PHENOTYPE PREDICTION

Abstract
Detection of epigenetic alteration, especially methylation, of a gene or a combination of genes, preferably in a perinatal tissue sample such as umbilical cord, for predicting diverse phenotypic characteristics such as propensity for obesity, altered body composition, impaired cognition, low bone mineral content, neuro-behavioural problems and altered cardiovascular structure and function occurring in an individual.
Description
FIELD OF THE INVENTION

The invention in general terms relates to the use of epigenetic markers, especially for example in perinatal tissues, as a means for predicting the propensity for the occurrence of a phenotype in an individual. In particular, for example, the invention relates to the prediction of a propensity for obesity, altered body composition, impaired cognition, neuro-behavioural characteristics and altered cardiovascular structure and function occurring in an individual. In addition, the invention provides methods of managing the propensity for the occurrence of a phenotype (e.g. obesity) in an individual and/or a population.


BACKGROUND ART

The term ‘epigenetic’ is used to refer to structural changes to genes that do not alter the nucleotide sequence. Of particular relevance is methylation of specific CpG dinucleotides in gene promoters and alterations in DNA packaging arising from chemical modifications of the chromatin histone core around which DNA wraps. Such epigenetic inheritance systems can be random with respect to the environment and have been termed epimutations, or specific epigenetic changes can be induced by the environment. DNA methylation is established during early development and persists into adulthood. The inventors have, however, now for the first time obtained data linking epigenetic change, more particularly degree of gene methylation, in perinatal tissues to phenotypic characteristics in later life


Epidemiological observations previously implicated early development in the etiology of common chronic diseases, including cardiovascular disease, type-2 diabetes, obesity, metabolic syndrome, non-alcoholic steatohepatitis, impaired skeletal growth, osteoporosis, chronic obstructive airways disease including asthma, susceptibility to infection, mental illness and affective disorder, impaired cognition, cancer, impaired renal function, reproductive health and auto-immune disorders. In humans, nutritional constraint before birth has been shown to be associated with increased risk of metabolic syndrome and cardiovascular disease (Godfrey & Barker (2001); Public Health Nutrition, 4, 611-624). This causal association has been replicated in animal models of nutritional constraint during pregnancy. The phenomenon may involve adaptations to fetal physiology which predict an unfavourable postnatal environment, such as limited nutrient availability, and so improve survival. However, if nutrients are abundant, the offspring may be less able to adapt to abundant nutrient supply which can ultimately result in disease (Gluckman & Hanson (2004) Science, 305, 1733-1736). In humans, a period of nutritional constraint during pregnancy can determine the risk of developing obesity in late middle age (Ravelli et al. (1999) Am. J. Clin. Nutr. 70, 811-816).


More recently it has been observed that maternal protein-restriction during rat pregnancy induces an alteration in the methylation of the glucocortioid receptor (GR) promoter associated with altered expression of the receptor in the liver of juvenile offspring (Lillycrop et al. (2005) J. Nutr. 135(6) 1382-1386). However, this observed epigenetic change associated with changed expression does not express itself as obesity. Alteration in the methylation of the GR promoter has therefore not been considered as a relevant epigenetic change in the treatment of obesity. Further, those knowledgeable in the art have previously considered that epigenetic change is only of relevance when a direct and reciprocal change in expression in the relevant gene is also observed (as in the case of Lillycrop et al. 2005). The evaluation of epigenetic change independent of changes in gene expression has not previously been considered of value and has not been investigated. The basis of this invention is the finding that epigenetic changes in themselves are of prognostic and diagnostic value.


That degree of gene methylation can be linked to propensity for a phenotypic characteristic was first recognised by the inventors as a result of rat studies linking altered methylation of the GR promoter in rat pup adipose tissue with maternal nutritional status and pup body weight on a high fat diet. Subsequent studies reported herein have confirmed that, equally, degree of methylation of specific genes in human perinatal tissue, e.g. umbilical cord, can be linked to a variety of future phenotypic characteristics including but not limited to characteristics of body composition/growth (total and/or proportionate body fat mass, total and/or proportionate lean body mass, bone mineral content or density and height), cognitive development (intellectual quotient (IQ)), neuro-behavioural status (e.g. hyperactivity) and cardiovascular structure and function (e.g. blood pressure, aortic compliance, left ventricular mass and coronary artery diameter). For the prediction of phenotypes, epigenetic markers may be used individually and in combinations. Such use of epigenetic markers may be of particular use not only in relation to managing propensity for occurrence of undesirable phenotypic characteristics in humans, e.g. obesity, but also, for example, in enabling economic decisions on future production characteristics in agricultural animals. Such use of epigenetic markers enables the targeting of corrective strategies where propensity for an undesirable phenotypic characteristic is identified.


SUMMARY OF THE INVENTION

In its broadest aspect, the present invention thus provides a method of a predicting a phenotypic characteristic of a human or non-human animal which comprises determining the degree of an epigenetic alteration of a gene or a combination of genes in a tissue, wherein the degree of said epigenetic alteration of the gene or genes of interest correlates with propensity for said phenotypic characteristic. As indicated above, the epigenetic alteration determined may be gene methylation, either across the entirety of the gene or genes of interest or gene promoter methylation. The tissue will desirably be a tissue readily available early in life. It may desirably be a perinatal tissue sample containing genomic DNA of the individual of interest taken prior to, at, or soon after birth (generally in the case of human neonates within a month of birth), preferably for example, an umbilical cord sample, but other tissue may prove useful including in addition to adipose tissue, blood (including fetal and cord blood), placenta, chorionic villus biopsy, amniotic fluid, hair follicles, buccal smears and muscle biopsies. Preferably such tissue will be from a newborn or taken from an infant within a few weeks of birth, more preferably within a few days of birth (less than a week), although samples taken much later, e.g. within about 6 months or longer may prove useful. As indicated above, adipose tissue may be a suitable choice particularly, for example, if the phenotypic characteristic of interest is obesity and may, for example, be a perinatal adipose tissue sample or a sample taken from an older infant.


More particularly, the invention provides in one embodiment a method of predicting the propensity for obesity in an individual including the step of testing for altered methylation of the gene encoding the glucocorticoid receptor (GR). However, other genes may be preferred for methylation determination for this purpose. For example, one or more genes may be chosen for which association has been identified between degree of promoter methylation and total and/or proportionate body fat mass. The gene or genes may be selected from any of those identified in the exemplification as exhibiting such association in 9 year old children relying on umbilical cord samples, especially for example the endothelial nitric oxide synthase (NOS3) gene, the matrix metalloproteinase-2 (MMP2) gene, the phosphoinositide-3-kinase, catalytic, δ-polypeptide (P13KCD) gene and the heparan-α-glucosaminide N-acetyltransferase (HGSNAT) gene.


Preferably, for example, alteration of methylation is tested for in the adipose tissue of the individual or in an umbilical cord sample obtained at or soon after birth.


Preferably the individual is a neonate, infant, child or young adult.


The invention also provides a method of managing incidence of obesity in an individual including the steps of:

    • (1) testing the individual for altered methylation of the glucocorticoid receptor gene or one or more other genes as discussed above; and
    • (2) managing the diet, behaviour or physical activity of the individual if the individual exhibits altered methylation of said gene or genes.


In another aspect, the invention provides a method of managing the incidence of obesity in a population including the steps of:

    • (1) screening individuals from the population for altered methylation of the glucocorticoid receptor gene or one or more other genes as discussed above; and
    • (2) changing the diet, behaviour or physical activity of the individuals in the population that exhibit altered methylation of said gene or genes.


Further aspects of the invention will be apparent from the more detailed description and examples below and with reference to the figures, which are now detailed.





BRIEF DESCRIPTION OF FIGURES


FIG. 1 shows from the rat studies noted above and further described in Example 1 absolute body weight change from weaning until day 170; control denotes offspring from mothers fed ad-libitum throughout pregnancy, UN denotes offspring from mothers fed at 30% of ad-libitum throughout pregnancy, chow denotes normal rodent diet, HF denotes high fat diet (45% kcals as fat). N=8 per group, data are mean ±SEM.



FIG. 2 shows from the same studies total body fat mass (% total body fat) at day 170 as quantified by dual energy x-ray absorbtiometry (DEXA) N=8 per group, data are mean ±SEM.



FIG. 3 shows from the same studies retroperitoneal fat depot mass expressed relative to body weight. N=8 per group, data are mean ±SEM.



FIG. 4 shows from the same studies glucocorticoid receptor (GR) gene promoter methylation expressed as a percentage relative to chow fed controls for progeny fed a high fat diet, N=6-8 per group, data are mean ±SEM.



FIG. 5 shows from the same studies glucocorticoid receptor gene expression expressed as a percentage relative to chow fed controls for progeny fed a high fat diet, N=6-8 per group, data are mean ±SEM.



FIG. 6 shows from the human studies described in Example 2, umbilical cord PI3KCD (PI-3-kinase, catalytic, delta polypeptide) gene promoter methylation (relative to control) in relation to the child's fat mass at age nine years measured by dual energy X-ray absorptiometry (DXA). N=15; r=0.68, P=0.005 by Spearman correlation.



FIG. 7 shows from the human studies described in Example 2, umbilical cord MMP2 (matrix metalloproteinase-2 gene) promoter methylation (relative to control) in relation to the child's proportionate fat mass at age nine years measured by dual energy X-ray absorptiometry (DXA). N=16; r=0.66, P=0.005 by Spearman correlation.



FIG. 8 shows from the human studies described in Example 2, umbilical cord HGSNAT (heparan-α-glucosaminide N-acetyltransferase) gene promoter methylation (relative to control) in relation to the child's proportionate fat mass at age nine years measured by dual energy X-ray absorptiometry (DXA). N=15; r=0.73, P=0.002 by Spearman correlation.



FIG. 9 shows from the human studies described in Example 2, umbilical cord RC3H2 (ring finger & CCCH-type zinc finger domains 2) gene promoter methylation (relative to control) in relation to the child's total lean mass at age nine years measured by dual energy X-ray absorptiometry (DXA). N=15; r=−0.70, P=0.004 by Spearman correlation.



FIG. 10 shows from the human studies described in Example 2, umbilical cord RXRA (retinoid X receptor, alpha) gene promoter methylation (relative to control) in relation to the child's total lean mass at age nine years measured by dual energy X-ray absorptiometry (DXA). N=15; r=0.76, P=0.001 by Spearman correlation.



FIG. 11 shows from the human studies described in Example 2, umbilical cord ESR1 (estrogen receptor-α) gene promoter methylation (relative to control) in relation to the child's height at age nine years. N=15; r=0.51, P=0.05 by Spearman correlation.



FIG. 12 shows from the human studies described in Example 2, umbilical cord FADS1 (fatty acid desaturase 1 (delta-5 desaturase)) gene promoter methylation (relative to control) in relation to the child's height at age nine years. N=16; r=0.62, P=0.01 by Spearman correlation.



