GRAPHICAL USER INTERFACE DISPLAYING RELATEDNESS BASED ON SHARED DNA
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.
1 . A computer-implemented method, comprising:
retrieving, by an online system, a family tree that includes a target potential relative of a focal individual who have a first genetic dataset, the target potential relative having a second genetic dataset;
determining a genetic relatedness between the target potential relative and the focal individual, wherein determining the genetic relatedness comprises:
determining a length of identity-by-descent (IBD) segments shared between the first genetic dataset of the target potential relative to the second genetic dataset of the focal individual;
determining, based on the family tree, a number of meiosis separations between the target potential relative and the focal individual;
determining a confidence level that the number of meiosis separations matches the genetic relatedness between the first and second genetic datasets; and
predicting a familial relationship between the target potential relative and the focal individual based on the confidence level.
2 . The computer-implemented method of claim 1 , wherein determining the genetic relatedness further comprises:
identifying a plurality of IBD segments shared between the first genetic dataset and the second genetic dataset;
filtering the IBD segments based on a predefined threshold length; and
classifying remaining IBD segments according to genomic location to determine genetic overlap.
3 . The computer-implemented method of claim 1 , wherein determining the genetic relatedness further comprises:
retrieving genetic markers from the first and second genetic datasets stored in a genetic data store;
comparing the retrieved genetic markers to identify regions of shared genetic material; and
generating a similarity score based on a percentage of matching genetic markers.
4 . The computer-implemented method of claim 1 , wherein determining the number of meiosis separations comprises:
retrieving, from a genealogical data store, a plurality of family trees associated with the focal individual and the target potential relative;
identifying, from the plurality of family trees, a most recent common ancestor (MRCA) of the focal individual and the target potential relative; and
counting a number of generational links between the MRCA and each of the focal individual and the target potential relative.
5 . The computer-implemented method of claim 1 , wherein determining the number of meiosis separations further comprises:
identifying inconsistencies between family trees by detecting mismatched ancestor assignments;
resolving inconsistencies using a machine learning model trained to determine whether different genealogical records correspond to the same individual; and
merging family trees by linking identified common individuals across different trees.
6 . The computer-implemented method of claim 1 , wherein determining the confidence level comprises:
retrieving a set of reference data for confirmed familial relationships from a genealogy data store;
comparing the length of shared IBD segments in the first and second genetic datasets against statistical distributions of known relatedness levels; and
assigning a probability score indicating a likelihood that the genetic relatedness corresponds to the number of meiosis separations.
7 . The computer-implemented method of claim 6 , wherein computing the relationship score comprises:
retrieving known pairs of confirmed relatives with established degrees of relatedness;
computing a probability distribution of shared IBD segment lengths for each known degree of relatedness; and
applying Bayesian inference to determine a conditional probability of a given relatedness degree based on observed IBD segment lengths.
8 . The computer-implemented method of claim 1 , further comprising generating a visual representation of the predicted familial relationship between the focal individual and the target potential relative, wherein generating the visual representation comprises:
retrieving genealogical data from a genealogy database to construct a relationship map;
identifying a visual format for displaying the relationship, including a family tree or a network graph; and
rendering the identified visual format within a user interface on a client device.
9 . The computer-implemented method of claim 8 , wherein generating the visual representation further comprises:
constructing a graphical tree structure that includes the focal individual, the target potential relative, and intermediate family members;
highlighting direct and collateral relationships between the focal individual and the target potential relative; and
adjusting the display dynamically based on user interactions, such as selecting alternative ancestral paths.
10 . The computer-implemented method of claim 1 , wherein retrieving the family tree comprises:
identifying multiple family trees in a genealogical database that contain records of the focal individual or the target potential relative;
concatenating the identified family trees into a large-scale network by linking individuals who appear in multiple trees; and
resolving discrepancies in ancestral relationships using conflict resolution algorithms.
