Ancestry.com DNA, LLC

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.

A system includes a computing device having one or more processors and memory configured to store instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to perform steps including receiving a first named entity dataset associated with a first named entity, receiving a second named entity dataset associated with a second named entity who is a potential named entity linked to the first named entity, identifying a plurality of data matches, each data match being a named entity whose data segments match the first named entity dataset, the second named entity dataset, or both, extracting features from the plurality of data matches, and inputting the extracted features into a machine learning model to determine a data-origin link between the first named entity and the second named entity.

Disclosed is a configuration for determining a genotyping label composition of a target individual using direct acyclic paths. The configuration includes receiving a phased genotype of the target individual, including a first haplotype and a second haplotype. The configuration initiates a full-ethnicity hidden Markov model (HMM) including nodes with a set of ethnicity labels. The first haplotype is input to determine a first subset of ethnicity labels that match the first haplotype. The second haplotype is input to determine a second subset of ethnicity labels that match the second haplotype. The first and second subsets of ethnicity labels are combined to create a candidate subset of ethnicity labels for the target individual. The configuration initiates a simplified HMM with nodes from the candidate subset of ethnicity labels. The phased genotype of the target individual is input to the simplified HMM to determine genotyping label composition of the target individual.

A computing device may receive a target data instance. The computing device may identify a plurality of matched segments that match to the target data instance for at least a threshold length. The computing device may define, based on overlapping of the matched segments, the target data instance as a plurality of data string ranges, wherein each divided data string is matched to a set of overlapping matched segments. The computing device may apply an iterative clustering algorithm to group the plurality of data string ranges based on values of a similarity metric among data string ranges that are assigned to a given group. The computing device may attribute a first set of data string ranges that are assigned to a first group to a first inheritance.

The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating a matched-data-link tree defining relationships among individuals according to genetic data. For example, the disclosed systems can determine matches of matches using a novel matching database structure. The disclosed systems encode integers based on data matches of a data identifier and populate a match database with the integers. Correlating data match integers for a data identifier with data match integers for data matches, the disclosed systems generate matches of matches. The disclosed systems can generate relationship clusters from matches of matches. For example, the disclosed systems compare sets of data matches to discover groups of data matches with stronger data-match levels.

The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating a matched-data-link tree defining relationships among individuals according to genetic data. For example, the disclosed systems can determine matches of matches using a novel matching database structure. The disclosed systems encode integers based on data matches of a data identifier and populate a match database with the integers. Correlating data match integers for a data identifier with data match integers for data matches, the disclosed systems generate matches of matches. The disclosed systems can generate relationship clusters from matches of matches. For example, the disclosed systems compare sets of data matches to discover groups of data matches with stronger data-match levels. The disclosed systems can generate and populate a data-link tree from a relationship cluster. In addition, the disclosed systems can merge one or more data-link trees into a single data-link tree.

A computing device may receive a target data instance. The computing device may identify a plurality of matched segments that match to the target data instance for at least a threshold length. The computing device may define, based on overlapping of the matched segments, the target data instance as a plurality of data string ranges, wherein each divided data string is matched to a set of overlapping matched segments. The computing device may apply an iterative clustering algorithm to group the plurality of data string ranges based on values of a similarity metric among data string ranges that are assigned to a given group. The computing device may attribute a first set of data string ranges that are assigned to a first group to a first inheritance.

Disclosed is a method for determining inheritance labels of users based on inheritance datasets of the users. The method includes generating a plurality of reference panels for a plurality of data-inheritance origins, each reference panel corresponding to a data-inheritance origin and comprising reference-panel datasets representative of the data-inheritance origin. The method constructs a plurality of simulated data trees that are built using the reference-panel datasets that are selected from the plurality of reference panels. The method generates a plurality of simulated inheritance datasets representing a plurality of simulated named entities, each representing a descendant named entity in one of the simulated data trees. The method trains a machine learning model to determine inheritance labels of an inheritance dataset.

A computing device may receive an inheritance dataset of a target domestic companion animal that belongs to a first owner, the first owner being a user of an online system. The computing device accesses inheritance datasets of reference panel animals. The reference panel animals are organized into breeds. The computing device compares the inheritance dataset of the target domestic companion animal to the inheritance datasets of the reference panel animals to identify breeds of the target domestic companion animal. The computing device identifies a plurality of matched domestic companion animals in the breeds of the target domestic companion animal. The computing device filters the matched domestic companion animals based on geographical proximity. The computing device causes to display a filtered matched domestic companion animal to the first owner of the target domestic companion animal to indicate potential social matches for the target domestic companion animal.

A computing server may generate a catalog of overrepresented data strings from a database that stores a plurality of data instances. An overrepresented data string is a data string that matches to a number of data instances and the number exceeds a number threshold. The computing server may receive a target data instance that is to be compared to a related data instance. The computing server may determine one or more matched data strings that match between the target data instance and the related data instance. The computing server may compare the matched data strings to the catalog to exclude a subset of matched data strings that are matched to the overrepresented data strings. The computing server may determine a total length of the matched data strings excluding the subset of matched data strings that are matched to the overrepresented data strings.