Finding relatives in a database

Information

  • Patent Grant
  • 11322227
  • Patent Number
    11,322,227
  • Date Filed
    Thursday, June 17, 2021
    2 years ago
  • Date Issued
    Tuesday, May 3, 2022
    2 years ago
  • CPC
  • Field of Search
    • US
    • NON E00000
  • International Classifications
    • G06F16/00
    • G16B50/30
    • G06F16/2457
    • G06F16/9535
    • G16B50/00
    • G16B10/00
    • G16B30/00
    • G06N5/04
    • Disclaimer
      This patent is subject to a terminal disclaimer.
Abstract
Determining relative relationships of people who share a common ancestor within at least a threshold number of generations includes: receiving recombinable deoxyribonucleic acid (DNA) sequence information of a first user and recombinable DNA sequence information of a plurality of users; processing, using one or more computer processors, the recombinable DNA sequence information of the plurality of users in parallel; determining, based at least in part on a result of processing the recombinable DNA information of the plurality of users in parallel, a predicted degree of relationship between the first user and a user among the plurality of users, the predicted degree of relative relationship corresponding to a number of generations within which the first user and the second user share a common ancestor.
Description
INCORPORATION BY REFERENCE

An Application Data Sheet is filed concurrently with this specification as part of the present application. Each application that the present application claims benefit of or priority to as identified in the concurrently filed Application Data Sheet is incorporated by reference herein in its entirety and for all purposes.


BACKGROUND OF THE INVENTION

Genealogy is the study of the history of families and the line of descent from ancestors. It is an interesting subject studied by many professionals as well as hobbyists. Traditional genealogical study techniques typically involve constructing family trees based on surnames and historical records. As gene sequencing technology becomes more accessible, there has been growing interest in genetic ancestry testing in recent years.


Existing genetic ancestry testing techniques are typically based on deoxyribonucleic acid (DNA) information of the Y chromosome (Y-DNA) or DNA information of the mitochondria (mtDNA). Aside from a small amount of mutation, the Y-DNA is passed down unchanged from father to son and therefore is useful for testing patrilineal ancestry of a man. The mtDNA is passed down mostly unchanged from mother to children and therefore is useful for testing a person's matrilineal ancestry. These techniques are found to be effective for identifying individuals that are related many generations ago (e.g., 10 generations or more), but are typically less effective for identifying closer relationships. Further, many relationships that are not strictly patrilineal or matrilineal cannot be easily detected by the existing techniques.





BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the invention are disclosed in the following detailed description and the accompanying drawings.



FIG. 1 is a block diagram illustrating an embodiment of a relative finding system.



FIG. 2 is a flowchart illustrating an embodiment of a process for finding relatives in a relative finding system.



FIG. 3 is a flowchart illustrating an embodiment of a process for connecting a user with potential relatives found in the database.



FIGS. 4A-4I are screenshots illustrating user interface examples in connection with process 300.



FIG. 5 is a diagram illustrating an embodiment of a process for determining the expected degree of relationship between two users.



FIG. 6 is a diagram illustrating example DNA data used for IBD identification by process 500.



FIG. 7 shows the simulated relationship distribution patterns for different population groups according to one embodiment.



FIG. 8 is a diagram illustrating an embodiment of a highly parallel IBD identification process.



FIG. 9 is a diagram illustrating an example in which phased data is compared to identify IBD.





DETAILED DESCRIPTION

The invention can be implemented in numerous ways, including as a process; an apparatus; a system; a composition of matter; a computer program product embodied on a computer readable storage medium; and/or a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor. In this specification, these implementations, or any other form that the invention may take, may be referred to as techniques. In general, the order of the steps of disclosed processes may be altered within the scope of the invention. Unless stated otherwise, a component such as a processor or a memory described as being configured to perform a task may be implemented as a general component that is temporarily configured to perform the task at a given time or a specific component that is manufactured to perform the task. As used herein, the term ‘processor’ refers to one or more devices, circuits, and/or processing cores configured to process data, such as computer program instructions.


A detailed description of one or more embodiments of the invention is provided below along with accompanying figures that illustrate the principles of the invention. The invention is described in connection with such embodiments, but the invention is not limited to any embodiment. The scope of the invention is limited only by the claims and the invention encompasses numerous alternatives, modifications and equivalents. Numerous specific details are set forth in the following description in order to provide a thorough understanding of the invention. These details are provided for the purpose of example and the invention may be practiced according to the claims without some or all of these specific details. For the purpose of clarity, technical material that is known in the technical fields related to the invention has not been described in detail so that the invention is not unnecessarily obscured.


Because of recombination and independent assortment of chromosomes, the autosomal DNA and X chromosome DNA (collectively referred to as recombinable DNA) from the parents is shuffled at the next generation, with small amounts of mutation. Thus, only relatives will share long stretches of genome regions where their recombinable DNA is completely or nearly identical. Such regions are referred to as “Identical by Descent” (IBD) regions because they arose from the same DNA sequences in an earlier generation. The relative finder technique described below is based at least in part on locating IBD regions in the recombinable chromosomes of individuals.


In some embodiments, locating IBD regions includes sequencing the entire genomes of the individuals and comparing the genome sequences. In some embodiments, locating IBD regions includes assaying a large number of markers that tend to vary in different individuals and comparing the markers. Examples of such markers include Single Nucleotide Polymorphisms (SNPs), which are points along the genome with two or more common variations; Short Tandem Repeats (STRs), which are repeated patterns of two or more repeated nucleotide sequences adjacent to each other; and Copy-Number Variants (CNVs), which include longer sequences of DNA that could be present in varying numbers in different individuals. Long stretches of DNA sequences from different individuals' genomes in which markers in the same locations are the same or at least compatible indicate that the rest of the sequences, although not assayed directly, are also likely identical.



FIG. 1 is a block diagram illustrating an embodiment of a relative finding system. In this example, relative finder system 102 may be implemented using one or more server computers having one or more processors, one or more special purpose computing appliances, or any other appropriate hardware, software, or combinations thereof. The operations of the relative finder system are described in greater detail below. In this example, various users of the system (e.g., user 1 (“Alice”) and user 2 (“Bob”)) access the relative finder system via a network 104 using client devices such as 106 and 108. User information (including genetic information and optionally other personal information such as family information, population group, etc.) pertaining to the users is stored in a database 110, which can be implemented on an integral storage component of the relative finder system, an attached storage device, a separate storage device accessible by the relative finder system, or a combination thereof. Many different arrangements of the physical components are possible in various embodiments. In various embodiments, the entire genome sequences or assayed DNA markers (SNPs, STRs, CNVs, etc.) are stored in the database to facilitate the relative finding process. For example, approximately 650,000 SNPs per individual's genome are assayed and stored in the database in some implementations.


System 100 shown in this example includes genetic and other additional non-genetic information for many users. By comparing the recombinable DNA information to identify IBD regions between various users, the relative finder system can identify users within the database that are relatives. Since more distant relationships (second cousins or further) are often unknown to the users themselves, the system allows the users to “opt-in” and receive notifications about the existence of relative relationships. Users are also presented with the option of connecting with their newly found relatives.



FIG. 2 is a flowchart illustrating an embodiment of a process for finding relatives in a relative finding system. Process 200 may be implemented on a relative finder system such as 100. The process may be invoked, for example, at a user's request to look for potential relatives this user may have in the database or by the system to assess the potential relationships among various users. At 202, recombinable DNA information of a first user (e.g., Alice) and of a second user (e.g., Bob) is received. In some embodiments, the information is retrieved from a database that stores recombinable DNA information of a plurality of users as well as any additional user information. For purposes of illustration, SNP information is described extensively in this and following examples. Other DNA information such as STR information and/or CNV information may be used in other embodiments.


At 204, a predicted degree of relationship between Alice and Bob is determined. In some embodiments, a range of possible relationships between the users is determined and a prediction of the most likely relationship between the users is made. In some embodiments, it is optionally determined whether the predicted degree of relationship at least meets a threshold. The threshold may be a user configurable value, a system default value, a value configured by the system's operator, or any other appropriate value. For example, Bob may select five generations as the maximum threshold, which means he is interested in discovering relatives with whom the user shares a common ancestor five generations or closer. Alternatively, the system may set a default value minimum of three generations, allowing the users to by default find relatives sharing a common ancestor at least three generations out or beyond. In some embodiments, the system, the user, or both, have the option to set a minimum threshold (e.g., two generations) and a maximum threshold (e.g., six generations) so that the user would discover relatives within a maximum number of generations, but would not be surprised by the discovery of a close relative such as a sibling who was previously unknown to the user.


At 206, Alice or Bob (or both) is notified about her/his relative relationship with the other user. In some embodiments, the system actively notifies the users by sending messages or alerts about the relationship information when it becomes available. Other notification techniques are possible, for example by displaying a list or table of users that are found to be related to the user. Depending on system settings, the potential relatives may be shown anonymously for privacy protection, or shown with visible identities to facilitate making connections. In embodiments where a threshold is set, the user is only notified if the predicted degree of relationship at least meets the threshold. In some embodiments, a user is only notified if both of the user and the potential relative have “opted in” to receive the notification. In various embodiments, the user is notified about certain personal information of the potential relative, the predicted relationship, the possible range of relationships, the amount of DNA matching, or any other appropriate information.


In some embodiments, at 208, the process optionally infers additional relationships or refines estimates of existing relationships between the users based on other relative relationship information, such as the relative relationship information the users have with a third user. For example, although Alice and Bob are only estimated to be 6th cousins after step 204, if among Alice's relatives in the system, a third cousin, Cathy, is also a sibling of Bob's, then Alice and Bob are deemed to be third cousins because of their relative relationships to Cathy. The relative relationships with the third user may be determined based on genetic information and analysis using a process similar to 200, based on non-genetic information such as family tree supplied by one of the users, or both.


In some embodiments, the relatives of the users in the system are optionally checked to infer additional relatives at 210. For example, if Bob is identified as a third cousin of Alice's, then Bob's relatives in the system (such as children, siblings, possibly some of the parents, aunts, uncles, cousins, etc.) are also deemed to be relatives of Alice's. In some embodiments a threshold is applied to limit the relationships within a certain range. Additional notifications about these relatives are optionally generated.


Upon receiving a notification about another user who is a potential relative, the notified user is allowed to make certain choices about how to interact with the potential relative. FIG. 3 is a flowchart illustrating an embodiment of a process for connecting a user with potential relatives found in the database. The process may be implemented on a relative finder system such as 102, a client system such as 106, or a combination thereof. In this example, it is assumed that it has been determined that Alice and Bob are possibly 4th cousins and that Alice has indicated that she would like to be notified about any potential relatives within 6 generations. In this example, process 300 follows 206 of process 200, where a notification is sent to Alice, indicating that a potential relative has been identified. In some embodiments, the identity of Bob is disclosed to Alice. In some embodiments, the identity of Bob is not disclosed initially to protect Bob's privacy.


Upon receiving the notification, Alice decides that she would like to make a connection with the newly found relative. At 302, an invitation from Alice to Bob inviting Bob to make a connection is generated. In various embodiments, the invitation includes information about how Alice and Bob may be related and any personal information Alice wishes to share such as her own ancestry information. Upon receiving the invitation, Bob can accept the invitation or decline. At 304, an acceptance or a declination is received. If a declination is received, no further action is required. In some embodiments, Alice is notified that a declination has been received. If, however, an acceptance is received, at 306, a connection is made between Alice and Bob. In various embodiments, once a connection is made, the identities and any other sharable personal information (e.g., genetic information, family history, phenotype/traits, etc.) of Alice and Bob are revealed to each other and they may interact with each other. In some embodiments, the connection information is updated in the database.


In some embodiments, a user can discover many potential relatives in the database at once. Additional potential relatives are added as more users join the system and make their genetic information available for the relative finding process. FIGS. 4A-4I are screenshots illustrating user interface examples in connection with process 300. In this example, the relative finder application provides two views to the user: the discovery view and the list view.



FIG. 4A shows an interface example for the discovery view at the beginning of the process. No relative has been discovered at this point. In this example, a privacy feature is built into the relative finder application so that close relative information will only be displayed if both the user and the close relative have chosen to view close relatives. This is referred to as the “opt in” feature. The user is further presented with a selection button “show close relatives” to indicate that he/she is interested in finding out about close relatives. FIG. 4B shows a message that is displayed when the user selects “show close relatives”. The message explains to the user how a close relative is defined. In this case, a close relative is defined as a first cousin or closer. In other words, the system has set a default minimum threshold of three degrees. The message further explains that unless there is already an existing connection between the user and the close relative, any newly discovered potential close relatives will not appear in the results unless the potential close relatives have also chosen to view their close relatives. The message further warns about the possibility of finding out about close relatives the user did not know he/she had. The user has the option to proceed with viewing close relatives or cancel the selection.



FIG. 4C shows the results in the discovery view. In this example, seven potential relatives are found in the database. The predicted relationship, the range of possible relationship, certain personal details a potential relative has made public, the amount of DNA a potential relative shares with the user, and the number of DNA segments the potential relative shares with the user are displayed. The user is presented with a “make contact” selection button for each potential relative.



FIG. 4D shows the results in the list view. The potential relatives are sorted according to how close the corresponding predicted relationships are to the user in icon form. The user may select an icon that corresponds to a potential relative and view his/her personal information, the predicted relationship, relationship range, and other additional information. The user can also make contact with the potential relative.



FIGS. 4E-4G show the user interface when the user selects to “make contact” with a potential relative. FIG. 4E shows the first step in making contact, where the user personalizes the introduction message and determine what information the user is willing to share with the potential relative. FIG. 4F shows an optional step in making contact, where the user is told about the cost of using the introduction service. In this case, the introduction is free. FIG. 4G shows the final step, where the introduction message is sent.



FIG. 4H shows the user interface shown to the potential relative upon receiving the introduction message. In this example, the discovery view indicates that a certain user/potential relative has requested to make a contact. The predicted relationship, personal details of the sender, and DNA sharing information are shown to the recipient. The recipient has the option to select “view message” to view the introduction message from the sender.



