Copyright © 2006 The American Society of Human Genetics. All rights reserved.
The American Journal of Human Genetics, Volume 79, Issue 2, 313-322, 1 August 2006
doi:10.1086/506276
Article
A Coalescence-Guided Hierarchical Bayesian Method for Haplotype Inference
Yu Zhanga, Tianhua Niub, c and Jun S. Liua, 
, 
a From the Department of Statistics, Harvard University, Cambridge, MA
b Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston
c Program in Molecular and Genetic Epidemiology, Department of Epidemiology, Harvard School of Public Health, Boston
Address for correspondence and reprints: Dr. Jun Liu, Department of Statistics, Harvard University, Science Center 7th Floor, 1 Oxford Street, Cambridge, MA 02138Abstract
Haplotype inference from phase-ambiguous multilocus genotype data is an important task for both disease-gene mapping and studies of human evolution. We report a novel haplotype-inference method based on a coalescence-guided hierarchical Bayes model. In this model, a hierarchical structure is imposed on the prior haplotype frequency distributions to capture the similarities among modern-day haplotypes attributable to their common ancestry. As a consequence, the model both allows distinct haplotypes to have different a priori probabilities according to the inferred hierarchical ancestral structure and results in a proper joint posterior distribution for all the parameters of interest. A Markov chain–Monte Carlo scheme is designed to draw from this posterior distribution. By using coalescence-based simulation and empirically generated data sets (Whitehead Institute's inflammatory bowel disease data sets and HapMap data sets), we demonstrate the merits of the new method in comparison with HAPLOTYPER and PHASE, with or without the presence of recombination hotspots and missing genotypes.
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