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PedMine—a simulated annealing algorithm to identify maximally unrelated individuals in population isolates
Author(s) -
Julie A. Douglas,
Conner I. Sandefur
Publication year - 2008
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btn087
Subject(s) - pedigree chart , simulated annealing , population , computer science , genetic algorithm , set (abstract data type) , algorithm , machine learning , genetics , demography , biology , programming language , sociology , gene
In family-based genetic studies, it is often useful to identify a subset of unrelated individuals. When such studies are conducted in population isolates, however, most if not all individuals are often detectably related to each other. To identify a set of maximally unrelated (or equivalently, minimally related) individuals, we have implemented simulated annealing, a general-purpose algorithm for solving difficult combinatorial optimization problems. We illustrate our method on data from a genetic study in the Old Order Amish of Lancaster County, Pennsylvania, a population isolate derived from a modest number of founders. Given one or more pedigrees, our program automatically and rapidly extracts a fixed number of maximally unrelated individuals.

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