FIG. 4I shows the message as it is displayed to the recipient. In addition to the content of the message, the recipient is given the option to accept or decline the invitation to be in contact with the sender. If the recipient accepts the invitation, the recipient and the sender become connected and may view each other's information and/or interact with each other.


Many other user interfaces can be used in addition to or as alternatives of the ones shown above. For example, in some embodiments, at least some of the potential relatives are displayed in a family tree.


Determining the relationship between two users in the database is now described. In some embodiments, the determination includes comparing the DNA markers (e.g., SNPs) of two users and identifying IBD regions. The standard SNP based genotyping technology results in genotype calls each having two alleles, one from each half of a chromosome pair. As used herein, a genotype call refers to the identification of the pair of alleles at a particular locus on the chromosome. Genotype calls can be phased or unphased. In phased data, the individual's diploid genotype at a particular locus is resolved into two haplotypes, one for each chromosome. In unphased data, the two alleles are unresolved; in other words, it is uncertain which allele corresponds to which haplotype or chromosome.


The genotype call at a particular SNP location may be a heterozygous call with two different alleles or a homozygous call with two identical alleles. A heterozygous call is represented using two different letters such as AB that correspond to different alleles. Some SNPs are biallelic SNPs with only two possible states for SNPs. Some SNPs have more states, e.g. triallelic. Other representations are possible.


In this example, A is selected to represent an allele with base A and B represents an allele with base G at the SNP location. Other representations are possible. A homozygous call is represented using a pair of identical letters such as AA or BB. The two alleles in a homozygous call are interchangeable because the same allele came from each parent. When two individuals have opposite-homozygous calls at a given SNP location, or, in other words, one person has alleles AA and the other person has alleles BB, it is very likely that the region in which the SNP resides does not have IBD since different alleles came from different ancestors. If, however, the two individuals have compatible calls, that is, both have the same homozygotes (i.e., both people have AA alleles or both have BB alleles), both have heterozygotes (i.e., both people have AB alleles), or one has a heterozygote and the other a homozygote (i.e., one has AB and the other has AA or BB), there is some chance that at least one allele is passed down from the same ancestor and therefore the region in which the SNP resides is IBD. Further, based on statistical computations, if a region has a very low rate of opposite-homozygote occurrence over a substantial distance, it is likely that the individuals inherited the DNA sequence in the region from the same ancestor and the region is therefore deemed to be an IBD region.



FIG. 5 is a diagram illustrating an embodiment of a process for determining the predicted degree of relationship between two users. Process 500 may be implemented on a relative finder system such as 102 and is applicable to unphased data. At 502, consecutive opposite-homozygous calls in the users' SNPs are identified. The consecutive opposite-homozygous calls can be identified by serially comparing individual SNPs in the users' SNP sequences or in parallel using bitwise operations as described below. At 504, the distance between consecutive opposite-homozygous calls is determined. At 506, IBD regions are identified based at least in part on the distance between the opposite-homozygous calls. The distance may be physical distance measured in the number of base pairs or genetic distance accounting for the rate of recombination. For example, in some embodiments, if the genetic distance between the locations of two consecutive opposite-homozygous calls is greater than a threshold of 10 centimorgans (cM), the region between the calls is determined to be an IBD region. This step may be repeated for all the opposite-homozygous calls. A tolerance for genotyping error can be built by allowing some low rate of opposite homozygotes when calculating an IBD segment. In some embodiments, the total number of matching genotype calls is also taken into account when deciding whether the region is IBD. For example, a region may be examined where the distance between consecutive opposite homozygous calls is just below the 10 cM threshold. If a large enough number of genotype calls within that interval match exactly, the interval is deemed IBD.



FIG. 6 is a diagram illustrating example DNA data used for IBD identification by process 500. 602 and 604 correspond to the SNP sequences of Alice and Bob, respectively. At location 606, the alleles of Alice and Bob are opposite-homozygotes, suggesting that the SNP at this location resides in a non-IBD region. Similarly, at location 608, the opposite-homozygotes suggest a non-IBD region. At location 610, however, both pairs of alleles are heterozygotes, suggesting that there is potential for IBD. Similarly, there is potential for IBD at location 612, where both pairs of alleles are identical homozygotes, and at location 614, where Alice's pair of alleles is heterozygous and Bob's is homozygous. If there is no other opposite-homozygote between 606 and 608 and there are a large number of compatible calls between the two locations, it is then likely that the region between 606 and 608 is an IBD region.


Returning to FIG. 5, at 508, the number of shared IBD segments and the amount of DNA shared by the two users are computed based on the IBD. In some embodiments, the longest IBD segment is also determined. In some embodiments, the amount of DNA shared includes the sum of the lengths of IBD regions and/or percentage of DNA shared. The sum is referred to as IBDhalf or half IBD because the individuals share DNA identical by descent for at least one of the homologous chromosomes. At 510, the predicted relationship between the users, the range of possible relationships, or both, is determined using the IBDhalf and number of segments, based on the distribution pattern of IBDhalf and shared segments for different types of relationships. For example, in a first degree parent/child relationship, the individuals have IBDhalf that is 100% the total length of all the autosomal chromosomes and 22 shared autosomal chromosome segments; in a second degree grandparent/grandchild relationship, the individuals have IBDhalf that is approximately half the total length of all the autosomal chromosomes and many more shared segments; in each subsequent degree of relationship, the percentage of IBDhalf of the total length is about 50% of the previous degree. Also, for more distant relationships, in each subsequent degree of relationship, the number of shared segments is approximately half of the previous number.


In various embodiments, the effects of genotyping error are accounted for and corrected. In some embodiments, certain genotyped SNPs are removed from consideration if there are a large number of Mendelian errors when comparing data from known parent/offspring trios. In some embodiments, SNPs that have a high no-call rate or otherwise failed quality control measures during the assay process are removed. In some embodiments, in an IBD segment, an occasional opposite-homozygote is allowed if there is sufficient opposite-homozygotes-free distance (e.g., at least 3 cM and 300 SNPs) surrounding the opposite-homozygote.


There is a statistical range of possible relationships for the same IBDhalf and shared segment number. In some embodiments, the distribution patterns are determined empirically based on survey of real populations. Different population groups may exhibit different distribution patterns. For example, the level of homozygosity within endogamous populations is found to be higher than in populations receiving gene flow from other groups. In some embodiments, the bounds of particular relationships are estimated using simulations of IBD using generated family trees. Based at least in part on the distribution patterns, the IBDhalf, and shared number of segments, the degree of relationship between two individuals can be estimated. FIG. 7 shows the simulated relationship distribution patterns for different population groups according to one embodiment. In particular, Ashkenazi Jews and Europeans are two population groups surveyed. In panels A-C, for each combination of IBDhalf and the number of IBD segments in an Ashkenazi sample group, the 95%, 50% and 5% of obtained nth degree cousinships from 1 million simulated pedigrees are plotted. In panels D-F, for each combination of IBDhalf and the number of IBD segments in a European sample group, the 95%, 50% and 5% of obtained nth degree cousinships from 1 million simulated pedigrees are plotted. In panels G-I, the differences between Ashkenazi and European distant cousinship for the prior panels are represented. Each nth cousinship category is scaled by the expected number of nth degree cousins given a model of population growth. Simulations are conducted by specifying an extended pedigree and creating simulated genomes for the pedigree by simulating the mating of individuals drawn from a pool of empirical genomes. Pairs of individuals who appear to share IBDhalf that was not inherited through the specified simulated pedigree are marked as “unknown” in panels A-F. Thus, special distribution patterns can be used to find relatives of users who have indicated that they belong to certain distinctive population groups such as the Ashkenazi.


The amount of IBD sharing is used in some embodiments to identify different population groups. For example, for a given degree of relationship, since Ashkenazi tend to have much more IBD sharing than non-Ashkenazi Europeans, users may be classified as either Ashkenazi or non-Ashkenazi Europeans based on the number and pattern of IBD matches.


In some embodiments, instead of, or in addition to, determining the relationship based on the overall number of IBD segments and percent DNA shared, individual chromosomes are examined to determine the relationship. For example, X chromosome information is received in some embodiments in addition to the autosomal chromosomes. The X chromosomes of the users are also processed to identify IBD. Since one of the X chromosomes in a female user is passed on from her father without recombination, the female inherits one X chromosome from her maternal grandmother and another one from her mother. Thus, the X chromosome undergoes recombination at a slower rate compared to autosomal chromosomes and more distant relationships can be predicted using IBD found on the X chromosomes.


In some embodiments, analyses of mutations within IBD segments can be used to estimate ages of the IBD segments and refine estimates of relationships between users.


In some embodiments, the relationship determined is verified using non-DNA information. For example, the relationship may be checked against the users' family tree information, birth records, or other user information.


In some embodiments, the efficiency of IBD region identification is improved by comparing a user's DNA information with the DNA information of multiple other users in parallel and using bitwise operations. FIG. 8 is a diagram illustrating an embodiment of a highly parallel IBD identification process. Alice's SNP calls are compared with those of multiple other users. Alice's SNP calls are pre-processed to identify ones that are homozygous. Alice's heterozygous calls are not further processed since they always indicate that there is possibility of IBD with another user. For each SNP call in Alice's genome that is homozygous, the zygosity states in the corresponding SNP calls in the other users are encoded. In this example, compatible calls (e.g., heterozygous calls and same homozygous calls) are encoded as 0 and opposite-homozygous calls are encoded as 1. For example, for homozygous SNP call AA at location 806, opposite-homozygous calls BB are encoded as 1 and compatible calls (AA and AB) are encoded as 0; for homozygous SNP call EE at location 812, opposite-homozygous calls FF are encoded as 1 and compatible calls (EE and EF) are encoded as 0, etc. The encoded representations are stored in arrays such as 818, 820, and 824. In some embodiments, the length of the array is the same as the word length of the processor to achieve greater processing efficiency. For example, in a 64-bit processing system, the array length is set to 64 and the zygosity of 64 users' SNP calls are encoded and stored in the array.


A bitwise operation is performed on the encoded arrays to determine whether a section of DNA such as the section between locations 806 and 810 includes opposite-homozygous calls. In this example, a bitwise OR operation is performed to generate a result array 824. Any user with no opposite-homozygous calls between beginning location 806 and ending location 816 results in an entry value of 0 in array 824. The corresponding DNA segment, therefore, is deemed as an IBD region for such user and Alice. In contrast, users with opposite-homozygotes result in corresponding entry values of 1 in array 824 and they are deemed not to share IBD with Alice in this region. In the example shown, user 1 shares IBD with Alice while other users do not.


In some embodiments, phased data is used instead of unphased data. These data can come directly from assays that produce phased data, or from statistical processing of unphased data. IBD regions are determined by matching the SNP sequences between users. In some embodiments, sequences of SNPs are stored in dictionaries using a hash-table data structure for the ease of comparison. FIG. 9 is a diagram illustrating an example in which phased data is compared to identify IBD. The sequences are split along pre-defined intervals into non-overlapping words. Other embodiments may use overlapping words. Although a preset length of 3 is used for purposes of illustration in the example shown, many implementations may use words of longer lengths (e.g. 100). Also, the length does not have to be the same for every location. In FIG. 9, in Alice's chromosome pair 1, chromosome 902 is represented by words AGT, CTG, CAA, . . . and chromosome 904 is represented by CGA, CAG, TCA, . . . . At each location, the words are stored in a hash table that includes information about a plurality of users to enable constant retrieval of which users carry matching haplotypes. Similar hash tables are constructed for other sequences starting at other locations. To determine whether Bob's chromosome pair 1 shares any IBD with Alice's, Bob's sequences are processed into words at the same locations as Alice's. Thus, Bob's chromosome 906 yields CAT, GAC, CCG, . . . and chromosome 908 yields AAT, CTG, CAA, . . . . Every word from Bob's chromosomes is then looked up in the corresponding hash table to check whether any other users have the same word at that location in their genomes. In the example shown, the second and third words of chromosome 908 match second and third words of Alice's chromosome 902. This indicates that SNP sequence CTGCAA is present in both chromosomes and suggests the possibility of IBD sharing. If enough matching words are present in close proximity to each other, the region would be deemed IBD.


In some embodiments, relative relationships found using the techniques described above are used to infer characteristics about the users that are related to each other. In some embodiments, the inferred characteristic is based on non-genetic information pertaining to the related users. For example, if a user is found to have a number of relatives that belong to a particular population group, then an inference is made that the user may also belong to the same population group. In some embodiments, genetic information is used to infer characteristics, in particular characteristics specific to shared IBD segments of the related users. Assume, for example, that Alice has sequenced her entire genome but her relatives in the system have only genotyped SNP data. If Alice's genome sequence indicates that she may have inherited a disease gene, then, with Alice's permission, Alice's relatives who have shared IBD with Alice in the same region that includes the disease gene may be notified that they are at risk for the same disease.


Although the foregoing embodiments have been described in some detail for purposes of clarity of understanding, the invention is not limited to the details provided. There are many alternative ways of implementing the invention. The disclosed embodiments are illustrative and not restrictive.