FIG. 13 shows from the human studies described in Example 2, umbilical cord RXRB (retinoid X receptor, beta) gene promoter methylation (relative to control) in relation to the child's whole body bone mineral content at age nine years measured by dual energy X-ray absorptiometry (DXA). N=15; r=0.74, P=0.002 by Spearman correlation.



FIG. 14 shows from the human studies described in Example 2, umbilical cord NOX5 (NADPH oxidase, EF-hand calcium binding domain 5) gene promoter methylation (relative to control) in relation to the child's full scale IQ at age nine years. N=16; r=0.64, P=0.008 by Spearman correlation.



FIG. 15 shows from the human studies described in Example 2, umbilical cord FADS3 (fatty acid desaturase 3 (delta-9 desaturase)) gene promoter methylation (relative to control) in relation to the child's full scale IQ at age nine years. N=16; r=0.76, P=0.001 by Spearman correlation.



FIG. 16 shows from the human studies described in Example 2, umbilical cord PIK3CD (PI-3-kinase, catalytic, delta polypeptide ) gene promoter methylation (relative to control) in relation to the child's full scale IQ at age nine years. N=15; r=0.66, P=0.007 by Spearman correlation.



FIG. 17 shows from the human studies described in Example 2, umbilical cord ECHDC2 (Enoyl Coenzyme A hydratase domain containing 2) gene promoter methylation (relative to control) in relation to the child's score on the hyperactivity scale at age 9 years using the strengths and difficulties questionnaire. N=15; r=−0.72, P=0.005 by Spearman correlation.



FIG. 18 shows from the human studies described in Example 2, umbilical cord HTR1A (5-hydroxytryptamine (serotonin) receptor 1A) gene promoter methylation (relative to control) in relation to the child's score on the conduct problems scale at age 9 years using the strengths and difficulties questionnaire. N=15; r=−0.76, P=0.001 by Spearman correlation.



FIG. 19 shows from the human studies described in Example 2, umbilical cord CDKN2A (cyclin-dependent kinase inhibitor 2A) gene promoter methylation (relative to control) in relation to the child's score on the emotional problems scale at age 9 years using the strengths and difficulties questionnaire. N=15; r=−0.74, P=0.002 by Spearman correlation.



FIG. 20 shows from the human studies described in Example 2, umbilical cord NOS3 (endothelial nitric oxide synthase) gene promoter methylation (relative to control) in relation to the child's systolic blood pressure at age nine years. N=15; r=0.68, P=0.005 by Spearman correlation.



FIG. 21 shows from the human studies described in Example 2, umbilical cord IGFBP1 (insulin like growth factor binding protein-1) gene promoter methylation (relative to control) in relation to the child's diastolic blood pressure at age nine years. N=15; r=0.72, P=0.002 by Spearman correlation.



FIG. 22 shows from the human studies described in Example 2, umbilical cord SOD1 (superoxide dismutase) gene promoter methylation (relative to control) in relation to the child's aorto-femoral pulse wave velocity at age nine years. N=14; r=0.82, P<0.001 by Spearman correlation.



FIG. 23 shows from the human studies described in Example 2, umbilical cord RC3H2 (ring finger and CCCH-type zinc finger domains 2) gene promoter methylation (relative to control) in relation to the child's left ventricular mass at age nine years measured by echocardiography. N=15; r=−0.57, P=0.026 by Spearman correlation.



FIG. 24 shows from the human studies described in Example 2, umbilical cord TRPC1 (transient receptor potential cation channel, subfamily C, member 1) gene promoter methylation (relative to control) in relation to the child's total coronary artery diameter at age nine years. N=8; r=−0.75, P=0.03 by Spearman correlation.





DETAILED DESCRIPTION

In general terms, this invention relates to a method of predicting the propensity for individuals, and populations of individuals, to develop obesity or another phenotypic characteristic using an epigenetic marker independent of changes in gene expression. The ability to predict the likely occurrence of obesity or other disorders from an early age in individuals, coupled with targeted intervention, will provide a valuable pre-emptive means of controlling the effect of the phenotype on the health of the individual at a later age. Obesity increases the incidence of diabetes, heart disease etc, therefore a reduction in the likelihood that an individual will develop obesity in later life is beneficial not only for the individual concerned but also for the community as a whole.


From this perspective the invention can also be seen to provide methods for predicting the propensity of individuals, or populations of individuals, to develop phenotypes such as diabetes and heart disease for example.


Methods of the invention can also be applied to the agricultural and domestic animal sector. Early prediction of obesity, by way of example, in farm animals (cattle, sheep, pigs, chickens, deer) would enable farmers to select and/or manage animals so as to maximise production efficiency and thereby economic returns (e.g. carcass yield of lean meat and breed or slaughter decisions). Similarly early diagnosis of predisposition to obesity would enable interventions to manage the condition and maximise animal performance in the bloodstock industries (e.g. horses) and manage obesity in companion animals (e.g. cats and dogs).


As described in more detail in Example 1, the inventors observed in rat adipose tissue that altered methylation of the glucocorticoid receptor gene, independent of any change in GR expression, provides at least a semi-permanent, if not permanent, marker that is predictive of obesity in later life. As noted above, altered methylation of the glucocorticoid receptor in the liver had previously been found to occur in neonates as a result of maternal undernutrition but was not known to be associated with obesity (Lillycrop et al. 2005, ibid.). The findings presented in Example 1 were novel and unexpected as there is no genetic change/expression link between the methylation alteration and obesity. The alteration is observable in the adipose tissue of the individual and the at least semi-permanent nature of the alteration allowed the determination of the relationship between the methylation change and the phenotype to be determined by the inventors. It is anticipated that samples for such testing, whether in animals or humans, could be taken not only from adipose tissue but also, for example, from placenta, chorionic villus biopsy, umbilical cord, blood (including fetal and cord blood), hair follicles, buccal smears and muscle biopsies, or other such readily accessible tissues.


Moreover, further data as now presented in Example 2, links degree of methylation of additional genes with indicators of obesity in humans and illustrates that determination of such epigenetic change at birth or soon after can be used to predict propensity for obesity in later life. While such studies employed umbilical cord samples taken at birth and frozen, it is to be expected that alternative perinatal tissue samples may be employed which will provide genomic DNA of the individual of interest, e.g. blood taken shortly after birth or amniotic fluid.


While the above discussion has focused principally on obesity, it is emphasised that as further illustrated by Example 2 the concept of the inventors of using epigenetic change in genes in perinatal tissues as a marker for propensity for later development of particular phenotypic characteristics extends to a wide variety of characteristics as diverse as bone mineral content and density, neuro-behavioural characteristics and IQ. For further information on specific genes for which promoter methylation status in umbilical cord has been correlated with particular phenotypic characteristics at age 9, the reader is referred to the gene tables in Example 2. Those genes for which strong correlation is noted will be favoured for use in phenotype prediction, particularly in humans.


By using microarray analysis or other methods to identify methylated promoters, methylation of combinations of different promoters may be conveniently assessed enabling propensity for a single phenotypic characteristic to be determined or propensity for multiple phenotypic characteristics to be determined simultaneously using a single tissue sample, e.g. an umbilical cord sample. A commercially available microarray may be employed such as the NimbleGen Human ChiP Epigenetic Promoter Tiling Array. However, it may be preferred to use a microarray specifically designed to detect methylation of a combination of promoters associated with propensity for one or more phenotypic characteristics. Such phenotype-specific microarrays form a further aspect of the invention. However, alternatively methylation-sensitive amplification, e.g. PCR amplification, may be carried out, particularly if the desire is to look at methylation status of a single gene or small number of genes strongly linked with one or more phenotypic characteristics. In this case, the gene or genes of interest will be amplified and the amplified nucleic acid treated with methylation-sensitive restriction enzymes, e.g. Aci1 and Hpa11, as described in more detail in Example 1. A primer and restriction enzyme kit suitable for carrying out methylation-sensitive amplification to detect methylation status of a combination of genes associated with propensity for one or more phenotypic characteristics constitutes a still further aspect of the invention.


Once the propensity for exhibiting phenotypes has been determined in an individual, management of that individual's lifestyle (diet, behaviour, exercise, etc) can be undertaken to reduce the risk of the actual occurrence of any undesirable characteristics such as obesity or low mineral bone content associated with osteoporosis.


Where a method of the invention identifies propensity for obesity in a newborn or child, then this may be used as a cue to also look at the nutritional status of the mother in view of the previously identified link between such propensity and poor maternal nutrition, and where poor maternal nutritional is found, improving the diet of the individual, e.g. by providing dietary advice and/or food supplements.


The methodology of the invention may be used to look at the occurrence of propensity for one or more phenotypic characteristics in a chosen population group and thereby used to identify external factors which contribute to the incidence of the characteristic(s), e.g. obesity, in the population of interest. In this way, information may be obtained useful in directing public health initiatives aimed at reducing the incidence of undesirable phenotypic characteristics in populations and thereby associated disorders such as diabetes, osteoporosis, sarcopenia, behavioural and mental disorders, hypertension, and cardiac problems.


The following examples illustrate the invention.


EXAMPLES
Example 1
Study Design

A previously developed maternal undernutrition model of fetal programming was utilized in this study (Vickers et al, 2000, American Journal of Physiology 279:E83-E87). Virgin Wistar rats (age 100±5 days) were time mated using a rat estrous cycle monitor to assess the stage of estrous of the animals prior to introducing the male. After confirmation of mating, rats were housed individually in standard rat cages with free access to water. All rats were kept in the same room with a constant temperature maintained at 25° C. and a 12-h light: 12-h darkness cycle. Animals were assigned to one of two nutritional groups: a) undernutrition (30% of ad-libitum) of a standard diet throughout gestation (UN group), b) standard diet ad-libitum throughout gestation (AD group). Food intake and maternal weights were recorded daily until the end of pregnancy. After birth, pups were weighed and litter size was adjusted to 8 pups per litter to assure adequate and standardized nutrition until weaning. Pups from undernourished mothers were cross-fostered on to dams that had received AD feeding throughout pregnancy. At weaning, AD and UN offspring were weight-matched and placed on either standard rat chow or a high fat diet (HF, Research Diets #12451, 45% kcals as fat). At postnatal day 170, rats were fasted overnight and sacrificed by halothane anaesthesia followed by decapitation. Blood was collected into heparinised vacutainers and stored on ice until centrifugation and removal of supernatant for analysis. Tissues, including retroperitoneal fat, were dissected, weighed, immediately frozen in liquid nitrogen and store at −80 until analysis. All animal work was approved by the Animal Ethics Committee of the University of Auckland.


Measurements

Body composition was assessed using dual energy x-ray absorbtiometry (DEXA, Hologic, Waltham, Mass., USA).


