g20240

At3g21510

At3g24470

At3g27180

At3g28270

At3g45930

At3g47510

At3g49750

At3g50810

At3g52370

At3g54250

At3g54820

At3g57000

At4g04790

At4g08140

At4g10280

At4g10320

At4g12430

At4g14420

At4g16700

At4g17180

At4g19100

At4g23720

At4g23750

At4g24670

At4g26140

At4g31210

At4g31540

At4g34740

At4g35990

At4g38050

At4g38760

At5g02050

At5g02180

At5g02590

At5g02740

At5g06050

At5g07800

At5g08180

At5g14370

At5g15050

At5g19920

At5g20240

At5g22430

At5g22790

At5g23570

At5g27330

At5g27660

At5g41480

At5g43880

At5g49555

At5g51050

At5g51350

At5g53760

At5g53770

At5g55400

At5g55710

At5g56620

At5g57960

At5g59350

At5g61770

At5g62575

orf121b

TABLE 6

Genes for prediction of oil content of seeds, % dry

weight (vernalised plants); Transcript ID (AGI code)

6A. Genes showing positive correlation

between transcript abundance and

trait value

At1g02640

At1g02750

At1g02890

At1g04170

At1g05550

At1g05720

At1g08110

At1g08560

At1g09200

At1g09575

At1g10170

At1g10590

At1g13250

At1g15260

At1g17590

At1g18650

At1g23370

At1g27590

At1g29180

At1g31020

At1g34030

At1g42480

At1g48140

At1g49660

At1g51950

At1g52800

At1g54850

At1g55300

At1g60010

At1g60230

At1g61810

At1g63780

At1g64105

At1g64450

At1g65260

At1g66130

At1g66180

At1g67350

At1g69690

At1g70730

At1g71970

At1g74670

At1g74690

At2g01090

At2g14890

At2g17650

At2g18400

At2g18550

At2g18990

At2g20210

At2g20220

At2g20840

At2g21860

At2g25170

At2g25900

At2g27260

At2g29550

At2g30050

At2g30530

At2g31120

At2g31640

At2g31955

At2g32440

At2g36490

At2g37050

At2g37410

At2g38120

At2g38720

At2g39850

At2g39870

At2g39990

At2g40040

At2g40570

At2g41370

At2g42300

At2g42590

At2g42740

At2g44130

At2g44530

At2g45190

At3g02500

At3g03310

At3g03380

At3g05410

At3g06470

At3g07080

At3g14240

At3g15550

At3g17850

At3g18390

At3g19170

At3g24660

At3g28345

At3g51150

At3g53110

At3g53170

At3g55480

At3g55610

At3g57340

At3g57490

At3g57860

At3g60390

At3g60520

At3g61180

At3g62720

At3g63000

At4g00180

At4g00600

At4g00860

At4g00930

At4g01120

At4g01460

At4g02440

At4g02700

At4g03050

At4g03070

At4g07400

At4g11790

At4g12600

At4g12880

At4g14550

At4g15780

At4g16490

At4g17560

At4g20070

At4g21650

At4g27830

At4g29750

At4g32760

At4g34250

At4g38670

At5g02770

At5g04600

At5g07000

At5g07030

At5g07300

At5g07640

At5g07840

At5g08330

At5g08500

At5g09330

At5g10390

At5g15390

At5g17100

At5g19530

At5g22290

At5g23420

At5g24210

At5g25180

At5g25760

At5g26270

At5g27360

At5g32470

At5g36210

At5g36900

At5g37510

At5g38140

At5g40150

At5g41650

At5g44860

At5g45260

At5g45270

At5g46160

At5g47030

At5g47760

At5g48900

At5g50230

At5g51660

At5g52110

At5g52250

At5g54190

At5g54580

At5g55670

At5g55900

At5g57660

At5g58600

At5g60850

At5g62530

At5g62550

At5g63860

At5g65650

6B. Genes showing negative correlation

between transcript abundance and

trait value

At1g01790

At1g03710

At1g04220

At1g04960

At1g04985

At1g06550

At1g06780

At1g10550

At1g11070

At1g11280

At1g11630

At1g12550

At1g15310

At1g16060

At1g16540

At1g16880

At1g18830

At1g22480

At1g23120

At1g27440

At1g29700

At1g31580

At1g34040

At1g34210

At1g47410

At1g47960

At1g49710

At1g50580

At1g51070

At1g51440

At1g51580

At1g51805

At1g53690

At1g54560

At1g55850

At1g61667

At1g62860

At1g63320

At1g64950

At1g65480

At1g66930

At1g69750

At1g70250

At1g70270

At1g72800

At1g73177

At1g74590

At1g74650

At1g75690

At1g77000

At1g77380

At1g78450

At1g78740

At1g78750

At1g79950

At1g80130

At1g80170

At2g02960

At2g11690

At2g13770

At2g19570

At2g19850

At2g20410

At2g20500

At2g21630

At2g22920

At2g23340

At2g26170

At2g27760

At2g30020

At2g31450

At2g31820

At2g32490

At2g33480

At2g37970

At2g37975

At2g44850

At2g47570

At2g47640

At3g01720

At3g01970

At3g05210

At3g05540

At3g09410

At3g09480

At3g14395

At3g14720

At3g16520

At3g17800

At3g18980

At3g19320

At3g19710

At3g20270

At3g22370

At3g22740

At3g23170

At3g24400

At3g25120

At3g26130

At3g27960

At3g28050

At3g29787

At3g30720

At3g42840

At3g43240

At3g45070

At3g45270

At3g46500

At3g47320

At3g49360

At3g50810

At3g51030

At3g51580

At3g53690

At3g57630

At3g57680

At3g57760

At3g60170

At3g62390

At3g62400

At3g62410

At4g00960

At4g01070

At4g01080

At4g02450

At4g03060

At4g03260

At4g03400

At4g03500

At4g03640

At4g04900

At4g09680

At4g10150

At4g12020

At4g13050

At4g13180

At4g14040

At4g17390

At4g18210

At4g18780

At4g19980

At4g20840

At4g21400

At4g22790

At4g24130

At4g24940

At4g25040

At4g25890

At4g26610

At4g28350

At4g32240

At4g32690

At4g33040

At4g34240

At4g37150

At4g39780

At5g02820

At5g05420

At5g08600

At5g08750

At5g10180

At5g11600

At5g15600

At5g16520

At5g17060

At5g17420

At5g17790

At5g20180

At5g23010

At5g24510

At5g24850

At5g25640

At5g25830

At5g26665

At5g28560

At5g35400

At5g35520

At5g37300

At5g38780

At5g38980

At5g39550

At5g39940

At5g42180

At5g43480

At5g43500

At5g44030

At5g44740

At5g45170

At5g46490

At5g47050

At5g47630

At5g48110

At5g48340

At5g49530

At5g49540

At5g52380

At5g53090

At5g53350

At5g54660

At5g54690

At5g56030

At5g56700

At5g58980

At5g59305

At5g59690

At5g60160

At5g61640

At5g63590

At5g64816

TABLE 7

Genes with transcript abundance correlating with ratio

of 18:2/18:1 fatty acids in seed oil (vernalised plants);

Transcript ID (AGI code)

7A. Genes showing positive correlation

between transcript abundance and

trait value

At1g01730

At1g15490

At1g16060

At1g16540

At1g23120

At1g26730

At1g34220

At1g35260

At1g50580

At1g54560

At1g59620

At1g61400

At1g62860

At1g67550

At1g74650

At1g76690

At1g77380

At1g77590

At1g78450

At1g78750

At1g79950

At1g80170

At2g01120

At2g02960

At2g03680

At2g13770

At2g17220

At2g20410

At2g21630

At2g27090

At2g34440

At2g37975

At2g38010

At2g44850

At2g44910

At3g01720

At3g05210

At3g05270

At3g05320

At3g11880

At3g13840

At3g14450

At3g16520

At3g19930

At3g22690

At3g24400

At3g42840

At3g45640

At3g48580

At3g49360

At3g57760

At4g02450

At4g03060

At4g04650

At4g10150

At4g12020

At4g13050

At4g13180

At4g15260

At4g17390

At4g24920

At4g24940

At4g32240

At5g06730

At5g06810

At5g08750

At5g13890

At5g17060

At5g19560

At5g20180

At5g23010

At5g28500

At5g28560

At5g38980

At5g43330

At5g44740

At5g47050

At5g49540

At5g56910

At5g60160

At5g64816

7B. Genes showing negative correlation

between transcript abundance and

trait value

At1g02050

At1g04170

At1g04790

At1g06580

At1g08110

At1g13250

At1g14700

At1g15280

At1g18650

At1g26920

At1g29180

At1g29950

At1g33055

At1g35720

At1g49660

At1g51950

At1g52800

At1g52810

At1g54450

At1g60190

At1g60390

At1g60800

At1g62500

At1g62510

At1g63780

At1g64105

At1g66180

At1g66250

At1g66900

At1g67590

At1g67830

At1g69690

At1g75710

At1g76320

At2g04700

At2g14900

At2g16800

At2g18990

At2g20210

At2g20220

At2g20360

At2g21860

At2g25900

At2g27970

At2g31120

At2g34560

At2g36490

At2g37410

At2g38120

At2g39450

At2g39870

At2g40040

At2g40570

At2g42740

At2g44860

At3g02500

At3g07200

At3g08000

At3g11420

At3g11760

At3g14240

At3g24660

At3g26310

At3g27420

At3g44010

At3g47060

At3g53230

At3g55480

At3g55610

At3g56060

At3g57860

At3g60520

At3g60530

At3g61830

At3g62430

At3g62460

At4g00600

At4g00930

At4g03050

At4g03070

At4g12600

At4g13980

At4g14550

At4g15780

At4g16920

At4g17560

At4g22160

At4g25150

At4g26555

At4g36140

At4g36740

At5g07000

At5g07030

At5g10390

At5g15120

At5g17020

At5g17100

At5g17220

At5g18070

At5g25590

At5g26270

At5g37510

At5g40150

At5g43280

At5g46160

At5g47760

At5g51080

At5g51660

At5g52230

At5g54190

At5g55670

At5g57660

At5g63860

At5g65390

At5g65650

At5g65880

18:2 = linoleic acid

18:1 = oleic acid

TABLE 8

Genes for prediction of ratio of 18:3/18:1 fatty

acids in seed oil (vernalised plants); Transcript ID (AGI code)

8A. Genes showing positive correlation

between transcript abundance and

trait value

At1g11940

At1g15490

At1g22200

At1g23890

At1g28030

At1g33560

At1g49030

At1g51430

At1g59265

At1g62610

At1g64190

At1g69450

At1g71140

At1g78210

At2g07050

At2g31770

At2g35736

At2g46640

At3g14780

At3g16700

At3g26430

At3g46540

At3g49360

At3g51580

At4g01690

At4g08240

At4g11900

At4g12300

At4g18593

At4g23300

At4g24940

At4g38930

At4g39390

At5g03290

At5g05750

At5g08590

At5g11270

At5g13890

At5g14700

At5g16250

At5g17880

At5g18400

At5g20180

At5g22860

At5g23510

At5g27760

At5g28940

At5g44240

At5g44290

At5g44520

At5g46630

At5g47410

At5g49540

At5g49630

At5g54970

At5g55760

At5g55930

At5g64110

8B. Genes showing negative correlation

between transcript abundance and

trait value

At1g05550

At1g06500

At1g06580

At1g10320

At1g10980

At1g16170

At1g21080

At1g24070

At1g29180

At1g30880

At1g32310

At1g33055

At1g59900

At1g61810

At1g63780

At1g63850

At1g65560

At1g66130

At1g67830

At1g70430

At1g72260

At1g76720

At2g01090

At2g17550

At2g18100

At2g20490

At2g20515

At2g20585

At2g21090

At2g21860

At2g31840

At2g32160

At2g36570

At3g06470

At3g07080

At3g11410

At3g14150

At3g15900

At3g18940

At3g22210

At3g23325

At3g24660

At3g26240

At3g44600

At3g44890

At3g50380

At3g51780

At3g52090

At3g53110

At3g53390

At3g54290

At3g57860

At3g62080

At3g62860

At4g01330

At4g02210

At4g03070

At4g05450

At4g10320

At4g14870

At4g14890

At4g14960

At4g16830

At4g17410

At4g18975

At4g23870

At4g26170

At4g35240

At4g35880

At4g36380

At5g07640

At5g08540

At5g11310

At5g13970

At5g17010

At5g17100

At5g19830

At5g22290

At5g23330

At5g25120

At5g25180

At5g26270

At5g41970

At5g47550

At5g47760

At5g48580

At5g48760

At5g49190

At5g49500

At5g50950

At5g51660

At5g64650

At5g65010

18:3 = linolenic acid

18:1 = oleic acid

TABLE 9

Genes with transcript abundance correlating with ratio

of 18:3/18:2 fatty acids in seed oil (vernalised plants);

Transcript ID (AGI code)

9A. Genes showing positive correlation

between transcript abundance and

trait value

At1g01370

At1g01530

At1g02300

At1g02710

At1g03420

At1g05650

At1g08170

At1g11940

At1g13280

At1g13810

At1g15050

At1g20810

At1g20980

At1g21710

At1g22200

At1g23670

At1g23890

At1g27210

At1g33880

At1g44960

At1g51430

At1g51980

At1g57760

At1g57780

At1g59740

At1g60300

At1g60560

At1g62630

At1g62770

At1g66520

At1g66620

At1g70830

At1g71690

At1g77490

At1g79000

At1g79060

At2g02590

At2g02770

At2g07050

At2g07702

At2g11270

At2g15790

At2g18115

At2g19310

At2g28100

At2g28160

At2g32330

At2g34310

At2g35890

At2g38140

At2g39700

At2g41600

At2g43320

At2g44100

At2g45150

At2g45710

At2g45920

At2g46640

At2g47600

At3g05520

At3g09140

At3g10810

At3g11090

At3g12920

At3g14780

At3g16370

At3g18060

At3g18270

At3g22710

At3g22850

At3g22880

At3g27325

At3g28090

At3g29770

At3g31415

At3g43960

At3g45440

At3g46670

At3g48730

At3g59860

At3g61160

At3g61170

At3g62430

At4g01350

At4g07420

At4g11835

At4g12300

At4g12510

At4g17650

At4g18460

At4g18593

At4g18820

At4g20140

At4g23300

At4g25570

At4g31870

At4g32960

At4g33160

At4g35530

At4g37220

At4g39390

At5g03730

At5g05840

At5g05890

At5g07250

At5g08280

At5g17210

At5g18390

At5g20590

At5g22500

At5g22860

At5g26140

At5g26180

At5g28620

At5g28940

At5g35490

At5g38120

At5g40230

At5g43070

At5g45120

At5g45320

At5g46630

At5g47400

At5g49630

At5g51080

At5g51230

At5g51960

At5g56370

At5g57345

At5g59660

At5g62030

At5g64110

At5g64970

At5g65100

At5g66985

cox1

orf154

9B. Genes showing negative correlation

between transcript abundance and

trait value

At1g02500

At1g02780

At1g03710

At1g06500

At1g06520

At1g12750

At1g13090

At1g14930

At1g14990

At1g15200

At1g19340

At1g22500

At1g22630

At1g26170

At1g28060

At1g29850

At1g30530

At1g31340

At1g32310

At1g47480

At1g50140

At1g52040

At1g53590

At1g54250

At1g59670

At1g59900

At1g60710

At1g62560

At1g63540

At1g64140

At1g64900

At1g66690

At1g67860

At1g72510

At1g73177

At1g74880

At1g76260

At1g76560

At1g76890

At1g77540

At1g77600

At1g78080

At1g78750

At1g78780

At1g79430

At1g80170

At2g15630

At2g19740

At2g19850

At2g20490

At2g21640

At2g22920

At2g25670

At2g25970

At2g27360

At2g28200

At2g28450

At2g29070

At2g29120

At2g30000

At2g36750

At2g37585

At2g39910

At2g40010

At2g45930

At2g47250

At2g48020

At3g01860

At3g03610

At3g06110

At3g06790

At3g07230

At3g09480

At3g11410

At3g12090

At3g13490

At3g13800

At3g15900

At3g16080

At3g17770

At3g18940

At3g21250

At3g22210

At3g23325

At3g25220

At3g25740

At3g28700

At3g31910

At3g44890

At3g46490

At3g47320

At3g48860

At3g51780

At3g53390

At3g53500

At3g53630

At3g53890

At3g54260

At3g55005

At3g55630

At3g56900

At3g57180

At3g59810

At3g61100

At3g61980

At3g62040

At4g02075

At4g03240

At4g04620

At4g05450

At4g10120

At4g13195

At4g14020

At4g14350

At4g14615

At4g15230

At4g17410

At4g18330

At4g18780

At4g19850

At4g21090

At4g22380

At4g25890

At4g29230

At4g29550

At4g30220

At4g30290

At4g30760

At4g31310

At4g31985

At4g32240

At4g35240

At4g37150

At5g02610

At5g02670

At5g03455

At5g03540

At5g04420

At5g04850

At5g05680

At5g07370

At5g07690

At5g08535

At5g08540

At5g13970

At5g16040

At5g17930

At5g25120

At5g28080

At5g28500

At5g39550

At5g40540

At5g45840

At5g47050

At5g47540

At5g48110

At5g48580

At5g49530

At5g50915

At5g50940

At5g50950

At5g51010

At5g51820

At5g55560

At5g57160

At5g58520

At5g59460

At5g61450

At5g61830

At5g62290

At5g63590

At5g64140

At5g64190

At5g66530

18:3 = linolenic acid

18:2 = linoleic acid

TABLE 10

Genes with transcript abundance correlating with ratio

of 20C + 22C/16C + 18C fatty acids in seed oil (vernalised

plants); Transcript ID (AGI code)

10A. Genes showing positive correlation

between transcript abundance and

trait value

At1g01370

At1g03420

At1g04790

At1g06730

At1g09850

At1g11800

At1g21690

At1g43650

At1g49200

At1g50660

At1g53460

At1g53850

At1g55120

At1g60390

At1g62150

At1g69670

At1g79060

At1g79460

At1g79970

At2g25450

At2g35155

At2g40070

At2g40480

At2g45710

At2g46710

At2g47380

At3g04680

At3g09710

At3g10650

At3g14240

At3g26090

At3g26310

At3g26380

At3g29770

At3g44500

At3g56060

At3g57880

At4g13360

At4g14090

At4g24390

At4g26555

At4g31570

At4g35900

At5g05230

At5g05370

At5g10400

At5g17210

At5g23940

At5g24280

At5g24520

At5g25940

At5g37290

At5g38630

At5g40880

At5g47320

At5g52410

At5g54860

At5g55810

10B. Genes showing negative correlation

between transcript abundance and

trait value

At1g02410

At1g02475

At1g02500

At1g05350

At1g05360

At1g07260

At1g17310

At1g17970

At1g21110

At1g21190

At1g21350

At1g22520

At1g22910

At1g27000

At1g32050

At1g32070

At1g32310

At1g33330

At1g33600

At1g34580

At1g35650

At1g44750

At1g47480

At1g47920

At1g49240

At1g50630

At1g51940

At1g53650

At1g58300

At1g59900

At1g60810

At1g60970

At1g61400

At1g62090

At1g64150

At1g66540

At1g66645

At1g72920

At1g73120

At1g73250

At1g73940

At1g74620

At1g77590

At1g77960

At1g77970

At1g78750

At1g79890

At1g80640

At1g80700

At2g02500

At2g02960

At2g05950

At2g14170

At2g15560

At2g15930

At2g16750

At2g17265

At2g19800

At2g19950

At2g21070

At2g22570

At2g23360

At2g24610

At2g28850

At2g28930

At2g29680

At2g30000

At2g30270

At2g32160

At2g34690

At2g35520

At2g38220

At2g40010

At2g41830

At2g45740

At2g46730

At3g01520

At3g01860

At3g04610

At3g06100

At3g06110

At3g08990

At3g09530

At3g11400

At3g11500

At3g11780

At3g13450

At3g15150

At3g17690

At3g19515

At3g22690

At3g24030

At3g27050

At3g27920

At3g42120

At3g44020

At3g44890

At3g45430

At3g46370

At3g46770

At3g46840

At3g48720

At3g48860

At3g50050

At3g55005

At3g59180

At3g61950

At3g63310

At3g63330

At4g00030

At4g00234

At4g00950

At4g01410

At4g02500

At4g02790

At4g02850

At4g02960

At4g04110

At4g05460

At4g11820

At4g12310

At4g14100

At4g19100

At4g19490

At4g19500

At4g19520

At4g19550

At4g21410

At4g22330

At4g24950

At4g29380

At4g31720

At4g32240

At4g33330

At4g34265

At4g38240

At4g38980

At5g01970

At5g02010

At5g02610

At5g03090

At5g03220

At5g05060

At5g08535

At5g08540

At5g14680

At5g16980

At5g25530

At5g27410

At5g33250

At5g35260

At5g35740

At5g36890

At5g37330

At5g42310

At5g43330

At5g44880

At5g44910

At5g45490

At5g45550

At5g45680

At5g46540

At5g49080

At5g50130

At5g51010

At5g51820

At5g52070

At5g52430

At5g58120

At5g60710

16C fatty acid = palmitic

18C fatty acids = oleic, stearic, linoleic, linolenic

20C fatty acids = eicosenoic

22C fatty acids = erucic

TABLE 11

Genes with transcript abundance showing correlation

with ratio of (ratio of 20C + 22C/16C + 18C fatty acids in seed

oil (vernalised plants))/(ratio of 20C + 22C/16C + 18C fatty

acids in seed oil (unvernalised plants)); transcript ID (AGI

code)