Claims
  • 1. A computer-implemented method for operating a relative finder database to display in a user interface a list of one or more potential relatives among users, the method comprising: (a) providing a system comprising a computer and the relative finder database comprising autosomal deoxyribonucleic acid (DNA) sequence information of a first user and autosomal DNA sequence information of a plurality of other users, wherein the system is configured to estimate relative relationships between the first user and one or more users of the plurality of other users from the autosomal DNA sequence information in the relative finder database;(b) receiving, from the first user an opt-in election to consent to being presented with information about potential relatives among users in the relative finder database;(c) receiving, from the first user a user selection to be presented with information about potential relatives among users in the relative finder database;(d) obtaining from the system, upon receiving the user selection, an estimated degree of relative relationship between the first user and the one or more users among the plurality of other users of the relative finder database; and(e) displaying, using a display device, a graphical display structure of the user interface to display the estimated degree of relative relationship of the first user and each of the one or more users among the plurality of other users, wherein the graphical display structure comprises one or more icons each of which corresponds to one of the one or more users among the plurality of other users and information pertaining to each of the one or more users among the plurality of other users, the information comprising: the estimated degree of relative relationship between the first user and each of the one or more users.
  • 2. The method of claim 1, wherein the information pertaining to each of the one or more users comprises: an entry of personal details that each of the one or more users has chosen to make public.
  • 3. The method of claim 1, wherein the information pertaining to each of the one or more users comprises: a number of identical-by-descent (IBD) segments shared between the first user and each of the one or more users.
  • 4. The method of claim 1, wherein the information pertaining to each of the one or more users comprises: a length of IBD segments shared between the first user and each of the one or more users or the length of the IBD segments shared between the first user and each of the one or more users represented as a percentage.
  • 5. The method of claim 1, further comprising displaying, before (b), a graphical user interface (GUI) comprising: (i) a first input component for receiving the opt-in election from the first user; and(ii) a second input component for receiving the user selection.
  • 6. The method of claim 5, wherein the GUI comprises: (iii) an indication of one or more users who may be potential relatives of the first user.
  • 7. The method of claim 1, further comprising: obtaining from the system an estimated range of possible relative relationships between the first user and each of the one or more users among the plurality of other users in the relative finder database, wherein the information pertaining to each of the one or more users comprises the estimated range of possible relative relationships between the first user and each of the one or more users among the plurality of other users in the relative finder database.
  • 8. The method of claim 1, wherein the one or more users includes multiple users, further comprising: obtaining the estimated degree of relative relationship between the first user and multiple users among the plurality of other users of the relative finder database who share a common ancestor within a threshold number of generations, wherein potential relationships between the first user and the multiple users among the plurality of other users in the relative finder database were previously unknown to the first user.
  • 9. The method of claim 8, the graphical display structure further comprising: a plurality of connection buttons each corresponding to one of the multiple users, each connection button configured to receive a user input selection to send a connection request to a corresponding user of the multiple users associated with the connection button.
  • 10. The method of claim 8, wherein the graphical display structure comprises multiple users that are sorted in descending order from a relative estimated to be closest to the first user positioned at a top of the list of the multiple users.
  • 11. The method of claim 8, wherein the graphical display structure comprises a family tree representation of the multiple users based on the estimated degree of relative relationship between the first user and each of the multiple users.
  • 12. The method of claim 1, wherein the system is configured to estimate relative relationships between the first user and one or more users of the plurality of other users from the autosomal DNA sequence information in the relative finder database based on a parallel comparison between the first user and the one or more users of the plurality of other users to estimate identical-by-descent (IBD) information between the first user and one or more users of the plurality of other users.
  • 13. The method of claim 12, further comprising: encoding the IBD information between the first user and the one or more users of the plurality of other users from the parallel comparison between the first user and the one or more users of the plurality of other users in the relative finder database in an array comprising a plurality of rows corresponding to the one or more users of the plurality of other users in the relative finder database and a plurality of columns corresponding to a location of the autosomal DNA information relative to a position on a chromosome.
  • 14. The method of claim 13, further comprising: estimating the degree of relative relationship between the first user and the one or more users of the plurality of other users based on the array; and storing the estimated degree of relative relationship between the first user and the one or more users in a database, wherein in step (d) obtaining from the system the estimated degree of relative relationship between the first user and the one or more users among the plurality of other users of the relative finder database includes retrieving the estimated degree of relative relationship from the relative finder database in which the estimated degree of relative relationship is stored.
  • 15. The method of claim 1, wherein the graphical display structure further comprises: a third input component for receiving an opt-in from the first user prior to processing the autosomal DNA sequence information of the first user and the plurality of other users in the relative finder database in parallel.
  • 16. The method of claim 1, wherein the one or more icons are configured to be selected by the first user and wherein the method further comprises, receiving from the first user via selection of one of the one or more icons an indication of the user corresponding to a selected icon.
  • 17. The method of claim 16, further comprising, in response to receiving an indication of the user corresponding to the selected icon, automatically updating the graphical display structure of the user interface to display additional information about the user corresponding to the selected icon.
  • 18. The method of claim 17, further comprising, in response to receiving an indication of the user corresponding to the selected icon, automatically updating the graphical display structure of the user interface to display a fourth input component configured to receive a user input selection to send a connection request to the user corresponding to the selected icon.
  • 19. The method of claim 18, further comprising, in response to receiving from the first user via the fourth input component the user input selection to send a connection request to the user corresponding to the selected icon, sending the connection request to the user corresponding to the selected icon.
  • 20. A system for operating a relative finder database to display in a user interface a list of one or more potential relatives among users, the system comprising: a relative finder database comprising autosomal deoxyribonucleic acid (DNA) sequence information of a first user and autosomal DNA sequence information of a plurality of other users;a computer comprising one or more processors and memory, the one or more processors configured to:(a) estimate relative relationships between the first user and one or more users of the plurality of other users from the autosomal DNA sequence information in the relative finder database;(b) receive, from the first user an opt-in election to consent to being presented with information about potential relatives among users in the relative finder database;(c) receive, from the first user a user selection to be presented with information about potential relatives among users in the relative finder database;(d) obtain from the system, upon receiving the user selection, an estimated degree of relative relationship between the first user and the one or more users among the plurality of other users of the relative finder database; and(e) display, using a display device, a graphical display structure of the user interface to display the estimated degree of relative relationship of the first user and each of the one or more users among the plurality of other users, wherein the graphical display structure comprises one or more icons each of which corresponds to one of the one or more users among the plurality of other users and information pertaining to each of the one or more users among the plurality of other users, the information comprising: the estimated degree of relative relationship between the first user and each of the one or more users.
  • 21. A computer program product comprising a non-transitory computer readable medium having stored thereon program code that, when executed by one or more processors of a computer system, cause the computer system to perform operations for operating a relative finder database to display in a user interface a list of one or more potential relatives among users, said program code comprising code for: (a) estimating relative relationships between a first user and one or more users of a plurality of other users from autosomal DNA sequence information in the relative finder database;(b) receiving, from the first user an opt-in election to consent to being presented with information about potential relatives among users in the relative finder database;(c) receiving, from the first user a user selection to be presented with information about potential relatives among users in the relative finder database;(d) obtaining from the system, upon receiving the user selection, an estimated degree of relative relationship between the first user and the one or more users among the plurality of other users of the relative finder database; and(e) displaying, using a display device, a graphical display structure of the user interface to display the estimated degree of relative relationship of the first user and each of the one or more users among the plurality of other users, wherein the graphical display structure comprises one or more icons each of which corresponds to one of the one or more users among the plurality of other users and information pertaining to each of the one or more users among the plurality of other users, the information comprising: the estimated degree of relative relationship between the first user and each of the one or more users.