Analysis of mRNA Expression


PPARα, PPARγ, AOX, CPT-1, lipoprotein lipase (LPL), leptin receptor (LR), glucocorticoid receptor and leptin mRNA concentrations were determined by RTPCR amplification and quantified by densitometry [Lillycrop et al. (2005)]. Briefly, total RNA was isolated from cells using TRIZOL reagent (InVitrogen), and 0.1 μg served as a template to prepare cDNA using 100 U Moloney-Murine Leukemia Virus reverse transcriptase. cDNA was amplified using primers specific to PPARα, PPARγ, AOX, CPT-1, LPL, LR, leptin and GR (Table 1). The PCR conditions in which the input cDNA was linearly proportional to the PCR product were initially established for each primer pair. One tenth of the cDNA sample was amplified for 25 cycles for the housekeeping gene ribosomal 18S and for 30 cycles for other genes. mRNA expression was normalized using the housekeeping gene ribosomal 18S RNA.


Methylation-Sensitive PCR

The methylation status of genes was determined using methylation-sensitive PCR as described [Lillycrop et al. (2005)]. Briefly, genomic DNA (5 μg), isolated from adipose tissue using a QIAquick PCR Purification Kit (QIAGEN, Crawley, Sussex, UK) and treated with the methylation-sensitive restriction enzymes Aci1 and HpaII as instructed by the manufacturer (New England Biolabs, Hitchin, Hertfordshire, UK). The resulting DNA was then amplified using real-time PCR (Table 1), which was performed in a total volume of 25 μL with SYBR® Green Jumpstart ready mix as described by the manufacturer (Sigma, Poole, Dorset, UK). As an internal control, the promoter region from the rat PPARγ2 gene, which contains no CpG islands and no Aci1 or HpaII recognition sites, was amplified. All CT values were normalized to the internal control.


Statistical analyses were carried out using SPSS (SPSS Inc., Chicago, USA) statistical packages. Differences between groups were determined by two-way factorial ANOVA (prenatal nutrition and postnatal diet as factors) followed by Bonferonni post-hoc analysis and data are shown as mean ±SEM.


Results and Discussion

Referring to the FIGS. 1-5 (in which * is p<0.05, NS=not significant),



FIG. 1 shows that the UNHF group has a significantly higher body weight gain on a high fat diet than control animals on the same diet. The increased body weight gain is indicative of diet-induced obesity as a result of developmental reprogramming of gene expression;



FIG. 2 illustrates that total body mass fat as determined by DEXA analysis is significant in the UNHF group compared with control animals on a high fat diet. This is once again illustrative of diet-induced obesity as a result of developmental reprogramming;



FIG. 3: Back fat is a readily dissectable depot of fat which is an in situ marker of total body adiposity. The back fat data shows a close relationship to whole body fat and further illustrates the increase in adiposity in UNHF animals compared with control animals on a high fat diet associated with developmental reprogramming:



FIG. 4: The data illustrates that the post natal HF diet has no significant effect on GR promoter gene methylation status in progeny from rats that had access to feed ad libitum throughout pregnancy whereas in progeny fed the high fat diet from rats under nourished through pregnancy methylation was significantly reduced. More broadly the data show that a decrease in GR promoter methylation is directly associated with increased propensity to obesity; and



FIG. 5: These data show that there was no significant effect of feeding a postnatal HF diet on GR expression with or without prenatal nutrient restriction. One explanation is that because gene expression requires the action of additional (transcription) factors, the offspring with lower GR methylation (UNHF) had potential for greater GR expression, but that in the absence of an appropriate stimulus GR expression was at a baseline levels.


Example 2
Samples

Umbilical cord samples were taken from pregnancies in the University of Southampton/UK Medical Research Council Princess Anne Hospital Nutrition Study.


Methods—Subjects and Phenotyping

In 1991-2 Caucasian women aged >16 years with singleton pregnancies of <17 weeks' gestation were recruited at the Princess Anne Maternity Hospital in Southampton, UK; diabetics and those who had undergone hormonal treatment to conceive were excluded. In early (15 weeks of gestation) and late (32 weeks of gestation) pregnancy, we administered a dietary and lifestyle questionnaire to the women. Anthropometric data on the child were collected at birth and a 1-inch section of umbilical cord was collected and stored at −40° C. Gestational age was estimated from menstrual history and scan data. 559 children were followed-up at age nine months, when data on anthropometry and infant feeding were recorded. Collection and analysis of human umbilical cord samples was carried out with written informed consent from all subjects and under IRB approval from the Southampton and South West Hampshire Joint Research Ethics Committee.


When these children approached age nine years, we wrote to the parents of those still living in Southampton inviting the children to participate in a further study. Of 461 invited, 216 (47%) agreed to attend a clinic. Height was measured using a stadiometer and weight using digital scales (SECA Model No. 835). The children underwent measurements of body composition by DXA (dual energy x-ray absorptiometry; Lunar DPX-L instrument using specific paediatric software; version 4.7c, GE Corporation, Madison, Wis., USA). The instrument was calibrated every day and all scans were done with the children wearing light clothing. The short-term and long-term coefficients of variation of the instrument were 0.8% and 1.4% respectively. After a five-minute rest, systolic and diastolic blood pressures were measured three times on the left arm placed at the level of the heart whilst the child was seated. Measurements were made using a Dinamap 1846 (Critikon, UK), with manufacturer's recommended cuff sizes based on the child's mid upper arm circumference. The mean of the three measurements was used in the analysis.


Cognitive function was measured using the Wechsler Abbreviated Scale of Intelligence (WASI) (Wechsler D., Wechsler Abbreviated Scale of Intelligence. The Psychological Corporation, Sam Antonio, 1999) and psychological health was assessed with the Strengths and Difficulties Questionnaire (SDQ), which was completed by the mother (Goodman R. The Strengths and Diffculties Questionnaire: a research note. J. Child Psychol. Psychiatr. (1997) 38:581-586). The SDQ is made up of 5 subscales assessing prosocial behaviour, hyperactivity, emotional symptoms, conduct problems and peer problems.


Arterial compliance was measured by a non-invasive optical method that determines the transit time of the wave of dilatation propagating in the arterial wall, as a result of the pressure wave generated by contraction of the left ventricle. Measurement of the time taken for the wave to travel a known distance allows the velocity of the pulse wave to be calculated. The optical method has been validated against intra-arterial determinations of pressure wave velocity (1Bonner et al. Validation and use of an optical technique for the measurement of pulse wave velocity in conduit arteries. Fortschritt Berichte (1995) 107: 43-52). Pulse wave velocities were measured in two arterial segments, aorta to femoral, extending from the common carotid artery near the arch of the aorta into the femoral artery just below the inguinal ligament, and aorta to foot, extending to the posterior tibial artery. Pulse wave velocity is inversely related to the square root of the compliance of the vessel wall. High pulse wave velocity therefore indicates a stiffer arterial wall.


When pulse rate and blood pressure measurements indicated hemodynamic stability, transthoracic echocardiography (Acuson 128 XP and a 3.5 MHz phased array transducer) was performed by a single ultrasonographer with the child in the left lateral recumbent position. Two dimensional, M-mode, and Doppler echocardiograms were recorded over five consecutive cardiac cycles and measurements were made off-line. Left ventricular mass (according to American Echocardiography Society convention) and total coronary artery diameter were measured as reported previously (Jiang B, Godfrey K M, Martyn C N, Gale C R (2006). Birth weight and cardiac structure in children. Pediatrics 117, e257-e261; Gale C R, Robinson S M, Harvey N C, Javaid M K, Jiang B, Martyn C N, Godfrey K M, Cooper C, Princess Anne Hospital Study Group. (2007) Maternal vitamin D status during pregnancy and child outcomes. Eur. J. Clin. Nutr.—in press).


Methods—Gene Promoter Methylation Assays in Umbilical Cord

Genomic DNA was extracted from the umbilical cord samples. We used an existing commercially available microarray (NimbleGen Human ChIP Epigenetic Promoter Tiling Array) to measure the degree of CpG methylation of putative promoters of 24,434 genes. This analyses 5-50 kb of promoter region for the comprehensive human genome in 2 arrays, tiling regions at 110 bp intervals and using variable length probes (˜5/promoter). All annotated splice variants and alternative splice sites are represented on the NimbleGen array.


In consequence of the high cost of the arrays (£1000 per subject), our approach was to maximise insights from using 18 arrays while also obtaining essential information on reproducibility; we therefore undertook measurements on 16 subjects, randomly selected from 4 strata of IQ at age 9 years, one of whom had measurements performed in triplicate.


Methods—Initial Data Analysis

The intra-subject variability in median gene promoter methylation as compared with controls across ˜12,000 gene promoters for the subject with a triplet of repeat assays showed a standard deviation of 0.11, as compared with a between subjects standard deviation of 0.23. This suggests that measurement error is relatively small in comparison with the biological variability between subjects.


Our initial analyses focussed on 24 genes with established candidacy for cardiovascular and bone development (Appendix 1) and the 50 genes with greatest between subject variability in gene promoter methylation measured using the NimbleGen microarray (Appendix 2). Subsequent analyses examined 55 genes with established candidacy for neural, cellular and metabolic processes (Appendix 3). Using SPSS 12.0 for Windows we used non-parametric tests (Spearman rank correlation) to relate the degree of methylation of the gene promoters in umbilical cord to offspring phenotype at age 9 years.


Summary of Findings—Body Composition
Childhood Fat Mass, Proportionate Fat Mass and Body Fat Distribution at Age 9 Years

For 27 of the 74 genes in our initial analyses and 32 of the 55 genes in subsequent analyses (see Table 1), we found evidence that the degree of gene promoter methylation was associated with the child's total and/or proportionate body fat mass and/or body fat distribution. The associations with total body fat mass were particularly strong for the NOS3 (endothelial nitric oxide synthase) gene (Spearman correlation coefficient r=0.66, P=0.007), the MMP2 (matrix metalloproteinase-2) gene (r=0.62, P=0.01) and the PI3KCD (phosphoinositide-3-kinase, catalytic, δ-polypeptide) gene (r=0.68, P=0.005; FIG. 6). The associations with proportionate body fat mass were particularly strong for the MMP2 (matrix metalloproteinase-2) gene (r=0.66, P=0.005; FIG. 7) and the HGSNAT (heparan-α-glucosaminide N-acetyltransferase) gene (r=0.73, P=0.002; FIG. 8). The associations with body fat distribution were particularly strong for the DRD1IP (dopamine receptor D1 interacting protein) gene (Spearman correlation coefficient r=−0.55, P=0.026) and the SYN3 (synapsin III) gene (Spearman correlation coefficient r=−0.56, P=0.025). Linkage of such gene promoter methylation with indicators of obesity is expected to be of assistance in identifying individuals at risk of, for example, type-2 diabetes, metabolic syndrome, cardiovascular disease and other conditions for which obesity is recognised to be a risk factor.










