We present a method adapted from graph theory that always identifies the maximum set of unrelated individuals in any dataset, and allows weighting parameters to be utilized in unrelated sample selection. PRIMUS reads in user-generated IBD estimates and outputs the maximum possible set of unrelated individuals, given a specified threshold of relatedness. Additional information for preferential selection of individuals may also be utilized. For example, when there are two equally sized maximum sets of unrelated individuals in a network, PRIMUS can preferentially select the set with more affected individuals. Primus can also preferentially select on quantitative traits such as data missingness of each sample.
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NEW VERSION OF PRIMUS THAT WILL RECONSTRUCT PEDIGREES COMING SOON. (Paper is currently under review).
- Identifies maximum unrelated set of individuals from a genetic dataset
- Groups samples into family networks
- Generates .dot files to visualize family networks
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