  • 22. A computer-implemented method for operating a relative finder database to display in a user interface a list of one or more potential relatives among users, the method comprising: (a) providing a system comprising a computer and the relative finder database comprising autosomal deoxyribonucleic acid (DNA) sequence information of a first user and autosomal DNA sequence information of a plurality of other users, wherein the system is configured to estimate relative relationships between the first user and one or more users of the plurality of other users from the autosomal DNA sequence information in the relative finder database;(b) receiving, from the first user an opt-in election to consent to being presented with information about potential relatives among users in the relative finder database;(c) receiving, from the first user a user selection to be presented with information about potential relatives among users in the relative finder database;(d) obtaining from the system, upon receiving the user selection, an estimated degree of relative relationship between the first user and the one or more users among the plurality of other users of the relative finder database;(e) displaying, using a display device, a graphical display structure of the user interface to display the estimated degree of relative relationship of the first user and each of the one or more users among the plurality of other users, wherein the graphical display structure comprises one or more icons each of which corresponds to one of the one or more users among the plurality of other users and information pertaining to each of the one or more users among the plurality of other users, the information comprising: the estimated degree of relative relationship between the first user and each of the one or more users;(f) receiving from the first user via selection of one of the one or more icons an indication of the user corresponding to the selected icon; and(g) in response to receiving an indication of the user corresponding to the selected icon in (f), automatically updating, by a processor of the computer, the graphical display structure of the user interface to display additional information about the user corresponding to the selected icon.
US Referenced Citations (409)
Number Name Date Kind
5376526 Brown et al. Dec 1994 A
5551880 Bonnstetter et al. Sep 1996 A
5649181 French et al. Jul 1997 A
5660176 Iliff Aug 1997 A
5752242 Havens May 1998 A
5769074 Barnhill et al. Jun 1998 A
5839120 Thearling Nov 1998 A
5940802 Hildebrand et al. Aug 1999 A
5985559 Brown Nov 1999 A
6063028 Luciano May 2000 A
6108647 Poosala et al. Aug 2000 A
6131092 Masand Oct 2000 A
6203993 Shuber et al. Mar 2001 B1
6216134 Heckerman et al. Apr 2001 B1
6253203 O'Flaherty et al. Jun 2001 B1
6269364 Kennedy et al. Jul 2001 B1
6321163 Graham et al. Nov 2001 B1
6393399 Even May 2002 B1
6487541 Aggarwal et al. Nov 2002 B1
6493637 Steeg Dec 2002 B1
6506562 Weissman et al. Jan 2003 B1
6507840 Ioannidis et al. Jan 2003 B1
6519604 Acharya et al. Feb 2003 B1
6601059 Fries Jul 2003 B1
6629097 Keith Sep 2003 B1
6629935 Miller et al. Oct 2003 B1
6640211 Holden Oct 2003 B1
6687696 Hofmann et al. Feb 2004 B2
6694311 Smith Feb 2004 B1
6730023 Dodds May 2004 B1
6738762 Chen et al. May 2004 B1
6873914 Winfield et al. Mar 2005 B2
6887666 Hager May 2005 B1
6912492 Johnson et al. Jun 2005 B1
6931326 Judson et al. Aug 2005 B1
6994962 Thilly Feb 2006 B1
7054758 Gill-Garrison et al. May 2006 B2
7062752 Simpson et al. Jun 2006 B2
7069308 Abrams Jun 2006 B2
7072794 Wittkowski Jul 2006 B2
7107155 Frudakis Sep 2006 B2
7127355 Cox et al. Oct 2006 B2
7162471 Knight et al. Jan 2007 B1
7271243 Dumas et al. Sep 2007 B2
7366719 Shaw Apr 2008 B2
7461006 Gogolak Dec 2008 B2
7572603 Small et al. Aug 2009 B2
7592910 Tuck et al. Sep 2009 B2
7720855 Brown May 2010 B2
7739247 Mount et al. Jun 2010 B2
7752215 Dettinger et al. Jul 2010 B2
7788358 Martino Aug 2010 B2
7797302 Kenedy et al. Sep 2010 B2
7818310 Kenedy et al. Oct 2010 B2
7844609 Kenedy et al. Nov 2010 B2
7877398 Kroeschel et al. Jan 2011 B2
7904511 Ryan et al. Mar 2011 B2
7917374 Walker Mar 2011 B2
7917438 Kenedy et al. Mar 2011 B2
7930156 Maruhashi et al. Apr 2011 B2
7933912 Kenedy et al. Apr 2011 B2
7941329 Kenedy et al. May 2011 B2
7941434 Kenedy et al. May 2011 B2
7957907 Sorenson et al. Jun 2011 B2
7996157 Zabeau et al. Aug 2011 B2
8024348 Kenedy et al. Sep 2011 B2
8051033 Kenedy et al. Nov 2011 B2
8055643 Kenedy et al. Nov 2011 B2
8065324 Kenedy et al. Nov 2011 B2
8073708 Igoe et al. Dec 2011 B1
8099424 Kenedy et al. Jan 2012 B2
8108406 Kenedy et al. Jan 2012 B2
8185461 Kenedy et al. May 2012 B2
8187811 Eriksson et al. May 2012 B2
8200509 Kenedy et al. Jun 2012 B2
8209319 Kenedy et al. Jun 2012 B2
8224835 Kenedy et al. Jul 2012 B2
8255403 Kenedy et al. Aug 2012 B2
8326648 Kenedy et al. Dec 2012 B2
8386519 Kenedy et al. Feb 2013 B2
8428886 Wong et al. Apr 2013 B2
8452619 Kenedy et al. May 2013 B2
8458097 Kenedy et al. Jun 2013 B2
8458121 Kenedy et al. Jun 2013 B2
8463554 Hon et al. Jun 2013 B2
8510057 Avey et al. Aug 2013 B1
8543339 Wojcicki et al. Sep 2013 B2
8589437 Khomenko et al. Nov 2013 B1
8606761 Kenedy et al. Dec 2013 B2
8635087 Igoe et al. Jan 2014 B1
8645343 Wong et al. Feb 2014 B2
8655899 Kenedy et al. Feb 2014 B2
8655908 Kenedy et al. Feb 2014 B2
8655915 Kenedy et al. Feb 2014 B2
8738297 Sorenson et al. May 2014 B2
8786603 Rasmussen et al. Jul 2014 B2
8788283 Kenedy et al. Jul 2014 B2
8788286 Kenedy et al. Jul 2014 B2
8855935 Myres et al. Oct 2014 B2
8990250 Chowdry et al. Mar 2015 B1
9031870 Kenedy et al. May 2015 B2
9116882 Macpherson et al. Aug 2015 B1
9170992 Kenedy et al. Oct 2015 B2
9213944 Do et al. Dec 2015 B1
9213947 Do et al. Dec 2015 B1
9218451 Wong et al. Dec 2015 B2
9336177 Hawthorne et al. May 2016 B2
9367663 Deciu et al. Jun 2016 B2
9367800 Do et al. Jun 2016 B1
9390225 Barber et al. Jul 2016 B2
9405818 Chowdry et al. Aug 2016 B2
9582647 Kenedy et al. Feb 2017 B2
9836576 Do et al. Dec 2017 B1
9864835 Avey et al. Jan 2018 B2
9977708 Do et al. May 2018 B1
10025877 Macpherson Jul 2018 B2
10162880 Chowdry et al. Dec 2018 B1
10275569 Avey et al. Apr 2019 B2
10296847 Do et al. May 2019 B1
10379812 Kenedy et al. Aug 2019 B2
10432640 Hawthorne et al. Oct 2019 B1
10437858 Naughton et al. Oct 2019 B2
10516670 Hawthorne et al. Dec 2019 B2
10572831 Do et al. Feb 2020 B1
10643740 Avey et al. May 2020 B2
10658071 Do et al. May 2020 B2
10691725 Naughton et al. Jun 2020 B2
10699803 Do et al. Jun 2020 B1
10755805 Do et al. Aug 2020 B1
10777302 Chowdry et al. Sep 2020 B2
10790041 Macpherson et al. Sep 2020 B2
10803134 Kenedy et al. Oct 2020 B2
10841312 Hawthorne et al. Nov 2020 B2
10854318 Macpherson et al. Dec 2020 B2
10891317 Chowdry et al. Jan 2021 B1
10896233 Kenedy et al. Jan 2021 B2
10936626 Naughton et al. Mar 2021 B1
10957455 Kenedy et al. Mar 2021 B2
10991467 Kenedy et al. Apr 2021 B2
10999285 Hawthorne et al. May 2021 B2
11003694 Kenedy et al. May 2021 B2
11031101 Hon et al. Jun 2021 B2
11049589 Hon et al. Jun 2021 B2
11170047 MacPherson Nov 2021 B2
11170873 Avey et al. Nov 2021 B2
11171962 Hawthorne et al. Nov 2021 B2
20010000810 Alabaster May 2001 A1
20020010552 Rienhoff, Jr. et al. Jan 2002 A1
20020019746 Rienhoff, Jr. et al. Feb 2002 A1
20020048763 Penn et al. Apr 2002 A1
20020052697 Serita May 2002 A1
20020052761 Fey et al. May 2002 A1
20020077775 Schork et al. Jun 2002 A1
20020094532 Bader et al. Jul 2002 A1
20020120623 Vivier et al. Aug 2002 A1
20020123058 Threadgill et al. Sep 2002 A1
20020126545 Warren et al. Sep 2002 A1
20020128860 Leveque et al. Sep 2002 A1
20020133299 Jacob et al. Sep 2002 A1
20020137086 Olek et al. Sep 2002 A1
20020138572 Delany et al. Sep 2002 A1
20020156043 Pfost Oct 2002 A1
20020161664 Shaya et al. Oct 2002 A1
20020169793 Sweeney Nov 2002 A1
20020174096 O'Reilly et al. Nov 2002 A1
20020179097 Atkins Dec 2002 A1
20020183965 Gogolak Dec 2002 A1
20030009295 Markowitz et al. Jan 2003 A1
20030030637 Grinstein et al. Feb 2003 A1
20030040002 Ledley Feb 2003 A1
20030046114 Davies et al. Mar 2003 A1
20030065241 Hohnloser Apr 2003 A1
20030065535 Karlov et al. Apr 2003 A1
20030101000 Bader et al. May 2003 A1
20030113727 Gim et al. Jun 2003 A1
20030130873 Nevin et al. Jul 2003 A1
20030135096 Dodds Jul 2003 A1
20030135488 Amir et al. Jul 2003 A1
20030165926 Olek et al. Sep 2003 A1
20030167260 Nakamura et al. Sep 2003 A1
20030171876 Markowitz et al. Sep 2003 A1
20030172065 Sorenson et al. Sep 2003 A1
20030195706 Korenberg Oct 2003 A1
20030198970 Roberts Oct 2003 A1
20030203370 Yakhini et al. Oct 2003 A1
20030204418 Ledley Oct 2003 A1
20030212579 Brown et al. Nov 2003 A1
20030224394 Schadt et al. Dec 2003 A1
20030233377 Kovac Dec 2003 A1
20040002816 Milosavljevic Jan 2004 A1
20040006488 Fitall et al. Jan 2004 A1
20040009495 O'Malley et al. Jan 2004 A1
20040014097 McGlennen et al. Jan 2004 A1
20040015337 Thomas et al. Jan 2004 A1
20040018500 Glassbrook et al. Jan 2004 A1
20040019598 Huang et al. Jan 2004 A1
20040019688 Nickerson et al. Jan 2004 A1
20040024534 Hsu Feb 2004 A1
20040034652 Hofmann et al. Feb 2004 A1
20040093331 Garner et al. May 2004 A1
20040093334 Scherer May 2004 A1
20040111410 Burgoon et al. Jun 2004 A1
20040122705 Sabol et al. Jun 2004 A1
20040158581 Kotlyar et al. Aug 2004 A1
20040172287 O'Toole et al. Sep 2004 A1
20040172313 Stein et al. Sep 2004 A1
20040175700 Geesaman Sep 2004 A1
20040177071 Massey et al. Sep 2004 A1
20040193019 Wei Sep 2004 A1
20040197799 Williamson et al. Oct 2004 A1
20040219493 Phillips Nov 2004 A1
20040221855 Ashton Nov 2004 A1
20040242454 Gallant Dec 2004 A1
20040243443 Asano et al. Dec 2004 A1
20040243545 Boone et al. Dec 2004 A1
20040254920 Brill et al. Dec 2004 A1
20050021240 Berlin et al. Jan 2005 A1
20050026117 Judson et al. Feb 2005 A1
20050026119 Ellis et al. Feb 2005 A1
20050032066 Heng et al. Feb 2005 A1
20050037405 Caspi et al. Feb 2005 A1
20050055365 Ramakrishnan et al. Mar 2005 A1
20050064476 Huang et al. Mar 2005 A1
20050090718 Dodds Apr 2005 A1
20050112684 Holzle May 2005 A1
20050120019 Rigoutsos et al. Jun 2005 A1
20050143928 Moser et al. Jun 2005 A1
20050154627 Zuzek et al. Jul 2005 A1
20050158788 Schork et al. Jul 2005 A1
20050164704 Winsor Jul 2005 A1
20050170321 Scully Aug 2005 A1
20050170528 West et al. Aug 2005 A1
20050176057 Bremer et al. Aug 2005 A1
20050181516 Dressman et al. Aug 2005 A1
20050191678 Lapointe et al. Sep 2005 A1
20050191731 Judson et al. Sep 2005 A1
20050203900 Nakamura et al. Sep 2005 A1
20050208454 Hall Sep 2005 A1
20050216208 Saito et al. Sep 2005 A1
20050228595 Cooke et al. Oct 2005 A1
20050256649 Roses Nov 2005 A1
20050260610 Kurtz et al. Nov 2005 A1
20050278125 Harwood et al. Dec 2005 A1
20060020398 Vernon et al. Jan 2006 A1
20060020614 Kolawa et al. Jan 2006 A1
20060025929 Eglington Feb 2006 A1
20060052945 Rabinowitz et al. Mar 2006 A1
20060059159 Truong et al. Mar 2006 A1
20060064415 Guyon et al. Mar 2006 A1
20060129034 Kasabov et al. Jun 2006 A1
20060136143 Avinash et al. Jun 2006 A1
20060195335 Christian et al. Aug 2006 A1
20060200319 Brown Sep 2006 A1
20060218111 Cohen Sep 2006 A1
20060235881 Masarie et al. Oct 2006 A1
20060257888 Zabeau et al. Nov 2006 A1
20060293921 McCarthy et al. Dec 2006 A1
20070011173 Agostino Jan 2007 A1
20070016568 Amir et al. Jan 2007 A1
20070027850 Chan et al. Feb 2007 A1
20070027917 Ariel et al. Feb 2007 A1
20070037182 Gaskin Feb 2007 A1
20070050354 Rosenberg Mar 2007 A1
20070061085 Fernandez Mar 2007 A1
20070061166 Ramasubramanian et al. Mar 2007 A1
20070061197 Ramer et al. Mar 2007 A1
20070061424 Mattaway Mar 2007 A1
20070078680 Wennberg Apr 2007 A1
20070106536 Moore May 2007 A1
20070106754 Moore May 2007 A1
20070111247 Stephens et al. May 2007 A1
20070116036 Moore May 2007 A1
20070122824 Tucker et al. May 2007 A1
20070220017 Zuzarte et al. Sep 2007 A1
20070239554 Lin et al. Oct 2007 A1
20070271292 Acharya et al. Nov 2007 A1
20070294113 Settimi Dec 2007 A1
20080040046 Chakraborty et al. Feb 2008 A1
20080040151 Moore Feb 2008 A1
20080081331 Myres et al. Apr 2008 A1
20080082955 Andreessen et al. Apr 2008 A1
20080108881 Stupp et al. May 2008 A1
20080114737 Neely et al. May 2008 A1
20080131887 Stephan et al. Jun 2008 A1
20080154566 Myres et al. Jun 2008 A1
20080162510 Baio et al. Jul 2008 A1
20080189047 Wong et al. Aug 2008 A1
20080227063 Kenedy et al. Sep 2008 A1
20080228043 Kenedy et al. Sep 2008 A1
20080228410 Kenedy et al. Sep 2008 A1
20080228451 Kenedy et al. Sep 2008 A1
20080228677 Kenedy et al. Sep 2008 A1
20080228698 Kenedy et al. Sep 2008 A1
20080228699 Kenedy et al. Sep 2008 A1
20080228700 Kenedy et al. Sep 2008 A1
20080228701 Kenedy et al. Sep 2008 A1
20080228702 Kenedy et al. Sep 2008 A1
20080228704 Kenedy et al. Sep 2008 A1
20080228705 Kenedy et al. Sep 2008 A1
20080228706 Kenedy et al. Sep 2008 A1
20080228708 Kenedy et al. Sep 2008 A1
20080228722 Kenedy et al. Sep 2008 A1
20080228753 Kenedy et al. Sep 2008 A1
20080228756 Kenedy et al. Sep 2008 A1
20080228757 Kenedy et al. Sep 2008 A1
20080228765 Kenedy et al. Sep 2008 A1
20080228766 Kenedy et al. Sep 2008 A1
20080228767 Kenedy et al. Sep 2008 A1
20080228768 Kenedy et al. Sep 2008 A1
20080228797 Kenedy et al. Sep 2008 A1
20080243843 Kenedy et al. Oct 2008 A1
20080256023 Nair Oct 2008 A1
20080300958 Gluck Dec 2008 A1
20090012928 Lussier et al. Jan 2009 A1
20090043752 Kenedy et al. Feb 2009 A1
20090083654 Nickerson et al. Mar 2009 A1
20090099789 Stephan et al. Apr 2009 A1
20090112871 Hawthorne et al. Apr 2009 A1
20090118131 Avey et al. May 2009 A1
20090119083 Avey et al. May 2009 A1
20090222517 Kalofonos et al. Sep 2009 A1
20090271375 Hyde et al. Oct 2009 A1
20090319610 Nikolayev et al. Dec 2009 A1
20090326832 Heckerman et al. Dec 2009 A1
20100041958 Leuthardt et al. Feb 2010 A1
20100042438 Moore et al. Feb 2010 A1
20100063830 Kenedy et al. Mar 2010 A1
20100063835 Kenedy et al. Mar 2010 A1