TABLE 1







AK090950
KIAA1345 protein


C1orf185
Chromosome 1 open reading frame 185


CIB4
Calcium and integrin binding family member 4


ECHDC2
Enoyl Coenzyme A hydratase domain containing 2


EDN1
Endothelin 1


FLAD1
FAD1 flavin adenine dinucleotide synthetase homolog


FLJ23577
KPL2 protein


GLO1
Glyoxalase I


HGSNAT
Heparan-alpha-glucosaminide N-acetyltransferase


HIVEP2
HIV type I enhancer binding protein 2


HSP90AB3P
Heat shock protein 90 kDa alpha (cytosolic), class B member 3 (pseudogene)


IL8
Interleukin-8


ISG15
ISG15 ubiquitin-like modifier


LOC401027
Hypothetical protein LOC401027


MCL1
Myeloid cell leukemia sequence 1 (BCL2-related)


MMP2
Matrix metalloproteinase-2


NOS3
Nitric oxide synthase 3 (endothelial cell) (eNOS)


NR3C1
Glucocorticoid receptor; nuclear receptor subfamily 3, group C, member 1


OGDH
Oxoglutarate (alpha-ketoglutarate) dehydrogenase (lipoamide)


OR5C1
Olfactory receptor, family 5, subfamily C, member 1


PI3KCD
Phosphoinositide-3-kinase, catalytic, delta polypeptide


PREB
Prolactin regulatory element binding


PSME4
Proteasome (prosome, macropain) activator subunit 4


RALGPS1
Ral GEF with PH domain and SH3 binding motif 1


TNF
Tumor necrosis factor (TNF superfamily, member 2)(TNFa)


TSN
Translin


VEGFA
Vascular endothelial growth factor A


CDK9
cyclin-dependent kinase 9 (CDC2-related kinase)


CDKN1A
cyclin-dependent kinase inhibitor 1A (p21, Cip1)


CDKN2A
cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4)


CDKN2B
cyclin-dependent kinase inhibitor 2B (p15, inhibits CDK4)


CREB1
cAMP responsive element binding protein 1


DRD1IP
dopamine receptor D1 interacting protein


DRD2
D(2) dopamine receptor


EGR1
early growth response 1


FADS1
fatty acid desaturase 1 (delta-5 desaturase)


FADS2
fatty acid desaturase 2 (delta-6-desaturase)


FADS3
fatty acid desaturase 3 (delta-9-desaturase)


FAF1
Fas (TNFRSF6) associated factor 1


FLT1
fms-related tyrosine kinase 1 (VEGF/vascular permeability factor receptor)


GABRB1
gamma-aminobutyric acid (GABA) A receptor, beta 1


GAD1
glutamate decarboxylase 1 (brain, 67 kDa)


GRIK2
glutamate receptor, ionotropic, kainate 2


GRIN3B
glutamate receptor, N-methyl-D-aspartate 3B (NMDA receptor subunit 3B)


HSFY1
heat shock transcription factor, Y-linked 1 (more centromeric copy)


HSFY2
HSFY2 heat shock transcription factor, Y linked 2 (more telomeric copy)


HTR2A
5-hydroxytryptamine (serotonin) receptor 2A


IGFBP1
insulin-like growth factor binding protein 1


MAOA
monoamine oxidase A


MAPK1
mitogen-activated protein kinase 1 (protein tyrosine kinase ERK2)


MYC
v-myc myelocytomatosis viral oncogene homolog (avian)


RXRA
retinoid X receptor, alpha


RXRB
retinoid X receptor, beta


S100B
S100 calcium binding protein B


SCD
stearoyl-CoA desaturase (delta-9-desaturase)


SEMA4F
sema domain, Ig domain, TM domain &short cytoplasmic domain, (semaphorin)



4F


SLC6A4
solute carrier family 6 (neurotransmitter transporter, serotonin), member 4 (5-



HTT)


SYN3
synapsin III


TNFRSF1A
tumor necrosis factor receptor superfamily, member 1A









In MRNA gene expression studies, both the FADS1 and FADS2 genes have also been found to be upregulated in liver from neonatal rats of undernourished mothers.


Childhood Lean Mass at Age 9 Years

For 34 of the 74 genes in our initial analyses and 29 of the 55 genes in subsequent analyses (see Table 2), we found evidence that the degree of gene promoter methylation was associated with child's total lean body mass and/or proportionate lean mass. Such gene methylation is of interest in relation to identifying individuals who may be susceptible to, for example sarcopenia and impaired muscle function. The associations with child's total lean body mass were particularly strong for the RC3H2 gene (ring finger and CCCH-type zinc finger domains 2) (r=−0.70, P=0.004; FIG. 9), the EGR (early growth response 1) gene (r=0.66, P=0.008), the FTO (fat mass and obesity associated) gene (r=−0.53, P=0.034), the RXRB (retinoid X receptor, beta) gene (r=0.75, P=0.001) and the RXRA (retinoid X receptor, alpha) gene (r=0.76, P=0.001; FIG. 10).










TABLE 2







AK090950
KIAA1345 protein


ATP2A3
ATPase, Ca++ transporting, ubiquitous (=PMCA3)


BDP1
B double prime 1, subunit of RNA polymerase III transcription initiation factor IIIB


C1orf185
Chromosome 1 open reading frame 185


CIB4
Calcium and integrin binding family member 4


COX6C
Cytochrome c oxidase subunit Vic


DEFB107A
Defensin, beta 107A


ECHDC2
Enoyl Coenzyme A hydratase domain containing 2


FCGR1B
Fc fragment of IgG, high affinity Ib, receptor (CD64)


FLAD1
FAD1 flavin adenine dinucleotide synthetase homolog


FLJ41821
FLJ41821 protein


HGSNAT
Heparan-α-glucosaminide N-acetyltransferase


HIVEP2
HIV type I enhancer binding protein 2


HSD11B2
Hydroxysteroid (11-beta) dehydrogenase 2


IL8
Interleukin-8


ISG15
ISG15 ubiquitin-like modifier


KLHL5
Kelch-like 5 (Drosophila)


LOC401027
Hypothetical protein LOC401027


MGC48628
Similar to KIAA1680 protein (=hypothetical protein LOC401145)


MMP2
Matrix metalloproteinase-2


MYH11
Smooth muscle myosin heavy chain 11


NOS3
Nitric oxide synthase 3 (endothelial cell) (eNOS)


NR3C1
Glucocorticoid receptor; nuclear receptor subfamily 3, group C, member 1


OGDH
Oxoglutarate (alpha-ketoglutarate) dehydrogenase (lipoamide)


OR5C1
Olfactory receptor, family 5, subfamily C, member 1


PIGK
Phosphatidylinositol glycan anchor biosynthesis, class K


PIK3CD
Phosphoinositide-3-kinase, catalytic, delta polypeptide


PREB
Prolactin regulatory element binding


RAI16
Retinoic acid induced 16


RALGPS1
Ral GEF with PH domain and SH3 binding motif 1


RC3H2
Ring finger and CCCH-type zinc finger domains 2


TSN
Translin


VCAM1
Vascular cell adhesion molecule 1


VEGFA
Vascular endothelial growth factor A


CDK9
cyclin-dependent kinase 9 (CDC2-related kinase)


CDKN2A
cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4)


CHRM1
cholinergic receptor, muscarinic 1


COMT
catechol-O-methyltransferase


DRD3
dopamine receptor D3


EGR1
early growth response 1


FADS1
fatty acid desaturase 1 (delta-5 desaturase)


FADS2
fatty acid desaturase 2 (delta-6-desaturase)


FADS3
fatty acid desaturase 3 (delta-9-desaturase)


FAF1
Fas (TNFRSF6) associated factor 1


FLT1
fms-related tyrosine kinase 1 (VEGF/vascular permeability factor receptor)


FOS
v-fos FBJ murine osteosarcoma viral oncogene homolog


FTO
fat mass and obesity associated


GABRB1
gamma-aminobutyric acid (GABA) A receptor, beta 1


GRIK2
glutamate receptor, ionotropic, kainate 2


GRIN3B
glutamate receptor, N-methyl-D-aspartate 3B (NMDA receptor subunit 3B)


HTR2A
5-hydroxytryptamine (serotonin) receptor 2A


IGFBP1
insulin-like growth factor binding protein 1


MAOA
monoamine oxidase A


MAPK1
mitogen-activated protein kinase 1 (protein tyrosine kinase ERK2)


MYC
v-myc myelocytomatosis viral oncogene homolog (avian)


OTX1
orthodenticle homeobox 1


RXRA
retinoid X receptor, alpha


RXRB
retinoid X receptor, beta


S100B
S100 calcium binding protein B


SCD
stearoyl-CoA desaturase (delta-9-desaturase)


SEMA4F
sema domain, Ig domain, TM domain &short cytoplasmic domain, (semaphorin)



4F


SLC6A3
Sodium-dependent dopamine transporter (DA transporter) (DAT)


SLC6A4
solute carrier family 6 (neurotransmitter transporter, serotonin), member 4 (5-HTT)









Childhood Bone Mineral Content and Height at Age 9 Years

We found evidence that gene promoter methylation of 35 genes was associated with the child's bone mineral content (see Table 3), including the ECHDC2 (Enoyl Coenzyme A hydratase domain containing 2) gene (r=0.65; P=0.015), the MAOA (monoamine oxidase A) gene (r=0.65, P=0.006), the RXRA (retinoid X receptor, alpha) gene (r=0.68, P=0.005) and the RXRB (retinoid X receptor, beta) gene (r=0.74, P=0.002; FIG. 13). Hence, determination of methylation of these genes is of interest in predicting impaired skeletal development and propensity for disease states linked with low bone mineral content such as osteoporosis in later life. For 17 of the 74 genes in our initial analyses and 13 of the 55 genes in our subsequent analyses (see Table 4), we found evidence that the degree of gene promoter methylation was associated with child's height, including the ESR1 (estrogen receptor-α) gene (r=0.51, P=0.05; FIG. 11), the FADS1 (fatty acid desaturase 1 (delta-5 desaturase)) gene (r=0.62, P=0.01; FIG. 12) and the OGDH (oxoglutarate dehydrogenase) gene (r=0.53, P=0.04). Determining methylation of such promotors may thus be utilised in identifying individuals liable to have impaired linear growth and may be of particular benefit at a very early age.










