Abstract

BackgroundKinship inference is the task of identifying genealogically related individuals. Kinship information is important for determining mating structures, notably in endangered populations. Although many solutions exist for reconstructing full sibling relationships, few exist for half-siblings.ResultsWe consider the problem of determining whether a proposed half-sibling population reconstruction is valid under Mendelian inheritance assumptions. We show that this problem is NP-complete and provide a 0/1 integer program that identifies the minimum number of individuals that must be removed from a population in order for the reconstruction to become valid. We also present SibJoin, a heuristic-based clustering approach based on Mendelian genetics, which is strikingly fast. The software is available at http://github.com/ddexter/SibJoin.git+.ConclusionsOur SibJoin algorithm is reasonably accurate and thousands of times faster than existing algorithms. The heuristic is used to infer a half-sibling structure for a population which was, until recently, too large to evaluate.

Highlights

  • Kinship inference is the task of identifying genealogically related individuals

  • The experimental results from real populations are contrasted with the the half-sibling minimum set cover (HS-MSC) and COLONY half-sibling approaches

  • For half-siblings, we have proved that even determining if such a structure obeys Mendelian laws is NP-complete, which is surprising since the same determination in monogomous populations is trivial

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Summary

Results

We consider the problem of determining whether a proposed half-sibling population reconstruction is valid under Mendelian inheritance assumptions. We show that this problem is NP-complete and provide a 0/1 integer program that identifies the minimum number of individuals that must be removed from a population in order for the reconstruction to become valid. We present SibJoin, a heuristic-based clustering approach based on Mendelian genetics, which is strikingly fast.

Conclusions
Background
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