Abstract: Disclosed are techniques for predicting a trait of an individual and identifying a set of enriched record collections of a genetic community. To predict a trait of an individual, DNA features and non-DNA features of the individual are accessed to generate a feature vector that is inputted into a machine learning model. The machine learning model generates a prediction of the trait. The prediction may be based on an inheritance prediction and/or a community prediction. To identify a set of enriched record collections, individuals belonging to a genetic community are identified and a set of candidate record collections are accessed. A community count and a background count is determined for each candidate record collection. The set of enriched record collections are identified based on a comparison of the community count and the background count. The genetic community may be annotated using the set of enriched record collections.

Abstract: Disclosed are techniques for predicting a trait of an individual and identifying a set of enriched record collections of a genetic community. To predict a trait of an individual, DNA features and non-DNA features of the individual are accessed to generate a feature vector that is inputted into a machine learning model. The machine learning model generates a prediction of the trait. The prediction may be based on an inheritance prediction and/or a community prediction. To identify a set of enriched record collections, individuals belonging to a genetic community are identified and a set of candidate record collections are accessed. A community count and a background count is determined for each candidate record collection. The set of enriched record collections are identified based on a comparison of the community count and the background count. The genetic community may be annotated using the set of enriched record collections.

Abstract: Disclosed are techniques for characterizing variants of interest and predicting assignments of individuals to communities based on obtained genetic information. To characterize a variant, DNA datasets of reference individuals are accessed and used to generate a cluster with additional individuals. Reference individuals carry a variant at a genetic locus and the additional individuals share IBD with reference individuals. Statistics of genealogical data of the cluster are generated. A result summarizing the characterization of the variant is generated based on the statistics. To determine if an individual belongs to a community, a subset of the individual's haplotypes are inputted into a community-specific model. The model is trained using the training samples that each include haplotypes of reference individuals and a label identifying whether the reference individual belongs to the community. Based on the output of the model, it is determined whether the individual is a member of the community.

Abstract: Disclosed are techniques for characterizing variants of interest and predicting assignments of individuals to communities based on obtained genetic information. To characterize a variant, DNA datasets of reference individuals are accessed and used to generate a cluster with additional individuals. Reference individuals carry a variant at a genetic locus and the additional individuals share IBD with reference individuals. Statistics of genealogical data of the cluster are generated. A result summarizing the characterization of the variant is generated based on the statistics. To determine if an individual belongs to a community, a subset of the individual's haplotypes are inputted into a community-specific model. The model is trained using the training samples that each include haplotypes of reference individuals and a label identifying whether the reference individual belongs to the community. Based on the output of the model, it is determined whether the individual is a member of the community.

Abstract: Example embodiments may be related to determining whether a trait may be commonly present among members of a genetic community using metadata about individuals such as data derived from survey responses or from other sources. A method may include receiving multiple sets of survey responses from a plurality of individuals. Each set of survey responses may correspond to one of the individuals. Each set may include survey responses to multiple survey questions. The method may define a plurality of traits. One of the traits may be a composite trait that is defined at least partially by a combination of the survey responses to the multiple survey questions. The method may further include retrieving data of the ethnicity composition of the plurality of individuals and determining a correlation between at least one trait and the ethnicity composition of the plurality of individuals. The determination of correlation may include an enrichment test.

Abstract: A system identifies ancestral birth locations or surnames estimated to be associated with an individual's ancestors using an individual's genetic sample. The system identifies users who are genetic matches to the individual and determines whether and how often a birth location or surname appears in the pedigrees of those users. Birth locations or surnames that appear frequently throughout the pedigrees of genetically matching users may represent birth locations or surnames that are affiliated with the individual's ancestors. The system determines whether the frequency of appearance of a birth location or surname is statistically significant to eliminate biases for certain birth locations or surnames that appear more frequently than others. The birth location or surname may be provided to the individual based on an also-determined enrichment score.

Abstract: Disclosed are techniques for predicting a trait of an individual and identifying a set of enriched record collections of a genetic community. To predict a trait of an individual, DNA features and non-DNA features of the individual are accessed to generate a feature vector that is inputted into a machine learning model. The machine learning model generates a prediction of the trait. The prediction may be based on an inheritance prediction and/or a community prediction. To identify a set of enriched record collections, individuals belonging to a genetic community are identified and a set of candidate record collections are accessed. A community count and a background count is determined for each candidate record collection. The set of enriched record collections are identified based on a comparison of the community count and the background count. The genetic community may be annotated using the set of enriched record collections.

Abstract: A system identifies ancestral birth locations or surnames estimated to be associated with an individual's ancestors using an individual's genetic sample. The system identifies users who are genetic matches to the individual and determines whether and how often a birth location or surname appears in the pedigrees of those users. Birth locations or surnames that appear frequently throughout the pedigrees of genetically matching users may represent birth locations or surnames that are affiliated with the individual's ancestors. The system determines whether the frequency of appearance of a birth location or surname is statistically significant to eliminate biases for certain birth locations or surnames that appear more frequently than others. The birth location or surname may be provided to the individual based on an also-determined enrichment score.