TABLE 3







DEFB107A
Defensin, beta 107A


ECHDC2
Enoyl Coenzyme A hydratase domain containing 2


FLAD1
FAD1 flavin adenine dinucleotide synthetase homolog


FLJ23577
KPL2 protein


HGSNAT
Heparan-alpha-glucosaminide N-acetyltransferase


LOC646870
Hypothetical protein LOC646870


MCL1
Myeloid cell leukemia sequence 1 (BCL2-related)


MMP9
Matrix metallopeptidase 9 (gelatinase B, 92 kDa gelatinase, 92 kDa type IV



collagenase)


NOS3
Nitric oxide synthase 3 (endothelial cell) (eNOS)


PI3KCD
Phosphoinositide-3-kinase, catalytic, delta polypeptide


PPARG
Peroxisome proliferator-activated receptor gamma


PREB
Prolactin regulatory element binding


RALGPS1
Ral GEF with PH domain and SH3 binding motif 1


TNF
Tumor necrosis factor (TNF superfamily, member 2)(TNFa)


TSN
Translin


VEGFA
Vascular endothelial growth factor A


CDK9
cyclin-dependent kinase 9 (CDC2-related kinase)


CDKN1A
cyclin-dependent kinase inhibitor 1A (p21, Cip1)


CDKN2A
cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4)


CDKN2B
cyclin-dependent kinase inhibitor 2B (p15, inhibits CDK4)


CDKN2C
cyclin-dependent kinase inhibitor 2C (p18, inhibits CDK4)


EGR1
early growth response 1


FADS3
fatty acid desaturase 3 (delta-9-desaturase)


FAF1
Fas (TNFRSF6) associated factor 1


FLT1
fms-related tyrosine kinase 1 (VEGF/vascular permeability factor receptor)


GAD1
glutamate decarboxylase 1 (brain, 67 kDa)


GRIK2
glutamate receptor, ionotropic, kainate 2


IGFBP1
insulin-like growth factor binding protein 1


MAOA
monoamine oxidase A


MYC
v-myc myelocytomatosis viral oncogene homolog (avian)


RXRA
retinoid X receptor, alpha


RXRB
retinoid X receptor, beta


SCD
stearoyl-CoA desaturase (delta-9-desaturase)


SEMA4F
sema domain, Ig domain, TM domain &short cytoplasmic domain, (semaphorin)



4F


SLC6A4
solute carrier family 6 (neurotransmitter transporter, serotonin), member 4 (5-



HTT)

















TABLE 4







AK090950
KIAA1345 protein


ECHDC2
Enoyl Coenzyme A hydratase domain containing 2


ESR1
Estrogen receptor 1 (alpha)


FLAD1
FAD1 flavin adenine dinucleotide synthetase homolog


FLJ23577
KPL2 protein


HSP90AB3P
Heat shock protein 90 kDa alpha (cytosolic),



class B member 3 (pseudogene)


IL8
Interleukin 8


ISG15
ISG15 ubiquitin-like modifier


KLHL5
Kelch-like 5 (Drosophila)


NOS3
Nitric oxide synthase 3 (endothelial cell) (eNOS)


OGDH
Oxoglutarate (alpha-ketoglutarate) dehydrogenase



(lipoamide)


OR2G2
Olfactory receptor, family 2, subfamily G, member 2


PCDH1
Protocadherin 1 (cadherin-like 1)


PI3KCD
Phosphoinositide-3-kinase, catalytic, delta polypeptide


RALGPS1
Ral GEF with PH domain and SH3 binding motif 1


TPM3
Tropomyosin 3


TSN
Translin


CDK9
cyclin-dependent kinase 9 (CDC2-related kinase)


CDKN2C
cyclin-dependent kinase inhibitor 2C (p18, inhibits CDK4)


EGR1
early growth response 1


FADS1
fatty acid desaturase 1 (delta-5 desaturase)


FAF1
Fas (TNFRSF6) associated factor 1


HTR1A
5-hydroxytryptamine (serotonin) receptor 1A


MAOA
monoamine oxidase A


MYC
v-myc myelocytomatosis viral oncogene homolog (avian)


RXRA
retinoid X receptor, alpha


RXRB
retinoid X receptor, beta


SEMA4F
sema domain, Ig domain, TM domain &short



cytoplasmic domain, (semaphorin) 4F


SYN1
synapsin I


SYN2
synapsin II









Summary of Findings—Cognitive and Neuro-Behavioural Status
Childhood IQ at Age 9 Years

For 20 of the 74 genes in our initial analyses and 25 of the 55 genes in our subsequent analyses (see Table 5), we found evidence that the degree of gene promoter methylation was associated with child's IQ. The associations with full scale IQ were particularly strong for the NOX5 (NADPH oxidase, EF-hand calcium binding domain 5) gene (r=0.64, P=0.008; FIG. 14), the CDKN2A (cyclin-dependent kinase inhibitor 2A) gene (r=0.68, P=0.005), the FADS3 (fatty acid desaturase 3 (delta-9 desaturase)) gene (r=0.76, P=0.001; FIG. 15) and for the PI3KCD (phosphoinositide-3-kinase, catalytic, δ-polypeptide) gene (r=0.66, P=0.007; FIG. 16).










TABLE 5







ATP2A3
ATPase, Ca++ transporting, ubiquitous (=PMCA3)


ATP2B1
ATPase, Ca++ transporting, plasma membrane 1 (=PMCA1)


C1orf185
Chromosome 1 open reading frame 185


ECHDC2
Enoyl Coenzyme A hydratase domain containing 2


HEMGN
Hemogen


HGSNAT
Heparan-alpha-glucosaminide N-acetyltransferase


HSD11B2
Hydroxysteroid (11-beta) dehydrogenase 2


IGF1R
Insulin-like growth factor 1 receptor


IL8
Interleukin 8


ISG15
ISG15 ubiquitin-like modifier


MCL1
Myeloid cell leukemia sequence 1 (BCL2-related)


MMP2
Matrix metallopeptidase 2 (gelatinase A, 72 kDa gelatinase, 72 kDa type IV



collagenase)


NOS3
Nitric oxide synthase 3 (endothelial cell) (eNOS)


NOX5
NADPH oxidase, EF-hand calcium binding domain 5


PI3KCD
Phosphoinositide-3-kinase, catalytic, delta polypeptide


OGDH
Oxoglutarate (alpha-ketoglutarate) dehydrogenase (lipoamide)


PPARG
Peroxisome proliferator-activated receptor gamma


QSCN6
Quiescin Q6


RAI16
Retinoic acid induced 16


VEGFA
Vascular endothelial growth factor A


CDK4
cyclin-dependent kinase 4


CDKN2A
cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4)


CHRM1
cholinergic receptor, muscarinic 1


COMT
catechol-O-methyltransferase


DRD2
D(2) dopamine receptor


EGR1
early growth response 1


FADS1
fatty acid desaturase 1 (delta-5 desaturase)


FADS2
fatty acid desaturase 2 (delta-6-desaturase)


FADS3
fatty acid desaturase 3 (delta-9-desaturase)


GABRB1
gamma-aminobutyric acid (GABA) A receptor, beta 1


GRIN3B
glutamate receptor, N-methyl-D-aspartate 3B (NMDA receptor subunit 3B)


HTR1A
5-hydroxytryptamine (serotonin) receptor 1A


HTR2A
5-hydroxytryptamine (serotonin) receptor 2A


MAOA
monoamine oxidase A


MAPK1
mitogen-activated protein kinase 1 (protein tyrosine kinase ERK2)


MYC
v-myc myelocytomatosis viral oncogene homolog (avian)


OTX1
orthodenticle homeobox 1


PGF
placental growth factor, vascular endothelial growth factor-related protein


RELN
reelin


RXRA
retinoid X receptor, alpha


SLC6A3
Sodium-dependent dopamine transporter (DA transporter) (DAT)


SLC6A4
solute carrier family 6 (neurotransmitter transporter, serotonin), member 4 (5-



HTT)


SYN1
synapsin I


SYN3
synapsin III


TNFRSF1A
tumor necrosis factor receptor superfamily, member 1A









Childhood Neuro-Behavioural Status at Age 9 Years

Results also indicate that degree of gene promoter methylation can be used as a useful marker for neuro-behavioural disorders including, but not limited to hyperactivity, emotional problems, conduct problems, peer problems and/or total difficulties. For 48 of the 74 genes in our initial analyses and 34 of the 55 genes in our subsequent analyses (See Table 6), we found evidence that the degree of gene promoter methylation was associated with child's score on hyperactivity, emotional problems, conduct problems, peer problems and/or total difficulties scales. The associations with hyperactivity score were particularly strong for the IL8 (interleukin-8) gene (r=−0.77, P=0.002) and the ECHDC2 (Enoyl Coenzyme A hydratase domain containing 2) gene (r=−0.72, P=0.005; FIG. 17). The associations with the conduct problem scores were particularly strong for the CDKN2C (cyclin-dependent kinase inhibitor 2C) gene (r=−0.67, P=0.007), the CHRM (cholinergic receptor, muscarinic 1) gene (r=−0.65, P=0.007) and the HTR1A (5-hydroxytryptamine (serotonin) receptor 1A) gene (r=−0.76, P=0.001; FIG. 18). Association with the emotional problems score was particularly strong for the CDKN2A (cyclin-dependent kinase inhibitor 2A) gene (r=−0.74, P=0.002; FIG. 19).










TABLE 6







ACTC1
Cardiac muscle alpha actin 1


AK090950
KIAA1345 protein


AK128539

Homo sapiens cDNA FLJ46698 fis, clone TRACH3013684



ATP2A3
ATPase, Ca++ transporting, ubiquitous (=PMCA3)


ATP2B1
ATPase, Ca++ transporting, plasma membrane 1 (=PMCA1)


CIB4
Calcium and integrin binding family member 4


C1orf185
Chromosome 1 open reading frame 185


DEFB107A
Defensin, beta 107A


ECHDC2
Enoyl Coenzyme A hydratase domain containing 2


ESR1
Estrogen receptor 1 (alpha)


FLAD1
FAD1 flavin adenine dinucleotide synthetase homolog


FLJ13231
Hypothetical protein FLJ13231


FLJ23577
KPL2 protein


GLO1
Glyoxalase I


HGSNAT
Heparan-alpha-glucosaminide N-acetyltransferase


HIVEP2
HIV type I enhancer binding protein 2


HSD11B2
Hydroxysteroid (11-beta) dehydrogenase 2


HSP90AB3P
Heat shock protein 90 kDa alpha (cytosolic), class B member 3 (pseudogene)


hTAK1
Human nuclear receptor hTAK1


IL8
Interleukin 8


ISG15
ISG15 ubiquitin-like modifier


KLHL5
Kelch-like 5 (Drosophila)


LOC401027
Hypothetical protein LOC401027


LOC644936
Similar to cytoplasmic beta-actin


LOC645156
Hypothetical protein LOC645156


MCL1
Myeloid cell leukemia sequence 1 (BCL2-related)


MGC48628
Similar to KIAA1680 protein (=hypothetical protein LOC401145)


MMP2
Matrix metallopeptidase 2 (gelatinase A, 72 kDa gelatinase, 72 kDa type IV



collagenase)


MMP9
Matrix metallopeptidase 9 (gelatinase B, 92 kDa gelatinase, 92 kDa type IV



collagenase)


MYOCD
Myocardin


NDUFA5
NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 5, 13 kDa


NOS3
Nitric oxide synthase 3 (endothelial cell) (eNOS)


NR3C1
Glucocorticoid receptor; nuclear receptor subfamily 3, group C, member 1


OGDH
Oxoglutarate (alpha-ketoglutarate) dehydrogenase (lipoamide)


OR2G2
Olfactory receptor, family 2, subfamily G, member 2


OR5C1
Olfactory receptor, family 5, subfamily C, member 1


PCDH1
Protocadherin 1 (cadherin-like 1)


PIK3CD
Phosphoinositide-3-kinase, catalytic, delta polypeptide


PSME4
Proteasome (prosome, macropain) activator subunit 4


PREB
Prolactin regulatory element binding


QSCN6
Quiescin Q6


RALGPS1
Ral GEF with PH domain and SH3 binding motif 1


SOD1
Superoxide dismutase 1, soluble (amyotrophic lateral sclerosis 1 (adult))


