Linking individual datasets to a database

1. A computer-implemented method for linking an individual dataset in a database, the computer-implemented method comprising:

receiving a target individual dataset associated with a target individual;

identifying a plurality of candidate individual datasets that are potentially related to the target individual dataset;

identifying a related individual dataset from the plurality of candidate individual datasets, wherein the related individual dataset has data bits that match at least a portion of data bits in the target individual dataset;

identifying a parent node that is a common parent node for both the related individual dataset and the target individual dataset, wherein identifying the parent node comprises determining a confidence score based on a meiosis between the target individual and a related individual represented by the related individual dataset and a generation value between the related individual and the parent node;

retrieving a data tree that the parent node belongs to, the data tree describing inter-relationships among datasets in the data tree;

identifying, based on strings of matched data bits and number of the strings of matched data bits between the target individual dataset and the datasets in the data tree, a position in the data tree to which the target individual dataset is assigned; and

outputting the data tree with the target individual dataset located in the position to the target individual to select an association between the target individual dataset and the database.

2. The method of claim 1 , wherein identifying a parent node further comprises:

identifying a plurality of candidate parent nodes, wherein a candidate parent node represents a candidate common ancestor for both the target individual dataset and one of the candidate individual datasets;

calculating confidence scores for the candidate parent nodes; and

selecting one of the candidate parent nodes as the parent node based on a ranking of the candidate parent nodes by the confidence scores.

3. The method of claim 2 , wherein identifying the parent node further comprises a pruning process, the pruning process comprising:

retrieving the meiosis between the related individual represented by the related individual dataset and the target individual;

determining the generation value between the related individual and one of the candidate parent nodes;

determining a range for the generation value based on the meiosis;

removing the one of the candidate parent nodes as a candidate in response to the generation value out of the range.

4. The method of claim 1 , wherein identifying the position in the data tree to which the target individual dataset is assigned comprises:

for each of one or more candidate positions in the data tree, generating a candidate data tree that includes datasets in the data tree and the target individual dataset at the candidate position.

5. The method of claim 4 , wherein generating the candidate data tree corresponding to each candidate position comprises one or more of the following:

(i) assigning the target individual dataset at an existing node in the data tree as the candidate position, the candidate position replacing the existing node;

(ii) adding a child node that descends from a leaf node in the data tree as the candidate position of the target individual dataset; and

(iii) adding a child node that descends from an inner node in the data tree wherein the child node is in a new branch descending from the inner node, the child node being the candidate position of the target individual dataset.

6. The method of claim 4 , wherein identifying the position in the data tree to which the target individual dataset is assigned further comprises:

calculating a likelihood score for each candidate data tree;

selecting a candidate data tree based on the likelihood score; and

assigning the target individual dataset to a corresponding node associated with the selected candidate data tree.

7. The method of claim 6 , wherein the likelihood score is a composite likelihood calculated based on individual datasets in each candidate data tree, wherein the individual datasets contain DNA information.

8. The method of claim 7 , wherein the composite likelihood for each candidate data tree is determined based on steps comprising:

determining a likelihood for each pairwise individual datasets between the target individual and other individuals in the candidate data tree, the pairwise individual datasets containing DNA information in the candidate data tree, the likelihood calculated based on matched DNA information and positions of the pair of individual datasets in the candidate data tree; and

generating the composite likelihood based on a product of the likelihood of each pair of individual datasets.

9. The method of claim 1 , wherein identifying the position in the data tree to which the target individual dataset is assigned is further based on metadata associated with the datasets.

10. The method of claim 9 , wherein the metadata comprises at least one of the following: sex, age, date of birth or date of death.

11. The method of claim 1 , wherein identifying the position in the data tree to which the target individual dataset is assigned is further based on a relationship between the target individual dataset and the related individual dataset determined based on matched DNA information.

12. The method of claim 1 , wherein:

the data bits contain information associated with DNA;

the strings of matched data bits contain information associated with matched DNA segments; and

the number of the strings contain information associated with number of matched DNA segments.