20100063865 Kenedy et al. Mar 2010 A1
20100063930 Kenedy et al. Mar 2010 A1
20100070292 Kenedy et al. Mar 2010 A1
20100070455 Halperin et al. Mar 2010 A1
20100076950 Kenedy et al. Mar 2010 A1
20100076988 Kenedy et al. Mar 2010 A1
20100169262 Kenedy et al. Jul 2010 A1
20100169313 Kenedy et al. Jul 2010 A1
20100169338 Kenedy et al. Jul 2010 A1
20100223281 Hon et al. Sep 2010 A1
20110004628 Armstrong et al. Jan 2011 A1
20110078168 Kenedy et al. Mar 2011 A1
20110184656 Kenedy et al. Jul 2011 A1
20110196872 Sims et al. Aug 2011 A1
20120270190 Kenedy et al. Oct 2012 A1
20120270794 Eriksson et al. Oct 2012 A1
20130013217 Stephan et al. Jan 2013 A1
20130345988 Avey et al. Dec 2013 A1
20140006433 Hon et al. Jan 2014 A1
20140067355 Noto et al. Mar 2014 A1
20140098344 Gierhart et al. Apr 2014 A1
20150100243 Myres et al. Apr 2015 A1
20150227610 Chowdry et al. Aug 2015 A1
20150248473 Kenedy et al. Sep 2015 A1
20150288780 El Daher Oct 2015 A1
20150347566 Kenedy et al. Dec 2015 A1
20160026755 Byrnes et al. Jan 2016 A1
20160091499 Sterling et al. Mar 2016 A1
20160103950 Myres et al. Apr 2016 A1
20160171155 Do et al. Jun 2016 A1
20160277408 Hawthorne et al. Sep 2016 A1
20160350479 Han et al. Dec 2016 A1
20170011042 Kermany et al. Jan 2017 A1
20170017752 Noto et al. Jan 2017 A1
20170053089 Kenedy et al. Feb 2017 A1
20170185719 Kenedy et al. Jun 2017 A1
20170220738 Barber et al. Aug 2017 A1
20170228498 Hon et al. Aug 2017 A1
20170277827 Granka et al. Sep 2017 A1
20170277828 Avey et al. Sep 2017 A1
20170329866 Macpherson Nov 2017 A1
20170329891 Macpherson et al. Nov 2017 A1
20170329899 Bryc et al. Nov 2017 A1
20170329901 Chowdry et al. Nov 2017 A1
20170329902 Bryc et al. Nov 2017 A1
20170329904 Naughton et al. Nov 2017 A1
20170329915 Kittredge et al. Nov 2017 A1
20170329924 Macpherson et al. Nov 2017 A1
20170330358 Macpherson et al. Nov 2017 A1
20180181710 Avey et al. Jun 2018 A1
20180307778 Macpherson Oct 2018 A1
20190012431 Hon et al. Jan 2019 A1
20190034163 Kenedy et al. Jan 2019 A1
20190114219 Do et al. Apr 2019 A1
20190139623 Bryc et al. May 2019 A1
20190206514 Avey et al. Jul 2019 A1
20190267115 Avey et al. Aug 2019 A1
20190281061 Hawthorne et al. Sep 2019 A1
20190384777 Naughton et al. Dec 2019 A1
20200137063 Hawthorne et al. Apr 2020 A1
20200210143 Kenedy et al. Jul 2020 A1
20200372974 Chowdry et al. Nov 2020 A1
20210020266 Freyman et al. Jan 2021 A1
20210043278 Hon et al. Feb 2021 A1
20210043279 Hon et al. Feb 2021 A1
20210043280 Hon et al. Feb 2021 A1
20210043281 Macpherson et al. Feb 2021 A1
20210058398 Hawthorne et al. Feb 2021 A1
20210074385 Hon et al. Mar 2021 A1
20210082167 Jewett et al. Mar 2021 A1
20210166452 Jewett et al. Jun 2021 A1
20210166823 Kenedy et al. Jun 2021 A1
20210193257 Freyman et al. Jun 2021 A1
20210209134 Kenedy et al. Jul 2021 A1
20210225458 Hon et al. Jul 2021 A1
20210233665 Kenedy et al. Jul 2021 A1
20210243094 Holness et al. Aug 2021 A1
20210250357 Hawthorne et al. Aug 2021 A1
20210375392 Polcari et al. Dec 2021 A1
20220044761 O'Connell et al. Feb 2022 A1
20220051751 Wilton et al. Feb 2022 A1
Foreign Referenced Citations (21)
Number Date Country
12291999 Dec 1999 EP
WO-0210456 Feb 2002 WO
WO-0222165 Mar 2002 WO
WO-02080079 Oct 2002 WO
WO-03060652 Jul 2003 WO
WO-03076895 Sep 2003 WO
WO 2004029298 Apr 2004 WO
WO-2004031912 Apr 2004 WO
WO-2004048551 Jun 2004 WO
WO-2004051548 Jun 2004 WO
WO-2004075010 Sep 2004 WO
WO-2004097577 Nov 2004 WO
WO-2005086891 Sep 2005 WO
WO-2005109238 Nov 2005 WO
WO-2006052952 May 2006 WO
WO 2006089238 Aug 2006 WO
WO-2006084195 Aug 2006 WO
WO-2007061881 May 2007 WO
WO 2008042232 Apr 2008 WO
WO 2016073953 May 2016 WO
WO-2022036178 Feb 2022 WO
Non-Patent Literature Citations (328)
Entry
Final Office Action dated Jun. 30, 2021 in U.S. Appl. No. 17/073,128.
Borsting et al. “Performance of the SNPfor1D 52 SNP-plex assay in paternity testing,” (Forensic Science International, vol. 2 (2008) pp. 292-300.
Office Action dated May 31, 2012 in U.S. Appl. No. 12/644,791.
Final Office Action dated Dec. 7, 2012 in U.S. Appl. No. 12/644,791.
Notice of Allowance dated Feb. 25, 2013 in U.S. Appl. No. 12/644,791.
Office Action dated Feb. 18, 2016 in U.S. Appl. No. 13/871,744.
Office Action dated Mar. 14, 2016 in U.S. Appl. No. 13/871,744.
Office Action dated May 18, 2018 in U.S. Appl. No. 15/264,493.
Office Action dated Apr. 23, 2021 in U.S. Appl. No. 16/129,645.
Notice of Allowance dated Jan. 21, 2021 in U.S. Appl. No. 17/073,095.
Notice of Allowance dated Jan. 7, 2021 in U.S. Appl. No. 17/073,110.
Notice of Allowance dated Apr. 29, 2021 in U.S. Appl. No. 17/073,110.
Office Action dated Dec. 24, 2020 in U.S. Appl. No. 17/073,122.
Final Office Action dated Jun. 14, 2021 in U.S. Appl. No. 17/073,122.
Office Action dated Feb. 3, 2021 in U.S. Appl. No. 17/073,128.
Office Action dated Mar. 3, 2020 in U.S. Appl. No. 15/664,619.
Notice of Allowance dated Aug. 19, 2020 in U.S. Appl. No. 15/664,619.
Office Action dated Dec. 21, 2020 in U.S. Appl. No. 17/077,930.
Final Office Action dated Apr. 20, 2021 in U.S. Appl. No. 17/077,930.
International Search Report and Written Opinion dated Mar. 3, 2010 in PCT/US2009/06706.
International Preliminary Report on Patentability dated Jul. 5, 2011 in PCT/US2009/06706.
International Preliminary Report on Patentability dated Apr. 7, 2009 in PCT Application No. PCT/US2007/020884.
Written Opinion dated Apr. 8, 2008 in PCT Application No. PCT/US2007/020884.
International Search Report dated Apr. 8, 2008 in PCT Application No. PCT/US2007/020884.
Extended European Search Report dated Oct. 25, 2016 in EP 09836517.4.
International Preliminary Report on Patentability dated Jul. 14, 2011 in PCT/US2009/006706.
Extended European Search Report dated Oct. 9, 2017 in Application No. 17172048.5.
European Examination Report dated Apr. 21, 2020 in Application No. EP 17172048.5.
“Parallel computing” Wikipedia.com, (2021) p. 1. <downloaded Apr. 14, 2021>.
“Bit-level parallelism” definition Wikipedia.com, (2021) p. 1. <downloaded Apr. 14, 2021>.
“Parallel processing definition” Techopedia.com, (2021), p. 1. <downloaded Apr. 14, 2021>.
“Parallel processing” definition Encyclopedia.com, (2021) p. 1. <downloaded Apr. 14, 2021>.
Garrett, Paul, “The mathematics of coding: Information, compression, error correction and finite fields,” University of Minnesota (2021) pp. 1-406, <downloaded Apr. 13, 2021>.
Keprt, et al., “Binary factor analysis with help of formal concepts” In Snasel et al (eds) CLA 2004, pp. 90-101, <ISBN 80-248-0597-9>.
Hon, et al., “Discovering Distant Relatives within a Diverse Set of Populations Using DNA Segments Identical by Descent” Advancing Human Genetics & Genomics Annual Meeting Poster Session, Oct. 20, 2009, 23andMe, Inc., pp. 1-2.
23andMe, Inc., v. Ancestry.com DNA, LLC, Ancestry.com Operations Inc., Ancestry.com LLC, No. 2019-1222, United States Court of Appeals for the Federal Circuit, Petition For Rehearing En Banc, filed Nov. 4, 2019, Case No. 18-cv-02791-EMC, pp. 1-28.
23andMe, Inc., v. Ancestry.com DNA, LLC, Ancestry.com Operations Inc., Ancestry.com LLC, No. 2019-1222, United States Court of Appeals for the Federal Circuit, Defendant's-Appellees' Response to Appellant 23ANDME, Inc.'s Petition for Rehearing En Banc, filed Dec. 19, 2019, Case No. 18-cv-02791-EMC, pp. 1-25.
23andMe, Inc., v. Ancestry.com DNA, LLC, Ancestry.com Operations Inc., Ancestry.com LLC, No. 2019-1222, United States Court of Appeals for the Federal Circuit, On Petition For Rehearing En Banc; Order, denied; filed Jan. 9, 2020, Case No. 18-cv-02791-EMC, pp. 1-2.
Abecasis, et al., “Extent and distribution of linkage disequilibrium in three genomic regions” Am. J. Hum. Genet. 68, (2001) pp. 191-197.
Abecasis, et al., “GOLD—Graphical overview of linkage disequilibrium” Bioinformatics, vol. 16, No. 2, (2000) pp. 182-183.
Abecasis, et al., “GRR: graphical representation of relationship errors” Bioinformatics 17, (2001) pp. 742-743.
Abecasis, et al., “Handling marker-marker linkage disequilibrium: pedigree analysis with clustered markers” Am. J. Hum. Genet. 77 (2005) pp. 754-767.
Abecasis, et al., “Linkage disequilibrium: ancient history drives the new genetics” Hum. Hered. 59, (2005) pp. 118-124.
Abecasis, et al., “MaCH: Using sequence and genotype data to estimate haplotypes and unobserved genotypes” Genetic Epidemiology 34 (2010) pp. 816-834.
Abecasis, et al., “Merlin-rapid analysis of dense genetic maps using sparse gene flow trees” Nat. Genet. 2002, 30 (2002) pp. 97-101.
Abney, et al., “Quantitative-Trait Homozygosity and Association Mapping and Empirical Genomewide Significance in Large, Complex Pedigrees: Fasting Serum-Insulin Level in the Hutterites” Am. J. Hum. Genet. 70, (2002) pp. 920-934.
Albers, et al., “Multipoint Approximations of Identity-by-descent probabilities for accurate linkage analysis of distantly related individuals,” The American Journal of Human Genetics 82, Mar. 2008, pp. 607-622.
Alexander, et al., “Fast model-based estimation of ancestry in unrelated individuals” Genome Research 19, (2009) pp. 1655-1664.
Almasy, et al. “Multipoint quantitative-trait linkage analysis in general pedigrees” Am. J. Hum. Genet. 62: (1998) pp. 1198-1211.
Almudevar, A., “A Bootstrap Assessment of Variability in Pedigree Reconstruction Based on DNA Markers” Biometrics, vol. 57, Sep. 2001, pp. 757-763.
Almudevar, A., “A simulated annealing algorithm for maximum likelihood pedigree reconstruction” Theoretical Population Biology, vol. 63, (2003) pp. 63-75.
Almudevar, et al., “Estimation of single-generation sibling relationship based on DNA markers” Journal Agricultural Biological, and Environmental Statistics, vol. 4, No. 2, (1999) pp. 136-165.
Almudevar, et al., “Most powerful permutation invariant tests for relatedness hypotheses based on genotypic data” Biometrics 57, Dec. 2001, pp. 1080-1088.
Altschul, et al. “Basic Local Alignment Search Tool” J. Mol. Biol. (1990) 215, pp. 403-410.
Amorim, et al., “Pros and cons in the use of SNP's in forensic kinship investigation: a comparative analysis with STRs” Forensic Sci. Int. 150, (2005) pp. 17-21.
Amos and Elston, “Robust Methods for the Detection of Genetic Linkage for Quantitative Data From Pedigree” Genetic Epidemiology 6, (1989) pp. 349-360.
Amos, et al., “The Probabilistic Determination of Identity-by-Descent Sharing for Pairs of Relatives from Pedigrees” Am. J. Hum. Genet. 47 (1990) pp. 842-853.
Ayers, et al. “Reconstructing Ancestral Haplotypes with a Dictionary Model” Department of Statistics Papers, Department of Statistics, UCLA, UC Los Angeles, Mar. 28, 2005, pp. 1-41.
Bacolod, et al., “The Signatures of Autozygosity among Patients with Colorectal Cancer” Cancer Res. vol. 68, No. 8, Apr. 15, 2008, pp. 2610-2621.
Balding, et al., “A method for quantifying differentiation between populations at multi-allelic loci and its implications for investigating identity and paternity” Genetica, 96 (1995) pp. 3-12.
Ballantyne, J., “Mass disaster genetics” Nature Genet. 15, (1997) pp. 329-331.
Belkhir, et al., “IDENTIX, a software to test for relatedness in a population using permutation methods” Molecular Ecology, 2, (2002) pp. 611-614.
Bieber, et al., “Finding criminals through DNA of their relatives” Science 312, (2006) pp. 1315-1316.
Blackwell et al., “Identity by Descent Genome Segmentation Based on Single Nucleotide Polymorphism Distributions,” American Association for Artificial Intelligence, 1999, pp. 54-59.
Blouin, M.S. et al., “Use of microsatellite loci to classify individuals by relatedness” Molecular Ecology, vol. 5, (1996) pp. 393-401.
Blouin, M.S., “DNA-based methods for pedigree reconstruction and kinship analysis in natural populations” Trends in Ecology and Evolution, vol. 18, No. 10, Oct. 2003, pp. 503-511.
Boehnke, et al., “Accurate Inference of Relationships in Sib-Pair Linkage Studies” Am. J. Hum. Genet. 61, (1997) pp. 423-429.
Boehnke, M., “Allele frequency estimation from data on relatives” Am. J. Hum. Genet. 48, (1991) pp. 22-25.
Brenner, C.H. “Kinship Analysis by DNA When There Are Many Possibilities” Progress in Forensic Genetics, vol. 8, (2000) pp. 94-96, Elsevier Science.
Brenner, C.H., “Issues and strategies in the DNA identification of World Trade Center victims” Theor. Popul. Biol. 63, (2003) pp. 173-178.
Brenner, C.H., “Symbolic kinship program” Genetics, 145, (1997) pp. 535-542.
Brief For Defendants-Appellees Ancestry.com DNA, LLC, Ancestry.com Operations Inc., And Ancestry.com LLC, Case No. 2019-1222, Document: 24, Filed on Mar. 18, 2019, in The US Court of Appeals For The Federal Circuit, pp. 1-76.
Brief of Appellant 23AndMe, Inc., Case No. 2019-1222, Document 19, Filed on Feb. 4, 2019, in The US Court of Appeals for the Federal Circuit, pp. 1-140.
Broman, et al., “Estimation of pairwise relationships in the presence of genotyping errors” Am. J. Hum. Genet. 63, (1998) pp. 1563-1564.