TNF
Tumor necrosis factor (TNF superfamily, member 2)(TNFa)


TPM3
Tropomyosin 3


TSN
Translin


VEGFA
Vascular endothelial growth factor A


VCAM1
Vascular cell adhesion molecule 1


CDK9
cyclin-dependent kinase 9 (CDC2-related kinase)


CDKN1A
cyclin-dependent kinase inhibitor 1A (p21, Cip1)


CDKN2A
cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4)


CDKN2B
cyclin-dependent kinase inhibitor 2B (p15, inhibits CDK4)


CDKN2C
cyclin-dependent kinase inhibitor 2C (p18, inhibits CDK4)


CHRM1
cholinergic receptor, muscarinic 1


COMT
catechol-O-methyltransferase


CREB1
cAMP responsive element binding protein 1


DRD1IP
dopamine receptor D1 interacting protein


DRD2
D(2) dopamine receptor


EGR1
early growth response 1


FADS1
fatty acid desaturase 1 (delta-5 desaturase)


FADS2
fatty acid desaturase 2 (delta-6-desaturase)


FADS3
fatty acid desaturase 3 (delta-9-desaturase)


FAF1
Fas (TNFRSF6) associated factor 1


GRIK2
glutamate receptor, ionotropic, kainate 2


HTR1A
5-hydroxytryptamine (serotonin) receptor 1A


HTR4
5-hydroxytryptamine (serotonin) receptor 4


IGFBP1
insulin-like growth factor binding protein 1


MAOA
monoamine oxidase A


MYC
v-myc myelocytomatosis viral oncogene homolog (avian)


NTRK2
neurotrophic tyrosine kinase, receptor, type 2


OTX1
orthodenticle homeobox 1


PGF
placental growth factor, vascular endothelial growth factor-related protein


RELN
reelin


RXRA
retinoid X receptor, alpha


RXRG
retinoid X receptor, gamma


S100B
S100 calcium binding protein B


SEMA4F
sema domain, Ig domain, TM domain &short cytoplasmic domain, (semaphorin)



4F


SLC6A3
Sodium-dependent dopamine transporter (DA transporter) (DAT)


SYN1
synapsin I


SYN2
synapsin II


SYN3
synapsin III


TNFRSF1A
tumor necrosis factor receptor superfamily, member 1A









Summary of Findings—Cardiovascular Structure and Function
Childhood Blood Pressure at Age 9 Years

For 31 of the 74 genes in our initial analyses and 27 of the 55 genes in our subsequent analyses (see Table 7), we found evidence that the degree of gene promoter methylation was associated with child's systolic and/or diastolic blood pressure. Methylation of such gene promoters is therefore of interest in predicting propensity for hypertension. The associations with child's systolic blood pressure were particularly strong for the NOS3 (endothelial nitric oxide synthase) gene (r=0.68, P=0.005; see FIG. 20), the NR3C1 (glucocorticoid receptor; nuclear receptor subfamily 3, group C, member 1) gene (r=0.62, P=0.01), the FADS3 (fatty acid desaturase-3) gene (r=0.64, P=0.007), the SEMA4F (sema domain, Ig domain, TM domain and short cytoplasmic domain, (semaphorin) 4F) gene (r=0.65, P=0.009) and the IL8 (interleukin-8) gene (r=0.71, P=0.003). The association with child's diastolic blood pressure was particularly strong for the IGFBP1 (insulin like growth factor binding protein-1) gene (r=0.72, P=0.002; FIG. 21).










TABLE 7







AK090950
KIAA1345 protein


ANXA4
Annexin A4


CIB4
Calcium and integrin binding family member 4


DEFB107A
Defensin, beta 107A


ECHDC2
Enoyl Coenzyme A hydratase domain containing 2


EDN1
Endothelin 1


ESR1
Estrogen receptor 1 (alpha)


FLAD1
FAD1 flavin adenine dinucleotide synthetase homolog


GLO1
Glyoxalase I


HGSNAT
Heparan-alpha-glucosaminide N-acetyltransferase


IGF1R
Insulin-like growth factor 1 receptor


IL8
Interleukin 8


KLHL5
Kelch-like 5 (Drosophila)


LOC401027
Hypothetical protein LOC401027


MCL1
Myeloid cell leukemia sequence 1 (BCL2-related)


MGC48628
Similar to KIAA1680 protein (=hypothetical protein LOC401145)


MMP9
Matrix metallopeptidase 9 (gelatinase B, 92 kDa gelatinase, 92 kDa type IV



collagenase)


MYH11
Smooth muscle myosin heavy chain 11


NDUFA5
NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 5, 13 kDa


NOS3
Nitric oxide synthase 3 (endothelial cell) (eNOS)


NR3C1
Glucocorticoid receptor; nuclear receptor subfamily 3, group C, member 1


OGDH
Oxoglutarate (alpha-ketoglutarate) dehydrogenase (lipoamide)


OR2G2
Olfactory receptor, family 2, subfamily G, member 2


PIK3CD
Phosphoinositide-3-kinase, catalytic, delta polypeptide


PPARG
Peroxisome proliferator-activated receptor gamma


PREB
Prolactin regulatory element binding


QSCN6
Quiescin Q6


RTN4
Reticulon 4


TRPC1
Transient receptor potential cation channel, subfamily C, member 1


TSN
Translin


VEGFA
Vascular endothelial growth factor A


CDK10
cyclin-dependent kinase (CDC2-like) 10


CDK9
cyclin-dependent kinase 9 (CDC2-related kinase)


CDKN1A
cyclin-dependent kinase inhibitor 1A (p21, Cip1)


CDKN2A
cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4)


COMT
catechol-O-methyltransferase


CREB1
cAMP responsive element binding protein 1


EGR1
early growth response 1


FADS1
fatty acid desaturase 1 (delta-5 desaturase)


FADS2
fatty acid desaturase 2 (delta-6-desaturase)


FADS3
fatty acid desaturase 3 (delta-9-desaturase)


FAF1
Fas (TNFRSF6) associated factor 1


FLT1
fms-related tyrosine kinase 1 (VEGF/vascular permeability factor receptor)


GABRB3
gamma-aminobutyric acid (GABA) A receptor, beta 3


HSFY2
HSFY2 heat shock transcription factor, Y linked 2 (more telomeric copy)


HTR2A
5-hydroxytryptamine (serotonin) receptor 2A


IGFBP1
insulin-like growth factor binding protein 1


MYC
v-myc myelocytomatosis viral oncogene homolog (avian)


OTX1
orthodenticle homeobox 1


PGF
placental growth factor, vascular endothelial growth factor-related protein


PPARD
peroxisome proliferator-activated receptor delta


RXRA
retinoid X receptor, alpha


RXRB
retinoid X receptor, beta


SEMA4F
sema domain, Ig domain, TM domain &short cytoplasmic domain, (semaphorin)



4F


SLC6A3
Sodium-dependent dopamine transporter (DA transporter) (DAT)


SLC6A4
solute carrier family 6 (neurotransmitter transporter, serotonin), member 4 (5-



HTT)


SYN1
synapsin I


TNFRSF1A
tumor necrosis factor receptor superfamily, member 1A









Childhood Aorto-Femoral Pulse Wave Velocity and Carotid Artery Intima Media Thickness at Age 9 Years

For 25 of the 74 genes in our initial analyses and 22 of the 55 genes in our subsequent analyses (see Table 8), we found evidence that the degree of gene promoter methylation was associated with child's aorto-femoral pulse wave velocity and/or carotid artery intima media thickness. Identification of such association may enable intervention to reduce disease risk associated with circulation problems, particularly atherosclerosis. The associations with child's aorto-femoral pulse wave velocity were particularly strong for the ATP2B1 (ATPase, Ca++ transporting, plasma membrane 1 (PMCA1)) gene (r=0.68, P=0.005) and the SOD1 (superoxide dismutase) gene (r=0.82, P<0.001, see FIG. 22). Association with carotid artery intima media thickness was particularly strong for the CREB1 (cAMP responsive element binding protein 1) gene (r=−0.58, P=0.024).










TABLE 8







ATP2B1
ATPase, Ca++ transporting, plasma membrane 1 (=PMCA1)


ECHDC2
Enoyl Coenzyme A hydratase domain containing 2


EDN1
Endothelin 1


ESR1
Estrogen receptor 1 (alpha)


FCGR1B
Fc fragment of IgG, high affinity Ib, receptor (CD64)


FLAD1
FAD1 flavin adenine dinucleotide synthetase homolog


FLJ41821
FLJ41821 protein


HEMGN
Hemogen


KLHL5
Kelch-like 5 (Drosophila)


LOC644936
Similar to cytoplasmic beta-actin


LOC646870
Hypothetical protein LOC646870


MGC48628
Similar to KIAA1680 protein (=hypothetical protein LOC401145)


MMP2
Matrix metallopeptidase 2 (gelatinase A, 72 kDa gelatinase, 72 kDa type IV



collagenase)


MYH11
Smooth muscle myosin heavy chain 11


MYOCD
Myocardin


NDUFA5
NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 5, 13 kDa


OGDH
Oxoglutarate (alpha-ketoglutarate) dehydrogenase (lipoamide)


POLQ
Polymerase (DNA directed), theta


PPARG
Peroxisome proliferator-activated receptor gamma


QSCN6
Quiescin Q6


RAI16
Retinoic acid induced 16


RALGPS1
Ral GEF with PH domain and SH3 binding motif 1


RTN4
Reticulon 4


SOD1
Superoxide dismutase 1, soluble (amyotrophic lateral sclerosis 1 (adult))


VEGFA
Vascular endothelial growth factor A


CDK4
cyclin-dependent kinase 4


CDKN2C
cyclin-dependent kinase inhibitor 20 (p18, inhibits CDK4)


CHRM1
cholinergic receptor, muscarinic 1


COMT
catechol-O-methyltransferase


CREB1
cAMP responsive element binding protein 1


DRD1IP
dopamine receptor D1 interacting protein


DRD2
D(2) dopamine receptor


FADS1
fatty acid desaturase 1 (delta-5 desaturase)


FADS2
fatty acid desaturase 2 (delta-6-desaturase)


FAF1
Fas (TNFRSF6) associated factor 1


HSFY1
heat shock transcription factor, Y-linked 1 (more centromeric copy)


HSFY2
HSFY2 heat shock transcription factor, Y linked 2 (more telomeric copy)


IGFBP1
insulin-like growth factor binding protein 1


MAPK1
mitogen-activated protein kinase 1 (protein tyrosine kinase ERK2)


NTRK2
neurotrophic tyrosine kinase, receptor, type 2


RELN
reelin


RXRG
retinoid X receptor, gamma


SEMA4F
sema domain, Ig domain, TM domain &short cytoplasmic domain, (semaphorin)



4F


SLC6A3
Sodium-dependent dopamine transporter (DA transporter) (DAT)


SLC6A4
solute carrier family 6 (neurotransmitter transporter, serotonin), member 4 (5-



HTT)


SYN1
synapsin I


TNFRSF1A
tumor necrosis factor receptor superfamily, member 1A









Childhood Left Ventricular Mass at Age 9 Years

For 7 of the 74 genes in our initial analyses and 3 of the 55 genes in our subsequent analyses (see Table 9), we found evidence that the degree of gene promoter methylation was associated with child's left ventricular mass. Determination of methylation of such genes is therefore of interest in predicting propensity for left ventricular hypertrophy. The associations were particularly strong for the KLHL5 (Kelch-like 5) gene (r=−0.67, P=0.006) and the RC3H2 (Ring finger and CCCH-type zinc finger domains 2) gene (r=−0.57, P=0.026; see FIG. 23).












