Ancestry.com Dna, LLC

Ancestryhealth.com, LLC

Julie M Granka

Keith D Noto

Kenneth Gregory Chahine

Catherine Ann Ball

Mathew J Barber

Shiya Song

Ahna R Girshick

Jake Kelly Byrnes

Natalie Telis

Shannon Hateley

Asher Keith Haug Baltzell

Ladan Doroud

Genevieve Heather Linnea Roberts

Shannon Ries McCurdy

Amir R Kermany

Jialiang Gu

Kristin A Rand

Elena P Sorokin

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.

Ancestral human genomes

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.

Predicting health outcomes

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.

Community assignments in identity by descent networks and genetic variant origination

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.

Estimation of phenotypes using DNA, pedigree, and historical data

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.

Genetic and genealogical analysis for identification of birth location and surname information

Growing community of inventors

San Francisco, CA, United States of America

Julie M Granka