11A. Genes showing positive

correlation between transcript

abundance and trait value

At1g01230

At1g02190

At1g02500

At1g02780

At1g02840

At1g03710

At1g06500

At1g06520

At1g06530

At1g10360

At1g11070

At1g12750

At1g13090

At1g13680

At1g14930

At1g15200

At1g17100

At1g19340

At1g22160

At1g22480

At1g22500

At1g23390

At1g26170

At1g27980

At1g28060

At1g29050

At1g29850

At1g30490

At1g30530

At1g31340

At1g31580

At1g32310

At1g32770

At1g37826

At1g52040

At1g52690

At1g52760

At1g53280

At1g53590

At1g54250

At1g55950

At1g56075

At1g59660

At1g59670

At1g59900

At1g60710

At1g62250

At1g62560

At1g63540

At1g64140

At1g64270

At1g64360

At1g64370

At1g64900

At1g66690

At1g67860

At1g68440

At1g69510

At1g69750

At1g70480

At1g72510

At1g73177

At1g73640

At1g74590

At1g74880

At1g76260

At1g76560

At1g76890

At1g77540

At1g77590

At1g77600

At1g78080

At1g78750

At1g78780

At1g79430

At1g80020

At1g80170

At2g01520

At2g01610

At2g06480

At2g14120

At2g14730

At2g15630

At2g18600

At2g19850

At2g19930

At2g20490

At2g21290

At2g21640

At2g21890

At2g22920

At2g25670

At2g25970

At2g27360

At2g28110

At2g28200

At2g28450

At2g29070

At2g29120

At2g32860

At2g33990

At2g36130

At2g36750

At2g36850

At2g37430

At2g37585

At2g38080

At2g38600

At2g39910

At2g40010

At2g44850

At2g45930

At2g47250

At2g47640

At2g48020

At3g01860

At3g02800

At3g03610

At3g04630

At3g06110

At3g06720

At3g06790

At3g07230

At3g07590

At3g08030

At3g09310

At3g09410

At3g09480

At3g10340

At3g11410

At3g12090

At3g13490

At3g13800

At3g14120

At3g15352

At3g15900

At3g16080

At3g16920

At3g17770

At3g18940

At3g20100

At3g20430

At3g21250

At3g22210

At3g22220

At3g22370

At3g22540

At3g22740

At3g25220

At3g25740

At3g26130

At3g28700

At3g29180

At3g29787

At3g31910

At3g44890

At3g45270

At3g46490

At3g46590

At3g47320

At3g47990

At3g48860

At3g49600

At3g50380

At3g51780

At3g52590

At3g53390

At3g53630

At3g53890

At3g54260

At3g54290

At3g55005

At3g55630

At3g56730

At3g56900

At3g57180

At3g57320

At3g59810

At3g60170

At3g60245

At3g60650

At3g61100

At3g61980

At3g62040

At4g00390

At4g02020

At4g02075

At4g03156

At4g04620

At4g04900

At4g05450

At4g09480

At4g10120

At4g12470

At4g13180

At4g13195

At4g14020

At4g14060

At4g14350

At4g14615

At4g15230

At4g15490

At4g15660

At4g17410

At4g18330

At4g18780

At4g19850

At4g21090

At4g21590

At4g22350

At4g22380

At4g22760

At4g24130

At4g25890

At4g27580

At4g29230

At4g29550

At4g30110

At4g30220

At4g30290

At4g31310

At4g31985

At4g32240

At4g32710

At4g35240

At4g35940

At4g36190

At4g37150

At4g37470

At4g37970

At4g39320

At5g01360

At5g02610

At5g03455

At5g03540

At5g03590

At5g04420

At5g04850

At5g05680

At5g06710

At5g07370

At5g07690

At5g08100

At5g08535

At5g08540

At5g08600

At5g09480

At5g10210

At5g10550

At5g11630

At5g13970

At5g16040

At5g17420

At5g17930

At5g18880

At5g20740

At5g24290

At5g25120

At5g28080

At5g28500

At5g28910

At5g29090

At5g39550

At5g40540

At5g40930

At5g42180

At5g42980

At5g43860

At5g45010

At5g45840

At5g47050

At5g47540

At5g48110

At5g48870

At5g49250

At5g49530

At5g50915

At5g50940

At5g50950

At5g51010

At5g51820

At5g52040

At5g53460

At5g54250

At5g55560

At5g57160

At5g58520

At5g58710

At5g59460

At5g59780

At5g60490

At5g61310

At5g61830

At5g62290

At5g63320

At5g63590

At5g64190

At5g65530

At5g66530

11B. Genes showing negative

correlation between transcript

abundance and trait value

At1g02300

At1g02710

At1g03420

At1g05650

At1g08170

At1g08770

At1g11940

At1g13280

At1g13810

At1g15050

At1g20810

At1g20980

At1g21710

At1g22200

At1g27210

At1g33880

At1g44960

At1g51430

At1g51980

At1g55130

At1g57760

At1g57780

At1g59520

At1g59740

At1g60560

At1g62050

At1g62630

At1g66620

At1g69450

At1g70830

At1g71690

At1g77490

At1g79000

At1g79060

At2g02770

At2g07050

At2g07702

At2g15790

At2g15810

At2g19310

At2g23180

At2g23560

At2g28100

At2g28160

At2g32330

At2g33540

At2g34310

At2g35780

At2g35890

At2g38140

At2g39700

At2g41600

At2g42590

At2g43130

At2g43320

At2g44100

At2g45150

At2g45710

At2g46640

At2g47600

At3g02290

At3g05520

At3g05750

At3g06710

At3g10810

At3g11090

At3g12920

At3g14780

At3g16370

At3g18060

At3g18270

At3g22710

At3g22850

At3g22880

At3g22990

At3g27325

At3g28090

At3g29770

At3g43510

At3g43960

At3g46510

At3g46670

At3g48730

At3g61160

At3g61170

At3g62430

At4g00860

At4g01350

At4g02610

At4g04750

At4g10780

At4g11835

At4g11900

At4g12300

At4g12510

At4g17650

At4g18460

At4g18593

At4g18820

At4g20140

At4g23300

At4g25570

At4g28740

At4g31870

At4g32960

At4g35530

At4g39390

At5g03730

At5g05840

At5g05890

At5g07250

At5g08280

At5g14800

At5g17210

At5g17570

At5g18390

At5g20590

At5g22860

At5g26180

At5g28940

At5g35490

At5g38120

At5g38310

At5g40230

At5g43070

At5g45320

At5g46630

At5g47400

At5g49630

At5g51080

At5g51230

At5g51960

At5g53580

At5g57345

At5g59660

At5g62030

At5g64110

orf154

16C fatty acid = palmitic

18C fatty acids = oleic, stearic, linoleic, linolenic

20C fatty acids = eicosenoic

22C fatty acids = erucic

TABLE 12

Genes with transcript abundance correlating with ratio

of polyunsaturated/monounsaturated + saturated 18C fatty acids

in seed oil (vernalised plants)

12A. Genes showing positive correlation

between transcript abundance and

trait value

At1g15490

At1g33560

At1g34220

At1g49030

At1g59620

At1g74650

At1g78210

At2g03680

At2g27090

At2g35736

At2g38010

At3g01720

At3g05210

At3g13840

At3g16520

At3g19930

At3g49360

At3g51580

At3g59660

At4g02450

At4g10150

At4g12020

At4g13050

At4g17390

At4g22840

At4g24940

At5g13890

At5g17060

At5g18400

At5g20180

At5g38980

At5g49540

At5g58910

12B. Genes showing negative correlation

between transcript abundance and

trait value

At1g02050

At1g05550

At1g06580

At1g08560

At1g10980

At1g13250

At1g15280

At1g29180

At1g33055

At1g34030

At1g51950

At1g52800

At1g52810

At1g60190

At1g60390

At1g60800

At1g61810

At1g62500

At1g63780

At1g64105

At1g65560

At1g66180

At1g66900

At1g67590

At1g67830

At1g69690

At1g76320

At2g20360

At2g20585

At2g21860

At2g25900

At2g27970

At2g36490

At2g39450

At2g39870

At2g40570

At2g41370

At2g44860

At3g02500

At3g07200

At3g07270

At3g11420

At3g14150

At3g14240

At3g24660

At3g27420

At3g44010

At3g44600

At3g53110

At3g53230

At3g55610

At3g57860

At3g60520

At4g00600

At4g00930

At4g03050

At4g03070

At4g12600

At4g12880

At4g15780

At4g17560

At4g20070

At4g21650

At4g22160

At4g26170

At4g36380

At4g36740

At5g07000

At5g07030

At5g09630

At5g17100

At5g18070

At5g25180

At5g25590

At5g26230

At5g26270

At5g40150

At5g46160

At5g47760

At5g48760

At5g49190

At5g51660

At5g52230

At5g54190

At5g63860

Polyunsaturated 18C fatty acids = linoleic, linolenic

Monounsaturated 18C fatty acid = oleic

Saturated 18C fatty acid = stearic

TABLE 13

Genes with transcript abundance showing correlation

with ratio of (ratio of polyunsaturated/monounsaturated +

saturated 18C fatty acids in seed oil (vernalised plants))/

(ratio of polyunsaturated/monounsaturated + saturated 18C fatty

acids in seed oil (unvernalised plants)); Transcript ID (AGI

code)

13A. Genes showing positive correlation

between transcript abundance and

trait value

At1g05040

At1g06225

At1g06650

At1g07640

At1g09740

At1g14340

At1g15410

At1g23130

At1g23880

At1g24490

At1g24530

At1g29410

At1g31240

At1g33265

At1g33790

At1g33900

At1g34400

At1g45180

At1g52590

At1g56270

At1g61090

At1g61180

At1g62540

At1g64190

At1g65330

At1g67910

At1g70870

At1g71140

At1g73630

At1g77070

At1g77310

At1g78720

At1g79460

At1g79640

At1g80190

At2g01350

At2g02080

At2g04520

At2g07550

At2g13570

At2g15040

At2g17600

At2g19110

At2g23560

At2g30695

At2g39750

At2g40313

At2g40980

At2g44740

At2g47300

At2g47340

At3g01510

At3g03780

At3g05165

At3g06060

At3g16190

At3g16500

At3g19490

At3g20390

At3g20950

At3g22850

At3g23570

At3g47750

At3g48730

At3g52750

At3g58830

At3g61160

At3g62580

At4g07960

At4g10470

At4g10920

At4g11560

At4g13050

At4g15440

At4g17180

At4g18810

At4g19470

At4g19770

At4g19985

At4g23920

At4g24940

At4g31920

At4g34480

At4g39560

At4g39660

At5g01690

At5g04740

At5g04750

At5g07580

At5g07630

At5g10140

At5g16140

At5g17210

At5g24230

At5g28410

At5g38360

At5g39080

At5g40670

At5g43830

At5g46030

At5g48800

At5g50250

At5g50970

At5g54095

At5g56185

At5g63020

At5g63150

At5g63370

At5g64630

At5g64830

At5g67060

orf107g

13B. Genes showing negative correlation

between transcript abundance and

trait value

At1g02500

At1g03430

At1g18570

At1g23750

At1g28670

At1g30530

At1g32310

At1g52550

At1g59840

At1g59900

At1g66970

At1g68560

At1g78970

At2g04550

At2g21830

At2g22425

At2g2g120

At2g29320

At2g29570

At2g35950

At3g01560

At3g01740

At3g01850

At3g04670

At3g09310

At3g10930

At3g17890

At3g17940

At3g19520

At3g20480

At3g23880

At3g26470

At3g27340

At3g44890

At3g45240

At3g46590

At3g47990

At3g50000

At3g50380

At3g51610

At3g52310

At3g53390

At3g55005

At3g58460

At3g61100

At3g62860

At4g01330

At4g01400

At4g02420

At4g02500

At4g02530

At4g05460

At4g08470

At4g10710

At4g14350

At4g15420

At4g15620

At4g16760

At4g18260

At4g19530

At4g23880

At5g01650

At5g04380

At5g23420

At5g24450

At5g25020

At5g25120

At5g40450

At5g42310

At5g42720

At5g44450

At5g45490

At5g45800

At5g49500

At5g50350

At5g57160

Polyunsaturated 18C fatty acids = linoleic, linolenic

Monounsaturated 18C fatty acid = oleic

Saturated 18C fatty acid = stearic

TABLE 14

Genes with transcript abundance showing correlation

with % 16:0 fatty acid in seed oil (vernalised plants);

Transcript ID (AGI code)

14A. Genes showing positive correlation between

transcript abundance and trait value

At1g03300
At1g74170
At2g41760
At3g60350
At5g10820

At1g03420
At1g74180
At2g42750
At3g60980
At5g13740

At1g04640
At1g75490
At2g43180
At3g61160
At5g15680

At1g08170
At1g78460
At2g45050
At3g61200
At5g17210

At1g13980
At1g79000
At2g48100
At3g61600
At5g19050

At1g20640
At1g80600
At3g01330
At3g63440
At5g20150

At1g22200
At1g80660
At3g02700
At4g00500
At5g22000

At1g24420
At1g80920
At3g04350
At4g00730
At5g22700

At1g25260
At2g05540
At3g04800
At4g02970
At5g24410

At1g27210
At2g05980
At3g05250
At4g03970
At5g25040

At1g28960
At2g07240
At3g11210
At4g04870
At5g27400

At1g33170
At2g07675
At3g11760
At4g10020
At5g35330

At1g33880
At2g07687
At3g12820
At4g11530
At5g38080

At1g34110
At2g07702
At3g14750
At4g12300
At5g38310

At1g35340
At2g07741
At3g15095
At4g13800
At5g38895

At1g35420
At2g11270
At3g15120
At4g16960
At5g38930

At1g36060
At2g15040
At3g15290
At4g18593
At5g39020

At1g47330
At2g15230
At3g15840
At4g18600
At5g41850

At1g47750
At2g15880
At3g16750
At4g20360
At5g41870

At1g48380
At2g18115
At3g17280
At4g26200
At5g42030

At1g52420
At2g18190
At3g18215
At4g28130
At5g44240

At1g52920
At2g19310
At3g20090
At4g30993
At5g47410

At1g52990
At2g19340
At3g20930
At4g32960
At5g50565

At1g53290
At2g22170
At3g21420
At4g33500
At5g50600

At1g54710
At2g23170
At3g22880
At4g33570
At5g51080

At1g56150
At2g23560
At3g25900
At4g35530
At5g51980

At1g61730
At2g25850
At3g26040
At4g37590
At5g53430

At1g63690
At2g27190
At3g26380
At4g40050
At5g54730

At1g64230
At2g27620
At3g27990
At5g01670
At5g55540

At1g65950
At2g29860
At3g29650
At5g02540
At5g55870

At1g66570
At2g35155
At3g46900
At5g03730
At5g65250

At1g66980
At2g35690
At3g49210
At5g05080
At5g65380

At1g67960
At2g37120
At3g53800
At5g05290
At5g66040

At1g70300
At2g38180
At3g55850
At5g05690
ndhG

At1g71000
At2g40070
At3g57270
At5g05700
ndhJ

At1g72650
At2g40970
At3g57470
At5g05750
orf111d

At1g73480
At2g41340
At3g60040
At5g05890
orf262

At1g73680
At2g41430
At3g60290
At5g06130
petD

14B. Genes showing negative correlation between

transcript abundance and trait value

At1g02500
At1g66200
At2g36880
At3g48130
At5g20110

At1g04040
At1g69250
At2g37020
At3g48720
At5g22630

At1g05760
At1g69700
At2g37110
At3g49720
At5g23540

At1g06410
At1g72450
At2g37400
At3g51780
At5g23750

At1g08580
At1g75390
At2g39560
At3g52500
At5g25920

At1g12310
At1g75590
At2g40010
At3g52900
At5g26330

At1g14780
At1g75780
At2g40230
At3g54430
At5g27990

At1g17620
At1g75840
At2g40660
At3g54980
At5g36890

At1g22710
At1g76260
At2g41830
At3g63200
At5g37330

At1g27000
At1g76550
At2g43290
At4g01100
At5g40770

At1g27700
At1g77970
At2g44745
At4g05530
At5g42150

At1g29310
At1g77990
At2g46730
At4g14350
At5g45550

At1g30510
At1g78090
At3g05020
At4g18570
At5g45650

At1g30690
At2g04780
At3g05230
At4g20120
At5g46280

At1g31340
At2g15860
At3g05490
At4g20410
At5g47210

At1g31660
At2g16280
At3g06160
At4g21090
At5g47540

At1g32050
At2g17670
At3g06510
At4g28780
At5g49510

At1g32450
At2g19540
At3g06930
At4g31480
At5g50740

At1g35670
At2g20270
At3g08990
At4g34870
At5g54900

At1g44800
At2g21580
At3g12370
At4g35510
At5g56350

At1g48830
At2g22470
At3g15150
At4g37190
At5g56950

At1g50010
At2g22475
At3g15260
At4g39280
At5g58030

At1g50500
At2g28510
At3g16340
At5g02740
At5g59290

At1g52040
At2g28760
At3g16760
At5g06160
At5g61660

At1g52910
At2g29070
At3g17780
At5g06190
At5g62165

At1g54830
At2g29540
At3g19590
At5g11630
At5g65710

At1g56170
At2g33430
At3g21020
At5g14680

At1g57620
At2g33620
At3g23620
At5g18280

At1g63000
At2g35120
At3g25220
At5g18690

At1g65010
At2g36620
At3g27200
At5g19910

16:0 = palmitic acid

TABLE 15

Genes with transcript abundance correlating with %

18:1 fatty acid in seed oil (vernalised plants); Transcript ID

(AGI code)

15A. Genes showing positive correlation between

transcript abundance and trait value

At1g05550
At1g67830
At3g14150
At4g20030
At5g18070

At1g06580
At1g69690
At3g19590
At4g20070
At5g19830

At1g08560
At1g70430
At3g24450
At4g21650
At5g23420

At1g10320
At1g72260
At3g24660
At4g22620
At5g25180

At1g10980
At1g74690
At3g26240
At4g23870
At5g25920

At1g13250
At1g75110
At3g28345
At4g28040
At5g26230

At1g15280
At2g01090
At3g44010
At4g30910
At5g26270

At1g21080
At2g17550
At3g44600
At4g32130
At5g40150

At1g23750
At2g19370
At3g48130
At4g35880
At5g41970

At1g29180
At2g20360
At3g53110
At4g36380
At5g47550

At1g33055
At2g20585
At3g53170
At4g36740
At5g47760

At1g34030
At2g21860
At3g54680
At5g06160
At5g48470

At1g51950
At2g25900
At3g57860
At5g06190
At5g48760

At1g52800
At2g32160
At3g60880
At5g07000
At5g49190

At1g52810
At2g36490
At3g62860
At5g07030
At5g49500

At1g61810
At2g37050
At4g00600
At5g07640
At5g50950

At1g62500
At2g39870
At4g01330
At5g08540
At5g51660

At1g63780
At2g41370
At4g03050
At5g10390
At5g54190

At1g64105
At2g44230
At4g03070
At5g11310
At5g58300

At1g65560
At3g02500
At4g12600
At5g13970
At5g63860

At1g66130
At3g06470
At4g12880
At5g14070
At5g64650

At1g67590
At3g08680
At4g15070
At5g17100
At5g65010

15B. Genes showing negative correlation between

transcript abundance and trait value

At1g04985
At2g27090
At3g51580
At5g05750
At5g39940

At1g15490
At2g35736
At3g59660
At5g08590
At5g44290

At1g26530
At2g38010
At4g02450
At5g11270
At5g47580

At1g28030
At3g01930
At4g12020
At5g13890
At5g49540

At1g33560
At3g05210
At4g12300
At5g16250
At5g55760

At1g49030
At3g16520
At4g13050
At5g18400

At1g59620
At3g17300
At4g17390
At5g20180

At1g76520
At3g20900
At4g24940
At5g23010

At1g78210
At3g49360
At4g32870
At5g27760

18:1 = oleic acid

TABLE 16

Genes with transcript abundance correlating with %

18:2 fatty acid in seed oil (vernalised plants); Transcript ID

(AGI code)

16A. Genes showing positive correlation between

transcript abundance and trait value

At1g02500
At1g65000
At2g44850
At3g54260
At5g04420

At1g06500
At1g67860
At2g46730
At3g54420
At5g06730

At1g10460
At1g72510
At3g01860
At3g55005
At5g07370

At1g11880
At1g73177
At3g02800
At3g55630
At5g08535

At1g13090
At1g73940
At3g03360
At3g57180
At5g08540

At1g13750
At1g74590
At3g05320
At3g61980
At5g08600

At1g14780
At1g76890
At3g06110
At4g00030
At5g09480

At1g14990
At1g77590
At3g07230
At4g01190
At5g11600

At1g19340
At1g77600
At3g08990
At4g01410
At5g16040

At1g21100
At1g78750
At3g09410
At4g02960
At5g16980

At1g21110
At1g79890
At3g09870
At4g03240
At5g19560

At1g21190
At1g79950
At3g10525
At4g04620
At5g27410

At1g22520
At1g80170
At3g11400
At4g09900
At5g28500

At1g23120
At1g80700
At3g15150
At4g10120
At5g38530

At1g26170
At2g01120
At3g15352
At4g10955
At5g38980

At1g30530
At2g02500
At3g17690
At4g11820
At5g39550

At1g32050
At2g02960
At3g19515
At4g12310
At5g42310

At1g32450
At2g05950
At3g20430
At4g13180
At5g43330

At1g33600
At2g13750
At3g22690
At4g14615
At5g45190

At1g34210
At2g13770
At3g22930
At4g15230
At5g47050

At1g34740
At2g15560
At3g24050
At4g15260
At5g47540

At1g35143
At2g15650
At3g27610
At4g18780
At5g48110

At1g35650
At2g17265
At3g27920
At4g19100
At5g50940

At1g42705
At2g21640
At3g28700
At4g19850
At5g51010

At1g47480
At2g22920
At3g30720
At4g21090
At5g51820

At1g47870
At2g27360
At3g30810
At4g25890
At5g53360

At1g50630
At2g28200
At3g31910
At4g27580
At5g55560

At1g52040
At2g28450
At3g44890
At4g29230
At5g56700

At1g52760
At2g29070
At3g46840
At4g32240
At5g57160

At1g54250
At2g30000
At3g48720
At4g34120
At5g57300

At1g55850
At2g35585
At3g48860
At4g37150
t5g61450

At1g59670
At2g37585
At3g48920
At5g01360
At5g61830

At1g59900
At2g37970
At3g50050
At5g02010
At5g64816

At1g60710
At2g37975
At3g53630
At5g02610
At5g66380

At1g62860
At2g40010
At3g53650
At5g03090
At5g66530

At1g63540
At2g41830
At3g53720
At5g03540

16B. Genes showing negative correlation between

transcript abundance and trait value

At1g01370
At1g66250
At2g34560
At3g56060
At5g05370

At1g02300
At1g66520
At2g39700
At3g57830
At5g08280

At1g02710
At1g68810
At2g40070
At3g57880
At5g17210

At1g03420
At1g70830
At2g41600
At3g60350
At5g17220

At1g04790
At1g71690
At2g43130
At3g61160
At5g18390

At1g06730
At1g79000
At2g44740
At3g62430
At5g22700

At1g11800
At1g79060
At2g44760
At4g00340
At5g24280

At1g12250
At1g79460
At2g45710
At4g01350
At5g24760

At1g15050
At1g80530
At3g05520
At4g12300
At5g26110

At1g20930
At2g04700
At3g07200
At4g12510
At5g26180

At1g20980
At2g06255
At3g11090
At4g13360
At5g28940

At1g21690
At2g07702
At3g11760
At4g13980
At5g35490

At1g21710
At2g15790
At3g14240
At4g17560
At5g38120

At1g22200
At2g17450
At3g18060
At4g17650
At5g45320

At1g28440
At2g18990
At3g22850
At4g24390
At5g51080

At1g47750
At2g23560
At3g26070
At4g26555
At5g52230

At1g50660
At2g28100
At3g26310
At4g31870
At5g55810

At1g53460
At2g29995
At3g26990
At4g32960
At5g59130

At1g55130
At2g32990
At3g29770
At4g35900
At5g59330

At1g57760
At2g33540
At3g48040
At4g39230
At5g63180

At1g62050
At2g34310
At3g55480
At5g05230
At5g64110

18:2 = linoleic acid

TABLE 17

Genes with transcript abundance correlating with %

18:3 fatty acid in seed oil (vernalised plants); Transcript ID

(AGI code)