TABLE 9









C1orf185
Chromosome 1 open reading frame 185



ISG15
ISG15 ubiquitin-like modifier



KLHL5
Kelch-like 5 (Drosophila)



MGC12538
Hypothetical protein MGC12538



PCDH1
Protocadherin 1 (cadherin-like 1)



POLQ
Polymerase (DNA directed), theta



RC3H2
Ring finger and CCCH-type zinc finger domains 2



GAD1
glutamate decarboxylase 1 (brain, 67 kDa)



MAPK1
mitogen-activated protein kinase 1




(protein tyrosine kinase ERK2)



RXRB
retinoid X receptor, beta










Childhood Coronary Artery Diameter at Age 9 Years

For 13 of the 74 genes in our initial analyses and 14 of the 55 genes in our subsequent analyses (see Table 10), we found evidence that the degree of gene promoter methylation was associated with child's total coronary artery diameter. Early age identification of propensity for small coronary artery clearly has important implications for combating coronary heart disease. The associations were particularly strong for the TRPC1 (Transient receptor potential cation channel, subfamily C, member 1) gene (r=−0.75, P=0.03, n=8; see FIG. 24), the FCGR1B (Fc fragment of IgG, high affinity Ib, receptor (CD64)) gene (r=−0.80, P=0.017, n=8), the DRD2 (D(2) dopamine receptor) gene (r=0.91, P=0.002, n=8) and the SOD1 (superoxide dismutase) gene (r=−0.74, P=0.038).










TABLE 10







ACTC1
Cardiac muscle alpha actin 1


AK090950
KIAA1345 protein


CIB4
Calcium and integrin binding family member 4


FCGR1B
Fc fragment of IgG, high affinity Ib, receptor (CD64)


HSP90AB3P
Heat shock protein 90 kDa alpha (cytosolic), class B member 3 (pseudogene)


IGF1R
Insulin-like growth factor 1 receptor


LOC401027
Hypothetical protein LOC401027


MGC48628
Similar to KIAA1680 protein (=hypothetical protein LOC401145)


PPARA
Peroxisome proliferator-activated receptor alpha


RAI16
Retinoic acid induced 16


SOD1
Superoxide dismutase 1, soluble (amyotrophic lateral sclerosis 1 (adult))


TRPC1
Transient receptor potential cation channel, subfamily C, member 1


VCAM1
Vascular cell adhesion molecule 1


CDK10
cyclin-dependent kinase (CDC2-like) 10


COMT
catechol-O-methyltransferase


CREB1
cAMP responsive element binding protein 1


DRD2
D(2) dopamine receptor


FADS2
fatty acid desaturase 2 (delta-6-desaturase)


FOS
v-fos FBJ murine osteosarcoma viral oncogene homolog


HSFY2
HSFY2 heat shock transcription factor, Y linked 2 (more telomeric copy)


IGFBP1
insulin-like growth factor binding protein 1


OTX1
orthodenticle homeobox 1


PGF
placental growth factor, vascular endothelial growth factor-related protein


RXRB
retinoid X receptor, beta


S100B
S100 calcium binding protein B


SLC6A4
solute carrier family 6 (neurotransmitter transporter, serotonin), member 4 (5-



HTT)


SYN1
synapsin I
















APPENDIX 1







Cardiovascular/bone candidate genes examined in preliminary analyses









Official




symbol
Gene name
MIM, GeneID





ACTC1
Cardiac muscle alpha actin 1
MIM: 102540, GeneID: 70


AGTR1
Angiotensin II receptor, type 1
MIM: 106165, GeneID: 185


ATP2A3
ATPase, Ca++ transporting, ubiquitous (=PMCA3)
MIM: 601929, GeneID: 489


ATP2B1
ATPase, Ca++ transporting, plasma membrane 1 (=PMCA1)
MIM: 108731, GeneID: 490


EDN1
Endothelin 1
MIM: 131240, GeneID: 1906


ESR1
Estrogen receptor 1 (alpha)
MIM: 133430, GeneID: 2099


HSD11B2
Hydroxysteroid (11-beta) dehydrogenase 2
MIM: 218030, GeneID: 3291


IGF1R
Insulin-like growth factor 1 receptor
MIM: 147370, GeneID: 3480


IL1A
Interleukin 1, alpha
MIM: 147760, GeneID: 3552


IL8
Interleukin 8
MIM: 146930, GeneID: 3576


MMP2
Matrix metallopeptidase 2 (gelatinase A, 72 kDa gelatinase, 72 kDa
MIM: 120360, GeneID: 4313



type IV collagenase)


MMP9
Matrix metallopeptidase 9 (gelatinase B, 92 kDa gelatinase, 92 kDa
MIM: 120361, GeneID: 4318



type IV collagenase)


MYH11
Smooth muscle myosin heavy chain 11
MIM: 160745, GeneID: 4629


MYOCD
Myocardin
MIM: 606127, GeneID: 93649


NOS3
Nitric oxide synthase 3 (endothelial cell) (eNOS)
MIM: 163729, GeneID: 4846


NOX5
NADPH oxidase, EF-hand calcium binding domain 5
MIM: 606572, GeneID: 79400


NR3C1
Glucocorticoid receptor; nuclear receptor subfamily 3, group C,
MIM: 138040, GeneID: 2908



member 1


PPARA
Peroxisome proliferator-activated receptor alpha
MIM: 170998, GeneID: 5465


PPARG
Peroxisome proliferator-activated receptor gamma
MIM: 601487, GeneID: 5468


SOD1
Superoxide dismutase 1, soluble (amyotrophic lateral sclerosis 1
MIM: 147450, GeneID: 6647



(adult))


TAGLN
Transgelin (=Smooth muscle protein 22-alpha)
MIM: 600818, GeneID: 6876


TNF
Tumor necrosis factor (TNF superfamily, member 2)(TNFa)
MIM: 191160, GeneID: 7124


VCAM1
Vascular cell adhesion molecule 1
MIM: 192225, GeneID: 7412


VEGFA
Vascular endothelial growth factor A
MIM: 192240, GeneID: 7422
















APPENDIX 2







Gene promoters with greatest inter-subject variation in methylation









Official




symbol


(/description)
Gene name
MIM, GeneID/Reporter





ADAR
Adenosine deaminase, RNA-specific
MIM: 601059, GeneID: 103


AK090950
KIAA1345 protein
AK090950


AK128539
Homo sapiens cDNA FLJ46698 fis, clone TRACH3013684
AK128539


ANXA4
Annexin A4
MIM: 106491, GeneID: 307


BDP1
B double prime 1, subunit of RNA polymerase III transcription
MIM: 607012, GeneID: 55814



initiation factor IIIB


C1orf185
Chromosome 1 open reading frame 185
GeneID: 284546


CIB4
Calcium and integrin binding family member 4
MIM: 610646, GeneID:




130106


COX6C
Cytochrome c oxidase subunit Vic
AK128382


DEFB107A
Defensin, beta 107A
GeneID: 245910


ECHDC2
Enoyl Coenzyme A hydratase domain containing 2
BX647186


FCGR1B
Fc fragment of IgG, high affinity Ib, receptor (CD64)
MIM: 601502, GeneID: 2210


FLAD1
FAD1 flavin adenine dinucleotide synthetase homolog
MIM: 610595, GeneID: 80308


FLJ13231
Hypothetical protein FLJ13231
GeneID: 65250


FLJ23577
KPL2 protein
MIM: 610172, GeneID: 79925


FLJ41821
FLJ41821 protein
GeneID: 401011


GLO1
Glyoxalase I
MIM: 138750, GeneID: 2739


HEMGN
Hemogen
MIM: 610715, GeneID: 55363


HGSNAT
Heparan-alpha-glucosaminide N-acetyltransferase
MIM: 610453, GeneID:




138050


HIVEP2
HIV type I enhancer binding protein 2
MIM: 143054, GeneID: 3097


HSP90AB3P
Heat shock protein 90 kDa alpha (cytosolic), class B member 3
GeneID: 3327



(pseudogene)


hTAK1
Human nuclear receptor hTAK1
U10990


IL6ST
Interleukin 6 signal transducer (gp130, oncostatin M receptor)
AB102799


ISG15
ISG15 ubiquitin-like modifier
MIM: 147571, GeneID: 9636


KLHL5
Kelch-like 5 (Drosophila)
MIM: 608064, GeneID: 51088


LOC401027
Hypothetical protein LOC401027
GeneID: 401027


LOC644936
Similar to cytoplasmic beta-actin
GeneID: 644936


LOC645156
Hypothetical protein LOC645156
GeneID: 645156


LOC646870
Hypothetical protein LOC646870
GeneID: 646870


MCL1
Myeloid cell leukemia sequence 1 (BCL2-related)
MIM: 159552, GeneID: 4170


MGC12538
Hypothetical protein MGC12538
GeneID: 84832


MGC48628
Similar to KIAA1680 protein (=hypothetical protein LOC401145)
GeneID: 401145


NDUFA5
NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 5,
MIM: 601677, GeneID: 4698



13 kDa


OGDH
Oxoglutarate (alpha-ketoglutarate) dehydrogenase (lipoamide)
MIM: 203740, GeneID: 4967


OR2G2
Olfactory receptor, family 2, subfamily G, member 2
GeneID: 81470


OR5C1
Olfactory receptor, family 5, subfamily C, member 1
GeneID: 392391


PCDH1
Protocadherin 1 (cadherin-like 1)
MIM: 603626, GeneID: 5097


PIGK
Phosphatidylinositol glycan anchor biosynthesis, class K
MIM: 605087, GeneID: 10026


PIK3CD
Phosphoinositide-3-kinase, catalytic, delta polypeptide
MIM: 602839, GeneID: 5293


POLQ
Polymerase (DNA directed), theta
MIM: 604419, GeneID: 10721


PREB
Prolactin regulatory element binding
MIM: 606395, GeneID: 10113


PSME4
Proteasome (prosome, macropain) activator subunit 4
MIM: 607705, GeneID: 23198


