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Most phasing and imputation algorithms build a model from the entire dataset, then thread each sequence in turn against it to provide a new phasing based effectively on a series of matches.
We apply the Bayesian network based model in a new phasing algorithm, called systematic long-range phasing (SLRP), that can capitalize on the close genetic relationships in isolated founder populations, and show with simulated and real genome-wide genotype data that SLRP substantially reduces the rate of phasing errors compared to previous phasing algorithms.
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