Abstract: Assembling a cohort includes: receiving genetic characteristic information pertaining to a desired genetic characteristic; using the genetic characteristic information to search a data storage comprising information of previously genotyped individuals to derive a candidate group having the desired genetic characteristic; and assembling the cohort based at least in part on the candidate group.

Abstract: Inferring a characteristic of an individual is disclosed. An indication that a first user and a second user have at least one shared chromosomal segment is received. Information about the second user is obtained. A characteristic of the first user is inferred based at least in part on the information about the second user.

Abstract: Inferring a characteristic of an individual is disclosed. An indication that a first user and a second user have at least one shared chromosomal segment is received. Information about the second user is obtained. A characteristic of the first user is inferred based at least in part on the information about the second user.

Abstract: The disclosed embodiments concern methods, apparatus, systems, and computer program products for storing and retrieving genetic data for individuals. In some implementations, a storage format is provided that allows genetic data to be defined by metadata for reproduce-ability.

Abstract: Determining a genetic sequence for a particular site on an individual's genome is disclosed, including: receiving a measurement associated with a particular sequence for the particular site on the individual's genome, receiving contextual information associated with a context of the individual within a larger collection of genetic information, and using the measurement associated with the particular sequence and the contextual information to compute an improved determination of the genetic sequence at the particular site on the individual's genome.

Abstract: A method, software, database and system for determining an optimal treatment for an illness in an individual and for determining the impact (e.g., side effects and intended benefits) of the treatment in the individual are presented in which an attribute profile of the individual containing genetic and non-genetic attributes is compared against a database containing combinations genetic and non-genetic attributes that are statistically associated with successful treatment of the illness in other individuals.

Abstract: Gamete donor selection includes receiving a specification including a phenotype of interest, receiving a genotype of a recipient and a plurality of genotypes of a respective plurality of donors, determining statistical information pertaining to the phenotype of interest based at least in part on different pairings of the genotype of the recipient and a genotype of a donor in the plurality of donors, and identifying a preferred donor among the plurality of donors, based at least in part on the statistical information determined.

Abstract: A method, software, database and system for attribute partner identification and social network based attribute analysis are presented in which attribute profiles associated with individuals can be compared and potential partners identified. Connections can be formed within social networks based on analysis of genetic and non-genetic data. Degrees of attribute separation (genetic and non-genetic) can be utilized to analyze relationships and to identify individuals who might benefit from being connected.

Abstract: A method, software, database and system for attribute partner identification and social network based attribute analysis are presented in which attribute profiles associated with individuals can be compared and potential partners identified. Connections can be formed within social networks based on analysis of genetic and non-genetic data. Degrees of attribute separation (genetic and non-genetic) can be utilized to analyze relationships and to identify individuals who might benefit from being connected.

Abstract: A method, software, database and system for attribute partner identification and social network based attribute analysis are presented in which attribute profiles associated with individuals can be compared and potential partners identified. Connections can be formed within social networks based on analysis of genetic and non-genetic data. Degrees of attribute separation (genetic and non-genetic) can be utilized to analyze relationships and to identify individuals who might benefit from being connected.

Abstract: Determining relative relationship includes receiving recombinable deoxyribonucleic acid (DNA) information of a first user and recombinable DNA information of a second user, determining, based at least in part on the recombinable DNA information of the first user and recombinable DNA information of the second user, a predicted degree of relationship between the first user and the second user, and in the event that the expected degree of relationship between the first user and the second user at least meets the threshold, notifying at least the first user about a relative relationship with the second user.

Abstract: Summarizing an aggregate contribution to a characteristic for an individual is disclosed, including determining the characteristic to be evaluated, identifying one or more markers associated with the characteristic, retrieving the measurement of the individual for each of the one or more markers from a database of individuals' markers, retrieving a statistical factor that measures the association between the marker associated with the characteristic and the characteristic for each of the one or more markers from the marker database, and determining the aggregate contribution based at least in part on the retrieved statistical factors.

Abstract: A technique of using collaborative family medical history (CFMH) to estimate disease risk includes establishing CFMH information of a user and a plurality of relatives of the user, the CFMH information including genetic information of at least some of the family members, genetic information of the user, or both. It further includes analyzing the CFMH information, including the genetic information. It further includes determining a potential risk condition of the user and a potential risk condition of at least a family member based on the CFMH information. It also includes outputting the potential risk condition of the user and the potential risk condition of the at least one family member.

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.

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.

Abstract: Summarizing an aggregate contribution to a phenotypic characteristic for an individual includes: receiving information pertaining to the phenotypic characteristic of an individual; identifying, using one or more computer processors, a set of one or more markers associated with the phenotypic characteristic; obtaining a set of one or more marker measurements of the individual that corresponds to the set of one or more markers; obtaining a set of one or more statistical factors that measure associations between the set of one or more markers and the phenotypic characteristic; determining an aggregate contribution to the phenotypic characteristic of the individual based at least in part on the retrieved set of one or more statistical factors; and outputting a display characteristic to be displayed that is associated with the aggregate contribution to the phenotypic characteristic for the individual.

Abstract: Databases and data processing systems for use with a network-based personal genetics services platform may include member information pertaining to a plurality of members of the network-based personal genetics services platform. The member information may include genetic information, family history information, environmental information, and phenotype information of the plurality of members. A data processing system may determine, based at least in part on the member information, a model for predicting a phenotype from genetic information, family history information, and environmental information, wherein determining the model includes training the model using the member information pertaining to a set of the plurality of members.

Abstract: Databases and data processing systems for use with a network-based personal genetics services platform may include member information pertaining to a plurality of members of the network-based personal genetics services platform. The member information may include genetic information, family history information, environmental information, and phenotype information of the plurality of members. A data processing system may determine, based at least in part on the member information, a model for predicting a phenotype from genetic information, family history information, and environmental information, wherein determining the model includes training the model using the member information pertaining to a set of the plurality of members.