Discovering population structure from patterns of identity-by-descent

Abstract

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.

1 . A computer-implemented method comprising:
 receiving DNA datasets of a plurality of individuals;   generating, based on the DNA datasets, a plurality of genetic clusters using a clustering algorithm based on shared DNA segments among the plurality of individuals;   identifying, for at least a particular genetic cluster, one or more reference panels based at least partially on birth locations of the one or more reference panels;   receiving a DNA dataset of a target individual;   determining that the target individual belongs to the particular genetic cluster, said determination comprising comparing the DNA dataset of the target individual to at least one of the reference panels; and   providing an annotation of the genetic cluster for presentation to the target individual, the annotation comprises one or more birth locations associated with the genetic cluster and one or more surnames associated with the genetic cluster.   
     
     
         2 . The computer-implemented method of  claim 1 , wherein the annotation further comprises family history data from one or more pedigrees associated with members of the particular genetic cluster. 
     
     
         3 . The computer-implemented method of  claim 1 , wherein the annotation further comprises ethnicity and geographic origins of one or more members of the particular genetic cluster. 
     
     
         4 . The computer-implemented method of  claim 1 , wherein determining that the target individual belongs to the particular genetic cluster is a binary determination of either the target individual belongs or does not belong to the particular genetic cluster. 
     
     
         5 . The computer-implemented method of  claim 1 , wherein the genetic clusters are represented by a plurality of nodes in a network, each node representing an individual of the plurality of individuals, and wherein two or more nodes are connected with edges, each edge connecting a first node and a second node and having a weight that is determined based on the shared DNA segments between two individuals represented by the first and the second nodes. 
     
     
         6 . The computer-implemented method of  claim 1 , wherein the clustering algorithm is a modularity algorithm. 
     
     
         7 . The computer-implemented method of  claim 1 , wherein the shared DNA segments are measured by shared identity-by-descent (IBD) segments between two individuals. 
     
     
         8 . The computer-implemented method of  claim 1 , wherein the one or more reference panels are individuals who each has more than a threshold number of ancestors that lived in the birth location corresponding to the reference panel. 
     
     
         9 . The computer-implemented method of  claim 1 , wherein determining that the target individual belongs to the particular genetic cluster comprises training a prediction model specific to the genetic cluster, wherein the prediction model is trained using the one or more reference panels. 
     
     
         10 . A system comprising:
 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 comprising:
 receiving DNA datasets of a plurality of individuals; 
 generating, based on the DNA datasets, a plurality of genetic clusters using a clustering algorithm based on shared DNA segments among the plurality of individuals; 
 identifying, for at least a particular genetic cluster, one or more reference panels based at least partially on birth locations of the one or more reference panels; 
 receiving a DNA dataset of a target individual; 
 determining that the target individual belongs to the particular genetic cluster, said determination comprising comparing the DNA dataset of the target individual to at least one of the reference panels; and 
 providing an annotation of the genetic cluster for presentation to the target individual, the annotation comprises one or more birth locations associated with the genetic cluster and one or more surnames associated with the genetic cluster. 
   
     
     
         11 . The system of  claim 10 , wherein the annotation further comprises family history data from one or more pedigrees associated with members of the particular genetic cluster. 
     
     
         12 . The system of  claim 10 , wherein the annotation further comprises ethnicity and geographic origins of one or more members of the particular genetic cluster. 
     
     
         13 . The system of  claim 10 , wherein determining that the target individual belongs to the particular genetic cluster is a binary determination of either the target individual belongs or does not belong to the particular genetic cluster. 
     
     
         14 . The system of  claim 10 , wherein the genetic clusters are represented by a plurality of nodes in a network, each node representing an individual of the plurality of individuals, and wherein two or more nodes are connected with edges, each edge connecting a first node and a second node and having a weight that is determined based on the shared DNA segments between two individuals represented by the first and the second nodes. 
     
     
         15 . The system of  claim 10 , wherein the clustering algorithm is a modularity algorithm. 
     
     
         16 . The system of  claim 10 , wherein the shared DNA segments are measured by shared identity-by-descent (IBD) segments between two individuals. 
     
     
         17 . The system of  claim 10 , wherein the one or more reference panels are individuals who each has more than a threshold number of ancestors that lived in the birth location corresponding to the reference panel. 
     
     
         18 . The system of  claim 10 , wherein determining that the target individual belongs to the particular genetic cluster comprises training a prediction model specific to the genetic cluster, wherein the prediction model is trained using the one or more reference panels. 
     
     
         19 . A non-transitory computer readable medium configured to store instructions, wherein the instructions, when executed by one or more processors, cause the one or more processors to perform steps comprising:
 receiving DNA datasets of a plurality of individuals;   generating, based on the DNA datasets, a plurality of genetic clusters using a clustering algorithm based on shared DNA segments among the plurality of individuals;   identifying, for at least a particular genetic cluster, one or more reference panels based at least partially on birth locations of the one or more reference panels;   receiving a DNA dataset of a target individual;   determining that the target individual belongs to the particular genetic cluster, said determination comprising comparing the DNA dataset of the target individual to at least one of the reference panels; and   providing an annotation of the genetic cluster for presentation to the target individual, the annotation comprises one or more birth locations associated with the genetic cluster and one or more surnames associated with the genetic cluster.   
     
     
         20 . The non-transitory computer readable medium of  claim 19 , wherein the one or more reference panels are individuals who each has more than a threshold number of ancestors that lived in the birth location corresponding to the reference panel.