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In this report, we compare and contrast three previously published Bayesian methods for inferring haplotypes from genotype data in a population sample. We review the methods, emphasizing the differences between them in terms of both the models ("priors") they use and the computational strategies they employ. We introduce a new algorithm that combines the modeling strategy of one method with the computational strategies of another. In comparisons using real and simulated data, this new algorithm outperforms all three existing methods. The new algorithm is included in the software package PHASE, version 2.0, available online (http://www.stat.washington.edu/stephens/software.html).

Original publication

DOI

10.1086/379378

Type

Journal article

Journal

Am J Hum Genet

Publication Date

11/2003

Volume

73

Pages

1162 - 1169

Keywords

Algorithms, Bayes Theorem, Genetics, Population, Haplotypes, Humans, Internet, Models, Genetic, Research Design, Software