Broman, et al., “Long Homozygous Chromosomal Segments in Reference Families from the Centre d'E 'tude du Polymorphisme Humain” Am. J. Hum. Genet. 65, (1999) pp. 1493-1500.
Browning, et al., “A unified approach to Genotype imputation and Haplotype-Phase inference for large data sets of Trios and unrelated individuals” Am. J. Hum. Genet. 84, (2009) pp. 210-223.
Browning, et al., “Efficient Multilocus Association Testing for Whole Genome Association Studies Using Localized Haplotype Clustering” Genetic Epidemiology, vol. 31, 2007 pp. 365-375.
Browning, et al., “On reducing the statespace of hidden markov models for the identity by descent process” Theor. Popul. Biol. 62, (2002) pp. 1-8.
Browning, et al., “Rapid and Accurate Haplotype Phasing and Missing-Data Inference for Whole-Genome Association Studies By Use of Localized Haplotype Clustering,” The American Journal of Human Genetics, vol. 81, Nov. 2007, pp. 1084-1097.
Browning, et al., “Identity by Descent Between Distant Relatives: Detection and Applications.” Annual Review of Genetics, vol. 46, Dec. 2012, pp. 617-633.
Browning, S. et al., “Estimation of pairwise identity by descent from dense genetic marker data in a population sample of haplotypes,” Genetics, vol. 178, No. 4, Apr. 22, 2008, pp. 2123-2132. <doi: 10.1534/genetics.107.084624>.
Cannings, C., “The identity by descent process along the chromosome” Human Heredity, 56 (2003) pp. 126-130.
Carlson, et al., “Mapping complex disease loci in whole-genome association studies” Nature 429 (2004) pp. 446-452.
Cavalli-Sforza, L., “The Human Genome Diversity Project: past, present and future,” Nature Reviews, Genetics, vol. 6, Apr. 2005, pp. 333-340.
Chapman, et al., “The effect of population history on the lengths of ancestral chromosome segments” Genetics, 162, Sep. 2002, pp. 449-458.
Chen et al., “Family-Based Association Test for Genomewide Association Scans,” The American Journal of Human Genetics, vol. 81, Nov. 2007, pp. 913-926.
Chen, et al., “Robust relationship inference in genome-wide association studies” Bioinformatics 26 No. 22, 2010, pp. 2867-2873.
Cheung, et al., “Linkage-disequilibrium mapping without genotyping” Nature Genetics 18, (1998) pp. 225-230.
Choi, et al., “Case-control association testing in the presence of unknown relationships” Genet. Epidem. 33, (2009) pp. 668-678.
Cockerman, C., “Higher order probability functions of identity of alleles by descent” Genetics 69, (1971) pp. 235-246.
Complaint filed In the United States District Court in and for the Northern District of California, captioned 23andMe, Inc. v. Ancestry.com DNA, LLC, Ancestry.com Operations Inc., and Ancestry.com LLC, filed on May 11, 2018, assigned Case No. 18-cv-02791-JCS, for “Complaint for Patent Infringement, Violations of the Lanham Act, Cal. Bus. & Prof. Code §§ 17200 And 17500, and Declaratory Relief of No Trademark Infringement and Trademark Invalidity.”
Cordell, et al., “Two-locus maximum Lod score analysis of a multifactorial trait: joint consideration of IDDM2 and IDDM4 with IDDM1 in Type 1 diabetes” Am. J. Hum. Genet. 57, (1995) pp. 920-934.
Cowell, R.G., “FINEX: A probabilistic expert system for forensic identification” Forensic Science International, 134, (2003) pp. 196-206.
Crawford, et al., “Evidence for substantial fine-scale variation in recombination rates across the human genome,” Nature Genetics, vol. 36, No. 7, Jul. 2004, pp. 700-706.
Cudworth, et al., “Evidence for HL-A-linked genes in “juvenile” diabetes mellitus” Br. Med. J. 3, (1975) pp. 133-135.
Curtis, et al., “Using risk calculation to implement an extended relative pair analysis” Ann. Hum. Genet. 58 (1994) pp. 151-162.
Defendants' Notice of Motion And Motion To Dismiss Plaintiffs Complaint, filed in the United States District Court in and for the Northern District of California LLC on Jun. 29, 2018, Case No. 18-cv-02791-JCS, Re 23andMe, Inc. v. Ancestry.com DNA, LLC, Ancestry.com Operations Inc., and Ancestry.com.
Delaneau, et al., “A Linear complexity phasing method for thousands of genomes,” Nature Methods, vol. 9, No. 2, Feb. 2012, pp. 179-184.
Denniston, C., “Probability and genetic relationship” Ann. Hum. Genet., Lond. (1975), 39, pp. 89-103.
Di Rienzo, et al., “An evolutionary framework for common diseases: the ancestral-susceptibility model” Trends Genet. 21 (2005) pp. 596-601.
Dodds, et al. “Using genetic markers in unpedigreed populations to detect a heritable trait” J. Zhejiang Univ, Sci. 2007 8(11):782-786.
Donnelly, K.P., “The probability that related individuals share some section of genome identical by descent” Theor. Popul. Biol. 23 (1983) pp. 34-63.
Douglas, et al., “A multipoint method for detecting genotyping errors and mutations in sibling-pair linkage data” Am. J. Hum. Genet. 66 (2000) pp. 1287-1297.
Duffy, et al. “An integrated genetic map for linkage analysis” Behav. Genet. 36, 2006, pp. 4-6.
Dupuis, et al., “Statistical methods for linkage analysis of complex traits from high resolution maps of identity by descent” Genetics 140 (1995) pp. 843-856.
Eding, et al., “Marker-based estimates of between and within population kinships for the conservation of genetic diversity” J. Anim. Breed. Genet. 118 (2001), pp. 141-159.
Ehm, et al., “A test statistic to detect errors in sib-pair relationships” Am. J. Hum. Genet. 62 (1998) pp. 181-188.
Elston, et al., “A general model for the genetic analysis of pedigree data” Hum. Hered. 21, (1971) pp. 523-542.
Epstein, et al., “Improved inference of relationship for pairs of individuals” Am. J. Hum. Genet., vol. 67, (2000) pp. 1219-1231.
Falush, et al., “Inference of population structure using multilocus genotype data:linked loci and correlated allele frequencies” Genetics 164, (2003) pp. 1567-1587.
Feingold, E. “Markov processes for modeling and analyzing a new genetic mapping method” J. Appl. Prob. 30 (1993) pp. 766-779.
Feingold, et al., “Gaussian Models for Genetic Linkage Analysis Using Complete High-Resolution Maps of Identity by Descent,” Am. J. Hum. Genet. 53 (1993) pp. 234-251.
Fisher, RA “A Fuller Theory of ‘Junctions’ in Inbreeding” Heredity, 8 (1954) pp. 187-197.
Fisher, RA “The theory of inbreeding” Department of Genetics, Cambridge University, Eng. Edinburgh, London, Oliver & Boyd, Ltd., (1949) pp. 97-100.
Frazer, et al., “A second generation human haplotype map of over 3.1 million SNPs” vol. 449, Oct. 18, 2007, pp. 851-861. <doi:10.1038/nature06258>.
Fuchsberger, et al., “Minimac2: faster genotype imputation,” Bioinformatics, vol. 31, No. 5, Oct. 22, 2014, pp. 782-784, <doi:10.1093/bioinformatics/btu704>.
Gaytmenn, et al., “Determination of the sensitivity and specificity of sibhip calculations using AmpF/STR Profiler Plus” Int. J. Legal Med. 116, (2002) pp. 161-164.
George, et al., “Discovering disease genes: Multipoint linkage analyses via a new Markov Chain Monte Carlo approach” Statistical Science, vol. 18, No. 4, (2003) pp. 515-535.
Gillanders, et al., “The Value of Molecular Haplotypes in a Family-Based Linkage Study” Am. J. Hum. Genet. 79 (2006) pp. 458-468.
Glaubitz, et al., “Prospects for inferring pairwise relationships with single nucleotide polymorphisms” Molecular Ecology, 12 (2003) pp. 1039-1047.
Goodnight, et al., “Computer software for performing likelihood tests of pedigree relationship using genetic markers” Molecular Ecology, vol. 8, (1999) pp. 1231-1234.
Grafen, A., “A geometric view of relatedness” Oxford Surveys in Evolutionary Biology, 2 (1985) pp. 39-89.
Grant, et al., “Significance testing for direct identity-by-descent mapping” Ann. Hum. Genet. 63, (1999) pp. 441-454.
Greenspan, et al., “Model-based inference of haplotype block variation” J. Comput. Biol. 11, (2004) pp. 493-504.
Griffiths, et al., “Ancestral inference of samples of DNA sequences with recombination” Journal of Computational Biology, vol. 3, No. 4 (1996) pp. 479-502.
Gudbjartsson, et al., “Allegro, a new computer program for multipoint linkage analysis” Nat. Genet. 25, (2000) pp. 12-13.
Guo, S., “Proportion of Genome Shared Identical by Descent by Relatives: Concept, Computation, and Applications” Am. J. Hum. Genet. 56 (1995) pp. 1468-1476.
Gusev, et al., “Whole population, genome-wide mapping of hidden relatedness,” Genome Research, vol. 19, 2009, pp. 318-326.
Hajnal, J., “Concepts of random mating and the frequency of consanguineous marriages,” Proceedings of the Royal Society of London. Series B. Biological Sciences 159, No. 974 (1963) pp. 125-177.
Hardy, O.J., “Estimation of pairwise relatedness between individuals and characterization of isolation-by-distance processes using dominant genetic markers” Molecular Ecology, 12 (2003) pp. 1577-1588.
Harris, D.L., “Genotypic covariances between inbred relatives” Genetics 50, (1964) pp. 1319-1348.
Hayward, et al., “Fibrillin-1 mutations in Marfan syndrome and other type-1 fibrillinopathies” Hum. Mutat. 10 (1997) pp. 415-423.
Heath, et al., “A novel approach to search for identity by descent in small samples of patients and controls from the same Mendelian breeding unit: a pilot study in myopia” Human Heredity, vol. 52, Feb. 2001, pp. 183-190.
Henn, B.M., et al. “Cryptic Distant Relatives Are Common in Both Isolated and Cosmopolitan Genetic Samples,” PLosOne, vol. 7, No. 4, Apr. 3, 2012, pp. 1-3. <doi:10.1371/journal.pone.0034267>.
Hepler, A.B., “Improving forensic identification using Bayesian Networks and Relatedness Estimation” Ph.D Thesis, NCSU, Raleigh (2005) pp. 1-131.
Hernández-Sánchez, et al., “On the prediction of simultaneous inbreeding coefficients at multiple loci” Genet. Res. 83 (2004) pp. 113-120.
Hernández-Sánchez, et al., “Prediction of IBD based on population history for fine gene mapping” Genet. Sel. Evol. 38 (2006) pp. 231-252.
Heyer, et al., “Variability of the genetic contribution of Quebec population founders associated to some deleterious genes” Am. J. Hum. Genet. 56 (1995) pp. 970-978.
Hill, et al. “Prediction of multilocus identity-by-descent” Genetics 176, Aug. 2007, pp. 2307-2315.
Hill, et al., “Linkage disequilibrium in finite populations” Theor. Appl. Genet. 38, (1968) pp. 226-231.
Hill, et al., “Prediction of multi-locus inbreeding coefficients and relation to linkage disequilibrium in random mating populations” Theor Popul Biol. Sep. 2007, 72(2), pp. 179-185. <doi:10.1016/j.tpb.2006.05.006>.
Hill, et al., “Variances and covariances of squared linkage disequilibria in finite populations” Theor. Pop. Biol., 33 (1988) pp. 54-78. [PubMed: 3376052].
Hill, W.G., “Disequilibrium among several linked neutral genes in finite population. II Variances and covariances of disequilibria” Theor. Pop. Biol., vol. 6, 1974, pp. 184-198.
Hinrichs, et al., “Multipoint identity-by-descent computations for single-point polymorphism and microsatellite maps,” BMC Genet. 6, Dec. 30, 2005, S34. <doi:10.1186/1471-2156-6-S1-S34>.
Houwen, et al., “Genomic screening by searching for shared segments: mapping a gene for benign recurrent intrahepatic cholestasis” Nature Genetics vol. 8, Dec. 1994, pp. 380-386.
Howie, et al., “A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies,” PLoS Genetics, vol. 5, No. 6, Jun. 2009, pp. 1-15.
Howie, et al., “Fast and accurate genotype imputation in genome-wide association studies through pre-phasing,” Nature Genetics, vol. 44, No. 8, Aug. 2012, pp. 955-960.
Hu, X.S., “Estimating the correlation of pairwise relatedness along chromosomes” Heredity 94, (2004) pp. 338-346, [PubMed: 15354191].
Huang, et al. “Whole genome DNA copy number changes identified by high density oligonucleotide arrays,” Hum, Genomics vol. 1, No. 4, May 2004, pp. 287-299.
Huang, et al., “Ignoring linkage disequilibrium among tightly linked markers induces false-positive evidence of linkage for affected sib pair analysis” Am. J. Hum. Genet. 75, (2004) pp. 1106-1112.
Idury, et al., “A faster and more general hidden Markov model algorithm for multipoint likelihood calculations” Hum. Hered. 47(1997) pp. 197-202.
Jacquard, A., “Genetic information given by a relative” Biometrics, 28, (1972) pp. 1101-1114.
Jiang, et al., “An efficient parallel implementation of the hidden Markov methods for genomic sequence-search on a massively parallel system.” IEEE Transactions on Parallel and Distributed Systems 19.1 (2007) pp. 15-23.
Jones, et al., “Methods of parentage analysis in natural populations” Molecular Ecology 12 (2003) pp. 2511-2523.
Karigl, G., “A recursive algorithm for the calculation of identity coefficients” Ann. Hum. Genet. 45, (1981) pp. 299-305.
Keith, J.M., et al., “Calculation of IBD Probabilities with Dense SNP or Sequence Data” Genetic Epidemiology 32 (2008) pp. 513-519.
Kent, J.W. “BLAT—The BLAST-Like Alignment Tool” Genome Res. 2002, vol. 12, pp. 656-664.
Kimmel, et al., “A block-free hidden Markov model for genotypes and its application to disease association” J, Comput. Biol. 12, (2005a) pp. 1243-1260.