17A. Genes showing positive correlation between

transcript abundance and trait value

At1g05060
At1g64230
At3g11090
At4g15960
At5g28940

At1g08170
At1g69450
At3g14780
At4g18460
At5g35350

At1g13280
At1g71800
At3g17840
At4g18593
At5g38310

At1g13580
At1g74290
At3g18270
At4g18820
At5g38460

At1g13810
At1g77140
At3g18650
At4g23300
At5g39790

At1g14660
At1g77490
At3g20230
At4g25570
At5g40230

At1g15330
At1g79000
At3g22710
At4g26870
At5g44240

At1g20370
At2g02360
At3g22850
At4g27900
At5g44290

At1g20810
At2g02770
At3g22880
At4g31150
At5g44520

At1g20980
At2g07050
At3g26430
At4g31870
At5g46270

At1g21710
At2g16090
At3g30140
At4g39390
At5g46630

At1g22200
At2g18115
At3g43790
At4g39920
At5g47400

At1g23890
At2g32330
At3g48730
At4g39930
At5g47410

At1g33265
At2g35890
At3g53680
At5g03290
At5g49630

At1g33880
At2g41600
At3g53900
At5g05840
At5g51960

At1g51430
At2g43180
At3g56590
At5g05890
At5g55760

At1g51980
At2g43320
At3g61480
At5g07250
At5g59660

At1g57780
At2g44690
At4g01690
At5g08280
At5g63370

At1g59780
At2g45150
At4g01970
At5g17210
At5g63740

At1g61830
At2g45560
At4g11835
At5g17520
At5g64110

At1g63200
At2g46640
At4g11900
At5g18400
orf114

At1g64190
At3g05520
At4g12300
At5g22860
ycf4

17B. Genes showing negative correlation between

transcript abundance and trait value

At1g02500
At1g76560
At3g09310
At4g02290
At5g07640

At1g05550
At1g76720
At3g10340
At4g03156
At5g08540

At1g06500
At1g77600
At3g11410
At4g04620
At5g09760

At1g06520
At1g78080
At3g12110
At4g05450
At5g13970

At1g06530
At1g78780
At3g12520
At4g09760
At5g16040

At1g07470
At1g78970
At3g13490
At4g10120
At5g16470

At1g09660
At1g79430
At3g14150
At4g10320
At5g18790

At1g10980
At2g01520
At3g15900
At4g12490
At5g19830

At1g13090
At2g15620
At3g16080
At4g13195
At5g24740

At1g13680
At2g18100
At3g20100
At4g14010
At5g25120

At1g14930
At2g18650
At3g21250
At4g14020
At5g25180

At1g15200
At2g19740
At3g22210
At4g14320
At5g27720

At1g18810
At2g20450
At3g22230
At4g14350
At5g35240

At1g18880
At2g20490
At3g23325
At4g14615
At5g40250

At1g21080
At2g20515
At3g25220
At4g16830
At5g42720

At1g23950
At2g20820
At3g25740
At4g17410
At5g45010

At1g24070
At2g21290
At3g26240
At4g18750
At5g45840

At1g26170
At2g21640
At3g29180
At4g21590
At5g47540

At1g28060
At2g21890
At3g46490
At4g22380
At5g47550

At1g29180
At2g23090
At3g47370
At4g23870
At5g47760

At1g29850
At2g25670
At3g47990
At4g25890
At5g48580

At1g30530
At2g25970
At3g48130
At4g26230
At5g49190

At1g33055
At2g26460
At3g49600
At4g26790
At5g49500

At1g50140
At2g27360
At3g50380
At4g29230
At5g49970

At1g52040
At2g28450
At3g51780
At4g29550
At5g50915

At1g52690
At2g29070
At3g52590
At4g30220
At5g50950

At1g53030
At2g29120
At3g53260
At4g30290
At5g51390

At1g54250
At2g36170
At3g53390
At4g31985
At5g51660

At1g59840
At2g36570
At3g53500
At4g35240
At5g52040

At1g59900
At2g41560
At3g53630
At4g35880
At5g53460

At1g61570
At2g41790
At3g53890
At4g35940
At5g57160

At1g61810
At2g47250
At3g54290
At4g36190
At5g58520

At1g63020
At2g47790
At3g55005
At4g36380
At5g59460

At1g63540
At3g03610
At3g56900
At4g37250
At5g61830

At1g64900
At3g04670
At3g58840
At4g39200
At5g64190

At1g66080
At3g05530
At3g59540
At5g01890
At5g64650

At1g66920
At3g06110
At3g62080
At5g03455
At5g65050

At1g72260
At3g06130
At3g62860
At5g04420
At5g65530

At1g74250
At3g06310
At4g02075
At5g04850
At5g65890

At1g74270
At3g06790
At4g02210
At5g05680

At1g74880
At3g08030
At4g02230
At5g06710

18:3 = linolenic acid

TABLE 18

Prediction of complex traits using models based on

accession transcriptome data

No. genes
Accession: Ga-0
Accession: Sorbo

Trait
in model
Measured
Predicted
Measured
Predicted
Ranking

Flowering time

Leaf number -
311
12.00
11.53
9.00
10.36
correct

vernalised

Leaf number -
339
16.10
18.87
24.20
20.33
correct

unvernalised

Leaf number -
485
0.75
0.71
0.37
0.61
correct

vern/unvern ratio

Seed oil content

Oil content % -
390
42.18
40.71
38.65
39.55
correct

vernalised

Seed fatty acid ratios

Chain length ratio -
228
0.21
0.21
0.14
0.18
correct

vernalised

Chain length ratio -
438
1.37
1.35
1.58
1.47
correct

vern/unvern

Desaturation ratio -
118
3.69
3.88
4.25
4.28
correct

vernalised

Desaturation ratio -
188
1.08
1.08
0.92
1.07
correct

vern/unvern

18:3/18:1 ratio -
151
1.98
2.15
1.91
2.07
correct

vernalised

18:3/18:2 ratio -
311
0.73
0.76
0.64
0.70
correct

vernalised

18:2/18:1 ratio -
197
2.72
2.86
3.01
3.37
correct

vernalised

Seed fatty acid absolute content

%16:0 - vernalised
337
9.29
10.34
8.37
9.90
correct

%18:1 - vernalised
151
11.97
11.83
13.14
11.18
not

correct

%18:2 - vernalised
288
32.40
32.31
38.38
34.85
correct

%18:3 - vernalised
313
23.81
24.36
24.10
24.06
not

correct

TABLE 19

Maize genes with transcript abundance in hybrids

correlating with heterosis

Probe Set ID
Representative Public ID

19A. Positive
Zm.18469.1.S1_at
BM378527

Correlation
ZmAffx.448.1.S1_at
AI677105

Zm.5324.1.A1_at
AI619250

Zm.886.5.S1_a_at
BU499802

Zm.5494.1.A1_at
AI622241

Zm.17363.1.S1_at
CK370960

Zm.1234.1.A1_at
BM073436

Zm.11688.1.A1_at
CK347476

Zm.695.1.A1_at
U37285.1

Zm.12561.1.A1_at
AI834417

Zm.17443.1.A1_at
CK347379

Zm.11579.2.S1_a_at
CF629377

Zm.342.2.A1_at
U65948.1

Zm.8950.1.A1_at
AY109015.1

Zm.18417.1.A1_at
CO528437

Zm.2553.1.A1_a_at
BQ619023

Zm.13487.1.A1_at
AY108830.1

Zm.13746.1.S1_at
CD998898

Zm.8742.1.A1_at
BM075443

Zm.17701.1.S1_at
CK370965

Zm.2147.1.A1_a_at
BM380613

Zm.10826.1.S1_at
BQ619411

ZmAffx.501.1.S1_at
AI691747

Zm.17970.1.A1_at
CK827393

Zm.12592.1.S1_at
CA830809

Zm.13810.1.S1_at
AB042267.1

Zm.4669.1.S1_at
AI737897

ZmAffx.351.1.S1_at
AI670538

Zm.5233.1.A1_at
CF626276

Zm.9738.1.S1_at
BM337426

Zm.8102.1.A1_at
CF005906

Zm.6393.4.A1_at
BQ048072

Zm.15120.1.A1_at
BM078520

Zm.17342.1.S1_at
CK370507

Zm.2674.1.A1_at
CF045775

Zm.4191.2.S1_a_at
BQ547780

Zm.14504.1.A1_at
AY107583.1

Zm.6049.3.A1_a_at
AI734480

Zm.2100.1.A1_at
CD001187

Zm.13795.2.S1_a_at
CF042915

Zm.5351.1.S1_at
AI619365

Zm.5939.1.A1_s_at
AI738346

Zm.2626.1.S1_at
AY112337.1

Zm.15454.1.A1_at
CD448347

Zm.4692.1.A1_at
AI738236

Zm.5502.1.A1_at
BM378399

Zm.2758.1.A1_at
AW067110

ZmAffx.752.1.S1_at
AI712129

Zm.14994.1.A1_at
BQ538997

Zm.12748.1.S1_at
AW066809

Zm.18006.1.A1_at
AW400144

ZmAffx.601.1.A1_at
AI715029

Zm.6045.7.A1_at
CK347781

Zm.81.1.S1_at
AY106090.1

ZmAffx.292.1.S1_at
AI670425

Zm.17917.1.A1_at
CF629332

ZmAffx.424.1.S1_at
AI676856

Zm.6371.1.A1_at
AY122273.1

Zm.1125.1.A1_at
BI993208

Zm.4758.1.S1_at
AY111436.1

Zm.17779.1.S1_at
CK370643

Zm.2964.1.S1_s_at
AY106674.1

Zm.17937.1.A1_at
CO529646

Zm.7162.1.A1_at
BM074641

Zm.13402.1.S1_at
AF457950.1

Zm.18189.1.S1_at
CN844773

Zm.4312.1.A1_at
BM266520

Zm.2141.1.A1_at
BM347927

Zm.19317.1.S1_at
CO521190

Zm.4164.2.A1_at
CF627018

Zm.8307.2.A1_a_at
CF635305

Zm.16805.2.A1_at
CF635679

Zm.19080.1.A1_at
CO522397

Zm.1489.1.A1_at
CO519391

Zm.13462.1.A1_at
CO522224

ZmAffx.191.1.S1_at
AI668423

Zm.19037.1.S1_at
CA404446

Zm.4109.1.A1_at
CD441071

Zm.2588.1.S1_at
AI714899

Zm.10920.1.A1_at
CA399553

Zm.1710.1.S1_at
AY106827.1

Zm.16301.1.S1_at
CK787019

Zm.4665.1.A1_at
CK370646

Zm.7336.1.A1_at
AF371263.1

Zm.16501.1.S1_at
AY108566.1

Zm.10223.1.S1_at
BM078528

Zm.3030.1.A1_at
CA402193

Zm.14027.1.A1_at
AW499409

Zm.8796.1.A1_at
BG841012

Zm.13732.1.S1_at
AY106236.1

Zm.4870.1.A1_a_at
CK985786

ZmAffx.555.1.A1_x_at
AI714437

Zm.7327.1.A1_at
AF289256.1

Zm.2933.1.A1_at
AW091233

Zm.949.1.A1_s_at
CF624182

Zm.15510.1.A1_at
CD441066

Zm.8375.1.A1_at
BM080176

Zm.4824.6.S1_a_at
AI665566

Zm.612.1.A1_at
AF326500.1

Zm.12881.1.A1_at
CA401025

Zm.7687.1.A1_at
BM072867

Zm.10587.1.A1_at
AY107155.1

Zm.17807.1.S1_at
CK371584

Zm.3947.1.S1_at
BE510702

Zm.6626.1.A1_at
AI491257

Zm.1527.2.A1_a_at
BM078218

Zm.6856.1.A1_at
AI065480

ZmAffx.1477.1.S1_at
40794996-104

Zm.12588.1.S1_at
CO530559

Zm.15817.1.A1_at
D87044.1

Zm.16278.1 A1_at
CO532740

Zm.18877.1.A1_at
CO529651

Zm.2090.1.A1_at
AI691653

Zm.5160.1.A1_at
CD995815

Zm.17651.1.A1_at
CF043781

Zm.15722.2.A1_at
CA404232

Zm.5456.1.A1_at
AI622004

Zm.13992.1.A1_at
CK827024

Zm.3105.1.S1_at
AY108981.1

ZmAffx.941.1.S1_at
AI820356

Zm.3913.1.A1_at
CF000034

Zm.1657.1.A1_at
BG842419

Zm.13200.1.A1_at
CF635119

Zm.18789.1.S1_at
CO525842

Zm.10090.1.A1_at
BM382713

Zm.312.1.A1_at
S72425.1

Zm.9118.1.A1_at
BM336433

Zm.9117.1.A1_at
CF636944

Zm.610.1.A1_at
AF326498.1

Zm.5725.1.A1_at
CK986059

Zm.6805.1.S1_a_at
BG266504

Zm.1621.1.S1_at
AY107628.1

Zm.1997.1.A1_at
BM075855

ZmAffx.1086.1.S1_at
AW018229

Zm.17377.1.A1_at
CK144565

Zm.15822.1.S1_at
AY313901.1

Zm.5486.1.A1_at
AI629867

Zm.4469.1.S1_at
AI734281

Zm.8620.1.S1_at
BM073355

Zm.18031.1.A1_at
CK985574

Zm.13597.1.A1_at
CF630886

Zm.75.2.S1_at
CK371662

Zm.4327.1.S1_at
BI993026

Zm.17157.1.A1_at
BM074525

Zm.7342.1.A1_at
AF371279.1

Zm.2781.1.S1_at
CF007960

Zm.3944.1.S1_at
M29411.1

Zm.98.1.S1_at
AY106729.1

Zm.3892.6.A1_x_at
CD441708

Zm.12051.1.A1_at
AI947869

Zm.4193.1.A1_at
AY106195.1

Zm.2197.1.S1_a_at
AF007785.1

Zm.12164.1.A1_at
CO521714

Zm.15998.1.A1_at
CA403811

ZmAffx.1186.1.A1_at
AY110093.1

Zm.19149.1.S1_at
CO526376

Zm.14820.1.S1_at
AY106101.1

Zm.15789.1.A1_a_at
CD440056

ZmAffx.655.1.A1_at
AI715083

Zm.19077.1.A1_at
CO526103

Zm.698.1.A1_at
AY112103.1

Zm.10332.1.A1_at
BQ048110

Zm.10642.1.A1_at
BQ539388

Zm.11901.1.A1_at
BM381636

ZmAffx.1494.1.S1_s_at
40794996-111

ZmAffx.871.1.A1_at
AI770769

Zm.13463.1.S1_at
AY109103.1

Zm.18502.1.A1_at
CF623953

Zm.2171.1.A1_at
BG841205

Zm.14069.2.A1_at
AY110342.1

Zm.6036.1.S1_at
AY110222.1

Zm.17638.1.S1_at
CK368502

Zm.813.1.S1_at
AF244683.1

Zm.8376.1.S1_at
BM073880

Zm.16922.1.A1_a_at
CD998944

Zm.16913.1.S1_at
BQ619268

Zm.12851.1.A1_at
CA400703

Zm.3225.1.S1_at
BE512131

Zm.13628.1.S1_at
CD437947

Zm.9998.1.A1_at
BM335619

Zm.15967.1.S1_at
CA404149

Zm.6366.2.A1_at
CA398774

Zm.1784.1.S1_at
BF728627

Zm.19031.1.A1_at
BU051425

Zm.6170.1.A1_a_at
AY107283.1

Zm.3789.1.S1_at
AW438148

Zm.4310.1.A1_at
BM078907

Zm.3892.10.A1_at
AI691846

RPTR-Zm-U47295-1_at
RPTR-Zm-U47295-1

Zm.15469.1.S1_at
CD438450

Zm.7515.1.A1_at
BM078765

Zm.6728.1.A1_at
CN844413

Zm.16798.2.A1_a_at
CF633780

Zm.455.1.S1_a_at
AF135014.1

Zm.10134.1.A1_at
BQ619055

19B. Negative
Zm.10492.1.S1_at
CA826941

Correlation
Zm.5113.2.A1_a_at
CF633388

Zm.3533.1.A1_at
AY110439.1

ZmAffx.674.1.S1_at
AI734487

ZmAffx.1060.1.S1_at
AI881420

ZmAffx.361.1.A1_at
AI670571

Zm.10190.1.S1_at
CF041516

Zm.12256.1.S1_at
BU049042

ZmAffx.1529.1.S1_at
40794996-124

Zm.19120.1.A1_at
CO523709

Zm.2614.2.A1_at
CD436098

Zm.10429.1.S1_at
BQ528642

Zm.13457.1.S1_at
AY109190.1

Zm.4040.1.A1_at
AI834032

Zm.5083.2.S1_at
AY109962.1

Zm.5704.1.A1_at
AI637031

Zm.3934.1.S1_at
AI947382

Zm.6478.1.S1_at
AI692059

Zm.1161.1.S1_at
BE511616

Zm.12135.1.A1_at
BM334402

Zm.4878.1.A1_at
AW288995

Zm.18825.1.A1_at
CO527281

Zm.4087.1.A1_at
AI834529

Zm.9321.1.A1_at
AY108492.1

Zm.9121.1.A1_at
CF631233

Zm.7797.1.A1_at
BM079946

Zm.1228.1.S1_at
CF006184

Zm.1118.1.S1_at
CF631214

Zm.3612.1.A1_at
AY103746.1

Zm.17612.1.S1_at
CK368134

Zm.7082.1.S1_at
CF637101

Zm.6188.2.A1_at
AY108898.1

Zm.6798.1.A1_at
CA400889

Zm.6205.1.A1_at
CK985870

Zm.582.1.S1_at
AF186234.2

Zm.5798.1.A1_at
BM072971

Zm.8598.1.A1_at
BM075029

Zm.15207.1.A1_at
BM268677

Zm.4164.3.A1_s_at
CF636517

Zm.1802.1.A1_at
BM078736

Zm.13583.1.S1_at
AY108161.1

ZmAffx.513.1.A1_at
AI692067

ZmAffx.853.1.A1_at
AI770653

Zm.2128.1.S1_at
AY105930.1

Zm.18488.1.A1_at
BM269253

Zm.10471.1.A1_at
CA399504

ZmAffx.716.1.S1_at
AI739804

Zm.10756.1.S1_at
CD975109

Zm.1482.5.S1_at
AI714961

ZmAffx.494.1.S1_at
AI770346

Zm.5688.1.A1_at
AY105372.1

Zm.4673.2.A1_a_at
CA400524

Zm.9542.1.A1_at
CF624708

Zm.10557.2.A1_at
BQ538273

ZmAffx.1051.1.A1_at
AI881809

Zm.3724.1.A1_x_at
CF627032

Zm.6575.1.A1_at
AI737943

Zm.18046.1.A1_at
BI993031

Zm.4990.1.A1_at
AI586885

ZmAffx.891.1.A1_at
AI770848

Zm.10750.1.A1_at
AY104853.1

Zm.6358.1.S1_at
CA402045

Zm.2150.1.A1_a_at
CD977294

Zm.4068.2.A1_at
BQ619512

Zm.1327.1.A1_at
BE643637

Zm.3699.1.S1_at
U92045.1

ZmAffx.175.1.S1_at
AI668276

Zm.311.1.A1_at
BM268583

Zm.19326.1.A1_at
CO530193

Zm.728.1.A1_at
BM338202

ZmAffx.963.1.A1_at
AI833792

Zm.5155.1.S1_at
CD433333

Zm.3186.1.S1_a_at
CK827152

ZmAffx.1164.1.A1_at
AW455679

Zm.10069.1.A1_at
AY108373.1

Zm.17869.1.S1_at
CK701080

Zm.1670.1.A1_at
AY109012.1

Zm.737.1.A1_at
D45403.1

Zm.9947.1.A1_at
BM349454

Zm.3553.1.S1_at
AY112170.1

Zm.11794.1.A1_at
BM380817

ZmAffx.139.1.S1_at
AI667769

Zm.5328.2.A1_at
AW258090

Zm.534.1.A1_x_at
AF276086.1

Zm.17724.3.S1_x_at
CK370253

Zm.13806.1.S1_at
AY104790.1

Zm.8710.1.A1_at
BM333560

Zm.14397.1.A1_at
BM351246

Zm.5495.1.S1_at
AY103870.1

Zm.4338.3.S1_at
AW000126

Zm.9199.1.A1_at
CO522770

Zm.15839.1.A1_at
AY109200.1

Zm.12386.1.A1_at
CF630849

Zm.7495.1.A1_at
CF636496

Zm.2181.1.S1_at
BF727788

ZmAffx.144.1.S1_at
AI667795

Zm.4449.1.A1_at
BM074466

Zm.8111.1.S1_at
CD972041

Zm.17784.1.S1_at
CK370703

Zm.16247.1.S1_at
AY181209.1

Zm.3699.5.S1_a_at
AY107222.1

Zm.7823.1.S1_at
BM078187

Zm.5866.1.S1_at
CF044154

Zm.6469.1.S1_at
BE345306

Zm.10434.1.S1_at
BQ577392

Zm.16929.1.S1_at
AW055615

Zm.7572.1.S1_at
CO521006

Zm.6726.1.S1_x_at
AI395973

ZmAffx.387.1.S1_at
AI673971

Zm.9543.1.A1_at
CK370330

Zm.1632.1.S1_at
AY104990.1

Zm.8897.1.S1_at
BM079371

Zm.14869.1.A1_at
AI586666

Zm.1059.2.A1_a_at
CO518029

Zm.4611.1.A1_s_at
BG842817

ZmAffx.1172.1.S1_at
AW787638

Zm.8751.1.A1_at
BM348137

Zm.1066.1.S1_a_at
AY104986.1

Zm.13931.1.S1_x_at
Z35302.1

Zm.9916.1.A1_at
BM348997

ZmAffx.1203.1.A1_at
BE128869

Zm.9468.1.S1_at
AY108678.1

Zm.4049.1.A1_at
AI834098

Zm.14325.1.S1_at
AY104177.1

Zm.9281.1.A1_at
BM267756

Zm.229.1.S1_at
L33912.1

Zm.2244.1.S1_a_at
CF348841

Zm.4587.1.A1_at
CO528135

Zm.9604.1.A1_at
BM333654

Zm.7831.1.A1_at
BM080062

Zm.648.1.S1_at
AF144079.1

Zm.5018.3.A1_at
AI668145

ZmAffx.962.1.A1_at
AI833777

Zm.11663.1.A1_at
CO531620

Zm.19167.2.A1_x_at
CF636656

ZmAffx.776.1.A1_at
AI746212

Zm.4736.1.A1_at
AY108189.1

ZmAffx.1053.1.A1_at
AI881846

Zm.4248.1.A1_at
AY110118.1

ZmAffx.1523.1.S1_at
40794996-120

