A coalescence-guided hierarchical Bayesian method for haplotype inference

Yu Zhang, Tianhua Niu, Jun S. Liu

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)313-322
Number of pages10
JournalAmerican Journal of Human Genetics
Issue number2
StatePublished - Aug 2006

All Science Journal Classification (ASJC) codes

  • Genetics
  • Genetics(clinical)


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