Kimmel, et al., “GERBIL: genotype resolution and block identification using likelihood” Proc. Natl. Acad. Sci. USA 102, (2005b) p. 158-162.
Kong, A. et al., “Detection of sharing by descent, long-range phasing and haplotype imputation,” Nature Genetics, vol. 40, No. 9, Sep. 2008, pp. 1068-1075.
Kong, et al., “A combined linkage-physical map of the human genome,” Am. J. Hum. Genet., vol. 75, 2004, pp. 1143-1148.
Kong, et al., “A high-resolution recombination map of the human genome” Nature Genetics, vol. 31, Jul. 2002, pp. 241-247.
Kong, et al., “Allele-sharing models—LOD scores and accurate linkage tests” Am. J. Hum. Genet. 61, (1997) pp. 1179-1188.
Kruglyak, et al., “Complete Multipoint Sib-Pair Analysis of Qualitative and Quantitative Traits” Am. J. Hum. Genet. 57, (1995) pp. 439-454.
Kruglyak, et al., “Faster multipoint linkage analysis using Fourier transforms” J. Comput. Biol. 5, (1998) pp. 1-7.
Kruglyak, et al., “Linkage thresholds for two-stage genome scans” Am. J. Hum. Genet. 62, (1998) pp. 994-997.
Kruglyak, et al., “Parametric and Nonparametric Linkage Analysis: A Unified Multipoint Approach” Am. J. Hum. Genet. 58, (1996) pp. 1347-1363.
Kruglyak, et al., “Rapid Multipoint Linkage Analysis of Recessive Traits in Nuclear Families, Including Homozygosity Mapping” Am. J. Hum. Genet. 56 (1995) pp. 519-527.
Kruglyak, L., “The use of a genetic map of biallelic markers in linkage studies” Nat. Genet. 17, (1997) pp. 21-24.
Kumar, et al., “Recurrent 16p11.2 microdeletions in autism” Human Molecular Genetics, 2008, vol. 17, No. 4, pp. 628-638.
Laberge, et al., “Population history and its impact on medical genetics in Quebec” Clin. Genet. 68 (2005) pp. 287-301.
Lafrate, et al., “Detection of large-scale variation in the human genome,” Nature Genetics, vol. 36, No. 9, Sep. 2004, pp. 949-951.
Lander, et al., “Construction of multilocus genetic linkage maps in humans” Genetics, vol. 84, Apr. 1987, pp. 2363-2367.
Lander, et al., “Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results” Nat. Genet. 11, (1995) pp. 241-247.
Lander, et al., “Homozygosity mapping: a way to map human recessive traits with the DNA of inbred children” Science 236, (1987) pp. 1567-1570.
Lange, et al., “Extensions to pedigree analysis I. Likelihood calculations for simple and complex pedigrees” Hum. Hered. 25 (1975) pp. 95-105.
Lavenier, Dominique, and J-L. Pacherie. “Parallel processing for scanning genomic data-bases.” Advances in Parallel Computing, vol. 12, North-Holland, 1998, pp. 81-88.
Leclair, et al., “Enhanced kinship analysis and STR-based DNA typing for human identification in mass fatality incidents: The Swissair Flight 111 disaster” Journal of Forensic Sciences, 49(5) (2004) pp. 939-953.
Leibon, et al., “A simple computational method for the identification of disease-associated loci in complex, incomplete pedigrees” arXiv:0710:5625vl [q-bio.GN] Oct. 30, 2007, pp. 1-20.
Leibon, et al.,“A SNP Streak Model for the Identification of Genetic Regions Identical-by-descent” Statistical Applications in Genetics and Molecular Biology, vol. No. 1, Article 16 (2008) pp. 1-17.
Leutenegger, et al., “Estimation of the Inbreeding Coefficient through Use of Genomic Data,” Am. J. Hum. Genet. 73, Jul. 29, 2003, pp. 516-523.
Leutenegger, et al., “Using genomic inbreeding coefficient estimates for homozygosity mapping of rare recessive traits: Application to Taybi-Linder syndrome” Am. J. Hum. Genet., vol. 79, Jul. 2006, pp. 62-66.
Li, et al., “Fast and accurate long-read alignment with Burrows-Wheeler transform” Bioinformatics vol. 26, No. 5, 2010, pp. 589-595.
Li, et al., “Joint modeling of linkage and association: identifying SNPs responsible for a linkage signal” Am. J. Hum. Genet. 76 (2005) pp. 934-949.
Li, et al., “Mapping short DNA sequencing reads and calling variants using mapping quality scores,” Genome Research, Aug. 19, 2008, pp. 1851-1858. <doi: 10.1101/gr.078212.108>.
Li, et al., “Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data” Genetics 165, (2003) pp. 2213-2233.
Li, et al., “Similarity of DNA fingerprints due to chance and relatedness” Hum. Hered. 43, 1993 pp. 45-52.
Li, et al., “The sequence alignment/map format and SAMtools” Bioinformatics vol. 25, No. 16 (2009) pp. 2078-2079.
Lien, et al. “Evidence for heterogeneity in recombination in the human pseudoautosomal region: High resolution analysis by sperm typing and radiation-hybrid mapping” Am. J. Hum. Genet. 66, 2000, pp. 557-566.
Lin, et al. “Haplotype inference in random population samples” Am. J. Hum. Genet. 71, 2002, pp. 1129-1137.
Liu, et al., “Affected sib-pair test in inbred populations” Ann. Hum. Genet. 68, (2004) pp. 606-619.
Long, et al., “An E-M Algorithm and Testing Strategy for Multiple-Locus Haplotypes” Am. J. Hum. Genet. 56 (1995) pp. 799-810.
Lowe, et al., “Genome-Wide Association Studies in an Isolated Founder Population from the Pacific Island of Kosrae,” PLoS Genet 5(2), 2009, e 1000365, pp. 1-17. <doi:10.1371/joumal.pgen.l000365>.
Lynch, et al., “Analysis of population genetic structure with RAPD markers” Molecular Ecology, 3, (1994) pp. 91-99.
Lynch, et al., “Estimation of pairwise relatedness with molecular markers” Genetics, vol. 152, (1999) pp. 1753-1766.
Lynch, M., “Estimation of relatedness by DNA fingerprinting” Molecular and Biological Evolution, 5, (1988) pp. 584-599.
Ma, et al., “PatternHunter: faster and more sensitive homology search” Bioinformatics, vol. 18, No. 3 (2002) pp. 440-445.
Mao, et al., “A Monte Carlo algorithm for computing the IBD matrices using incomplete marker information” Heredity (2005) 94, pp. 305-315.
Marchini, et al., “A comparison of phasing algorithms for trios and unrelated individuals,” Am. J. Hum. Genet. 78, 2006, pp. 437-450.
Matsuzaki, et al., “Genotyping over 100,000 SNPs on a pair of oligonucleotide arrays” Nat. Methods, vol. 1, No. 2, Nov. 2004, pp. 109-111.
Matsuzaki, et al., “Parallel genotyping of over 10,000 SNPs using a one-primer assay on a high-density oligonucleotide array” Genome Res., vol. 14, No. 3, Mar. 2004, pp. 414-425.
McPeek, et al., “Statistical test for detection of misspecified relationships by use of genome-screen data” Am. J. Hum. Genet. 66, (2000) pp. 1076-1094.
Meuwissen, et al., “Fine mapping of a quantitative trait locus for twinning rate using combined linkage and linkage disequilibrium mapping,” Genetics 161 (2002) pp. 373-379.
Meuwissen, et al., “Multipoint Identity-by-Descent Prediction Using Dense Markers to Map Quantitative Trait Loci and Estimate Effective Population Size,” Genetics 176, Aug. 2007, pp. 2551-2560.
Meuwissen, et al., “Prediction of identity by descent probabilities from marker-haplotypes,” Genetics Selelction Evolution, vol. 33, Nov. 15, 2001, pp. 605-634. <doi: 10.1186/1297-9686-33-6-605>.
Miano, et al., “Pitfalls in homozygosity mapping” Am. J. Hum. Genet. 67, (2000) pp. 1348-1351.
Milligan, B.G., “Maximum-Likelihood Estimation of Relatedness” Genetics 163, (2003) pp. 1153-1167.
Miyazawa et al., “Homozygosity Haplotype Allows a Genomewide Search for the Autosomal Segments Shared among Patients,” Am. J. Hum. Genet. vol. 80, Jun. 2007, pp. 1090-1102.
Morris, et al., “The avuncular index and the incest index” Advances in Forensic Haemogenetics 2, (1988) pp. 607-611.
Morton, N.E., “Sequential test for the detection of linkage” Am. J. Hum. Genet. 7 (1955) pp. 277-318.
Motro, et al., “The affected sib method. I. Statistical features of the affected sib-pair method” Genetics 110, (1985) pp. 525-538.
Nelson, et al., “Genomic mismatch scanning: A new approach to genetic linkage mapping” Nature Genetics, vol. 4, May 1993, pp. 11-18.
Newton, et al., “Inferring the location and effect of tumor suppressor genes by instability-selection modeling of allelic-loss data” Biometrics 56, (2000) pp. 1088-1097.
Newton, et al., “On the statistical analysis of allelic-loss data” Statistics in Medicine 17, (1998) pp. 1425-1445.
Ning et al., “SSAHA: A Fast Search Method for Large DNA Databases,” Genome Research, Oct. 2001, vol. 11, No. 10, pp. 1725-1729.
Nuanmeesri, S., et al., “Genealogical Information Searching System,” Proceedings of the 2008 IEEE ICMIT, pp. 1255-1259.
Nyholt, Dale R., “GENEHUNTER: Your ‘One-Stop Shop’ for Statistical Genetic Analysis?” Hum, Hered. 53 (2002) pp. 2-7.
O'Connell, J.R., “Rapid multipoint linkage analysis via inheritance vectors in the Elston-Stewart algorithm” Hum. Hered. 51, (2001) pp. 226-240.
O'Connell, J.R., “The VITESSE algorithm for rapid exact multilocus linkage analysis via genotype set-recoding and fuzzy inheritance” Nature. Genet. 11, (1995) pp. 402-408.
Oliehoek, et al., “Estimating relatedness between individuals in general populations with a focus on their use in conservation programs” Genetics 173 (2006) pp. 483-496.
Olson, et al., “Relationship estimation by Markov-process models in sib-pair linkage study” Am. J. Hum. Genet. 64, (1999) pp. 1464-1472.
Opposition to Defendants' Motion to Dismiss Plaintiffs Complaint, filed in the United States District Court in and for the Northern District of California LLC on Jul. 13, 2018, Case No. 18-cv-02791-JCS, Re 23andMe, Inc. v. Ancestry.com DNA, LLC, Ancestry.com Operations Inc., and Ancestry.com.
Order Granting In Part and Denying In Part Defendants' Motion to Dismiss, dated Aug. 23, 2018, Case No. 18-cv-02791-JCS, from the United States District Court in and for the Northern District of California LLC, Re 23andMe, Inc. v. Ancestry.com DNA, LLC, Ancestry.com Operations Inc., and Ancestry.com.
Patterson, et al., “Population Structure and Eigenanalysis,” PLoS Genetics, vol. 2, No. 12, e190, Dec. 2006, pp. 2074-2093.
Paynter, et al., “Accuracy of Multiplexed Illumina Platform-Based Single-Nucleotide Polymorphism Genotyping Compared between Genomic and Whole Genome Amplified DNA Collected from Multiple Sources,” Cancer Epidemiol Biomarkers Prev. 15, Dec. 2006, pp. 2533-2536.
Pemberton et al., “Inference of unexpected Genetic relatedness among individuals in HapMap phase III” Am. J. Hum. Genet. 87, (2010) pp. 457-464.
Perry, et al., “The fine-scale and complex architecture of human copy-number variation,” Am. J. Hum. Genet. 82, Mar. 2008, pp. 685-695.
Pinto, et al., “Copy-number variation in control population cohorts,” Human Molecular Genetics, 2007, vol. 16, review issue No. 2, pp. R168-R173. <doi:10.1093/hmg/ddm241>.
Porras-Hurtado, et al., “An overview of STRUCTURE: applications, parameter settings, and supporting software,” Frontiers in Genetics, vol. 4, No. 96, May 29, 2013, pp. 1-13.
Pritchard, et al., “Association Mapping in Structured Populations,” Am. J. Hum. Genet., vol. 67, 2000, pp. 170-181.
Pritchard, et al., “Inference of population structure using multilocus genotype data” Genetics 155, (2000) pp. 945-959.
Purcell, et al., “PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analysis,” The American Journal of Human Genetics, vol. 81, Sep. 2007, pp. 559-575.
Queller, et al., “Estimating relatedness using genetic markers” Evolution, vol. 43, No. 2, (1989) pp. 258-275.
Rabiner, L., “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,” Proceedings of the IEEE, vol. 77, No. 2, Feb. 1989, pp. 257-286.
Rannala, et al., “Detecting immigration by using multilocus genotypes” Proc. Natl. Acad. Sci. USA 94, (1997) pp. 9197-9201.
Rastas, et al., “A hidden Markov technique for haplotype reconstruction” Lect. Notes Comput. Sci. 3692, (2005) pp. 140-151.
Redon, et al., “Global variation in copy number in the human genome,” Nature vol. 444, Nov. 23, 2006, pp. 444-454. <doi:10.1038/nature05329>.
Reid, et al., “Specificity of sibship determination using the ABI identifier multiplex system” J. Forensic Sci. 49, (2004) pp. 1262-1264.
Reply Brief of Appellant 23AndMe, Inc., Case No. 2019-1222, Document: 25, Filed on Apr. 8, 2019, In The US Court of Appeals for Federal Circuit, pp. 1-38.
Riquet, J. et al., “Fine-mapping of quantitative trait loci by identity by descent in outbred populations: application to milk production in dairy cattle,” Proceedings of the National Academy of Sciences, vol. 96, No. 16, Aug. 3, 1999, pp. 9252-9257. <doi: 10.1073/pnas.96.16.9252>.
Risch, et al., “Linkage strategies for genetically complex traits. II. The power of affected relative pairs” Am. J. Hum. Genet. 46 (1990) pp. 229-241.
Risch, N., “Linkage Strategies for Genetically Complex Traits. II. The Power of Affected Relative Pairs” Am. J. Hum. Genet. 46, (1990 a,b) pp. 222-241.
Ritland, et al., “Inferences about quantitative inheritance based on natural population structure in the yellow monkeyflower, Mimulus guttatus” Evolution 50, (1996) pp. 1074-1082.
Ritland, K., “A marker-based method for inferences about quantitative inheritance in natural populations” Evolution 50, (1996b) pp. 1062-1073.