Zm.4922.1.A1_at
AI586404

Zm.6601.2.A1_a_at
BM078978

Zm.18355.1.A1_at
CO532040

Zm.16351.1.A1_at
CF623648

Zm.12150.1.S1_at
AY106576.1

ZmAffx.1428.1.S1_at
11990232-13

Zm.11468.1.A1_at
BM382262

Zm.11550.1.A1_at
BG320003

Zm.12235.1.A1_at
CF972364

Zm.10911.1.A1_x_at
BM340657

Zm.1497.1.S1_at
AF050631.1

Zm.2440.1.A1_a_at
BM347886

Zm.6638.1.A1_at
AI619165

ZmAffx.840.1.S1_at
AI770592

Zm.15800.2.A1_at
CD998623

Zm.2220.4.S1_at
AY110053.1

Zm.5791.1.A1_at
AY103953.1

Zm.9435.1.A1_at
BM268868

Zm.2565.1.S1_at
AY112147.1

ZmAffx.964.1.A1_at
AI833796

Zm.3134.1.A1_at
AY112040.1

Zm.8549.1.A1_at
BM339103

Zm.10807.2.A1_at
CD970321

Zm.3286.1.A1_at
BG265986

Zm.11983.1.A1_at
BM382368

ZmAffx.841.1.A1_at
AI770596

Zm.2950.1.A1_at
AI649878

Zm.900.1.S1_at
BF728342

Zm.8147.1.A1_at
BM073080

Zm.18430.1.S1_at
CO524429

Zm.15859.1.A1_at
D14578.1

Zm.17164.1.S1_at
AY188756.1

Zm.1204.1.S1_at
BE519063

Zm.17968.1.A1_at
CK827143

TABLE 20

Maize genes with transcript abundance in hybrids used

for prediction of average yield in hybrids

Probe Set ID
Representative Public ID

20A. Positive
Zm.4900.2.A1_at
AY105715.1

Correlation
Zm.6390.1.S1_at
BU098381

Zm.17314.1.S1_at
CK369303

Zm.8720.1.S1_at
AY303682.1

ZmAffx.435.1.A1_at
AI676952

Zm.4807.1.A1_at
CO518291

Zm.16794.1.A1_at
AF330034.1

Zm.19357.1.A1_at
CO533449

Zm.13190.1.A1_at
CD433968

Zm.16025.1.A1_at
BM340438

AFFX-r2-TagC_at
AFFX-r2-TagC

ZmAffx.844.1.S1_at
AI770609

Zm.6342.1.S1_at
AW052791

Zm.9453.1.A1_at
CO521132

Zm.13708.1.A1_at
AY106587.1

Zm.10609.1.A1_at
BQ538614

Zm.6589.1.A1_at
AI622544

ZmAffx.1308.1.S1_s_at
11990232-76

Zm.4024.1.S1_at
AY105692.1

Zm.16805.4.A1_at
AI795617

Zm.10032.1.S1_at
CN844905

Zm.4943.1.A1_at
BG320867

Zm.6970.1.A1_a_at
AY111674.1

Zm.8150.1.A1_at
BM073089

Zm.4696.1.S1_at
BG266403

ZmAffx.994.1.A1_at
AI855283

Zm.11585.1.A1_at
BM379130

ZmAffx.45.1.S1_at
AI664925

Zm.6214.1.A1_a_at
BQ538548

Zm.9102.1.A1_at
BM333481

Zm.4909.1.A1_at
AY111633.1

Zm.13916.1.S1_at
AF037027.1

Zm.17317.1.S1_at
CK370700

Zm.5684.1.A1_at
BM334571

AFFX-r2-TagJ-3_at
AFFX-r2-TagJ-3

Zm.2232.1.S1_at
BM380334

Zm.15667.1.S1_at
CD437700

Zm.1996.1.S1_at
CK347826

Zm.9642.1.A1_at
BM338826

Zm.12716.1.S1_at
AY112283.1

Zm.6556.1.A1_at
AY109683.1

ZmAffx.54.1.S1_at
AI665038

Zm.5099.1.S1_at
AI600819

Zm.5550.1.S1_at
AI622648

Zm.1352.1.A1_at
AY106566.1

Zm.4312.3.S1_at
CF075294

Zm.2202.1.A1_at
AY105037.1

Zm.14089.1.S1_at
AW324724

Zm.13601.1.S1_at
AY107674.1

Zm.4.1.S1_a_at
CD434423

ZmAffx.219.1.S1_at
AI670227

ZmAffx.122.1.S1_at
AI665696

ZmAffx.109.1.S1_at
AI665560

ZmAffx.331.1.A1_at
AI670513

Zm.4118.1.A1_at
AY105314.1

Zm.6369.3.A1_at
AI881634

Zm.15323.1.A1_at
BM349667

Zm.3050.3.A1_at
CF630494

Zm.2957.1.A1_at
CK371564

ZmAffx.439.1.A1_at
AI676966

Zm.4860.2.A1_at
AI770577

Zm.19141.1.A1_at
CF625022

Zm.5268.1.S1_at
CF626642

Zm.5791.2.A1_a_at
AW438331

Zm.4616.1.A1_x_at
BQ538201

Zm.12940.1.S1_at
AY104675.1

Zm.4265.1.A1_at
CA402796

Zm.8412.1.A1_at
AY108596.1

Zm.18041.1.A1_at
BQ620926

Zm.13365.1.A1_at
CK827054

Zm.2734.2.S1_at
BF727671

Zm.16299.2.A1_a_at
BM336250

Zm.13007.1.S1_at
CO532826

Zm.12716.1.A1_at
AY112283.1

Zm.11827.1.A1_at
BM381077

Zm.14824.1.S1_at
AJ430693.1

Zm.15083.2.A1_at
AY107613.1

Zm.445.2.A1_at
AF457968.1

Zm.5834.1.A1_a_at
BM335098

ZmAffx.823.1.S1_at
AI770503

Zm.8924.1.A1_at
BM381215

Zm.722.1.A1_at
AW288498

Zm.13341.1.S1_at
CF044863

Zm.12037.1.S1_at
BI894209

Zm.2557.1.S1_at
CF649649

ZmAffx.1152.1.A1_at
AW424633

Zm.5423.1.S1_at
CD997936

ZmAffx.243.1.S1_at
AI670255

Zm.17696.1.A1_at
BM073027

Zm.13194.2.A1_at
AY108895.1

Zm.13059.1.S1_at
AB112938.1

Zm.3255.2.A1_a_at
BM073865

ZmAffx.57.1.A1_at
AI665066

Zm.18764.1.A1_at
CO519979

20B. Negative
Zm.4875.1.S1_at
AI691556

Correlation
Zm.5980.2.A1_a_at
AI666161

Zm.6045.2.A1_a_at
BM337093

Zm.14497.15.A1_x_at
CF016873

Zm.281.1.S1_at
U06831.1

Zm.2376.1.A1_x_at
AF001634.1

Zm.6007.1.S1_at
AI666154

ZmAffx.316.1.A1_at
AI670498

Zm.17786.1.S1_at
CF623596

Zm.18419.1.A1_at
CF631047

Zm.16237.1.A1_at
CF624893

Zm.6594.1.A1_at
CF972362

Zm.18998.1.S1_at
BF727820

ZmAffx.421.1.S1_at
AI676853

Zm.3198.2.A1_a_at
CN844169

Zm.1551.1.A1_at
BM339714

Zm.936.1.A1_at
CF052340

Zm.6194.1.A1_at
AW519914

AFFX-ThrX-M_at
AFFX-ThrX-M

Zm.4304.1.S1_at
AI834719

Zm.3616.1.A1_at
BM380107

Zm.16207.1.A1_at
AW355980

Zm.5917.2.A1_at
BM379236

ZmAffx.914.1.A1_at
AI770970

Zm.18260.1.A1_at
CF602623

Zm.16879.1.A1_at
CF645954

Zm.19203.1.S1_at
CO520849

Zm.17500.1.A1_at
CK371009

Zm.5705.1.S1_at
AI637038

Zm.7892.1.A1_at
CO520489

ZmAffx.586.1.A1_at
AI715014

Zm.11783.1.A1_at
BM380733

Zm.18254.2.A1_at
CF632979

Zm.4258.1.A1_at
BM348441

Zm.13790.1.S1_at
AY105115.1

Zm.14428.1.S1_at
AY106109.1

Zm.13947.2.A1_at
AI737859

Zm.12517.1.A1_at
CF624446

Zm.5507.1.S1_at
CN071496

Zm.11055.1.A1_at
BM336314

Zm.13417.1.A1_at
CA400681

Zm.12101.2.S1_at
AI833552

Zm.10202.1.A1_at
AY112463.1

ZmAffx.273.1.A1_at
AI670401

Zm.784.1.A1_at
CF005849

Zm.7858.1.A1_at
AY108500.1

Zm.9839.1.A1_at
BM339393

ZmAffx.1198.1.S1_at
BE056195

Zm.4326.1.A1_at
AI711615

Zm.9735.1.A1_at
BM336891

Zm.3634.1.A1_at
CF638013

Zm.1408.1.A1_at
CN845023

Zm.16848.1.A1_at
CK369421

Zm.8114.1.A1_at
BM072985

ZmAffx.138.1.A1_at
AI667759

Zm.5803.1.A1_at
AI691266

Zm.10681.1.A1_at
BQ538977

Zm.9867.1.A1_at
AY106142.1

Zm.1511.1.S1_at
CO532736

Zm.7150.1.A1_x_at
AY103659.1

Zm.9614.1.A1_at
BM335440

Zm.1338.1.S1_at
W49442

Zm.8900.1.A1_at
CK827399

ZmAffx.721.1.A1_at
AI665110

Zm.7596.1.A1_at
BM079087

Zm.19034.1.S1_at
BQ833817

Zm.8959.1.A1_at
BM335622

Zm.2243.1.A1_at
BM349368

Zm.13403.1.S1_x_at
AF457949.1

AFFX-Zm-r2-Ec-bioB-3_at
AFFX-Zm-r2-Ec-bioB-3

Zm.3633.1.A1_at
U33816.1

Zm.17529.1.S1_at
CK394827

Zm.18275.1.A1_at
CO526155

Zm.7056.6.A1_at
CF051906

Zm.5796.1.A1_at
BM332299

ZmAffx.1106.1.S1_at
AW216267

Zm.12965.1.A1_at
CA402509

Zm.13845.1.A1_at
AY103950.1

Zm.12765.1.A1_at
AI745814

ZmAffx.1500.1.S1_at
40794996-117

Zm.10867.1.A1_at
BM073190

Zm.19144.1.A1_at
CO518283

ZmAffx.262.1.A1_s_at
AI670379

Zm.7012.9.A1_at
BE123180

ZmAffx.1295.1.S1_s_at
40794996-25

Zm.4682.1.S1_at
AI737946

Zm.2367.1.S1_at
AW497505

Zm.8847.1.A1_at
BM075896

Zm.2813.1.A1_at
BM381379

ZmAffx.586.1.S1_at
AI715014

Zm.14450.1.A1_at
AI391911

Zm.1454.1.A1_at
BG841866

Zm.18933.2.S1_at
AI734652

Zm.1118.1.S1_at
CF631214

Zm.18416.1.A1_at
CO524449

ZmAffx.939.1.S1_at
AI820322

Zm.16251.1.A1_at
AI711812

Zm.18427.1.S1_at
CO523584

Zm.10053.1.A1_at
CO523900

Zm.18439.1.A1_at
BM267666

Zm.12356.1.S1_at
BQ547740

ZmAffx.507.1.A1_at
AI691932

Zm.10718.1.A1_at
BM339638

Zm.15796.1.S1_at
BE640285

ZmAffx.270.1.A1_at
AI670398

Zm.54.1.S1_at
L25805.1

Zm.8391.1.A1_at
BM347365

Zm.9238.1.A1_at
CO533275

Zm.3633.2.S1_x_at
CF634876

Zm.4505.1.S1_at
AY111153.1

Zm.12070.1.A1_at
BM418472

Zm.17977.1.A1_s_at
CK827616

Zm.5789.3.S1_at
X83696.1

ZmAffx.771.1.A1_at
AI746147

Zm.11620.1.A1_at
BM379366

Zm.5571.2.A1_a_at
AY107402.1

Zm.12192.1.A1_at
BM380585

Zm.19243.1.A1_at
AW181224

Zm.12382.1.S1_at
BU097491

Zm.7538.1.A1_at
BM337034

Zm.1738.2.A1_at
CF630684

Zm.1313.1.A1_s_at
BM078737

Zm.9389.2.A1_x_at
BQ538340

ZmAffx.678.1.A1_at
AI734611

Zm.18105.1.S1_at
CO527288

Zm.19042.1.A1_at
CO521963

ZmAffx.782.1.A1_at
AI759014

Zm.5957.1.S1_at
AY105442.1

Zm.18908.1.S1_at
CO531963

Zm.1004.1.S1_at
BE511241

Zm.6743.1.S1_at
AF494284.1

Zm.8118.1.A1_at
AY107915.1

ZmAffx.960.1.S1_at
AI833639

Zm.17425.1.S1_at
CK145186

Zm.8106.1.S1_at
BM079856

ZmAffx.277.1.S1_at
AI670405

Zm.13686.1.A1_at
AY106861.1

Zm.1068.1.S1_at
BM381276

Zm.778.1.A1_a_at
CO529433

Zm.11834.1.S1_at
BM381120

Zm.16324.1.A1_at
CF032268

Zm.18774.1.S1_at
CO524725

Zm.14811.1.S1_at
CF629330

Zm.6654.1.A1_at
CF038689

Zm.17243.1.S1_at
CK786707

Zm.6000.1.S1_at
BG265807

Zm.17212.1.A1_at
CO529021

Zm.8233.2.S1_a_at
BM381462

Zm.138842.A1_at
AF099414.1

ZmAffx.1362.1.S1_at
11990232-90

Zm.7904.1.A1_at
BM080363

Zm.16742.1.A1_at
AW499330

Zm.5119.1.A1_a_at
CF634150

Zm.152.1.S1_at
J04550.1

Zm.15451.1.S1_at
CD439729

Zm.5492.1.A1_at
AI622235

Zm.2710.1.S1_at
CO520765

Zm.8937.1.A1_at
BM080734

Zm.14283.4.S1_at
BG841525

Zm.6437.1.A1_a_at
CA402215

Zm.10175.1.A1_at
BM379420

Zm.6228.1.A1_at
AI739920

Zm.5558.1.A1_at
AY072298.1

Zm.10269.1.S1_at
BM660878

Zm.1894.2.S1_at
CK371174

Zm.12875.1.A1_at
CA400938

Zm.3138.1.A1_a_at
AI621861

Zm.15984.1.A1_at
CD441218

ZmAffx.1073.1.A1_at
AI947671

Zm.8489.1.A1_at
BQ538173

Zm.14962.1.A1_at
BM268018

Zm.9799.1.A1_at
AY111917.1

Zm.3833.1.A1_at
AW288806

Zm.15467.1.A1_at
CD219385

Zm.4316.1.S1_a_at
AI881448

Zm.4246.1.A1_at
AI438854

Zm.9521.1.A1_x_at
CF624102

Zm.17356.1.A1_at
CF634567

Zm.17913.1.S1_at
CF625344

Zm.17630.1.A1_at
CK348094

Zm.3350.1.A1_x_at
BM266649

Zm.2031.1.S1_at
AY103664.1

Zm.5623.1.A1_at
BG840990

Zm.16338.1.A1_at
CF348862

Zm.6430.1.A1_at
AY111839.1

Zm.10210.1.A1_at
CF627510

Zm.4418.1.A1_at
BM378152

ZmAffx.791.1.A1_at
AI759133

Zm.9048.1.A1_at
CF024226

Zm.2542.1.A1_at
CF636373

Zm.19011.2.A1_at
AY108328.1

Zm.9650.1.S1_at
BM380250

Zm.7804.1.S1_at
AF453836.1

Zm.17656.1.S1_at
CK369512

Zm.7860.1.A1_at
BM333940

Zm.3395.1.A1_at
AY103867.1

Zm.14505.2.A1_at
CF059379

Zm.3099.1.S1_at
CO522746

Zm.12133.1.S1_at
CF636936

Zm.4999.1.S1_at
AI600285

Zm.16080.1.A1_at
AY108583.1

Zm.2715.1.A1_at
AW066985

Zm.5797.1.S1_at
CF012679

ZmAffx.844.1.A1_at
AI770609

Zm.13263.1.A1_at
AY109418.1

Zm.3852.1.S1_at
CD998914

Zm.12391.1.S1_at
CF349132

Zm.6624.1.S1_at
AI491254

Zm.13961.1.S1_at
AY540745.1

Zm.8632.1.A1_at
BM268513

Zm.15102.1.A1_at
AI065586

Zm.11831.1.S1_a_at
CA401860

Zm.4460.1.A1_at
AI714963

Zm.4546.1.A1_at
BG266283

RPTR-Zm-U55943-1_at
RPTR-Zm-U55943-1

Zm.7915.1.A1_at
BM080414

ZmAffx.188.1.S1_at
AI668391

Zm.3889.5.A1_x_at
AI737901

Zm.2078.1.A1_at
CF675000

Zm.7648.1.A1_at
CO517814

Zm.3167.1.S1_s_at
U89342.1

Zm.19347.1.S1_at
AI902024

Zm.1881.1.A1_at
AY110751.1

Zm.6982.1.S1_at
AY105052.1

Zm.4187.1.S1_at
AY105088.1

Zm.6298.1.A1_at
CD444675

Zm.9529.1.A1_at
CA399003

Zm.1383.1.A1_at
BG873830

Zm.9339.1.A1_at
BM332063

Zm.6318.1.A1_at
BM073937

Zm.16926.1.S1_at
CO522465

ZmAffx.485.1.S1_at
AI691349

Zm.3795.1.A1_at
BM335144

Zm.5367.1.A1_at
CF638282

Zm.2040.2.S1_a_at
CB331475

Zm.7056.12.S1_at
AI746152

Zm.5656.1.A1_at
BG837879

Zm.1212.1.S1_at
CF011510

Zm.9098.1.A1_a_at
BM336161

Zm.3805.1.S1_at
AY112434.1

Zm.6645.1.S1_at
CF637989

Zm.9250.1.S1_at
CF016507

Zm.2656.2.S1_s_at
AY111594.1

Zm.13585.1.S1_at
AY107846.1

ZmAffx.261.1.S1_at
AI670366

Zm.1056.1.S1_a_at
AW120162

ZmAffx.474.1.S1_at
AI677507

Zm.2225.1.S1_at
BF728179

Zm.8292.1.S1_at
AY106611.1

Zm.6569.9.A1_x_at
AW091447

Zm.4230.1.S1_at
CO523811

RPTR-Zm-J01636-4_at
RPTR-Zm-J01636-4

Zm.13326.1.S1_at
CF042397

ZmAffx.728.1.A1_at
AI740010

Zm.6048.2.S1_at
AI745933

Zm.9513.1.A1_at
BM349310

Zm.5944.1.A1_at
BG874229

ZmAffx.1059.1.A1_at
AI881930

Zm.14352.2.S1_at
AY104356.1

ZmAffx.607.1.S1_at
AI715035

Zm.2199.2.S1_at
CA404051

Zm.9169.2.S1_at
CO521754

ZmAffx.630.1.S1_at
AI715058

Zm.16285.1.S1_at
CD970925

Zm.9747.1.S1_at
BM337726

Zm.9783.1.A1_at
BM347856

ZmAffx.827.1.A1_at
AI770520

Zm.3133.1.S1_at
CK371248

Zm.15512.1.S1_at
CD436002

Zm.4531.1.A1_at
AI734623

Zm.12810.1.A1_at
CA399348

Zm.17498.1.A1_at
CK144816

ZmAffx.821.1.A1_at
AI770497

Zm.5723.1.A1_at
BM079835

Zm.16535.2.A1_s_at
CF062633

Zm.14502.1.S1_at
CO531791

Zm.10792.1.A1_at
AY106092.1

Zm.14170.1.A1_a_at
BG841910

ZmAffx.1005.1.A1_at
AI881362

Zm.5048.6.A1_at
BM380925

Zm.8270.1.A1_at
AY649984.1

Zm.1899.1.A1_at
BM333426

Zm.17843.1.A1_at
BM380806

Zm.7005.1.A1_at
BM333037

Zm.15576.1.A1_a_at
CK827910

Zm.13930.1.A1_x_at
Z35298.1

Zm.12433.1.S1_at
AY105016.1

ZmAffx.1031.1.A1_at
AI881675

ZmAffx.237.1.S1_at
AI670249

Zm.13103.1.S1_at
CO534624

Zm.16538.1.S1_at
BM337996

Zm.10271.1.S1_at
CA452443

Zm.6625.2.S1_at
BM347999

Zm.8756.1.A1_at
BM333012

Zm.885.1.S1_at
BM080781

ZmAffx.1077.1.A1_at
AI948123

Zm.14463.1.A1_at
BM336602

ZmAffx.58.1.S1_at
AI665082

Zm.5112.1.A1_at
AI600906

Zm.14076.2.A1_a_at
CO526265

Zm.3077.2.S1_x_at
CF061929

Zm.9814.1.A1_at
BM351590

Zm.161.2.S1_x_at
X70153.1

Zm.16266.1.S1_at
CF243553

Zm.17657.1.A1_at
CK369553

Zm.19019.1.A1_at
BM080703

Zm.10514.1.S1_at
BQ485919

Zm.2473.1.S1_at
AY104610.1

Zm.13720.1.S1_s_at
AY106348.1

Zm.2266.1.A1_at
AW330883

Zm.5228.1.A1_at
AW061845

AFFX-Zm-r2-Ec-bioC-3_at
AFFX-Zm-r2-Ec-bioC-3

Zm.13858.1.S1_at
CO524282

Zm.5847.1.A1_at
BM078382

Zm.9056.1.A1_at
BM334642

Zm.4894.1.A1_at
BM076024

ZmAffx.1032.1.S1_at
AI881679

Zm.9757.1.A1_at
BM338070

Zm.4616.1.A1_a_at
BQ538201

Zm.4287.1.A1_at
BG266567

Zm.5988.1.A1_at
AI666062

Zm.4187.1.A1_at
AY105088.1

Zm.8665.1.A1_at
BM075117

Zm.5080.1.A1_at
AI600750

Zm.5930.1.S1_at
CF018694

TABLE 21

Pedigree and seedling growth characteristics of the

maize inbred lines used in Example 6a

Seedling characteristics

Group
Subgroup
after 2 weeks' growth

Line
Pedigree [72]
[72]
[72]
Weight/g
Height/mm

Parent in all crosses

B73
lowa Stiff Stalk Synthetic
SS
B73
1.62
204

C5

Training dataset

B97
derived from BSCB1(R)C9
NSS
NSS-mixed
1.30
204

CML52
Pop. 79?
TS
TZI
2.18
262

CML69
Pop. 36 = Cogollero
TS
Suwan
2.56
273

(Caribbean)

CML228
Suwan-1/SR
TS
Suwan
0.88
159

CML247
Pool 24 (Tuxpeño)
TS
CML-early
2.11
227

CML277
Pop. 43 = La Posta (Tux.)
TS
CML-P
1.26
205

CML322
Recyc. US + Mex
TS
CML-early
1.29
173

CML333
Pop. 590 = ?
TS
CML-P
1.46
184

II14H
White Narrow Grain
Sweet

1.68
264

Evergreen
corn

Ki11
Suwan 1
TS
Suwan
2.04
174

Ky21
Boone County White
NSS
K64W
1.40
191

M37W
AUSTRALIA/JELLICORSE
Mixed

1.12
204

Mo17
C.I.187-2*C103
NSS
CO109:Mo17
2.39
231

Mo18W
Wf9*Mo22(2)
Mixed

1.12
197

NC350
H5*PX105A/H101
TS
NC
1.49
206

NC358
TROPHY SYN
TS
TZI
1.12
161

Oh43
Oh40B*W8
NSS
M14:Oh43
3.13
293

P39
Purdue Bantam
Sweet

0.49
146

corn

Tx303
Yellow Surcropper
Mixed

1.10
179

Tzi8
TZB × TZSR
TS
TZI
1.22
206

Test dataset

CML103
Pop. 44
TS
CML-late
1.52
199

HP301
Supergold
Popcorn

1.02
240

Ki3
Suwan-1 lines
TS
Suwan
1.79
230

Oh7B
Oh07B = [(Oh07*38-
Mixed

0.72
149

11)Oh07]

