Ancestry.com Dna, LLC

Ross E Curtis

Shiya Song

Brett Frederick Jorgensen

Brian Jerel Kerr

Bryce Damon Ririe

Eunjung Han

Peter Carbonetto

Neal Craig Varner

Kelly McCloy Becker

Michael Joseph Mulligan

Justin Matthew Robert Van Dyke

Michaela Black Bonkemeyer

Kenneth Gregory Chahine

Catherine Ann Ball

Mathew J Barber

Ahna R Girshick

Jingwen Pei

Ariel Hippen Anderson

Andre Everson Kim

Alisa Elnaz Sedghifar

Aaron James Stern

Yong Wang

Milos Pavlovic

Jeffrey Adrion

Natalie Anne Swinford

David A Turissini

Benjamin Wilson

Caitlyn Elizabeth Bruns

Ju Zhang

Disclosed are methods and system for predicting data-source influences on one or more data manifestations of a named entity. The method includes receiving an inheritance dataset of the named entity. The method determines first and second portions of the inheritance dataset of the named entity. The method determines an aggregated data-bit association score for the named entity based on the inheritance dataset at an identified subset of the data-bit regions. The method determines aggregated data-bit association scores associated with the first and second data source based on the first and second portions of the inheritance dataset at the identified subset of the data-bit regions. The method selects one of the first and second data sources as having a measure of influence on a data manifestation of the named entity corresponding to the identified subset of the data-bit regions.

Determination of data-source influence on data manifestations

A user of a genetic database may create and build upon their family tree in the database. For new users, creating a family tree can be difficult and time consuming. Even for users with established family trees, extending their family tree is a challenge requiring extensive research. Disclosed herein are embodiments for assisting users of a genetic database with building their family trees. In some embodiments, a method for assisting with constructing family trees includes receiving a target individual's genetic dataset. The method identifies a plurality of matched individuals who genetically match the target individual. The method identifies potential ancestors who are potential common ancestors between the target individual and one of the matched individuals. The method inputs a set of features related to the target individual to a machine learning model and filters the potential common ancestors to determine a subset of likely common ancestors for the target individual.

Filtering individual datasets in a database

A computing device may receive an inheritance dataset of a target named entity. The device may access a plurality of clusters associated with a region, each cluster comprising inheritance data for a plurality of reference panel named entities. The device may determine that the inheritance dataset of the target named entity has at least a threshold amount of inheritance sequences that are classified to the region. The device may compare, for each cluster, the inheritance dataset of the target named entity to the reference panel named entities in the cluster to identify similarities and shared inheritance segments between the target named entity and the reference panel named entities. The device may determine, for each cluster, a metric based on the inheritance segments shared. The device may assign the target named entity to one or more ethnicities based on the comparison between the metric and the threshold specific to the cluster.

Systems and methods for determining ethnicity subregions

The disclosed system links an individual dataset to a database. The system receives a target individual dataset associated with a target individual and identifies candidate individual datasets that are potentially related to the target individual dataset. The system identifies a related individual dataset that has data bits that match some data bits in the target individual dataset. The system then identifies a parent node that is a common parent node to both the target individual dataset and the related individual dataset. The system retrieves a data tree that the parent node belongs to with the data tree containing information describing inter-relationships among datasets in the data tree. A node in the data tree is identified to assign the target individual dataset based on strings of matched data bits and number of the matched strings between the target individual dataset and the datasets in the data tree.

Linking individual datasets to a database

A user may select one or more potential common ancestors with a DNA match to view the target individual's relationship with them. The process may include identifying, from a first genealogical profile of the target individual. A first individual has a first linkage that connects the target individual towards the selected potential common ancestor. The process may also include identifying, from a second genealogical profile of the DNA match, a second individual who has a second linkage that connects the DNA match towards the selected potential common ancestor. The process may further include connecting the first linkage and the second linkage with the selected potential common ancestor by adding one or more individuals whose profiles are retrieved from other searchable genealogical profiles stored in the online system. With the nodes and connections available, the process may generate a map of visual connections between the target individual and the DNA match.

Graphical user interface displaying relatedness based on shared DNA

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.

Discovering population structure from patterns of identity-by-descent

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

Described embodiments enable identification of family networks using combinations of DNA analysis and genealogical information. Genealogical data is provided by users of a genealogical research service or collected from other sources and used to create family trees for each user. DNA samples are also received from the users. By analyzing the DNA samples, potential genetic relationships can be identified between some users. Once these DNA-suggested relationships have been identified, common ancestors can be sought in the respective trees of the potentially related users. Where these common ancestors exist, an inference is drawn that the DNA-suggested relationship accurately represents a familial overlap between the individuals in question. People descended from the same common ancestor are each members of a family network. Members of a family network not in a user's tree may be identified for the user, enabling the user to discover additional ancestors that might otherwise have remained unknown.

Family networks

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Cedar Hills, UT, United States of America

Ross E Curtis