Ritland, K., “Estimators for pairwise relatedness and individual inbreeding coefficients” Genet. Res. 67 (1996a) pp. 175-185.
Ritland, K., “Marker-inferred relatedness as a tool for detecting heritability in nature” Mol. Ecol. 9, (2000) pp. 1195-1204.
Sanda, et al., “Genomic analysis I: inheritance units and genetic selection in the rapid discovery of locus linked DNA makers” Nucleic Acids Research, vol. 14, No. 18 (1986) pp. 7265-7283.
Schaid, et al., “Caution on pedigree haplotype inference with software that assumes linkage equilibrium” Am. J. Hum. Genet. 71, (2002) pp. 992-995.
Scheet, et al., “A Fast and Flexible Statistical Model for Large-Scale Population Genotype Data: Applications to Inferring Missing Genotypes and Haplotypic Phase,” The American Journal of Human Genetics, vol. 78, Apr. 2006, pp. 629-644.
Schork, N.J., “Extended Multipoint Identity-by-Descent Analysis of Human Quantitative Traits: Efficiency, Power, and Modeling Considerations” Am. J. Hum. Genet. 53 (1993) pp. 1306-1319.
Shmulewitz, et al., “Linkage analysis of quantitative traits for obesity, diabetes, hypertension, and dyslipidemia on the island of Kosrae, Federated States of Micronesia,” Proc. Natl. Acad. Sci. vol. 103, No. 10, Mar. 7, 2006, pp. 3502-3509.
Shore, et al., “A recurrent mutation in the BMP type I receptor ACVR1 causes inherited and sporadic fibrodysplasia ossificans progressiva” Nat. Genet. 38 (2006) pp. 525-527.
Siegmund, et al., “Statistical Analysis of Direct Identity-by-descent Mapping,” Annals of Human Genetics (2003) 67,464-470.
Slager, et al., “Evaluation of candidate genes in case-control studies: a statistical method to account for related subjects” Am. J. Hum. Genet. 68, (2001) pp. 1457-1462.
Smouse, et al., “A genetic mixture analysis for use with incomplete source population data” Can J Fisheries Aquatic Sci. 47 (1990) pp. 620-634.
Sobel, et al., “Descent graphs in pedigree analysis: Applications to haplotyping, location scores, and marker-sharing statistics” Am. J. Hum. Genet. 58, (1996) pp. 1323-1337.
Stam, P., “The distribution of the fraction of the genome identical by descent in finite random mating populations” Genet. Res. Camb. 35, (1980) pp. 131-155.
Stephens, et al., “A Comparison of Bayesian Methods for Haplotype Reconstruction from Population Genotype Data,” Am. J. Hum. Genet., vol. 73, 2003, pp. 1162-1169.
Stephens, et al., “A New Statistical Method for Haplotype Reconstruction from Population Data,” Am. J. Hum. Genet., vol. 68, 2001, pp. 978-989.
Stephens, et al., “Accounting for Decay of Linkage Disequilibrium in Haplotype Inference and Missing-Data Imputation,” Am. J. Hum. Genet., vol. 76, 2005, pp. 449-462.
Stone, et al., “DELRIOUS: a computer program designed to analyze molecular marker data and calculate delta and relatedness estimates with confidence” Molecular Ecology Notes, vol. 1, (2001) pp. 209-212.
Tang, et al., “Estimation of individual admixture: Analytical and study design considerations” Genet. Epidem. 28, (2005) pp. 289-301.
Te Meerman, et al., “Genomic Sharing Surrounding Alleles Identical by Descent: Effects of Genetic Drift and Population Growth” Genetic Epidemiology vol. 14 (1997) pp. 1125-1130.
Teo, et al., “Singapore Genome Variation Project: A haplotype map of three Southeast Asian populations” Genome Res. 19, (2009) pp. 2154-2162.
The International HapMap Consortium, “A haplotype map of the human genome” vol. 437, Oct. 27, 2005, pp. 1300-1320, <doi:10.1038/nature04226>.
The International HapMap Consortium, “A haplotype map of the human genome,” Nature, vol. 437, Oct. 27, 2005, pp. 1299-1320, <doi:10.1038/nature04226>.
The International HapMap Consortium, “A second generation human haplotype map of over 3.1 million SNPs,” Nature, vol. 449, Oct. 18, 2007, pp. 851-860. <doi: 10.1038/nature06258>.
Thomas, et al., “Genomic mismatch scanning in pedigrees” IMA Journal of Mathematics Applied in Medicine and Biology, vol. 11, (1994) pp. 1-16.
Thomas, et al., “Multilocus linkage analysis by blocked Gibbs sampling” Statistics and Computing, vol. 10, (2000), pp. 259-269.
Thomas, et al., “Shared genomic segment analysis. Mapping disease predisposition genes in extended pedigrees using SNP genotype assays,” Ann. Hum. Genet. 72, Mar. 2008, pp. 279-287.
Thompson, E.A., “Estimation of relationships from genetic data” In Handbook of Statistics, vol. 8, (1991) pp. 255-269.
Thompson, E.A., “Inference of genealogical structure” Soc. Sci. Inform. 15, (1976) pp. 477-526.
Thompson, E.A., “The estimation of pairwise relationships” Ann. Hum. Genet., Lond. 39, (1975) pp. 173-188.
Thompson, et al., “The IBD process along four chromosomes,” Theor. Popul. Biol. May 73(3) May 2008, pp. 369-373.
Tishkoff, et al., “The Genetic Structure and History of Africans and African Americans,” Science, vol. 324(5930), May 22, 2009, pp. 1035-1044. <doi: 10.1126/science.1172257>.
Todorov, et al., “Probabilities of identity-by-descent patterns in sibships when the parents are not genotyped” Genet. Epidemiol, 14 (1997) pp. 909-913.
Transcript of Proceedings dated Aug. 16, 2018, Case No. 18-cv-02791-JCS, Re Defendant's Motion to Dismiss, heard in the United States District Court in and for the Northern District of California LLC, in the matter of 23andMe, Inc. v. Ancestry.com DNA, LLC, Ancestry.com Operations Inc., and Ancestry.com.
Tu, et al., “The maximum of a function of a Markov chain and application to linkage analysis” Adv. Appl. Probab. 31, (1999) pp. 510-531.
Tzeng, et al., “Determination of sibship by PCR-amplified short tandem repeat analysis in Taiwan” Transfusion 40, (2000) pp. 840-845.
Van De Casteele, et al., “A comparison of microsatellite-based pairwise relatedness estimators” Molecular Ecology 10, (2001) pp. 1539-1549.
Wang, et al., “An estimator of pairwise relatedness using molecular markers” Genetics, vol. 160 (2002) pp. 1203-1215.
Wang, et al., “An integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data,” Genome Res. 17, 2007, pp. 665-1674.
Weir, et al., “A maximum-likelihood method for the estimation of pairwise relatedness in structured populations” Genetics 176, (2007) pp. 421-440.
Weir, et al., “Allelic association patterns for a dense SNP map” Genetic Epidemiology 24, (2004) pp. 442-450.
Weir, et al., “Behavior of pairs of loci in finite monoecious populations” Theor. Popul. Biol. 6 (1974) pp. 323-354.
Weir, et al., “Estimating F-statistics” Annual Review of Genetics, 36, (2002) pp. 721-750.
Weir, et al., “Genetic relatedness analysis: modem data and new challenges” Nature Genetics 7, (2006) pp. 771-780.
Weir, et al., “Group inbreeding with two linked loci” Genetics 63 (1969) pp. 711-742.
Weir, et al., “Measures of human population structure show heterogeneity among genomic regions” Genome Res. 15 (2005) pp. 1468-1476, [PubMed: 16251456].
Weiss, et al., “Association between microdeletion and microduplication at 16p11.2 and autism” New England Journal of Medicine, vol. 358, No. 7, Feb. 14, 2008, pp. 667-675.
Whittemore, et al., “A Class of Tests for Linkage Using Affected Pedigree Members” Biometrics 50, (1994) pp. 118-127.
Wijsman, et al., “Multipoint linkage analysis with many multiallelic or dense diallelic markers: Markov chain-Monte Carlo provides practical approaches for genome scans on general pedigrees” Am. J. Hum. Genet. 79, (2006) pp. 846-858.
Wright, S. “Systems of Mating. I. The Biometric Relations Between Parent and Offspring,” Genetics, 6:111.
Yu, et al., “A unified mixed-model method for association mapping accounting for multiple levels of relatedness” Nature Genet. 38, (2006) pp. 203-208.
Zhang, et al., “A comparison of several methods for haplotype frequency estimation and haplotype reconstruction for tightly linked markers from general pedigrees” Genet. Epidemiol. 30 (2006) pp. 423-437.
Zhao et al., “On Relationship Inference Using Gamete Identity by Descent Data”, Journal of Computational Biology, vol. 8, No. 2, Nov. 2, 2001, pp. 191-200.
U.S. Appl. No. 12/774,546, filed May 5, 2010, Macpherson et al.
U.S. Appl. No. 17/301,129, filed Mar. 25, 2021, Hon et al.
Office Action dated Jan. 3, 2014 in U.S. Appl. No. 12/774,546.
Final Office Action dated Jan. 8, 2015 in U.S. Appl. No. 12/774,546.
Office Action dated Aug. 12, 2015 in U.S. Appl. No. 12/774,546.
Final Office Action dated Feb. 2, 2016 in U.S. Appl. No. 12/774,546.
Office Action dated Feb. 1, 2017 in U.S. Appl. No. 12/774,546.
Office Action dated Jun. 8, 2021 in U.S. Appl. No. 17/301,129.
Abe, H. et al., “Implementing an Integrated Time-Series Data Mining Environment Based on Temporal Pattern Extraction Methods: A Case Study of an Interferon Therapy Risk Mining for Chronic Hepatitis”, New Frontiers in Artificial Intelligence, Lecture Notes in Computer Science, vol. 4012, Jun. 2005, pp. 425-435.
Anonymous , “Frequency” (Web Definition ) , Feb. 24, 2011, Wikipedia, Harvard School of Public Health I Harvard Center for Cancer Prevention , “Your Disease Risk” website for calculating disease risk , 34 exemplary pages submitted including heart disease risk estimation and listings of risk factors , last accessed via the world wide web on Apr. 30, 2007, at the URL address:http://www.yourdiseaserisk.harvard.edu/english/index.htm).
Batzoglou, S. et al., “Human And Mouse Gene Structure: Comparative Analysis and Application to Exon Prediction”, Genome Research, Cold Spring Harbor Laboratory Press, vol. 10, No. 7, Jul. 2000, pp. 952-958.
Carson, R.C. et al. , Abnormal Psychology and Modern Life , 8th edition, Scott Foresman and Company, Glenview , IL , 1988, pp. 56-57.
Cespivova, H., “Roles of Medical Ontology in Association Mining CRISP-DM Cycle”, Proceedings of the ECML/PKDD04 Workshop on Knowledge Discovery and Ontologies, PISA, 2004, pp. 12.
Cooper, D.N. et al., “The Mutational Spectrum Of Single Base-Pair Substitutions Causing Human Genetic Disease: Patterns And Predictions”, Human Genetics, vol. 85, No. 1, Jun. 1990, pp. 55-74.
Das, S., “Filters, Wrappers And a Boosting-Based Hybrid For Feature Selection”, In Proceedings Of The Eighteenth International Conference On Machine Learning, Jun. 28, 2001, pp. 74-81.
Duan, K.B. et al., “Multiple SVM-RFE For Gene Selection In Cancer Classification With Expression Data”, IEEE Transactions On Nanobioscience, vol. 4, No. 3, Aug. 29, 2005, pp. 228-234.
EP Office action dated Oct. 27, 2021, in EP Application No. EP17172048.5.
Hitsch, G.J. et al., “What Makes You Click ?—Mate Preference and Matching Outcomes in Online Dating”, MIT Sloan Research, Apr. 2006, pp. 603-606.
Klein, T.E. et al., “Integrating Genotype And Phenotype Information: An Overview Of The PharmGKB Project”, The Pharmacogenetics Journal, vol. 1, No. 3, 2001, pp. 167-170.
Mani, S. et al., “Causal Discovery From Medical Textual Data”, 2000, pp. 542-546.
Miyamoto, K. et al., “Diagnostic and Therapeutic Applications of Epigenetics”, Japanese Journal of Clinical Oncology, Keigakul Publishing Company, vol. 35, No. 6, Jun. 1, 2005, pp. 293-301.
Nadkarni, P.M. et al., “Data Extraction And Ad Hoc Query Of An Entity-Attribute-Value Database”, Journal of the American Medical Informatics Association, vol. 5, No. 6, Nov.-Dec. 1998, pp. 511-527.
Nielsen, O.T. et al., “Molecular Characterization Of Soft Tissue Tumours: A Gene Expression Study”, The Lancet, vol. 359, Apr. 13, 2002, pp. 1301-1307.
Office Action Advisory Action dated Sep. 10, 2021 in U.S. Appl. No. 17/077,930.
Peedicayil, J., “Epigenetic Therapy—a New Development in Pharmacology”, Indian Journal of Medical Research, vol. 123, No. 1, Jan. 2006, pp. 17-24.
Prather, J.C. et al., “Medical Data Mining: Knowledge Discovery In A Clinical Data Warehouse”, Proceedings Of The AMIA Annual Fall Symposium, 1997, pp. 101-105.
Roddick, J.F. et al., “Exploratory Medical Knowledge Discovery: Experiences And Issues”, vol. 5, No. 1, Jul. 1, 2003, ACM, pp. 94.99.
Smith., “SNPs and Human Disease”, Nature, vol. 435, Jun. 2005, pp. 993.
Stadler, E.L, et al., “Personality Traits and Platelet Monoamine Oxidase Activity in a Swedish Male Criminal Population”, Neuropsychobiology, vol. 46, No. 4, 2002, pp. 202-208.
U.S. Non-Final Office Action dated Nov. 16, 2021, in U.S. Appl. No. 17/077,930.
Vrbsky, S.V. et al., Approximate—a Query Processor That Produces Monotonically Improving Approximate Answers, IEEE Transactions on Knowledge and Data Engineering, vol. 5, No. 6, Dec. 1993, pp. 1056-1068.
Wagner, S.F.,“ Introduction To Statistics, HarperCollins Publishers”, 1992, pp. 23-30.
Related Publications (1)
Number Date Country
20210313013 A1 Oct 2021 US
Provisional Applications (1)
Number Date Country
61204195 Dec 2008 US
Continuations (5)
Number Date Country
Parent 17073110 Oct 2020 US
Child 17351052 US
Parent 16129645 Sep 2018 US
Child 17073110 US
Parent 15264493 Sep 2016 US
Child 16129645 US
Parent 13871744 Apr 2013 US
Child 15264493 US
Parent 12644791 Dec 2009 US
Child 13871744 US