TABLE 22

Maize genes for which transcript abundance in inbred

lines of the training dataset is correlated (P < 0.00001) with plot

yield of hybrids with line B73

Systematic Name
P value
R2
Slope
Intercept
GenBank entry

Zm.3907.1.S1_at
0
0.648
−0.1182
1.773
gb: L81162.2

DB_XREF = gi: 50957230

Zm.18118.1.S1_at
0
0.5906
−0.3374
5.653
gb: CN844890

DB_XREF = gi: 47962181

Zm.2741.1.A1_at
1.13E−12
0.585
−0.3268
5.597
gb: CB603857

DB_XREF = gi: 29543461

Zm.13075.1.A1_at
4.58E−12
0.5647
−0.8445
12.26
gb: CA403748

DB_XREF = gi: 24768619

Zm.11896.1.A1_at
4.62E−12
0.5646
−0.523
7.705
gb: CO530711

DB_XREF = gi: 50335585

Zm.8790.1.A1_at
3.76E−11
0.5324
−0.1699
3.336
gb: CF005102

DB_XREF = gi: 32865420

Zm.14547.1.S1_a_at
4.19E−11
0.5307
−0.2015
2.891
gb: BG840169

DB_XREF = gi: 14243004

Zm.17578.1.A1_at
5.68E−11
0.5258
−3.303
48.37
gb: CK368635

DB_XREF = gi: 40334565

ZmAffx.1036.1.S1_at
8.13E−11
0.52
−0.1258
1.934
gb: AI881726

DB_XREF = gi: 5566710

Zm.6469.1.S1_at
8.45E−11
0.5194
0.0888
−0.1612
gb: BE345306

DB_XREF = gi: 9254838

ZmAffx.1211.1.A1_at
9.65E−11
0.5172
−0.5151
8.386
gb: BG842238

DB_XREF = gi: 14244259

Zm.17743.1.S1_at
1.06E−10
0.5156
−0.8687
12.7
gb: CK370833

DB_XREF = gi: 40336763

Zm.11126.1.S1_at
3.41E−10
0.496
0.103
−0.3613
gb: AA979835

DB_XREF = gi: 3157213

Zm.17115.1.S1_at
4.19E−10
0.4925
−0.395
6.294
gb: CN844978

DB_XREF = gi: 47962269

Zm.1465.1.A1_at
1.08E−09
0.476
−1.141
17.41
gb: BG840947

DB_XREF = gi: 14243198

ZmAffx.175.1.A1_at
1.58E−09
0.4692
−0.7394
11.35
gb: AI668276

DB_XREF = gi: 4827584

Zm.7407.1.A1_a_at
1.77E−09
0.4672
−0.1588
3.222
gb: BM074289

DB_XREF = gi: 16919636

Zm.12072.1.S1_at
1.86E−09
0.4663
−0.2694
3.894
gb: BM417375

DB_XREF = gi: 18384175

Zm.17209.1.A1_at
2.01E−09
0.4648
0.07619
−0.06023
gb: BM073068

DB_XREF = gi: 16916971

Zm.1615.1.S1_at
2.37E−09
0.4618
−0.1839
3.377
gb: AY106014.1

DB_XREF = gi: 21209092

Zm.1835.2.A1_at
2.76E−09
0.459
−0.1609
2.806
gb: CK985959

DB_XREF = gi: 45568216

Zm.5605.1.S1_at
3.21E−09
0.4563
−0.1728
3.327
gb: CO528780

DB_XREF = gi: 50333654

Zm.17923.1.A1_at
3.99E−09
0.4523
−0.2692
4.808
gb: AY110526.1

DB_XREF = gi: 21214935

Zm.7407.1.A1_x_at
4.46E−09
0.4502
−0.1987
3.798
gb: BM074289

DB_XREF = gi: 16919636

Zm.1143.1.S1_at
4.54E−09
0.4499
−0.166
3.287
gb: CD443909

DB_XREF = gi: 31359552

Zm.5656.1.A1_at
5.20E−09
0.4473
0.1137
−0.4548
gb: BG837879

DB_XREF = gi: 14204202

Zm.7397.1.A1_at
5.31E−09
0.4469
0.168
−1.328
gb: BQ539216

DB_XREF = gi: 28984830

Zm.11141.1.S1_at
7.30E−09
0.441
−0.1185
2.511
gb: AY106810.1

DB_XREF = gi: 21209888

Zm.6221.1.S1_at
7.80E−09
0.4397
−0.06997
1.969
gb: AW585256

DB_XREF = gi: 7262313

Zm.4741.1.A1_a_at
8.01E−09
0.4392
−0.2734
4.707
gb: AI600480

DB_XREF = gi: 4609641

Zm.8535.1.A1_at
1.06E−08
0.4338
−0.1364
2.904
gb: AY104401.1

DB_XREF = gi: 21207479

Zm.14547.1.S1_at
1.39E−08
0.4287
−0.2202
3.814
gb: BG840169

DB_XREF = gi: 14243004

Zm.16839.1.A1_at
1.67E−08
0.4251
0.0764
0.004757
gb: CF630748

DB_XREF = gi: 37387111

Zm.19172.1.A1_at
1.90E−08
0.4226
−0.1808
3.45
gb: CO528850

DB_XREF = gi: 50333724

Zm.5170.1.S1_at
2.20E−08
0.4197
0.11
−0.4471
gb: CF349172

DB_XREF = gi: 33942572

Zm.5851.11.A1_x_at
2.71E−08
0.4156
−0.7137
11.37
gb: CO527835

DB_XREF = gi: 50332709

Zm.7006.2.A1_at
2.84E−08
0.4147
0.07037
0.09825
gb: AW225324

DB_XREF = gi: 6540662

Zm.8914.1.S1_at
2.95E−08
0.414
0.0947
−0.2888
gb: BM073720

DB_XREF = gi: 16918380

Zm.1974.1.A1_at
3.19E−08
0.4124
−0.3785
6.334
gb: CF920129

DB_XREF = gi: 38229816

Zm.13497.1.S1_at
3.62E−08
0.4099
0.08851
−0.1197
gb: CK368613

DB_XREF = gi: 40334543

Zm.10640.1.S1_at
3.96E−08
0.4081
−0.08601
2.231
gb: AY107547.1

DB_XREF = gi: 21210625

Zm.19062.1.S1_at
4.74E−08
0.4045
−0.08075
2.065
gb: CO531568

DB_XREF = gi: 50336442

Zm.18060.1.A1_at
4.79E−08
0.4043
−0.2694
4.583
gb: CK985812

DB_XREF = gi: 45567918

Zm.878.1.S1_x_at
5.24E−08
0.4025
0.1231
−0.4754
gb: AI855310

DB_XREF = gi: 5499443

Zm.5159.1.A1_at
6.20E−08
0.3991
0.0685
0.06159
gb: CA403363

DB_XREF = gi: 24768234

Zm.4632.1.A1_at
6.24E−08
0.399
−0.1062
2.425
gb: AI737439

DB_XREF = gi: 5058963

Zm.11189.1.A1_at
6.86E−08
0.3971
−0.08985
1.381
gb: BM339882

DB_XREF = gi: 18170042

Zm.1541.2.S1_at
8.18E−08
0.3935
0.09864
−0.363
gb: CF650678

DB_XREF = gi: 37425858

Zm.15307.1.A1_at
8.20E−08
0.3934
−4.65
68.91
gb: CF014037

DB_XREF = gi: 32909225

Zm.12775.1.A1_x_at
8.37E−08
0.393
−0.1098
1.876
gb: CA398576

DB_XREF = gi: 24763400

Zm.5086.1.A1_at
1.03E−07
0.3887
0.05381
0.329
gb: CF625592

DB_XREF = gi: 37377894

Zm.5851.9.S1_at
1.15E−07
0.3865
−0.2305
3.44
gb: AY105349.1

DB_XREF = gi: 21208427

Zm.3182.1.A1_at
1.31E−07
0.3838
−0.06838
1.868
gb: CK827062

DB_XREF = gi: 44900517

Zm.5415.1.A1_at
1.32E−07
0.3837
−0.3297
5.269
gb: BM074945

DB_XREF = gi: 16921022

Zm.16855.1.A1_at
1.34E−07
0.3833
−0.1675
2.758
gb: AF036949.1

DB_XREF = gi: 2865393

Zm.5851.11.A1_a_at
1.35E−07
0.3832
−2.667
40.08
gb: CO527835

DB_XREF = gi: 50332709

ZmAffx.106.1.A1_at
1.42E−07
0.3822
−0.317
5.565
gb: AI665540

DB_XREF = gi: 4776537

Zm.5688.2.A1_at
1.73E−07
0.3781
−0.733
12.07
gb: BM338540

DB_XREF = gi: 18168700

Zm.9294.1.A1_at
1.99E−07
0.3751
−0.4105
6.62
gb: BM335301

DB_XREF = gi: 18165462

Zm.11189.1.A1_x_at
2.14E−07
0.3736
−0.1475
2.193
gb: BM339882

DB_XREF = gi: 18170042

Zm.8904.1.A1_at
2.24E−07
0.3726
−0.2324
3.566
gb: CK371274

DB_XREF = gi: 40337204

Zm.9631.1.A1_at
2.37E−07
0.3714
−0.1776
2.7
gb: BM336220

DB_XREF = gi: 18166381

Zm.2106.1.S1_at
2.38E−07
0.3713
−0.2349
4.515
gb: CK786800

DB_XREF = gi: 44681752

Zm.552.1.A1_at
2.74E−07
0.3683
0.1283
−0.6816
gb: AF244691.1

DB_XREF = gi: 11385502

Zm.9371.1.A1_x_at
3.1E−07
0.3657
−0.1302
2.806
gb: BM350310

DB_XREF = gi: 18174922

Zm.16747.1.A1_at
3.18E−07
0.3652
0.06149
0.2381
gb: BM335125

DB_XREF = gi: 18165286

Zm.878.1.S1_at
3.2E−07
0.365
0.2286
−1.663
gb: AI855310

DB_XREF = gi: 5499443

Zm.12188.1.A1_at
3.43E−07
0.3636
−0.08906
1.631
gb: BM382754

DB_XREF = gi: 18181544

Zm.4452.1.A1_at
3.5E−07
0.3631
−0.1109
2.573
gb: AI691174

DB_XREF = gi: 4938761

Zm.17790.1.S1_at
3.51E−07
0.363
0.1348
−0.6063
gb: CK370971

DB_XREF = gi: 40336901

Zm.13843.1.A1_at
3.79E−07
0.3614
0.06967
0.1099
gb: AY104026.1

DB_XREF = gi: 21207104

Zm.4271.4.A1_at
3.88E−07
0.3609
0.05597
0.2215
gb: BG316519

DB_XREF = gi: 13126069

Zm.8922.1.S1_at
3.95E−07
0.3605
−0.1195
2.683
gb: BM080861

DB_XREF = gi: 16927792

Zm.6092.1.S1_at
4.22E−07
0.3591
0.07163
0.03375
gb: CB885460

DB_XREF = gi: 30087252

Zm.5851.6.S1_x_at
4.64E−07
0.3571
−1.814
27.33
gb: L46399.1

DB_XREF = gi: 939782

Zm.3467.1.A1_at
4.7E−07
0.3568
−0.11
2.537
gb: CF626421

DB_XREF = gi: 37379355

Zm.495.1.A1_at
5.15E−07
0.3548
0.05399
0.3248
gb: AF236369.1

DB_XREF = gi: 7716457

Zm.446.1.S1_at
5.28E−07
0.3543
−0.764
12.28
gb: AF529266.1

DB_XREF = gi: 27544873

Zm.5960.1.A1_at
5.32E−07
0.3541
−0.215
3.564
gb: AI665953

DB_XREF = gi: 4804087

Zm.4213.1.A1_at
5.5E−07
0.3534
−0.1478
3.071
gb: BG841480

DB_XREF = gi: 14243777

Zm.4728.1.A1_at
5.59E−07
0.3531
−0.1074
2.592
gb: AI855200

DB_XREF = gi: 5499333

Zm.9580.1.A1_at
5.62E−07
0.3529
−0.2372
4.381
gb: BM332976

DB_XREF = gi: 18163137

Zm.13808.1.S1_at
5.75E−07
0.3524
−0.105
2.492
gb: AY104740.1

DB_XREF = gi: 21207818

Zm.2626.1.A1_at
6.12E−07
0.3511
−0.05262
1.708
gb: AY112337.1

DB_XREF = gi: 21216927

Zm.15868.1.A1_at
6.23E−07
0.3507
0.1032
−0.2451
gb: BM336226

DB_XREF = gi: 18166387

Zm.4180.1.S1_at
6.88E−07
0.3485
0.1176
−0.5887
gb: CD964540

DB_XREF = gi: 32824818

Zm.5851.15.A1_x_at
7.11E−07
0.3478
−0.3181
5.392
gb: AI759130

DB_XREF = gi: 5152832

Zm.1739.1.A1_at
7.48E−07
0.3467
0.1393
−0.8398
gb: BM337820

DB_XREF = gi: 18167980

Zm.5390.1.A1_at
7.81E−07
0.3458
−0.1602
3.31
gb: BM078263

DB_XREF = gi: 16925195

Zm.3097.1.A1_at
7.87E−07
0.3456
0.1663
−0.8862
gb: AY103827.1

DB_XREF = gi: 21206905

Zm.6736.1.S1_at
8.55E−07
0.3438
−0.1797
3.458
gb: AY108079.1

DB_XREF = gi: 21211157

Zm.2910.1.S1_at
8.67E−07
0.3435
0.09427
−0.2644
gb: CK145276

DB_XREF = gi: 38688245

Zm.8697.1.A1_at
8.83E−07
0.3431
−0.1124
2.472
gb: BM079294

DB_XREF = gi: 16926226

Zm.4046.1.S1_at
8.85E−07
0.343
0.1288
−0.7911
gb: CA400292

DB_XREF = gi: 24765132

Zm.1285.1.A1_at
9.43E−07
0.3416
0.05565
0.2897
gb: AY111542.1

DB_XREF = gi: 21216132

Zm.2563.1.A1_at
9.52E−07
0.3414
−0.05074
1.192
gb: BE638571

DB_XREF = gi: 9951988

Zm.17952.1.A1_at
9.87E−07
0.3406
−0.6734
10.55
gb: CF632730

DB_XREF = gi: 37390982

Zm.5766.1.S1_x_at
1E−06
0.3403
−0.3844
5.842
gb: BG840404

DB_XREF = gi: 14242680

Zm.15977.1.S1_at
1.17E−06
0.3368
0.08845
−0.8911
gb: AY108613.1

DB_XREF = gi: 21211748

Zm.3913.1.A1_at
1.24E−06
0.3355
0.1163
−0.4099
gb: CF000034

DB_XREF = gi: 32860352

Zm.303.1.S1_at
1.3E−06
0.3346
−0.07128
2.002
gb: AF236373.1

DB_XREF = gi: 7716465

Zm.4332.1.A1_at
1.36E−06
0.3336
−0.3654
6.262
gb: AI711854

DB_XREF = gi: 5005792

Zm.9376.1.A1_at
1.41E−06
0.3326
0.09554
−0.3578
gb: BM332576

DB_XREF = gi: 18162737

Zm.1423.1.A1_at
1.46E−06
0.3319
−0.0643
1.871
gb: CF047935

DB_XREF = gi: 32943116

Zm.1792.1.A1_at
1.49E−06
0.3314
0.06852
0.04595
gb: AY107188.1

DB_XREF = gi: 21210266

Zm.17540.1.A1_at
1.51E−06
0.3311
−0.07019
1.93
gb: CO525036

DB_XREF = gi: 50329910

Zm.3561.1.A1_at
1.52E−06
0.3311
−0.6223
9.644
gb: CK826673

DB_XREF = gi: 44900128

ZmAffx.566.1.A1_at
1.62E−06
0.3297
−0.07933
1.337
gb: AI714636

DB_XREF = gi: 5018443

Zm.5597.1.A1_at
1.63E−06
0.3295
−0.2103
3.985
gb: AI629497

DB_XREF = gi: 4680827

Zm.13082.1.S1_a_at
1.68E−06
0.3288
−0.2151
3.969
gb: CD438478

DB_XREF = gi: 31354121

Zm.6216.1.S1_at
1.69E−06
0.3287
−0.04754
1.586
gb: CO531189

DB_XREF = gi: 50336063

Zm.2742.1.A1_at
1.72E−06
0.3283
−0.1419
3.028
gb: AY111235.1

DB_XREF = gi: 21215825

Zm.1559.1.S1_at
1.72E−06
0.3282
−0.07846
1.413
gb: BF729152

DB_XREF = gi: 12058302

Zm.3154.1.A1_at
1.74E−06
0.328
−0.03944
1.529
gb: BM333548

DB_XREF = gi: 18163709

Zm.3357.1.A1_at
1.75E−06
0.3279
0.08751
−0.1318
gb: BM347858

DB_XREF = gi: 18172470

Zm.2924.1.A1_a_at
1.8E−06
0.3273
−0.05843
1.786
gb: BM349722

DB_XREF = gi: 18174334

Zm.10301.1.A1_at
1.86E−06
0.3265
0.1287
−0.5513
gb: BU050993

DB_XREF = gi: 22491070

Zm.5992.1.A1_at
1.87E−06
0.3264
0.07232
0.08961
gb: AY108021.1

DB_XREF = gi: 21211099

Zm.13693.1.S1_at
1.87E−06
0.3264
−0.1718
3.323
gb: AY106770.1

DB_XREF = gi: 21209848

Zm.6117.1.A1_at
1.89E−06
0.3262
−0.05436
1.737
gb: BM074413

DB_XREF = gi: 16919905

Zm.8911.1.A1_at
2.03E−06
0.3246
−0.2179
4.077
gb: BM350783

DB_XREF = gi: 18175488

Zm.7595.1.A1_at
2.11E−06
0.3237
−0.05045
1.648
gb: CD437071

DB_XREF = gi: 31352714

Zm.2424.1.A1_at
2.28E−06
0.3219
−0.3084
5.458
gb: BG841655

DB_XREF = gi: 14243883

Zm.2391.1.A1_at
2.44E−06
0.3204
−0.3225
5.482
gb: CK826632

DB_XREF = gi: 44900087

Zm.2455.1.A1_at
2.47E−06
0.3201
−0.09311
2.332
gb: BM416746

DB_XREF = gi: 18383546

Zm.12934.1.A1_a_at
2.55E−06
0.3194
−0.3145
4.903
gb: AY106367.1

DB_XREF = gi: 21209445

Zm.13266.2.S1_at
2.6E−06
0.3189
−0.2755
4.818
gb: CO533594

DB_XREF = gi: 50338468

Zm.9364.1.A1_at
2.63E−06
0.3187
0.1468
−0.7177
gb: BM334062

DB_XREF = gi: 18164223

Zm.6293.1.A1_at
2.68E−06
0.3182
−0.08441
2.061
gb: CF038760

DB_XREF = gi: 32933948

Zm.2530.1.A1_at
2.71E−06
0.318
−0.1539
3.168
gb: CF637153

DB_XREF = gi: 37399642

Zm.8204.1.A1_at
2.8E−06
0.3172
−0.07345
2.051
gb: BM073273

DB_XREF = gi: 16917409

Zm.843.1.A1_a_at
2.81E−06
0.3172
0.06446
0.1415
gb: AY111573.1

DB_XREF = gi: 21216163

Zm.13288.1.S1_at
2.82E−06
0.3171
−0.07191
1.268
gb: CA826847

DB_XREF = gi: 26455264

Zm.19018.1.A1_at
2.87E−06
0.3167
−0.05674
1.775
gb: CO532922

DB_XREF = gi: 50337796

Zm.14036.1.S1_at
2.89E−06
0.3165
−0.05461
0.846
gb: X55388.1

DB_XREF = gi: 22270

Zm.13248.1.S1_at
2.98E−06
0.3158
−0.04989
0.7365
gb: Y09301.1

DB_XREF = gi: 3851330

Zm.14272.2.A1_at
3.07E−06
0.3151
0.1132
−0.5078
gb: D10622.1

DB_XREF = gi: 217961

Zm.14318.1.A1_at
3.33E−06
0.3133
0.1184
−0.4017
gb: AY104313.1

DB_XREF = gi: 21207391

Zm.19303.1.S1_at
3.4E−06
0.3128
0.04973
0.3873
gb: CA829102

DB_XREF = gi: 26457519

ZmAffx.909.1.S1_at
3.54E−06
0.3119
−0.1389
2.793
gb: AI770947

DB_XREF = gi: 5268983

Zm.2293.1.A1_at
3.65E−06
0.3112
−0.3914
5.735
gb: AW331208

DB_XREF = gi: 6827565

Zm.3796.1.A1_at
3.66E−06
0.3111
−0.1047
2.305
gb: BG836961

DB_XREF = gi: 14203284

Zm.6560.1.S1_a_at
3.95E−06
0.3094
−0.1021
2.428
gb: Z29518.1

DB_XREF = gi: 575959

Zm.6560.1.S1_at
4.13E−06
0.3083
−0.5382
9.188
gb: Z29518.1

DB_XREF = gi: 575959

ZmAffx.667.1.A1_at
4.19E−06
0.308
−0.1973
3.638
gb: AI734359

DB_XREF = gi: 5055472

Zm.9931.1.A1_at
4.36E−06
0.3071
−0.2746
4.617
gb: BM339241

DB_XREF = gi: 18169401

Zm.11852.1.A1_x_at
4.54E−06
0.3062
0.1797
−1.23
gb: CF013366

DB_XREF = gi: 32908553

Zm.520.1.S1_x_at
4.74E−06
0.3052
0.1057
−0.5001
gb: AF200528.1

DB_XREF = gi: 9622879

Zm.16977.1.S1_at
4.76E−06
0.3051
−0.04535
1.634
gb: AB102956.1

DB_XREF = gi: 38347685

Zm.16227.1.A1_at
4.77E−06
0.305
−0.2137
4.017
gb: BI180294

DB_XREF = gi: 14646105

Zm.5379.1.S1_at
4.91E−06
0.3043
0.4236
−3.132
gb: AI621513

DB_XREF = gi: 4630639

Zm.17720.1.A1_at
4.93E−06
0.3042
−0.08202
1.488
gb: BM340967

DB_XREF = gi: 18171127

Zm.588.1.S1_at
5.14E−06
0.3033
0.06464
0.1791
gb: AF142322.1

DB_XREF = gi: 4927258

Zm.18033.1.A1_at
5.17E−06
0.3031
−0.08471
2.06
gb: BM080835

DB_XREF = gi: 16927766

Zm.663.1.S1_at
5.22E−06
0.3029
−0.178
3.527
gb: AF318075.1

DB_XREF = gi: 14091009

Zm.16513.1.A1_at
5.27E−06
0.3027
−0.07343
1.845
gb: CF634462

DB_XREF = gi: 37394377

Zm.17307.1.S1_at
5.53E−06
0.3016
0.06901
−0.101
gb: CK367910

DB_XREF = gi: 40333840

Zm.13719.1.A1_at
5.64E−06
0.3011
−0.04963
1.62
gb: AY106357.1

DB_XREF = gi: 21209435

Zm.1611.1.A1_at
5.7E−06
0.3009
−0.09719
2.327
gb: AW787466

DB_XREF = gi: 7844244

Zm.6251.1.A1_at
5.77E−06
0.3006
−0.05725
1.778
gb: CD434479

DB_XREF = gi: 31350122

Zm.16854.1.S1_at
6.1E−06
0.2993
−0.08796
2.166
gb: CF674957

DB_XREF = gi: 37621904

Zm.7731.1.A1_at
6.19E−06
0.299
0.0859
−0.1337
gb: AI612464

DB_XREF = gi: 4621631

Zm.7074.1.A1_at
6.21E−06
0.2989
0.09015
−0.1237
gb: CF634632

DB_XREF = gi: 37394712

Zm.8376.1.S1_at
6.34E−06
0.2984
−0.07696
1.936
gb: BM073880

DB_XREF = gi: 16918753

Zm.14497.8.A1_x_at
6.36E−06
0.2983
0.06997
0.1062
gb: CO527469

DB_XREF = gi: 50332343

Zm.14590.1.A1_x_at
6.39E−06
0.2982
−0.1306
2.728
gb: AY110683.1

DB_XREF = gi: 21215273

Zm.15293.1.S1_a_at
6.49E−06
0.2978
−0.1162
2.534
gb: AF232008.2

DB_XREF = gi: 9313026

Zm.15282.1.A1_at
6.52E−06
0.2977
−0.1326
2.786
gb: BM382478

DB_XREF = gi: 18181268

Zm.520.1.S1_at
6.67E−06
0.2972
0.1149
−0.623
gb: AF200528.1

DB_XREF = gi: 9622879

Zm.10553.1.A1_at
6.93E−06
0.2963
−0.2323
4.09
gb: CD441187

DB_XREF = gi: 31356830

Zm.3428.1.A1_at
7.38E−06
0.2948
−0.1968
3.706
gb: AI964613

DB_XREF = gi: 5757326

ZmAffx.1083.1.A1_at
7.6E−06
0.2942
−0.09468
2.276
gb: AI974922

DB_XREF = gi: 5777303

Zm.6997.1.A1_at
7.72E−06
0.2938
0.045
0.4419
gb: BG874061

DB_XREF = gi: 14245479

Zm.16489.1.S1_at
7.76E−06
0.2937
0.06034
0.2686
gb: CF637893

DB_XREF = gi: 37401062

Zm.5851.3.A1_at
7.91E−06
0.2932
−0.4542
7.864
gb: AY104012.1

DB_XREF = gi: 21207090

Zm.19019.1.A1_at
8.06E−06
0.2928
−0.06012
1.716
gb: BM080703

DB_XREF = gi: 16927634

Zm.4880.1.S1_at
8.19E−06
0.2924
−0.0599
1.721
gb: CF627543

DB_XREF = gi: 37381330

Zm.3243.1.A1_at
8.21E−06
0.2924
0.08508
−0.1167
gb: AY105697.1

DB_XREF = gi: 21208775

Zm.19022.1.S1_at
8.43E−06
0.2917
−0.246
3.664
gb: CO526898

DB_XREF = gi: 50331772

Zm.13991.1.S1_at
8.5E−06
0.2915
0.07005
0.1974
gb: AW424608

DB_XREF = gi: 6952540

Zm.9867.1.A1_at
8.51E−06
0.2915
0.3098
−3.067
gb: AY106142.1

DB_XREF = gi: 21209220

Zm.6480.2.S1_a_at
8.6E−06
0.2912
0.04572
0.403
gb: AI065715

DB_XREF = gi: 30052426

Zm.6931.1.S1_a_at
9.14E−06
0.2898
−0.09601
2.355
gb: AY588275.1

DB_XREF = gi: 46560601

Zm.12942.1.A1_at
9.16E−06
0.2898
−0.5247
7.489
gb: CA402151

DB_XREF = gi: 24767006

Zm.889.2.S1_at
9.29E−06
0.2894
−0.6597
10.97
gb: CD439290

DB_XREF = gi: 31354933

Zm.6816.1.A1_at
9.86E−06
0.288
0.0469
0.3894
gb: AY104584.1

DB_XREF = gi: 21207662

TABLE 23

Maize Plot Yield Data

Grain yield/lb

per plot²

Hybrid¹
Plot 1
Plot 2
Mean

Training dataset

B97 × B73
15.42
12.60
14.01

CML228 × B73
15.11
15.23
15.17

B73 × CML69
13.12
12.75
12.94

B73 × CML247
13.95
14.35
14.15

B73 × CML277
12.29
13.49
12.89

B73 × CML322
10.20
11.72
10.96

CML333 × B73
12.88
12.76
12.82

CML52 × B73
13.97
14.99
14.48

B73 × IL14H
9.43
7.06
8.24

B73 × Ki11
12.28
13.69
12.98

Ky21 × B73
11.82
12.43
12.13

B73 × M37W
13.88
13.80
13.84

B73 × Mo17
12.99
10.10
11.55

B73 × Mo18W
14.51
14.19
14.35

NC350 × B73
18.27
19.43
18.85

B73 × NC358
14.41
13.11
13.76

Oh43 × B73
11.83
12.11
11.97

P39 × B73
5.84
7.07
6.45

B73 × Tx303
10.25
13.42
11.83

Tzi8 B73
12.82
14.21
13.51

Test dataset

B73 × CML103
14.16
14.86
14.51

B73 × Hp301
8.06
9.92
8.99

B73 × Ki3
12.14
14.15
13.15

B73 × OH7B
11.94
11.17
11.55

¹Maternal parent listed first

²Corrected to 15% moisture

Program 1

job ‘kondara br-0 heterosis work’

output [width=132]1

variate [nvalues=22810]sec1,sec2,sec3,sec4,sec5,sec6,sec7,sec8,sec9,\

DK22,DKLD,DKSD,DB22,DBLD,DBSD,DBH22,DBHLD,DBHSD,DKH22,DKHLD,DKHSD,

\

HBK22,HBKLD,HBKSD,KBH22,KBHLD,KBHSD,D_K22,D_KLD,D_KSD,H22,HLD,HSD,

\

BDK22,BDKLD,BDKSD,HB22,HBLD,HBSD,HK22,HKLD,HKSD,B_K22,B_KLD,B_KSD,

\

r22kb,rldkb,rsdkb,r22bk,rldbk,rsdbk,KHB22,KHBLD,KHBSD,BHK22,BHKLD,

BHKSD,\

KDB22,KDBLD,KDBSD,BDK22,BDKLD,BDKSD,H22h,HLDh,HSDh,H22l,HLDl,HSDl,

A,B,C,\

b_k22,b_kLD,b_kSD,K_H22h,K_HLDh,K_HSDh,B_H22h,B_HLDh,B_HSDh,\

HB22l,HBLDl,HBSDl,HB22h,HBLDh,HBSDh,\

HK22l,HKLDl,HKSDl,HK22h,HKLDh,HKSDh

variate [values=1...22810]gene

“*********************************READ BASIC EXPRESSION

DATA*************************”

open ‘x:\\daves\\reciprocals\\hk 22k.txt’;ch=2

read [ch=2;print=e,s;serial=n]h22,hld,hsd,k22,kld,ksd,b22,bld,bsd

close ch=2

“ INITIAL SEED FOR RANDOM NUMBER GENERATION

”

scalar int,x,y

scalar [value=54321]a

& [value=78656]b

& [value=17345]c

output [width=132]1

“ OPEN OUTPUT FILE
”

open ‘x:\\daves\\reciprocals\\hk 22k.out’;ch=3;width=132;filetype=o

scalar [value=12345]a

scalar [value=*]miss

scalar [value=1]int

“ CALCULATES COMPARISONS FOR THREEOFOLD DIFFERENCES
”

