Ancestry.com Dna, LLC

Ahna R Girshick

Ross E Curtis

Shiya Song

Julie M Granka

Natalie Telis

Asher Keith Haug Baltzell

Ariel Hippen Anderson

Jack Reese

Luong Ruiz

Benjamin Wilson

Genevieve Heather Linnea Roberts

David A Turissini

Shannon Ries McCurdy

Jialiang Gu

Systems, methods, and/or computer-program products for generating a photo composite are configured to identify a facial photo, assign a community such as a genetic community to the identified facial photo, select and retrieve a subset of photos from a genetic community, determine a landmark in each photo of the subset of photos, compute and apply a similarity transform to each photo based and its landmarks, update estimates of transformed averaged landmarks, calculate a triangularization of the transformed averaged landmarks, warp images to the average image landmarks, and combine the intensities of the warped images. The communities are identified from genetic information. The facial photos are identified from a network of genealogical trees and/or a collection of historical records.

Photo composites

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

Described are techniques for determining population structure from identity-by-descent (IBD) of individuals. The techniques may be used to predict that an individual belongs to zero, one or more of a number of communities identified within an IBD network. Additional data may be used to annotate the communities with birth location, surname, and ethnicity information. In turn, these data may be used to provide to an individual a prediction of membership to zero, one or more communities, accompanied by a summary of the information annotated to those communities. Ethnicity heterogeneity and age information may be tabulated and provided based on community membership information.

Characterizing heterogeneity with fine-scale population structure

A computing server generates a graph such as an identity-by-descent (IBD) network. The graph includes a plurality of nodes. Each node represents one of the individuals. Two or more nodes are connected through edges. Each edge connecting two nodes and associated with a weight that is derived from affinity between the genetic data of the two individuals represented by the two nodes. The computing system filters the graph based on features that are associated with the edges or the nodes. The filtered graph includes a subset of nodes. The computing system divides the filtered graph into a plurality of clusters to identify genetic communities that may not be discoverable without filtering. The computing server may also perform a multi-path hierarchical community detection process to assign an individual represented by a node to more than one communities.

Filtering genetic networks to discover populations of interest

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Berkeley, CA, United States of America

Ahna R Girshick