13. A non-transitory computer readable medium for storing computer code comprising instructions for linking an individual dataset to a database, the instructions, when executed by one or more computer processors, cause the one or more computer processors to perform steps comprising:

receiving a target individual dataset associated with a target individual;

identifying a plurality of candidate individual datasets that are potentially related to the target individual dataset;

identifying a related individual dataset from the plurality of candidate individual datasets, wherein the related individual dataset has data bits that match at least a portion of data bits in the target individual dataset;

identifying a parent node that is a common parent node for both the related individual dataset and the target individual dataset, wherein identifying the parent node comprises determining a confidence score based on a meiosis between the target individual and a related individual represented by the related individual dataset and a generation value between the related individual and the parent node;

retrieving a data tree that the parent node belongs to, the data tree describing inter-relationships among datasets in the data tree;

identifying, based on strings of matched data bits and number of the strings of matched data bits between the target individual dataset and the datasets in the data tree, a position in the data tree to which the target individual dataset is assigned; and

outputting the data tree with the target individual dataset located in the position to the target individual to select an association between the target individual dataset and the database.

14. The non-transitory computer readable medium of claim 13 , wherein identifying a parent node further comprising:

identifying a plurality of candidate parent nodes, wherein a candidate parent node represents a candidate common ancestor for both the target individual dataset and one of the candidate individual datasets;

calculating confidence scores for the candidate parent nodes; and

selecting one of the candidate parent nodes as the parent node based on a ranking of the candidate parent nodes by the confidence scores.

15. The non-transitory computer readable medium of claim 14 , wherein identifying the parent node further comprises a pruning process, the pruning process comprising:

retrieving the meiosis between the related individual represented by the related individual dataset and the target individual;

determining the generation value between the related individual and one of the candidate parent nodes;

determining a range for the generation value based on the meiosis;

removing the one of the candidate parent nodes as a candidate in response to the generation value out of the range.

16. The non-transitory computer readable medium of claim 13 , wherein identifying the candidate nodes further comprising:

for each of one or more candidate positions in the data tree, generating a candidate data tree that includes datasets in the data tree and the target individual dataset at the candidate position, wherein generating the candidate data tree corresponding to each candidate position further comprising one or more of the following:

assigning the target individual dataset at an existing node in the data tree as the candidate position, the candidate position replacing the existing node;

adding a child node that descends from a leaf node in the data tree as the candidate position of the target individual dataset; and

adding a child node that descends from an inner node of the data tree wherein the child node is in a new branch descending from the inner node, the child node being the candidate position of the target individual dataset.

17. The non-transitory computer readable medium of claim 16 further comprising:

calculating a likelihood score for each candidate data tree;

selecting a candidate data tree based on the likelihood score; and

assigning the target individual dataset to a corresponding node associated with selected candidate data tree.

18. The non-transitory computer readable medium of claim 17 wherein the likelihood score for each candidate data tree is a composite likelihood determined based on steps comprising:

determining a likelihood for each pairwise individual datasets between the target individual and other individuals in the candidate data tree, the pairwise individual datasets containing DNA information in the candidate data tree, the likelihood calculated based on matched DNA information and positions of the pair of individual datasets in the candidate data tree; and

generating the composite likelihood based on a product of the likelihood of each pair of individual datasets.

19. A system comprising:

one or more processors;

and memory for storing computer code comprising instructions for linking an individual dataset to a database, the instructions, when executed by one or more computer processors, cause the one or more computer processors to perform steps comprising:

receiving a target individual dataset associated with a target individual;

identifying a plurality of candidate individual datasets that are potentially related to the target individual dataset;

identifying a related individual dataset from the plurality of candidate individual datasets, wherein the related individual dataset has data bits that match at least a portion of data bits in the target individual dataset;

identifying a parent node that is a common parent node for both the related individual dataset and the target individual dataset, wherein identifying the parent node comprises determining a confidence score based on a meiosis between the target individual and a related individual represented by the related individual dataset and a generation value between the related individual and the parent node;

retrieving a data tree that the parent node belongs to, the data tree describing inter-relationships among datasets in the data tree;

identifying, based on strings of matched data bits and number of the strings of matched data bits between the target individual dataset and the datasets in the data tree, a position in the data tree to which the target individual dataset is assigned; and

outputting the data tree with the target individual dataset located in the position to the target individual to select an association between the target individual dataset and the database.