“************************************* ratio of K : B

*****************************”

calc r22kb=k22/b22

& rldkb=kld/bld

& rsdkb=ksd/bsd

“************************************* ratio of B : K

*****************************”

& r22bk=b22/k22

& rldbk=bld/kld

& rsdbk=bsd/ksd

“************************************* ratio of H : K

*****************************”

& r22hk=h22/k22

& rldhk=hld/kld

& rsdhk=hsd/ksd

“************************************* ratio of H : B

*****************************”

& r22hb=h22/b22

& rldhb=hld/bld

& rsdhb=hsd/bsd

for k=1...22810

“************************************* B = H (within 2)

*****************************”

for

i=r22hb,rldhb,rsdhb;j=A,B,C;m=b22,bld,bsd;n=h22,hld,hsd;o=HB22l,HBLDl,HB

SDl;p=HB22h, HBLDh, HBSDh

if ((elem(i;k).gt.0.5).and.(elem(i;k).lt.2))

calc elem(j;k)=int

else

calc elem(j;k)=miss

endif

calc x=elem(m;k)

& y=elem(n;k)

“ LOWEST VALUE OF B OR H
”

if (y.gt.x).and.(elem(j;k).eq.1)

calc elem(o;k)=x

elsif (x.gt.y).and.(elem(j;k).eq.1)

calc elem(o;k)=y

else

calc elem(o;k)=miss

endif

“ HIGHEST VALUE OF B OR H
”

if (x.gt.y).and.(elem(j;k).eq.1)

calc elem(p;k)=x

elsif (y.gt.x).and.(elem(j;k).eq.1)

calc elem(p;k)=y

else

calc elem(p;k)=miss

endif

endfor

“************************************* K = H (within

2)*****************************”

for

i=r22hk,rldhk,rsdhk;j=A,B,C;m=k22,kld,ksd;n=h22,hld,hsd;o=HK22l,HKLDl,HK

SDl;p=HK22h,HKLDh,HKSDh

if ((elem(i;k).gt.0.5).and.(elem(i;k).lt.2))

calc elem(j;k)=int

else

calc elem(j;k)=miss

endif

calc x=elem(m;k)

& y=elem(n;k)

“ LOWEST VALUE OF K OR H
”

if (x.lt.y).and.(elem(j;k).eq.1)

calc elem(o;k)=x

elsif (y.lt.x).and.(elem(j;k).eq.1)

calc elem(o;k)=y

else

calc elem(o;k)=miss

endif

“ HIGHEST VALUE OF K OR H
”

if (x.gt.y).and.(elem(j;k).eq.1)

calc elem(p;k)=x

elsif (y.gt.x).and.(elem(j;k).eg.1)

calc elem(p;k)=y

else

calc elem(p;k)=miss

endif

endfor

“************************************* K = B (within 2)

*****************************”

for i=r22kb,rldkb,rsdkb;j=A,B,C;m=k22,kld,ksd;n=b22,bld,bsd

if ((elem(i;k).gt.0.5).and.(elem(i;k).lt.2))

calc elem(j;k)=int

else

calc elem(j;k)=miss

endif

endfor

“*********************************K = B (highest & lowest

values)********************”

for

i=r22kb,rldkb,rsdkb;j=A,B,C;m=k22,kld,ksd;n=b22,bld,bsd;o=B_K22,B_KLD,B_—

KSD;p=b_k22,b_kLD,b_kSD

calc x=elem(m;k)

& y=elem(n;k)

if (x.gt.y)

calc elem(o;k)=x

else

calc elem(o;k)=y

endif

if (x.lt.y)

calc elem(p;k)=x

else

calc elem(p;k)=y

endif

endfor

endfor

“************************************ratio of H : (K = B) high

values**************”

calc H22h=h22/B_K22

& HLDh=hld/B_KLD

& HSDh=hsd/B_KSD

“*************************************ratio of H : (K = B) low

values***************”

calc H22l=h22/b_k22

& HLDl=hld/b_kLD

& HSDl=hsd/b_kSD

“***********************************ratio of K : (B = H)

****************************”

calc KDB22=k22/HB22h

& KDBLD=kld/HBLDh

& KDBSD=ksd/HBSDh

“************************************ratio of B : (K =

H)****************************”

calc BDK22=b22/HK22h

& BDKLD=bld/HKLDh

& BDKSD=bsd/HKSDh

“************************************ratio of (K = H − low values) : B

************”

calc KHB22=HK22l/b22

& KHBLD=HKLDl/bld

& KHBSD=HKSDl/bsd

“*************************************ratio of (B = H) :

K***************************”

calc BHK22=HB22l/k22

& BHKLD=HBLDl/kld

& BHKSD=HBSDl/ksd

“***********************************************************************

*************”

for k=1...22810

“*********************** SEC 1 ---- K>BR-0

********************************”

if

(elem(r22kb;k).gt.2).and.(elem(rldkb;k).gt.2).and.(elem(rsdkb;k).gt.2)

calc elem(sec1;k)=int

else

calc elem(sec1;k)=miss

endif

“***********************SEC 2 ---- BR-0>K

*********************************”

if

(elem(r22bk;k).gt.2).and.(elem(rldbk;k).gt.2).and.(elem(rsdbk;k).gt.2)

calc elem(sec2;k)=int

else

calc elem(sec2;k)=miss

endif

“***********************SEC 3 ---- K AND H > B (BUT K = H)

*****************”

if

(elem(KHB22;k).gt.2).and.(elem(KHBLD;k).gt.2).and.(elem(KHBSD;k).gt.2)

calc elem(sec3;k)=int

else

calc elem(sec3;k)=miss

endif

“***********************SEC 4 ---- B AND H > K (BUT B = H)

*******************”

if

(elem(BHK22;k).gt.2).and.(elem(BHKLD;k).gt.2).and.(elem(BHKSD;k).gt.2)

calc elem(sec4;k)=int

else

calc elem(sec4;k)=miss

endif

“***********************SEC 5 K > B and H (BUT B = H)

************************”

if

(elem(KDB22;k).gt.2).and.(elem(KDBLD;k).gt.2).and.(elem(KDBSD;k).gt.2)

calc elem(sec5;k)=int

else

calc elem(sec5;k)=miss

endif

“***********************SEC 6 ---- B > K and H (BUT K = H)

************************”

if

(elem(BDK22;k).gt.2).and.(elem(BDKLD;k).gt.2).and.(elem(BDKSD;k).gt.2)

calc elem(sec6;k)=int

else

calc elem(sec6;k)=miss

endif

“***********************SEC 7 ---- H > B and

K*********************************”

if

(elem(H22h;k).gt.2).and.(elem(HLDh;k).gt.2).and.(elem(HSDh;k).gt.2)

calc elem(sec7;k)=int

else

calc elem(sec7;k)=miss

endif

“***********************SEC 8 ---- H < B and

K************************************”

if

(elem(H22l;k).lt.0.5).and.(elem(HLDl;k).lt.0.5).and.(elem(HSDl;k).lt.0.5

)

calc elem(sec8;k)=int

else

calc elem(sec8;k)=miss

endif

endfor

“***********************************************************************

*************”

for i=sec1,sec2,sec3,sec4,sec5,sec6,sec7,sec8;\

j=No1,No2,No3,No4,No5,No6,No7,No8;\

k=N1,N2,N3,N4,N5,N6,N7,N8;\

l=mv1,mv2,mv3,mv4,mv5,mv6,mv7,mv8

calc k=nvalues(i)

& l=nmv(i)

& j=k−l

endfor

print No1,No2,No3,No4,No5,No6,No7,No8

print [ch=3;iprint=*;rlprint=*;clprint=*]No1,No2,No3,No4,No5,No6,No7,No8

endfor

stop

Program 2

job ‘kondara br-0 heterosis work’

output [width=132]1

variate [nvalues=22810]sec1,sec2,sec3,sec4,sec5,sec6,sec7,sec8,sec9,\

DK22,DKLD,DKSD,DB22,DBLD,DBSD,DBH22,DBHLD,DBHSD,DKH22,DKHLD,DKHSD,

\

HBK22,HBKLD,HBKSD,KBH22,KBHLD,KBHSD,D_K22,D_KLD,D_KSD,H22,HLD,HSD,

\

BDK22,BDKLD,BDKSD,HB22,HBLD,HBSD,HK22,HKLD,HKSD,B_K22,B_KLD,B_KSD,

\

r22kb,rldkb,rsdkb,r22bk,rldbk,rsdbk,KHB22,KHBLD,KHBSD,BHK22,BHKLD,

BHKSD,\

KDB22,KDBLD,KDBSD,BDK22,BDKLD,BDKSD,H22h,HLDh,HSDh,H22l,HLDl,HSDl,

A,B,C,\

b_k22,b_kLD,b_kSD,K_H22h,K_HLDh,K_HSDh,B_H22h,B_HLDh,B_HSDh,\

HB22l,HBLDl,HBSDl,HB22h,HBLDh,HBSDh,\

HK22l,HKLDl,HKSDl,HK22h,HKLDh,HKSDh

variate [values=1...22810]gene

“*******************************READ BASIC EXPRESSION

DATA***************************”

open ‘x:\\daves\\reciprocals\\hk 22k.txt’;ch=2

read [ch=2;print=e,s;serial=n]h22,hld,hsd,k22,kld,ksd,b22,bld,bsd

close ch=2

“ INITIAL SEED FOR RANDOM NUMBER GENERATION

”

scalar int,x,y

scalar [value=54321]a

& [value=78656]b

& [value=17345]c

output [width=132]1

“ OPEN OUTPUT FILE
”

open ‘x:\\daves\\reciprocals\\hk 22k.out’;ch=3;width=132;filetype=o

scalar [value=16598]a

scalar [value=*]miss

scalar [value=1]int

for [ntimes=250] “START OF LOOP FOR BOOTSTRAPPING”

“ RANDOMISES ALL NINE VARIATES ”

for i=b22,h22,k22,bld,hld,hsd,bsd,kld,ksd;\

j=b22,h22,k22,bld,hld,hsd,bsd,kld,ksd

calc a=a+1

calc xx=urand(a;22810)

calc j=sort(i;xx)

end for

“ CALCULATES COMPARISONS FOR THREEOFOLD DIFFERENCES
”

“**********************************ratio of K : B

*****************************”

calc r22kb=k22/b22

& rldkb=kld/bld

& rsdkb=ksd/bsd

“**********************************ratio of B : K

*****************************”

& r22bk=b22/k22

& rldbk=bld/kld

& rsdbk=bsd/ksd

“***********************************ratio of H : K

*****************************”

& r22hk=h22/k22

& rldhk=hld/kld

& rsdhk=hsd/ksd

“********************************** ratio of H : B

*****************************”

& r22hb=h22/b22

& rldhb=hld/bld

& rsdhb=hsd/bsd

for k=1...22810

“********************************* B = H (within 2)

*****************************”

for

i=r22hb,rldhb,rsdhb;j=A,B,C;m=b22,bld,bsd;n=h22,hld,hsd;o=HB22l,HBLDl,HBSDl

;p=HB22h,HBLDh,HBSDh

if ((elem(i;k).gt.0.5).and.(elem(i;k).lt.2))

calc elem(j;k)=int

else

calc elem(j;k)=miss

endif

calc x=elem(m;k)

& y=elem(n;k)

“ LOWEST VALUE OF B OR H
”

if (y.gt.x).and.(elem(j;k).eq.1)

calc elem(o;k)=x

elsif (x.gt.y).and.(elem(j;k).eq.1)

calc elem(o;k)=y

else

calc elem(o;k)=miss

endif

“ HIGHEST VALUE OF B OR H
”

if (x.gt.y).and.(elem(j;k).eq.1)

calc elem(p;k)=x

elsif (y.gt.x).and.(elem(j;k).eq.1)

calc elem(p;k)=y

else

calc elem(p;k)=miss

endif

endfor

“*********************************K = H (within 2)

*****************************”

for

i=r22hk,rldhk,rsdhk; j=A,B,C;m=k22,kld,ksd;n=h22,hld,hsd;o=HK22l,HKLDl,HKSDl

;p=HK22h,HKLDh,HKSDh

if ((elem(i;k).gt.0.5).and.(elem(i;k).lt.2))

calc elem(j;k)=int

else

calc elem(j;k)=miss

endif

calc x=elem(m;k)

& y=elem(n;k)

“ LOWEST VALUE OF K OR H
”

if (x.lt.y).and.(elem(j;k).eq.1)

calc elem(o;k)=x

elsif (y.lt.x).and.(elem(j;k).eq.1)

calc elem(o;k)=y

else

calc elem(o;k)=miss

endif

“ HIGHEST VALUE OF K OR H
”

if (x.gt.y).and.(elem(j;k).eq.1)

calc elem(p;k)=x

elsif (y.gt.x).and.(elem(j;k).eq.1)

calc elem(p;k)=y

else

calc elem(p;k)=miss

endif

endfor

“************************************K = B (within 2)

*****************************”

for i=r22kb,rldkb,rsdkb;j=A,B,C;m=k22,kld,ksd;n=22,bld,bsd

if ((elem(i;k).gt.0.5).and.(elem(i;k).lt.2))

calc elem(j;k)=int

else

calc elem(j;k)=miss

endif

endfor

“**********************************K = B (highest & lowest

values)*******************”

for

i=r22kb,rldkb,rsdkb;j=A,B,C;m=k22,kld,ksd;n=b22,bld,bsd;o=B_K22,B_KLD,B_—

KSD;p=b_k22,b_kLD,b_kSD

calc x=elem(m;k)

& y=elem(n;k)

if (x.gt.y)

calc elem(o;k)=x

else

calc elem(o;k)=y

endif

if (x.lt.y)

calc elem(p;k)=x

else

calc elem(p;k)=y

endif

endfor

endfor

“***********************************ratio of H : (K = B) high values

**************”

calc H22h=h22/B_K22

& HLDh=hld/B_KLD

& HSDh=hsd/B_KSD

“************************************ratio of H : (K = B) low

values***************”

calc H22l=h22/b_k22

& HLDl=hld/b_kLD

& HSDl=hsd/b_kSD

“***********************************ratio of K : (B = H)

****************************”

calc KDB22=k22/HB22h

& KDBLD=kld/HBLDh

& KDBSD=ksd/HBSDh

“***********************************ratio of B : (K = H)

****************************”

calc BDK22=b22/HK22h

& BDKLD=bld/HKLDh

& BDKSD=bsd/HKSDh

“***********************************ratio of (K = H − low values) : B

************”

calc KHB22=HK22l/b22

& KHBLD=HKLDl/bld

& KHBSD=HKSDl/bsd

“************************************ratio of (B = H) : K

***************************”

calc BHK22=HB22l/k22

& BHKLD=HBLDl/kld

& BHKSD=HBSDl/ksd

“***********************************************************************

*************”

for k=1...22810

“*********************** SEC 1 ---- K>BR-0

********************************”

if

(elem(r22kb;k).gt.2).and.(elem(rldkb;k).gt.2).and.(elem(rsdkb;k).gt.2)

calc elem(sec1;k)=int

else

calc elem(sec1;k)=miss

endif

“***********************SEC 2 ---- BR-0>K

*********************************”

if

(elem(r22bk;k).gt.2).and.(elem(rldbk;k).gt.2).and.(elem(rsdbk;k).gt.2)

calc elem(sec2;k)=int

else

calc elem(sec2;k)=miss

endif

“**********************SEC 3 ---- K AND H > B (BUT K = H)

******************”

if

(elem(KHB22;k).gt.2).and.(elem(KHBLD;k).gt.2).and.(elem(KHBSD;k).gt.2)

calc elem(sec3;k)=int

else

calc elem(sec3;k)=miss

endif

“**********************SEC 4 ---- B AND H > K (BUT B = H)

*******************”

if

(elem(BHK22;k).gt.2).and.(elem(BHKLD;k).gt.2).and.(elem(BHKSD;k).gt.2)

calc elem(sec4;k)=int

else

calc elem(sec4;k)=miss

endif

“***********************SEC 5 ---- K > B and H (BUT B = H)

*********************”

if

(elem(KDB22;k).gt.2).and.(elem(KDBLD;k).gt.2).and.(elem(KDBSD;k).gt.2)

calc elem(sec5;k)=int

else

calc elem(sec5;k)=miss

endif

“********************* SEC 6 ---- B > K and H (BUT K = H)

************************”

if

(elem(BDK22;k).gt.2).and.(elem(BDKLD;k).gt.2).and.(elem(BDKSD;k).gt.2)

calc elem(sec6;k)=int

else

calc elem(sec6;k)=miss

endif

“********************* SEC 7 ---- H > B and K

*********************************”

if

(elem(H22h;k).gt.2).and.(elem(HLDh;k).gt.2).and.(elem(HSDh;k).gt.2)

calc elem(sec7;k)=int

else

calc elem(sec7;k)=miss

endif

“***********************SEC 8 ---- H < B and K

************************************”

if

(elem(H221;k).lt.0.5).and.(elem(HLDl;k).lt.0.5).and.(elem(HSDl;k).lt.0.5

)

calc elem(sec8;k)=int

else

calc elem(sec8;k)=miss

endif

endfor

“***********************************************************************

*************”

for i=sec1,sec2,sec3,sec4,sec5,sec6,sec7,sec8;\

j=No1,No2,No3,No4,No5,No6,No7,No8;\

k=N1,N2,N3,N4,N5,N6,N7,N8;\

l=mv1,mv2,mv3,mv4,mv5,mv6,mv7,mv8

calc k=nvalues(i)

& l=nmv(i)

& j=k−l

endfor

print No1,No2,No3,No4,No5,No6,No7,No8

endfor

stop

Program 3

job ‘correlation & linear regression analysis of expression data for 30

22k chips hybrid‘

“ MID PARENT ADVANTAGE ”

set [diagnostic=fault]

unit [32]

output [width=132]1

open ‘x:\\daves\\linreg\\all 32 hybs data.txt’;channel=2;width=250

open ‘x:\\daves\\linreg\\fprob 32 hybs lin

midp.out’;channel=3;filetype=o

variate

values=220.29,147.22,242.86,188.79,125.42,97.38,123.46,76.92,

104.48,103.61,

270.27,200.00,137.50,184.62,127.50,66.10,110.53,97.50,

121.26,138.46,63.53,124.56,103.23,108.33,128.74,122.89,

94.38,158.14,230.95,143.75,248.10,186.21]mpadv

scalar [value=45454]a

for [ntimes=22810]

read [ch=2;print=*;serial=n]exp

model exp

fit [print=*]mpadv

rkeep exp;meandev=resms;tmeandev=totms;tdf=df

calc totss=totms*31
“= number of genotypes-1”

& resss=resms*30
“= number of genotypes-2”

& regms=(totss-resss)/1

& regvr=regms/resms

& fprob=1−(clf(regvr;1;30))

print [ch=3;iprint=*;squash=y] fprob,df

endfor

close ch=2

stop

Program 4

job ‘correlation & linear regression analysis of expression data for 30

22k chips hybrid’

“ MID PARENT ADVANTAGE ”

set [diagnostic=fault]

unit [32]

output [width=132]1

open ‘x:\\daves\\linreg\\all 32 hybs data.txt’;channel=2;width=250

open ‘x:\\daves\\linreg\\fprob 32 hybs lin midpA

boot.out’;channel=2;filetype=o

& ‘x:\\daves\\linreg\\fprob 32 hybs lin midpB

boot.out’;channel=3;filetype=o

& ‘x:\\daves\\linreg\\fprob 32 hybs lin midpC

boot.out’;channel=4;filetype=o

& ‘x:\\daves\\linreg\\fprob 32 hybs lin midpD

boot.out’;channel=5;filetype=o

variate

values=220.29,147.22,242.86,188.79,125.42,97.38,123.46,76.92,

104.48,103.61,

270.27,200.00,137.50,184.62,127.50,66.10,110.53,97.50,121.26,

138.46,63.53,124.56,103.23,108.33,128.74,122.89,94.38,158.14,

230.95,143.75,248.10,186.21]mpadv

scalar [value=89849]a

for [ntimes=6000]

read [ch=2;print=*;serial=n]exp

for [ntimes1000]

calc a=a+1

calc y=urand(a;32)

& pex=sort(exp;y)

model pex

fit [print=*]mpadv

rkeep pex;meandev=resms;tmeandev=totms

calc totss=totms*31
“= number of

genotypes-1”

& resss=resms*30
“= number of

genotypes-2”

& regms=(totss-resss)/1

& regvr=regms/resms

& fprob=1−(clf(regvr;1;30))

print [ch=2;iprint=*;squash=yfprob

endfor

print [ch=2;iprint=*;squash=y]‘:’

endfor

for [ntimes=6000]

read [ch=2;print=*;serial=n]exp

for [ntimes=1000]

calc a=a+1

calc y=urand(a;32)

& pex=sort(exp;y)

model pex

fit [print=*]mpadv

rkeep pex;meandev=resms;tmeandev=totms

calc totss=totms*31
“= number of

genotypes-1”

& resss=resms*30
“= number of

genotypes-2”

& regms=(totss-resss)/1

& regvr=regms/resms

& fprob=1−(clf(regvr;1;30))

print [ch=3;iprint=*;squash=y] fprob

endfor

print [ch=3;iprint=*;squash=y]‘:’

endfor

for [ntimes=6000]

read [ch=2;print=*;serial=n]exp

for [ntimes=1000]

calc a=a+1

calc y=urand(a;32)

& pex=sort(exp;y)

model pex

fit [print=*]mpadv

rkeep pex;meandev=resms;tmeandev=totms

calc totss=totms*31
“= number of

genotypes-1”

& resss=resms*30
“= number of

genotypes-2”

& regms=(totss-resss)/1

& regvr=regms/resms

& fprob=1−(clf(regvr;1;30))

print [ch=4;iprint=*;squash=y]fprob

endfor

print [ch=4;iprint=*;squash=y]‘:’

endfor

for [ntimes=4810]

read [ch=2;print=*;serial=n]exp

for [ntimes=1000]

calc a=a+1

calc y=urand(a;32)

& pex=sort(exp;y)

model pex

fit [print=*]mpadv

rkeep pex;meandev=resms;tmeandev=totms

calc totss=totms*31
“= number of

genotypes-1”

& resss=resms*30
“= number of

genotypes-2”

& regms=(totss-resss)/1

& regvr=regms/resms

& fprob=1−(clf(regvr;1;30))

print [ch=5;iprint=*;squash=y]fprob

endfor

print [ch=5;iprint=*;squash=y]‘:’

endfor

close ch=2

close ch=3

close ch=4

close ch=5

stop

Program 5

job ‘BOOTSTRAP of linear regression analysis of expression data for 32

hybrid 22k chips ’

“ MID PARENT ADVANTAGE ”

open ‘x:\\daves\\linreg\\fprob 32 hybs lin midpA boot.out’;channel=2

& ‘x:\\daves\\linreg\\fprob 32 hybs lin midpB boot.out’;channel=3

& ‘x:\\daves\\linreg\\fprob 32 hybs lin midpC boot.out’;channel=4

& ‘x:\\daves\\linreg\\fprob 32 hybs lin midpD boot.out’;channel=5

for [ntimes=6000]

read [ch=2;print=*;serial=y]coeff

sort [dir=d]coeff;bootstrap

calc p05minus=elem(bootstrap;950)

& p01minus=elem(bootstrap;990)

& p001minus=elem(bootstrap;999)

print [iprint=*;squash=y]p05minus,p01minus,p001minus

endfor

close ch=2

for [ntimes=6000]

read [ch=3;print=*;serial=y]coeff

sort [dir=d]coeff;bootstrap

calc p05minus=elem(bootstrap;950)

& p01minus=elem(bootstrap;990)

& p001minus=elem(bootstrap;999)

print [iprint=*;squash=y]p05minus,p01minus,p01minus

endfor

close ch=3

for [ntimes=6000]

read [ch=4;print=*;serial=y]coeff

sort [dir=d]coeff;bootstrap

calc p05minus=elem(bootstrap;950)

& p01minus=elem(bootstrap;990)

& p001minus=elem(bootstrap;999)

print [iprint=*;squash=y]p05minus,p01minus,p001minus

endfor

close ch=4

for [ntimes=4810]

read [ch=5;print=*;serial=y]coeff

sort [dir=d]coeff;bootstrap

calc p05minus=elem(bootstrap;950)

& p01minus=elem(bootstrap;990)

& p001minus=elem(bootstrap;999)

print [iprint=*;squash=y]p05minus,p01minus,p001minus

endfor

close ch=5

stop

GenStat Programme 1˜Basic Regression Programme

job ‘Basic Regression Programme’