QSCN6
Quiescin Q6
MIM: 603120, GeneID: 5768


RAI16
Retinoic acid induced 16
GeneID: 64760


RALGPS1
Ral GEF with PH domain and SH3 binding motif 1
GeneID: 9649


RC3H2
Ring finger and CCCH-type zinc finger domains 2
GeneID: 54542


RTN4
Reticulon 4
MIM: 604475, GeneID: 57142


SDHALP2
Succinate dehydrogenase complex, subunit A, flavoprotein
GeneID: 727956



pseudogene 2


TPM3
Tropomyosin 3
MIM: 191030, GeneID: 7170


TRPC1
Transient receptor potential cation channel, subfamily C,
MIM: 602343, GeneID: 7220



member 1


TSN
Translin
MIM: 600575, GeneID: 7247
















APPENDIX 3







Additional candidate genes examined









Human




gene


symbol
Human gene name
MIM, GeneID/Reporter





CDK10
cyclin-dependent kinase (CDC2-like) 10
MIM: 603464, GeneID: 8558


CDK4
cyclin-dependent kinase 4
MIM: 123829, GeneID: 1019


CDK9
cyclin-dependent kinase 9 (CDC2-related kinase)
MIM: 603251, GeneID: 1025


CDKN1A
cyclin-dependent kinase inhibitor 1A (p21, Cip1)
MIM: 116899, GeneID: 1026


CDKN2A
cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4)
MIM: 600160, GeneID: 1029


CDKN2B
cyclin-dependent kinase inhibitor 2B (p15, inhibits CDK4)
MIM: 600431, GeneID: 1030


CDKN2C
cyclin-dependent kinase inhibitor 2C (p18, inhibits CDK4)
MIM: 603369, GeneID: 1031


CHRM1
cholinergic receptor, muscarinic 1
MIM: 118510, GeneID: 1128


COMT
catechol-O-methyltransferase
MIM: 116790, GeneID: 1312


CREB1
cAMP responsive element binding protein 1
MIM: 123810, GeneID: 1385


DRD1IP
dopamine receptor D1 interacting protein
MIM: 604647, GeneID: 50632


DRD2
D(2) dopamine receptor
MIM: 126450, GeneID: 1813


DRD3
dopamine receptor D3
MIM: 126451, GeneID: 1814


DRD4
Dopamine receptor D4
MIM: 126452, GeneID: 1815


EGR1
early growth response 1
MIM: 128990, GeneID: 1958


FADS1
fatty acid desaturase 1 (delta-5 desaturase)
MIM: 606148, GeneID: 3992


FADS2
fatty acid desaturase 2 (delta-6-desaturase)
MIM: 606149, GeneID: 9415


FADS3
fatty acid desaturase 3 (delta-9-desaturase)
MIM: 606150, GeneID: 3995


FAF1
Fas (TNFRSF6) associated factor 1
MIM: 604460, GeneID: 11124


FLT1
fms-related tyrosine kinase 1 (VEGF/vascular permeability factor receptor)
MIM: 165070, GeneID: 2321


FOS
v-fos FBJ murine osteosarcoma viral oncogene homolog
MIM: 164810, GeneID: 2353


FTO
fat mass and obesity associated
GeneID: 79068


GABRB1
gamma-aminobutyric acid (GABA) A receptor, beta 1
MIM: 137190, GeneID: 2560


GABRB3
gamma-aminobutyric acid (GABA) A receptor, beta 3
MIM: 137192, GeneID: 2562


GAD1
glutamate decarboxylase 1 (brain, 67 kDa)
MIM: 605363, GeneID: 2571


GRIK2
glutamate receptor, ionotropic, kainate 2
MIM: 138244, GeneID: 2898


GRIN3B
glutamate receptor, N-methyl-D-aspartate 3B (NMDA receptor subunit 3B)
MIM: 606651, GeneID: 116444


HSFY1
heat shock transcription factor, Y-linked 1 (more centromeric copy)
MIM: 400029, GeneID: 86614


HSFY2
HSFY2 heat shock transcription factor, Y linked 2 (more telomeric copy)
GeneID: 159119


HTR1A
5-hydroxytryptamine (serotonin) receptor 1A
MIM: 109760, GeneID: 3350


HTR2A
5-hydroxytryptamine (serotonin) receptor 2A
MIM: 182135, GeneID: 3356


HTR4
5-hydroxytryptamine (serotonin) receptor 4
MIM: 602164, GeneID: 3360


IGFBP1
insulin-like growth factor binding protein 1
MIM: 146730, GeneID: 3484


MAOA
monoamine oxidase A
MIM: 309850, GeneID: 4128


MAPK1
mitogen-activated protein kinase 1 (protein tyrosine kinase ERK2)
MIM: 176948, GeneID: 5594


MYC
v-myc myelocytomatosis viral oncogene homolog (avian)
MIM: 190080, GeneID: 4609


NCAM1
neural cell adhesion molecule 1
MIM: 116930, GeneID: 4684


NTRK2
neurotrophic tyrosine kinase, receptor, type 2
MIM: 600456, GeneID: 4915


OTX1
orthodenticle homeobox 1
MIM: 600036, GeneID: 5013


PGF
placental growth factor, vascular endothelial growth factor-related protein
MIM: 601121, GeneID: 5228


PPARD
peroxisome proliferator-activated receptor delta
MIM: 600409, GeneID: 5467


PRKCA
protein kinase C, alpha
MIM: 176960, GeneID: 5578


RELN
reelin
MIM: 600514, GeneID: 5649


RXRA
retinoid X receptor, alpha
MIM: 180245, GeneID: 6256


RXRB
retinoid X receptor, beta
MIM: 180246, GeneID: 6257


RXRG
retinoid X receptor, gamma
MIM: 180247, GeneID: 6258


S100B
S100 calcium binding protein B
MIM: 176990, GeneID: 6285


SCD
stearoyl-CoA desaturase (delta-9-desaturase)
MIM: 604031, GeneID: 6319


SEMA4F
sema domain, Ig domain, TM domain &short cytoplasmic domain, (semaphorin) 4F
MIM: 603706, GeneID: 10505


SLC6A3
Sodium-dependent dopamine transporter (DA transporter) (DAT)
MIM: 126455, GeneID: 6531


SLC6A4
solute carrier family 6 (neurotransmitter transporter, serotonin), member 4 (5-HTT)
MIM: 182138, GeneID: 6532


SYN1
synapsin I
MIM: 313440, GeneID: 6853


SYN2
synapsin II
MIM: 600755, GeneID: 6854


SYN3
synapsin III
MIM: 602705, GeneID: 8224


TNFRSF1A
tumor necrosis factor receptor superfamily, member 1A
MIM: 191190, GeneID: 7132








Claims
  • 1. A method of predicting a phenotypic characteristic of a human or non-human animal which comprises determining the degree of an epigenetic alteration of a gene or a combination of genes in a tissue sample, wherein the degree of said epigenetic alteration of the gene or genes of interest correlates with propensity for said phenotypic characteristic.
  • 2. A method as claimed in claim 1 wherein said epigenetic alteration is gene methyation, either across the entirety of the gene or genes of interest or gene promoter methylation.
  • 3. A method as claimed in claim 1 wherein said tissue sample is a perinatal tissue sample.
  • 4. A method as claimed in claim 3 wherein said tissue sample is umbilical cord.
  • 5. A method as claimed in claim 1 wherein said sample is adipose tissue.
  • 6. A method as claimed in claim 2 for predicting propensity for obesity or predicting total body fat mass and/or percentage body fat mass and/or body fat distribution.
  • 7. A method as claimed in claim 6 for predicting propensity for obesity wherein said tissue is adipose tissue and methylation of the glucocorticoid receptor gene is determined.
  • 8. A method as claimed in claim 6 wherein methylation of the promoter of one or more genes selected from the genes listed in the following Table 1 is determined:
  • 9.-15. (canceled)
  • 16. A method for identifying mothers liable to have poor nutritional status which comprises identifying an offspring with propensity for obesity in accordance with claim 6.
  • 17.-19. (canceled)
  • 20. A method as claimed in claim 2 for predicting total body lean mass and/or percentage lean mass and propensity for disease conditions associated therewith including sarcopenia.
  • 21. A method as claimed in claim 20 wherein methylation of the promoter of one or more genes selected from the genes listed in the following Table 2 is determined:
  • 22.-27. (canceled)
  • 28. A method as claimed in claim 2 for predicting impaired skeletal development associated with reduced bone mineral content and propensity for disease conditions associated with low bone mineral content such as osteoporosis.
  • 29. A method as claimed in claim 28 wherein methylation of the promoter of one or more genes selected from the genes listed in the following Table 3 is determined:
  • 30.-34. (canceled)
  • 35. A method as claimed in claim 2 for predicting height or impaired linear growth.
  • 36. A method as claimed in claim 35 wherein methylation of the promoter of one or more genes selected from the genes listed in the following Table 4 is determined:
  • 37.-40. (canceled)
  • 41. A method as claimed in claim 2 for predicting cognitive development.
  • 42. A method as claimed in claim 41 wherein methylation of the promoter of one or more genes selected from the genes listed in the following Table 5 is determined:
  • 43.-47. (canceled)
  • 48. A method as claimed in claim 2 for predicting propensity for a neuro-behavioural disorder including, but not limited to, one or more of hyperactivity, emotional problems, conduct problems, peer problems and total difficulties.
  • 49. A method as claimed in claim 48 wherein methylation of the promoter of one or more genes selected from the genes listed in the following Table 6 is determined:
  • 50.-56. (canceled)
  • 57. A method as claimed in claim 2 for predicting systolic and/or diastolic blood pressure and propensity for hypertension.
  • 58. A method as claimed in claim 57 wherein methylation of the promoter of one or more genes selected from the genes listed in the following Table 7 is determined:
  • 59.-65. (canceled)
  • 66. A method as claimed in claim 2 for predicting propensity for atherosclerosis.
  • 67. A method as claimed in claim 66 wherein methylation of the promoter of one or more genes selected from the genes listed in the following Table 8 is determined:
  • 68.-71. (canceled)
  • 72. A method as claimed in claim 2 for predicting left ventricular mass and propensity for left ventricular hypertrophy.
  • 73. A method as claimed in claim 72 wherein methylation of the promoter of one or more genes selected from the genes listed in the following Table 9 is determined:
  • 74.-76. (canceled)
  • 77. A method as claimed in claim 2 for predicting coronary artery diameter and propensity for coronary heart disease
  • 78. A method as claimed in claim 77 wherein methylation of the promoter of one or more genes selected from the genes listed in the following Table 10 is determined:
  • 79.-84. (canceled)
  • 85. A microarray for predicting one or more phenotypic characteristics in accordance with claim 2, said microarray being specific for detecting methylation status of a combination of genes, the methylation status of each gene being correlated with at least one phenotypic characteristic to be predicted.
  • 86. (canceled)
  • 87. A kit comprising primers and methylation-sensitive restriction enzymes for use in carrying out methylation-sensitive amplification of a combination of genes for predicting one or more phenotypic characteristics in accordance with claim 2.
  • 88. (canceled)
Priority Claims (1)
Number Date Country Kind
546953 May 2006 NZ national
PCT Information
Filing Document Filing Date Country Kind 371c Date
PCT/GB2007/050229 5/2/2007 WO 00 10/30/2008
Provisional Applications (1)
Number Date Country
60919104 Mar 2007 US