Abstract: Described are methods for identification of likelihood of health outcomes such as the development of a medical condition using health histories from genetically related individuals. Embodiments include: receiving a first set of genetic data associated with the human subject; comparing the first set of genetic data to a plurality of sets of genetic data from a plurality of other individuals; identifying from the comparison a family network comprising individuals genetically related to the human subject as defined by identity by descent; receiving a set of health history data for each individual and each individual in the family network; analyzing the set of health history data to generate a health outcome score for the human subject, the health outcome score being a measure of risk for the human subject to develop a pre-defined health outcome that is associated with the health outcome score; and reporting the health outcome score.

Abstract: Disclosed are techniques for characterizing variants of interest and predicting assignments of individuals to communities based on obtained genetic information. To characterize a variant, DNA datasets of reference individuals are accessed and used to generate a cluster with additional individuals. Reference individuals carry a variant at a genetic locus and the additional individuals share IBD with reference individuals. Statistics of genealogical data of the cluster are generated. A result summarizing the characterization of the variant is generated based on the statistics. To determine if an individual belongs to a community, a subset of the individuals haplotypes are inputted into a community-specific model. The model is trained using the training samples that each include haplotypes of reference individuals and a label identifying whether the reference individual belongs to the community. Based on the output of the model, it is determined whether the individual is a member of the community.

Abstract: Disclosed are techniques for predicting a trait of an individual and identifying a set of enriched record collections of a genetic community. To predict a trait of an individual, DNA features and non-DNA features of the individual are accessed to generate a feature vector that is inputted into a machine learning model. The machine learning model generates a prediction of the trait. The prediction may be based on an inheritance prediction and/or a community prediction. To identify a set of enriched record collections, individuals belonging to a genetic community are identified and a set of candidate record collections are accessed. A community count and a background count is determined for each candidate record collection. The set of enriched record collections are identified based on a comparison of the community count and the background count. The genetic community may be annotated using the set of enriched record collections.

Abstract: Described are methods for identification of likelihood of health outcomes such as the development of a medical condition using health histories from genetically related individuals. Embodiments include: receiving a first set of genetic data associated with the human subject; comparing the first set of genetic data to a plurality of sets of genetic data from a plurality of other individuals; identifying from the comparison a family network comprising individuals genetically related to the human subject as defined by identity by descent; receiving a set of health history data for each individual and each individual in the family network; analyzing the set of health history data to generate a health outcome score for the human subject, the health outcome score being a measure of risk for the human subject to develop a pre-defined health outcome that is associated with the health outcome score; and reporting the health outcome score.

Abstract: Disclosed are techniques for predicting a trait of an individual and identifying a set of enriched record collections of a genetic community. To predict a trait of an individual, DNA features and non-DNA features of the individual are accessed to generate a feature vector that is inputted into a machine learning model. The machine learning model generates a prediction of the trait. The prediction may be based on an inheritance prediction and/or a community prediction. To identify a set of enriched record collections, individuals belonging to a genetic community are identified and a set of candidate record collections are accessed. A community count and a background count is determined for each candidate record collection. The set of enriched record collections are identified based on a comparison of the community count and the background count. The genetic community may be annotated using the set of enriched record collections.

Abstract: A system and method predicts a phenotypic trait for an individual. The system identifies a subset of SNP loci with predictive ability of the phenotypic trait. The system calculates a PRS for the individual based on the individual's genetic dataset at the identified subset of SNP loci. The system compares the PRS to a threshold PRS. The threshold PRS is determined by calculating, for each training individual of a plurality of training individuals including some reported to have and some reported to not have the phenotypic trait, a PRS, sweeping through a domain of PRS while calculating a true positive rate and a false positive rate, and then identifying an optimal threshold PRS as the threshold PRS. The system generates a prediction whether the individual has the phenotypic trait based on the comparison.

Abstract: Described are computational methods to reconstruct the chromosomes (and genomes) of ancestors given genetic data, IBD information, and full or partial pedigree information of some number of their descendants

Abstract: Described are computational methods to reconstruct the chromosomes (and genomes) of ancestors given genetic data, IBD information, and full or partial pedigree information of some number of their descendants.

Abstract: Described are methods for identification of likelihood of health outcomes such as the development of a medical condition using health histories from genetically related individuals. Embodiments include: receiving a first set of genetic data associated with the human subject; comparing the first set of genetic data to a plurality of sets of genetic data from a plurality of other individuals; identifying from the comparison a family network comprising individuals genetically related to the human subject as defined by identity by descent; receiving a set of health history data for each individual and each individual in the family network; analyzing the set of health history data to generate a health outcome score for the human subject, the health outcome score being a measure of risk for the human subject to develop a pre-defined health outcome that is associated with the health outcome score; and reporting the health outcome score.

Abstract: Described are computational methods to reconstruct the chromosomes (and genomes) of ancestors given genetic data, IBD information, and full or partial pedigree information of some number of their descendants.

Abstract: A system identifies ancestral birth locations or surnames estimated to be associated with an individual's ancestors using an individual's genetic sample. The system identifies users who are genetic matches to the individual and determines whether and how often a birth location or surname appears in the pedigrees of those users. Birth locations or surnames that appear frequently throughout the pedigrees of genetically matching users may represent birth locations or surnames that are affiliated with the individual's ancestors. The system determines whether the frequency of appearance of a birth location or surname is statistically significant to eliminate biases for certain birth locations or surnames that appear more frequently than others. The birth location or surname may be provided to the individual based on an also-determined enrichment score.