“ ORDER OF ORIGINAL DATA

Ag-0 P1 Ag-0 P2 Ag-0 P3 BR-0 P1 Br-0 P2 Br-0 P3 Col-0 P1 Ct-1

P1 Ct-1 P2 Ct-1 P3 Cvi-0 P1 Cvi-0 P2 Cvi-0 P3

Ga-0 P1 Gy-0 P1 Gy-0 P2 Gy-0 P3 Kondara P1 Kondara P2 Kondara P3

Mz-0 P1Mz-0 P2 Mz-0 P3 Nok-2 P1

Sorbo P1 Ts-5 P1 Wt-5 P1 ms1 1 ms1 2 ms1 3 ms1 4 ms1

5 ” “DATA ORDER IS OPTIONAL”

“ Data Input Files ”

set [diagnostic=fault]

unit [32] “NUMBER OF GENECHIPS”

output [width=132]1

open ‘x:\\daves\\linreg\\all 32 hybs data.txt’;channel=2;width=250

“FILE WITH EXPRESSION DATA ”

open ‘x:\\daves\\linreg\\fprob 32 hybs lin

midp.out’;channel=3;filetype=o “OUTPUT FILE”

variate [values=220.29,147.22,242.86,188.79,125.42,97.38,123.46,

76.92,104.48,103.61,270.27,200.00,137.50,184.62,\

127.50,66.10,110.53,97.50,121.26,138.46,63.53,124.56,103.23,108.33,

128.74,122.89,94.38,158.14,\

230.95,143.75,248.10,186.21]mpadv “TRAIT DATA”

scalar [value=45454]a

for [ntimes=22810] “NUMBER OF GENES”

read [ch=2;print=*;serial=n]exp

model exp

fit [print=*]mpadv

rkeep exp;meandev=resms;tmeandev=totms;tdf=df;“est=fd”

“Use to calculate Rsq Slope and Intercept”

“scalar intcpt,slope

equate
[oldform=!(1,−1)]fd;intcpt

&
[oldform=!(−1,1)]fd;slope”

“Regression Model”

calc totss=totms*31
“= number of GeneChips −1”

& resss=resms*30
“= number of GeneChips −2”

& regms=(totss−resss)/1

& regvr=regms/resms

& fprob=1−(clf(regvr;1;30))“= number of GeneChips −2”

print

[ch=3;iprint=*;squash=y]“resms,totms,regms,resss,totss,regvr,“fprob,df,”

rsq,slope,intcpt” “OUTPUT OPTIONS”

endfor

close ch=2

stop

GenStat Programme 2˜Basic Prediction Regression Programme

job ‘Basic Prediction Regression Programme’

set [diagnostic=fault]

unit [33]

output [width=250]1

open ‘x:\\Heterosis\\daves\\Predict\\MPH sept05\\BPH pred\\maleparhet

0.1% genes.txt’;channel=2;width=250 “INPUT FILE ”

open ‘x:\\Heterosis\\daves\\Predict\\MPH sept05\\BPH pred\\maleparhet

0.1% genes.out’;channel=3;filetype=o “OUTPUT FILE ”

variate

[values=97.70,97.70,97.70,130.90,130.90,130.90,103.44,103.44,

103.44,138.89,\

138.89,138.89,96.18,96.18,141.41,141.41,156.36,156.36,145.77,

145.77,150.80,\

150.80,150.80,282.42,282.42,385.39,385.39,430.10,430.10,

430.10,205.71,205.71,\

205.71]mpadv “TRAIT DATA”

scalar [value=68342]a

for [ntimes=706]“Number of Genes”

read [ch=2;print=*;serial=n]exp

model exp

fit [print=*]mpadv

rkeep exp;meandev=resms;tmeandev=totms;tdf=df

calc totss=totms*32
“= number of genotypes-1”

& resss=resms*31
“= number of genotypes-2”

& regms=(totss−resss)/1

& regvr=regms/resms

& fprob=1−(clf(regvr;1;31))“= number of genotypes-2”

predict

[print=*;prediction=bin]mpadv;levels=!(95,105,115,125,135,145,155,

165,175,185,195,250,350,450 )“BINS, COVERING RANGE OF DATA”

print
[ch=3;iprint=*;clprint=*;rlprint=*]bin

&
[ch=3;iprint=*;clprint=*]‘:’

endfor

close ch=2

stop

GenStat Programme 3˜Prediction Extraction Programme

job ‘Prediction Extraction Programme ’

“ MID PARENT ADVANTAGE ”

set [diagnostic=fault]

variate

[values=95,105,115,125,135,145,155,165,175,185,195,250,350,

450]mpadv

“BIN DATA FROM PREDICTION REGRESSION PROGRAMME”

variate [values=*]miss

scalar [value=0]gene,Estimate

output [width=200]1

open ‘x:\\Heterosis\\daves\\predict\\MPH sept05\\BPH pred\\KasLLSha

MalepredprobesSept05_0.1%.txt’;channel=2;width=500 “file with

test parent data”

open ‘x:\\Heterosis\\daves\\Predict\\MPH sept05\\BPH pred\\maleparhet

0.1% genes.out’;channel=3“file with calibration data”

calc y=0

& z=1

for [ntimes=2118] “Number of test genes X Number of Parents”

calc y=y+1

if y.eq.z

read [ch=3;print*;serial=n]bin “ 11 bins = 11 values”

calc z=z+3 “No of test parents”

print ‘:’

endif

read [ch=2;print=*;serial=n]exp

model mpadv

fit [print=*]bin

rkeep mpadv;meandev=resms;tmeandev=totms;tdf=df

calc totss=totms*10
“= number of genotypes-

1”

& resss=resms*9
“= number of genotypes-

2”

& regms=(totss−resss)/1

& regvr=regms/resms

& fprob=1−(clf(regvr;1;9))“= number of genotypes-2”

predict [print=*;prediction=estimate]bin;levels=exp

“should be scalar == or restricted variate”

if (estimate.lt.50) “FOR CAPPED PREDICTION, THIS IS THE LOWER

CAP”

calc Estimate=miss

elsif (estimate.gt.455)“FOR CAPPED PREDICTION, THIS IS THE

UPPER CAP”

calc Estimate=miss

else

calc Estimate=estimate

endif

calc gene=gene+1

print

[iprint=*;rlprint=*;squash=y]gene,Estimate,estimate

endfor

close ch=2

stop

GenStat Programme 4˜Basic Best Predictor Programme

job ‘Basic Best Predictor Programme’

text

[values=B73×B97,CML103,CML228,CML247,CML277,CML322,

CML333,CML52,IL14H,\Ki11,Ky21,M37W,Mo18W,

NC350,NC358,Oh43,P39,Tx303,Tzi8]l “Name of Accessions”

& [values=‘chip 1’,‘chip 2’]c “Number of Replicates”

factor
[labels=l]line

&
[labels=c]chip

factor gene

open ‘X:\\Heterosis\\daves\\Predictive gene id\\prediction

data.dat’;ch=2 “Input File”

read [ch=2;print=*;serial=n]gene,raw,line,chip,actual;frep=l,*,l,l,*

calc delta=raw-actual

& ratio=raw/actual

tabulate [class=gene;print=*]delta;means=Delta;nobs=number;var=t3

calc se_delta=sqrt(t3)/sqrt(number)

tabulate [class=gene;print=*]ratio;means=Ratio;var=t7

calc se_ratio=sqrt(t7)/sqrt(number)

print number,Delta,se_delta,Ratio,se_ratio;fieldwidth=20;dec=0,2,2,3,4

stop

GenStat Programme 5˜Basic Linear Regression Bootstrapping Programme

job ‘Basic Linear Regression Bootstrapping Programme’

“ Data Input Files ”

set [diagnostic=fault]

unit [32]“NUMBER OF GENECHIPS”

output [width=132]1

open ‘x:\\daves\\linreg\\all 32 hybs data.txt’;channel=2;width=250

“FILE WITH EXPRESSION DATA ”

open ‘x:\\daves\\linreg\\fprob 32 hybs lin midpA

boot.out’;channel=2;filetype=o “OUTPUT FILES ”

& ‘x:\\daves\\linreg\\fprob 32 hybs lin midpB

boot.out’;channel=3;filetype=o

& ‘x:\\daves\\linreg\\fprob 32 hybs lin midpC

boot.out’;channel=4;filetype=o

& ‘x:\\daves\\linreg\\fprob 32 hybs lin midpD

boot.out’;channel=5;filetype=o

variate

[values=220.29,147.22,242.86,188.79,125.42,97.38,123.46,76.92,104.48,

103.61,270.27,200.00,137.50,184.62,\

127.50,66.10,110.53,97.50,121.26,138.46,63.53,124.56,103.23,108.33,

128.74,122.89,94.38,158.14,\

230.95,143.75,248.10,186.21]mpadv “TRAIT DATA”

scalar [value=89849]a “SEED NUMBER”

for [ntimes=6000]“NUMBER OF GENES TO ANALYSE IN THIS

SECTION”

read [ch=2;print=*;serial=n]exp

for [ntimes=1000]“NUMBER OF RANDOMISATIONS”

calc a=a+1

calc y=urand(a;32)“NUMBER OF GENECHIPS TO

RANDOMISE”

& pex=sort(exp;y)

model pex

fit [print=*]mpadv

rkeep pex;meandev=resms;tmeandev=totms

calc totss=totms*31
“= number of

genotypes-1”

& resss=resms*30
“= number of

genotypes-2”

& regms=(totss-resss)/1

& regvr=regms/resms

& fprob=1−(clf(regvr;1;30)) “= number of

genotypes-2”

print

[ch=2;iprint=*;squash=y]“resms,totms,regms,resss,totss,regvr,”fprob

endfor

print [ch=2;iprint=*;squash=y]‘:’

endfor

for [ntimes=6000] “NUMBER OF GENES TO ANALYSE IN THIS

SECTION“

read [ch=2;print=*;serial=n]exp

for [ntimes=1000]“NUMBER OF RANDOMISATIONS”

calc a=a+1

calc y=urand(a;32)“NUMBER OF GENECHIPS TO

RANDOMISE”

& pex=sort(exp;y)

model pex

fit [print=*]mpadv

rkeep pex;meandev=resms;tmeandev=totms

calc totss=totms*31
“= number of

genotypes-1”

& resss=resms*30
“= number of

genotypes-2”

& regms=(totss−resss)/1

& regvr=regms/resms

& fprob=1−(clf(regvr;1;30))“= number of

genotypes-2”

print

[ch=3;iprint=*;squash=y]“resms,totms,regms,resss,totss,regvr,”fprob

endfor

print [ch=3;iprint=*;squash=y]‘:’

endfor

for [ntimes=6000]“NUMBER OF GENES TO ANALYSE IN THIS

SECTION”

read [ch=2;print=*;serial=n]exp

for [ntimes=1000]“NUMBER OF RANDOMISATIONS”

calc a=a+1

calc y=urand(a;32)“NUMBER OF GENECHIPS TO

RANDOMISE”

& pex=sort(exp;y)

model pex

fit [print=*]mpadv

rkeep pex;meandev=resms;tmeandev=totms

calc totss=totms*31
“= number of

genotypes-1”

& resss=resms*30
“= number of

genotypes-2”

& regms=(totss−resss)/1

& regvr=regms/resms

& fprob=1−(clf(regvr;1;30))“= number of

genotypes-2”

print

[ch=4;iprint=*;squash=y]“resms,totms,regms,resss,totss,regvr,”fprob

endfor

print [ch=4;iprint*;squash=y]‘:’

endfor

for [ntimes=4810]“NUMBER OF GENES TO ANALYSE IN THIS

SECTION”

read [ch=2;print=*;serial=n]exp

for [ntimes=1000]“NUMBER OF RANDOMISATIONS”

calc a=a+1

calc y=urand(a;32)“NUMBER OF GENECHIPS TO

RANDOMISE”

& pex=sort(exp;y)

model pex

fit [print=*]mpadv

rkeep pex;meandev=resms;tmeandev=totms

calc totss=totms*31
“= number of

genotypes-1”

& resss=resms*30
“= number of

genotypes-2”

& regms=(totss−resss)/1

& regvr=regms/resms

& fprob=1−(clf(regvr;1;30))“= number of

genotypes-2”

print

[ch=5;iprint=*;squash=y]“resms,totms,regms,resss,totss,regvr,”fprob

endfor

print [ch=5;iprint=*;squash=y]‘:’

endfor

close ch=2

close ch=3

close ch=4

close ch=5

stop

GenStat Programme 6˜Basic Linear Regression Bootstrapping Data Extraction Programme

job ‘Basic Linear Regression Bootstrapping Data Extraction Programme ’

“ DATA INPUT FILES ”

open ‘x:\\daves\\linreg\\fprob 32 hybs lin midpA boot.out’;channel=2

“INPUT FILES”

& ‘x:\\daves\\linreg\\fprob 32 hybs lin midpB boot.out ’;channel=3

& ‘x:\\daves\\linreg\\fprob 32 hybs lin midpC boot.out’;channel=4

& ‘x:\\daves\\linreg\\fprob 32 hybs lin midpD boot.out’;channel=5

for [ntimes=6000] “FIRST INPUT FILE NUMBER OF GENES”

read [ch=2;print=*;serial=y]coeff

sort [dir=a]coeff;bootstrap

calc p05plus=elem(bootstrap;50)

& p01plus=elem(bootstrap;10)

& p001plus=elem(bootstrap;1)

print [iprint=*;squash=y]p05plus,p01plus,p001plus “Extracts 5, 1 and

0.1% Significance levels”

endfor

close ch=2

for [ntimes=6000] “SECOND INPUT FILE NUMBER OF GENES”

read [ch=3;print=*;serial=y]coeff

sort [dir=a]coeff;bootstrap

calc p05plus=elem(bootstrap;50)

& p01plus=elem(bootstrap;10)

& p001plus=elem(bootstrap;1)

print [iprint=*;squash=y]p05plus,p01plus,p001plus

endfor

close ch=3

for [ntimes=6000] “THIRD INPUT FILE NUMBER OF GENES”

read [ch=4;print=*;serial=y]coeff

sort [dir=a]coeff;bootstrap

calc p05plus=elem(bootstrap;50)

& p01plus=elem(bootstrap;10)

& p001plus=elem(bootstrap;1)

print [iprint=*;squash=y]p05plus,p01plus,p001plus

print

[iprint=*;squash=y]“p05plus,p01plus,p001plus,”p05minus,p01minus,

p001minus

endfor

close ch=4

12 for [ntimes=4810] “FOURTH INPUT FILE NUMBER OF GENES”

read [ch=5;print=*;serial=y]coeff

sort [dir=a]coeff;bootstrap

calc p05plus=elem(bootstrap;50)

& p01plus=elem(bootstrap;10)

& p001plus=elem(bootstrap;1)

print [iprint=*;squash=y]p05plus,p01plus,p001plus

endfor

close ch=5

stop

GenStat Programme 7˜Basic Transcriptome Remodelling Programme

job ‘Basic Transcriptome Remodelling Programme ’

output [width=132]1

variate [nvalues=22810]sec1,sec2,sec3,sec4,sec5,sec6,sec7,sec8,sec9,\

DK22,DKLD,DKSD,DB22,DBLD,DBSD,DBH22,DBHLD,DBHSD,DKH22,DKHLD,DKHSD,

\

HBK22,HBKLD,HBKSD,KBH22,KBHLD,KBHSD,D_K22,D_KLD,D_KSD,H22,HLD,HSD,

\

BDK22,BDKLD,BDKSD,HB22,HBLD,HBSD,HK22,HKLD,HKSD,B_K22,B_KLD,B_KSD,

\

r22kb,rldkb,rsdkb,r22bk,rldbk,rsdbk,KHB22,KHBLD,KHBSD,BHK22,BHKLD,

BHKSD,\

KDB22,KDBLD,KDBSD,BDK22,BDKLD,BDKSD,H22h,HLDh,HSDh,H22l,HLDl,HSDl,

A,B,C,\

b_k22,b_kLD,b_kSD,K_H22h,K_HLDh,K_HSDh,B_H22h,B_HLDh,B_HSDh,\

HB22l,HBLDl,HBSDl,HB22h,HBLDh,HBSDh,\

HK22l,HKLDl,HKSDl,HK22h,HKLDh,HKSDh “FILE IDENTIFIERS-IGNORE”

variate [values=1...22810]gene

“********************************* READ BASIC EXPRESSION DATA

******************************”

open ‘x:\\daves\\reciprocals\\hb 22k.txt’;ch=2 “INPUT FILE”

read [ch=2;print=e,s;serial=n]h22,hld,hsd,k22,kld,ksd,b22,bld,bsd

close ch=2

“ INITIAL SEED FOR RANDOM NUMBER GENERATION

”

scalar int,x,y

scalar
[value=54321]a

&
[value=78656]b

&
[value=17345]c

output [width=132]1

“ OPEN OUTPUT FILE

”

open ‘x:\\daves\\reciprocals\\hk 22k.out’;ch=3;width=132;filetype=o

“OUTPUT FILE”

scalar
[value=12345]a

scalar
[value=*]miss

scalar
[value=1]int

“ CALCULATES COMPARISONS FOR THREEOFOLD DIFFERENCES ”

“************************************* ratio of K : B

*****************************”

calc r22kb=k22/b22

& rldkb=kld/bld

& rsdkb=ksd/bsd

“************************************* ratio of B : K

*****************************”

& r22bk=b22/k22

& rldbk=bld/kld

& rsdbk=bsd/ksd

“************************************* ratio of H : K

*****************************”

& r22hk=h22/k22

& rldhk=hld/kld

& rsdhk=hsd/ksd

“************************************* ratio of H : B

*****************************”

& r22hb=h22/b22

& rldhb=hld/bld

& rsdhb=hsd/bsd

for k=1...22810

“************************************* B = H (within 2)

*****************************”

for

i=r22hb,rldhb,rsdhb;j=A,B,C;m=b22,bld,bsd;n=h22,hld,hsd;o=HB22l,HBLDl,HBSDl

;p=HB22h,HBLDh,HBSDh

if ((elem(i;k).gt.0.5).and.(elem(i;k).lt.2)) “SETS FOLD

LEVELS”

calc elem(j;k)=int

else

calc elem(j;k)=miss

endif

calc x=elem(m;k)

& y=elem(n;k)

“ LOWEST VALUE OF B OR H ”

if (y.gt.x).and.(elem(j;k).eq.1)

calc elem(o;k)=x

elsif (x.gt.y).and.(elem(j;k).eq.1)

calc elem(o;k)=y

else

calc elem(o;k)=miss

endif

“ HIGHEST VALUE OF B OR H ”

if (x.gt.y).and.(elem(j;k).eq.1)

calc elem(p;k)=x

elsif (y.gt.x).and.(elem(j;k).eq.1)

calc elem(p;k)=y

else

calc elem(p;k)=miss

endif

endfor

“************************************* K = H (within 2)

*****************************”

for

i=r22hk,rldhk,rsdhk;j=A,B,C;m=k22,kld,ksd;n=h22,hld,hsd;o=HK22l,HKLDl,HKSDl

;p=HK22h,HKLDh,HKSDh

if ((elem(i;k).gt.0.5).and.(elem(i;k).lt.2))

calc elem(j;k)=int

else

calc elem(j;k)=miss

endif

calc x=elem(m;k)

& y=elem(n;k)

“ LOWEST VALUE OF K OR H ”

if (x.lt.y).and.(elem(j;k).eq.1)

calc elem(o;k)=x

elsif (y.lt.x).and.(elem(j;k).eq.1)

calc elem(o;k)=y

else

calc elem(o;k)=miss

endif

“ HIGHEST VALUE OF K OR H ”

if (x.gt.y).and.(elem(j;k).eq.1)

calc elem(p;k)=x

elsif (y.gt.x).and.(elem(j;k).eq.1)

calc elem(p;k)=y

else

calc elem(p;k)=miss

endif

endfor

“************************************* K = B (within 2)

*****************************”

for i=r22kb,rldkb,rsdkb;j=A,B,C;m=k22,kld,ksd;n=b22,bld,bsd

if ((elem(i;k).gt.0.5).and.(elem(i;k).lt.2))

calc elem(j;k)=int

else

calc elem(j;k)=miss

endif

endfor

“************************************* K = B (highest & lowest values)

*************************”

for

i=r22kb,rldkb,rsdkb;j=A,B,C;m=k22,kld,ksd;n=b22,bld,bsd;o=B_K22,B_KLD,B_—

KSD;p=b_k22,b_kLD,b_kSD

calc x=elem(m;k)

& y=elem(n;k)

if (x.gt.y)

calc elem(o;k)=x

else

calc elem(o;k)=y

endif

if (x.lt.y)

calc elem(p;k)=x

else

calc elem(p;k)=y

endif

endfor

endfor

“************************************* ratio of H : (K = B) high

values **************”

calc H22h=h22/B_K22

& HLDh=hld/B_KLD

& HSDh=hsd/B_KSD

“************************************* ratio of H : (K = B) low

values ***************”

calc H22l=h22/b_k22

& HLDl=hld/b_kLD

& HSDl=hsd/b_kSD

“************************************* ratio of K : (B = H)

****************************”

calc KDB22=k22/HB22h

& KDBLD=kld/HBLDh

& KDBSD=ksd/HBSDh

“************************************* ratio of B : (K = H)

****************************”

calc BDK22=b22/HK22h

& BDKLD=bld/HKLDh

& BDKSD=bsd/HKSDh

“************************************* ratio of (K = H − low values) :

B ************”

calc KHB22=HK22l/b22

& KHBLD=HKLDl/bld

& KHBSD=HKSDl/bsd

“************************************* ratio of (B = H) : K

****************************”

calc BHK22=HB22l/k22

& BHKLD=HBLDl/kld

& BHKSD=HBSDl/ksd

“***********************************************************************

****************”

for k=1...22810

“*********************** SEC 1 ---- K>BR-0

********************************”

if

(elem(r22kb;k).gt.2).and.(elem(rldkb;k).gt.2).and.(elem(rsdkb;k).gt.2)

calc elem(sec1;k)=int

else

calc elem(sec1;k)=miss

endif

“*********************** SEC 2 ---- BR-0>K

*********************************”

if

(elem(r22bk;k).gt.2).and.(elem(rldbk;k).gt.2).and.(elem(rsdbk;k).gt.2)

calc elem(sec2;k)=int

else

calc elem(sec2;k)=miss

endif

“*********************** SEC 3 ---- K AND H > B (BUT K = H)

******************”

if

(elem(KHB22;k).gt.2).and.(elem(KHBLD;k).gt.2).and.(elem(KHBSD;k).gt.2)

calc elem(sec3;k)=int

else

calc elem(sec3;k)=miss

endif

“*********************** SEC 4 ---- B AND H > K (BUT B = H)

*******************”

if

(elem(BHK22;k).gt.2).and.(elem(BHKLD;k).gt.2).and.(elem(BHKSD;k).gt.2)

calc elem(sec4;k)=int

else

calc elem(sec4;k)=miss

endif

“*********************** SEC 5 ---- K > B and H (BUT B = H)

*********************”

if

(elem(KDB22;k).gt.2).and.(elem(KDBLD;k).gt.2).and.(elem(KDBSD;k).gt.2)

calc elem(sec5;k)=int

else

calc elem(sec5;k)=miss

endif

“*********************** SEC 6 ---- B > K and H (BUT K = H)

************************”

if

(elem(BDK22;k).gt.2).and.(elem(BDKLD;k).gt.2).and.(elem(BDKSD;k).gt.2)

calc elem(sec6;k)=int

else

calc elem(sec6;k)=miss

endif

“*********************** SEC 7 ---- H > B and K

*********************************”

if

(elem(H22h;k).gt.2).and.(elem(HLDh;k).gt.2).and.(elem(HSDh;k).gt.2)

calc elem(sec7;k)=int

else

calc elem(sec7;k)=miss

endif

“*********************** SEC 8 ---- H < B and K

************************************”

if

(elem(H22l;k).lt.0.5).and.(elem(HLDl;k).lt.0.5).and.(elem(HSDl.k).lt.0.5)

calc elem(sec8;k)=int

else

calc elem(sec8;k)=miss

endif

endfor

“***********************************************************************

******************************”

print gene,sec1,sec2,sec3,sec4,sec5,sec6,sec7,sec8

for i=sec1,sec2,sec3,sec4,sec5,sec6,sec7,sec8;\

j=No1,No2,No3,No4,No5,No6,No7,No8;\

k=N1,N2,N3,N4,N5,N6,N7,N8;\

l=mv1,mv2,mv3,mv4,mv5,mv6,mv7,mv8

calc k=nvalues(i)

& l=nmv(i)