11 . The computer-implemented method of claim 10 , wherein concatenating multiple family trees further comprises:
detecting duplicate individuals appearing across different family trees using entity resolution models;
determining whether two records correspond to the same individual based on name similarity, birthdates, and associated relatives; and
merging duplicate records while preserving genealogical integrity.
12 . The computer-implemented method of claim 1 , wherein determining the confidence level further comprises:
identifying at least one surrogate individual related to both the focal individual and the target potential relative;
calculating the surrogate's genetic similarity to the focal individual and the target potential relative; and
incorporating genetic data of the surrogate into the confidence level calculation to enhance accuracy.
13 . The computer-implemented method of claim 12 , wherein incorporating information from surrogate relatives further comprises:
selecting a surrogate based on a genetic similarity threshold indicating a close familial connection;
determining a surrogate's estimated relatedness level by analyzing shared IBD segments; and
using a surrogate's genetic data to refine the number of meiosis separations.
14 . A system, comprising:
one or more processors;
memory configured to store code comprising instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to perform steps comprising:
retrieving, by an online system, a family tree that includes a target potential relative of a focal individual who have a first genetic dataset, the target potential relative having a second genetic dataset;
determining a genetic relatedness between the target potential relative and the focal individual, wherein determining the genetic relatedness comprises:
determining a length of identity-by-descent (IBD) segments shared between the first genetic dataset of the target potential relative to the second genetic dataset of the focal individual;
determining, based on the family tree, a number of meiosis separations between the target potential relative and the focal individual;
determining a confidence level that the number of meiosis separations matches the genetic relatedness between the first and second genetic datasets; and
predicting a familial relationship between the target potential relative and the focal individual based on the confidence level.
15 . The system of claim 14 , wherein determining the genetic relatedness further comprises:
identifying a plurality of IBD segments shared between the first genetic dataset and the second genetic dataset;
filtering the IBD segments based on a predefined threshold length; and
classifying remaining IBD segments according to genomic location to determine genetic overlap.
16 . The system of claim 14 , wherein determining the genetic relatedness further comprises:
retrieving genetic markers from the first and second genetic datasets stored in a genetic data store;
comparing the retrieved genetic markers to identify regions of shared genetic material; and
generating a similarity score based on a percentage of matching genetic markers.
17 . The system of claim 14 , wherein determining the number of meiosis separations comprises:
retrieving, from a genealogical data store, a plurality of family trees associated with the focal individual and the target potential relative;
identifying, from the plurality of family trees, a most recent common ancestor (MRCA) of the focal individual and the target potential relative; and
counting a number of generational links between the MRCA and each of the focal individual and the target potential relative.
18 . The system of claim 14 , wherein determining the number of meiosis separations further comprises:
identifying inconsistencies between family trees by detecting mismatched ancestor assignments;
resolving inconsistencies using a machine learning model trained to determine whether different genealogical records correspond to the same individual; and
merging family trees by linking identified common individuals across different trees.
19 . The system of claim 14 , wherein determining the confidence level comprises:
retrieving a set of reference data for confirmed familial relationships from a genealogy data store;
comparing the length of shared IBD segments in the first and second genetic datasets against statistical distributions of known relatedness levels; and
assigning a probability score indicating a likelihood that the genetic relatedness corresponds to the number of meiosis separations.
20 . A non-transitory computer-readable medium configured to store code comprising instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to perform steps comprising:
retrieving, by an online system, a family tree that includes a target potential relative of a focal individual who have a first genetic dataset, the target potential relative having a second genetic dataset;
determining a genetic relatedness between the target potential relative and the focal individual, wherein determining the genetic relatedness comprises:
determining a length of identity-by-descent (IBD) segments shared between the first genetic dataset of the target potential relative to the second genetic dataset of the focal individual;
determining, based on the family tree, a number of meiosis separations between the target potential relative and the focal individual;
determining a confidence level that the number of meiosis separations matches the genetic relatedness between the first and second genetic datasets; and
predicting a familial relationship between the target potential relative and the focal individual based on the confidence level.