& j=k−l

endfor

print No1,No2,No3,No4,No5,No6,No7,No8

stop

GenStat Programme 8˜Dominance Pattern Programme

job ‘Dominance Pattern Programme’

scalar AG1M,AG1,AG2M,AG2,AG3M,AG3,CT1M,CT1,CT2M,CT2,CT3M,CT3,\

CV1M,CV1,CV2M,CV2,CV3M,CV3,GY1M,GY1,GY2M,GY2,GY3M,GY3,K1M,\

K1,K2M,K2,K3M,K3,MZ1M,MZ1,MZ2M,MZ2,MZ3M,MZ3,BK1M,BK1,BK2M,\

BK2,BK3M,BK3,KB1M,KB1,KB2M,KB2,KB3M,KB3 “genotypes

names/bins for calculations”

scalar [value=48]a
“starting value

for equate directive”

& [value=12345]seed
“seed value for

randomisation”

& [value=*]miss
“missing value”

&

[value=0]AGEQ,AGGT,AGLT,CTEQ,CTGT,CTLT,CVEQ,CVGT,CVLT,GYEQ,GYGT,GYLT,\

KEQ,KGT,KLT,MZEQ,MZGT,MZLT,BKEQ,BKGT,BKLT,KBEQ,KBGT,KBLT

“scalars for total signifiant genes”

variate [nvalues=48]gene

& [nvalues=22810]AG,CT,CV,GY,K,MZ,BK,KB

&

[nvalues=3]eqAG,gtAG,ltAG,eqCT,gtCT,ltCT,eqCV,gtCV,ltCV,eqGY,gtGY,ltGY,\

eqK,gtK,ltK,eqMZ,gtMZ,ltMZ,eqBK,gtBK,ltBK,eqKB,gtKB,ltKB

output [width=400]1

“ OPEN OUTPUT FILE

”

open ‘x:\\daves\\Dominance method\\dom 2

fold.out’;ch=3;width=300;filetype=o “OUTPUT FILE”

open ‘x:\\daves\\Dominance method\\Expression datab.txt’;ch=2;width=500

“INPUT FILE”

read [ch=2;print=e,s;serial=n]EXP

close ch=2

for i=1...22810
“reads through

data gene by gene”

calc a=a−48
“incremnets data”

equate [oldformat=!(a,48)]EXP;gene
“puts data in one

variate per gene”

“randomises variate for subsequent calculations

calc nege=rand(gene;seed)”

“places data for 1 gene at a time into variate bins”

for

geno=AG1M,AG1,AG2M,AG2,AG3M,AG3,CT1M,CT1,CT2M,CT2,CT3M,CT3,CV1M,CV1,CV2M,

CV2,CV3M,CV3,\

GY1M,GY1,GY2M,GY2,GY3M,GY3,K1M,K1,K2M,K2,K3M,K3,MZ1M,MZ1,MZ2M,MZ2,

NZ3M,MZ3,BK1M,BK1,\

BK2M,BK2,BK3M,BK3,KB1M,KB1,KB2M,KB2,KB3M,KB3;\

j=1...48

calc geno=elem(gene;j)

endfor

“calculation of ratios”

for

genom=AG1M,AG2M,AG3M,CT1M,CT2M,CT3M,CV1M,CV2M,CV3M,GY1M,GY2M,GY3M,K1M,\

K2M,K3M,MZ1M,MZ2M,MZ3M,BK1M,BK2M,BK3M,KB1M,KB2M,KB3M;\

genoh=AG1,AG2,AG3,CT1,CT2,CT3,CV1,CV2,CV3,GY1,GY2,GY3,\

K1,K2,K3,MZ1,MZ2,MZ3,BK1,BK2,BK3,KB1,KB2,KB3;\

ratio=rAG1,rAG2,rAG3,rCT1,rCT2,rCT3,rCV1,rCV2,rCV3,rGY1,rGY2,rGY3,\

rK1,rK2,rK3,rMZ1,rMZ2,rMZ3,rBK1,rBK2,rBK3,rKB1,rKB2,rKB3;\

hEQmp=eqAG,eqAG,eqAG,eqCT,eqCT,eqCT,eqCV,eqCV,eqCV,eqGY,eqGY,eqGY,\

eqK,eqK,eqK,eqMZ,eqMZ,eqMZ,eqBK,eqBK,eqBK,eqKB,eqKB,eqKB;\

hGTmp=gtAG,gtAG,gtAG,gtCT,gtCT,gtCT,gtCV,gtCV,gtCV,gtGY,gtGY,gtGY,\

gtK,gtK,gtK,gtMZ,gtMZ,gtMZ,gtBK,gtBK,gtBK,gtKB,gtKB,gtKB;\

hLTmp=ltAG,ltAG,ltAG,ltCT,ltCT,ltCT,ltCV,ltCV,ltCV,ltGY,ltGY,ltGY,\

ltK,ltK,ltK,ltMZ,ltMZ,ltMZ,ltBK,ltBK,ltBK,ltKB,ltKB,ltKB;\

k=1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2,3

calc ratio=genoh/genom “calculates

ratios”

calc heqmp=miss

& hgtmp=miss “sets default flag

values”

& hltmp=miss

if (ratio.ge.0.5).and.(ratio.le.2)
“SETS FOLD LEVEL”

calc heqmp=1

elsif (ratio.gt.2)
“SETS UPPER FOLD LEVEL”

calc hgtmp=1

elsif (ratio.lt.0.5)
“SETS LOWER FOLD LEVEL”

calc hltmp=1

else

calc heqmp=miss

& hgtmp=miss

& hltmp=miss

endif

calc elem(hEQmp;k)=heqmp

& elem(hGTmp;k)=hgtmp

& elem(hLTmp;k)=hltmp

endfor

for

X=eqAG,gtAG,ltAG,eqCT,gtCT,ltCT,eqCV,gtCV,ltCV,eqGY,gtGY,ltGY,\

eqK,gtK,ltK,eqMZ,gtMZ,ltMZ,eqBK,gtBK,ltBK,eqKB,gtKB,ltKB;\

Y=AGeq,AGgt,AGlt,CTeq,CTgt,CTlt,CVeq,CVgt,CVlt,GYeq,GYgt,GYlt,\

Keq,Kgt,Klt,MZeq,MZgt,MZlt,BKeq,BKgt,BKlt,KBeq,KBgt,KBlt;\

Z=AGEQ,AGGT,AGLT,CTEQ,CTGT,CTLT,CVEQ,CVGT,CVLT,GYEQ,GYGT,GYLT,\

KEQ,KGT,KLT,MZEQ,MZGT,MZLT,BKEQ,BKGT,BKLT,KBEQ,KBGT,KBLT

calc Y=sum(X)

if Y.eq.3

calc Y=1

else

calc Y=0

endif

calc Z=Z+Y

endfor

print

[ch=3;iprint=*;squash=y]AGeq,AGgt,AGlt,CTeq,CTgt,CTlt,CVeq,CVgt,CVlt,GYeq,

GYgt,GYlt,\

Keq,Kgt,Klt,MZeq,MZgt,MZlt,BKeq,BKgt,BKlt,KBeq,KBgt,KBlt;fieldwidth=8;

dec=0

endfor

stop

GenStat Programme 9˜Dominance Permutation Programme

job ‘Dominance Permutation Programme’

scalar AG1M,AG1,AG2M,AG2,AG3M,AG3,CT1M,CT1,CT2M,CT2,CT3M,CT3,\

CV1M,CV1,CV2M,CV2,CV3M,CV3,GY1M,GY1,GY2M,GY2,GY3M,GY3,K1M,\

K1,K2M,K2,K3M,K3,MZ1M,MZ1,MZ2M,MZ2,MZ3M,MZ3,BK1M,BK1,BK2M,\

BK2,BK3M,BK3,KB1M,KB1,KB2M,KB2,KB3M,KB3
“genotypes

names/bins for calculations”

scalar [value=48]a
“starting value

for equate directive”

& [value=12345]seed
“seed value for

randomisation”

&

[value=0]AGEQ,AGGT,AGLT,CTEQ,CTGT,CTLT,CVEQ,CVGT,CVLT,GYEQ,GYGT,GYLT,\

KEQ,KGT,KLT,MZEQ,MZGT,MZLT,BKEQ,BKGT,BKLT,KBEQ,KBGT,KBLT

“scalars for total signifiant genes”

variate [nvalues=48]gene

& [nvalues=22810]AG,CT,CV,GY,K,MZ,BK,KB

&

[nvalues=3]eqAG,gtAG,ltAG,eqCT,gtCT,ltCT,eqCV,gtCV,ltCV,eqGY,gtGY,ltGY,\

eqK,gtK,ltK,eqMZ,gtMZ,ltMZ,eqBK,gtBK,ltBK,eqKB,gtKB,ltKB

output [width=400]1

“ OPEN OUTPUT FILE

”

open ‘x:\\daves\\Dominance

method\\domperm.out’;ch=3;width=300;filetype=o “OUTPUT FILE”

open ‘x:\\daves\\Dominance method\\Expression datab.txt’;ch=2;width=500

“INPUT FILE”

read [ch=2;print=e,s;serial=n]EXP

close ch=2

for [ntimes=1000]
“NUMBER OF

PERMUTATIONS”

calc seed=seed+1

for [ntimes=22810] “NUMBER OF GENES”

“***********************************************************************

***********”

calc a=a−48

equate [oldformat=!(a,48)]EXP;gene
“puts data

in one variate per gene”

“randomises variate for subsequent calculations”

calc y=urand(seed;48)

& nege=sort(gene;y)

“places data for 1 gene at a time into variate bins”

for

geno=AG1M,AG1,AG2M,AG2,AG3M,AG3,CT1M,CT1,CT2M,CT2,CT3M,CT3,CV1M,CV1,CV2M

,CV2,CV3M,CV3,\

GY1M,GY1,GY2M,GY2,GY3M,GY3,K1M,K1,K2M,K2,K3M,K3,MZ1M,MZ1,MZ2M,MZ2,

MZ3M,MZ3,BK1M,BK1,\

BK2M,BK2,BK3M,BK3,KB1M,KB1,KB2M,KB2,KB3M,KB3;\

j=1...48

calc geno=elem(nege;j)

endfor

“***********************************************************************

********”

“calculation of ratios”

for

genom=AG1M,AG2M,AG3M,CT1M,CT2M,CT3M,CV1M,CV2M,CV3M,GY1M,GY2M,GY3M,K1M,\

K2M,K3M,MZ1M,MZ2M,MZ3M,BK1M,BK2M,BK3M,KB1M,KB2M,KB3M;\

genoh=AG1,AG2,AG3,CT1,CT2,CT3,CV1,CV2,CV3,GY1,GY2,GY3,\

K1,K2,K3,MZ1,MZ2,MZ3,BK1,BK2,BK3,KB1,KB2,KB3;\

ratio=rAG1,rAG2,rAG3,rCT1,rCT2,rCT3,rCV1,rCV2,rCV3,rGY1,rGY2,rGY3,\

rK1,rK2,rK3,rMZ1,rMZ2,rMZ3,rBK1,rBK2,rBK3,rKB1,rKB2,rKB3;\

hEQmp=eqAG,eqAG,eqAG,eqCT,eqCT,eqCT,eqCV,eqCV,eqCV,eqGY,eqGY,eqGY,\

eqK,eqK,eqK,eqMZ,eqMZ,eqMZ,eqBK,eqBK,eqBK,eqKB,eqKB,eqKB;\

hGTmp=gtAG,gtAG,gtAG,gtCT,gtCT,gtCT,gtCV,gtCV,gtCV,gtGY,gtGY,gtGY,\

gtK,gtK,gtK,gtMZ,gtMZ,gtMZ,gtBK,gtBK,gtBK,gtKB,gtKB,gtKB;\

hLTmp=ltAG,ltAG,ltAG,ltCT,ltCT,ltCT,ltCV,ltCV,ltCV,ltGY,ltGY,ltGY,\

ltK,ltK,ltK,ltMZ,ltMZ,ltMZ,ltBK,ltBK,ltBK,ltKB,ltKB,ltKB;\

k=1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2,3

calc ratio=genoh/genom “calculates

ratios”

calc heqmp=0

& hgtmp=0
“sets

default flag values”

& hltmp=0

if (ratio.le.2.0).and.(ratio.ge.0.5)
“SETS FOLD

LEVEL”

calc heqmp=1

elsif (ratio.gt.2.0)
“SETS UPPER

FOLD LEVEL”

calc hgtmp=1

elsif (ratio.lt.0.5)
“SETS LOWER

FOLD LEVEL”

calc hltmp=1

else

calc heqmp=0

& hgtmp=0

& hltmp=0

endif

calc elem(hEQmp;k)=heqmp

& elem(hGTmp;k)=hgtmp

& elem(hLTmp;k)=hltmp

endfor

for

X=eqAG,gtAG,ltAG,eqCT,gtCT,ltCT,eqCV,gtCV,ltCV,eqGY,gtGY,ltGY,\

eqK,gtK,ltK,eqMZ,gtMZ,ltMZ,eqBK,gtBK,ltBK,eqKB,gtKB,ltKB;\

Y=AGeq,AGgt,AGlt,CTeq,CTgt,CTlt,CVeq,CVgt,CVlt,GYeq,GYgt,GYlt,\

Keq,Kgt,Klt,MZeq,MZgt,MZlt,BKeq,BKgt,BKlt,KBeq,KBgt,KBlt;\

Z=AGEQ,AGGT,AGLT,CTEQ,CTGT,CTLT,CVEQ,CVGT,CVLT,GYEQ,GYGT,GYLT,\

KEQ,KGT,KLT,MZEQ,MZGT,MZLT,BKEQ,BKGT,BKLT,KBEQ,KBGT,KBLT

calc Y=sum(X)

if Y.eq.3

calc Y=1

else

calc Y=0

endif

calc Z=Z+Y

endfor

endfor

print

[ch=3;iprint=*;squash=y]AGEQ,AGGT,AGLT,CTEQ,CTGT,CTLT,CVEQ,CVGT,CVLT,GYEQ

,GYGT,GYLT,\

KEQ,KGT,KLT,MZEQ,MZGT,MZLT,BKEQ,BKGT,BKLT,KBEQ,KBGT,KBLT;fieldwidth

=8; dec=0

for list=AGEQ,AGGT,AGLT,CTEQ,CTGT,CTLT,CVEQ,CVGT,CVLT,GYEQ,GYGT,GYLT,\

KEQ,KGT,KLT,MZEQ,MZGT,MZLT,BKEQ,BKGT,BKLT,KBEQ,KBGT,KBLT

calc list=0

endfor

endfor

stop

GenStat Programme 10˜Transcriptome Remodelling Bootstrap Programme

job ‘Transcriptome Remodelling Bootstrap Programme’

output [width=132]1

variate [nvalues=22810]sec1,sec2,sec3,sec4,sec5,sec6,sec7,sec8,sec9,\

DK22,DKLD,DKSD,DB22,DBLD,DBSD,DBH22,DBHLD,DBHSD,DKH22,DKHLD,DKHSD,

\

HBK22,HBKLD,HBKSD,KBH22,KBHLD,KBHSD,D_K22,D_KLD,D_KSD,H22,HLD,HSD,

\

BDK22,BDKLD,BDKSD,HB22,HBLD,HBSD,HK22,HKLD,HKSD,B_K22,B_KLD,B_KSD,

\

r22kb,rldkb,rsdkb,r22bk,rldbk,rsdbk,KHB22,KHBLD,KHBSD,BHK22,BHKLD,

BHKSD,\

KDB22,KDBLD,KDBSD,BDK22,BDKLD,BDKSD,H22h,HLDh,HSDh,H22l,HLDl,HSDl,

A,B,C,\

b_k22,b_kLD,b_kSD,K_H22h,K_HLDh,K_HSDh,B_H22h,B_HLDh,B_HSDh,\

HB22l,HBLDl,HBSDl,HB22h,HBLDh,HBSDh,\

HK22l,HKLDl,HKSDl,HK22h,HKLDh,HKSDh “FILE IDENTIFIERS-IGNORE”

variate [values=1...22810]gene

“********************************* READ BASIC EXPRESSION DATA

******************************”

open ‘x:\\daves\\reciprocals\\hb 22k.txt’;ch=2 “INPUT FILE”

read [ch=2;print=e,s;serial=n]h22,hld,hsd,k22,kld,ksd,b22,bld,bsd

close ch=2

“ INITIAL SEED FOR RANDOM NUMBER GENERATION

”

scalar int,x,y

scalar
[value=54321]a

&
[value=78656]b

&
[value=17345]c

output
[width=132]1

“ OPEN OUTPUT FILE

”

open ‘x:\\daves\\reciprocals\\hb 22k.out’;ch=3;width=132;filetype=o

“OUTPUT FILE”

scalar [value=17589]a

scalar [value=*]miss

scalar [value=1]int

“START OF LOOP FOR BOOTSTRAPPING”

for [ntimes=1000] “NUMBER OF RANDOMISATIONS”

“ RANDOMISES ALL NINE VARIATES ”

for
i=b22,h22,k22,bld,hld,hsd,bsd,kld,ksd;\

j=b22,h22,k22,bld,hld,hsd,bsd,kld,ksd

calc a=a+1

calc xx=urand(a;22810)“NUMBER OF GENES”

calc j=sort(i;xx)

endfor

“ CALCULATES COMPARISONS FOR THREEOFOLD DIFFERENCES ”

“************************************* ratio of K : B

*****************************”

calc r22kb=k22/b22

& rldkb=kld/bld

& rsdkb=ksd/bsd

“************************************* ratio of B : K

*****************************”

& r22bk=b22/k22

& rldbk=bld/kld

& rsdbk=bsd/ksd

“************************************* ratio of H : K

*****************************”

& r22hk=h22/k22

& rldhk=hld/kld

& rsdhk=hsd/ksd

“************************************* ratio of H : B

*****************************”

& r22hb=h22/b22

& rldhb=hld/bld

& rsdhb=hsd/bsd

for k=1...22810

“************************************* B = H (within 2)

*****************************”

for

i=r22hb,rldhb,rsdhb;j=A,B,C;m=b22,bld,bsd;n=h22,hld,hsd;o=HB22l,HBLDl,HBSDl;

p=HB22h,HBLDh,HBSDh

if ((elem(i;k).gt.0.5).and.(elem(i;k).lt.2))“SETS FOLD

LEVELS”

calc elem(j;k)=int

else

calc elem(j;k)=miss

endif

calc x=elem(m;k)

& y=elem(n;k)

“ LOWEST VALUE OF B OR H ”

if (y.gt.x).and.(elem(j;k).eq.1)

calc elem(o;k)=x

elsif (x.gt.y).and.(elem(j;k).eq.1)

calc elem(o;k)=y

else

calc elem(o;k)=miss

endif

“ HIGHEST VALUE OF B OR H ”

if (x.gt.y).and.(elem(j;k).eq.1)

calc elem(p;k)=x

elsif (y.gt.x).and.(elem(j;k).eq.1)

calc elem(p;k)=y

else

calc elem(p;k)=miss

endif

endfor

“************************************* K = H (within 2)

*****************************”

for

i=r22hk,rldhk,rsdhk;j=A,B,C;m=k22,kld,ksd;n=h22,hld,hsd,o=HK22l,HKLDl,HKSDl;

p=HK22h,HKLDh,HKSDh

if ((elem(i;k).gt.0.5).and.(elem(i;k).lt.2))

calc elem(j;k)=int

else

calc elem(j;k)=miss

endif

calc x=elem(m;k)

& y=elem(n;k)

“ LOWEST VALUE OF K OR H ”

if (x.lt.y).and.(elem(j;k).eq.1)

calc elem(o;k)=x

elsif (y.lt.x).and.(elem(j;k).eq.1)

calc elem(o;k)=y

else

calc elem(o;k)=miss

endif

“ HIGHEST VALUE OF K OR H ”

if (x.gt.y).and.(elem(j;k).eq.1)

calc elem(p;k)=x

elsif (y.gt.x).and.(elem(j;k).eq.1)

calc elem(p;k)=y

else

calc elem(p;k)=miss

endif

endfor

“************************************* K = B (within 2)

*****************************”

for i=r22kb,rldkb,rsdkb;j=A,B,C;m=k22,kld,ksd;n=b22,bld,bsd

if ((elem(i;k).gt.0.5).and.(elem(i;k).lt.2))

calc elem(j;k)=int

else

calc elem(j;k)=miss

endif

endfor

“************************************* K = B (highest & lowest values)

*************************”

for

i=r22kb,rldkb,rsdkb;j=A,B,C;m=k22,kld,ksd;n=b22,bld,bsd;o=B_K22,B_KLD,B_—KSD;

p=b_k22,b_kLD,b_kSD

calc x=elem(m;k)

& y=elem(n;k)

if (x.gt.y)

calc elem(o;k)=x

else

calc elem(o;k)=y

endif

if (x.lt.y)

calc elem(p;k)=x

else

calc elem(p;k)=y

endif

endfor

endfor

“************************************* ratio of H : (K = B) high

values **************”

calc H22h=h22/B_K22

& HLDh=hld/B_KLD

& HSDh=hsd/B_KSD

“************************************* ratio of H : (K = B) low

values ***************”

calc H22l=h22/b_k22

& HLDl=hld/b_kLD

& HSDl=hsd/b_kSD

“************************************* ratio of K : (B = H)

****************************”

calc KDB22=k22/HB22h

& KDBLD=kld/HBLDh

& KDBSD=ksd/HBSDh

“************************************* ratio of B : (K = H)

****************************”

calc BDK22=b22/HK22h

& BDKLD=bld/HKLDh

& BDKSD=bsd/HKSDh

“************************************* ratio of (K = H − low values) :

B ************”

calc KHB22=HK22l/b22

& KHBLD=HKLDl/bld

& KHBSD=HKSDl/bsd

“************************************* ratio of (B = H) : K

****************************”

calc BHK22=HB22l/k22

& BHKLD=HBLDl/kld

& BHKSD=HBSDl/ksd

“***********************************************************************

****************”

for k=1...22810

“*********************** SEC 1 ---- K>BR-0

********************************”

if

(elem(r22kb;k).gt.2).and.(elem(rldkb;k).gt.2).and.(elem(rsdkb;k).gt.2)

calc elem(sec1;k)=int

else

calc elem(sec1;k)=miss

endif

“*********************** SEC 2 ---- BR-0>K

*********************************”

if

(elem(r22bk;k).gt.2).and.(elem(rldbk;k).gt.2).and.(elem(rsdbk;k).gt.2)

calc elem(sec2;k)=int

else

calc elem(sec2;k)=miss

endif

“*********************** SEC 3 ---- K AND H > B (BUT K = H)

******************”

if

(elem(KHB22;k).gt.2).and.(elem(KHBLD;k).gt.2).and.(elem(KHBSD;k).gt.2)

calc elem(sec3;k)=int

else

calc elem(sec3;k)=miss

endif

“*********************** SEC 4 ---- B AND H > K (BUT B = H)

*******************”

if

(elem(BHK22;k).gt.2).and.(elem(BHKLD;k).gt.2).and.(elem(BHKSD;k).gt.2)

calc elem(sec4;k)=int

else

calc elem(sec4;k)=miss

endif

“*********************** SEC 5 ---- K > B and H (BUT B = H)

*********************”

if

(elem(KDB22;k).gt.2).and.(elem(KDBLD;k).gt.2).and.(elem(KDBSD;k).gt.2)

calc elem(sec5;k)=int

else

calc elem(sec5;k)=miss

endif

“*********************** SEC 6 ---- B > K and H (BUT K = H)

************************”

if

(elem(BDK22;k).gt.2).and.(elem(BDKLD;k).gt.2).and.(elem(BDKSD.k).gt.2)

calc elem(sec6;k)=int

else

calc elem(sec6;k)=miss

endif

“*********************** SEC 7 ---- H > B and K

*********************************”

if

(elem(H22h;k).gt.2).and.(elem(HLDh;k).gt.2).and.(elem(HSDh;k).gt.2)

calc elem(sec7;k)=int

else

calc elem(sec7;k)=miss

endif

“*********************** SEC 8 ---- H < B and K

************************************”

if

(elem(H22l;k).lt.0.5).and.(elem(HLDl;k).lt.0.5).and.(elem(HSDl;k).lt.0.5)

calc elem(sec8;k)=int

else

calc elem(sec8;k)=miss

endif

endfor

“***********************************************************************

******************************”

“print gene,sec1,sec2,sec3,sec4,sec5,sec6,sec7,sec8”

for i=sec1,sec2,sec3,sec4,sec5,sec6,sec7,sec8;\

j=No1.No2,No3,No4,No5,No6,No7,No8;\

k=N1,N2,N3,N4,N5,N6,N7,N8;\

l=mv1,mv2,mv3,mv4,mv5,mv6,mv7,mv8

calc k=nvalues (i)

& l=nmv(i)

& j=k−l

endfor

print No1,No2,No3,No4,No5,No6,No7,No8

endfor

stop

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PREDICTION OF HETEROSIS AND OTHER TRAITS BY TRANSCRIPTOME ANALYSIS

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