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Vol. 11, Issue 10, 1716-1724, October 2001

METHODS
Bayesian Analysis of Haplotypes for Linkage Disequilibrium Mapping

Jun S. Liu,1,6 Chiara Sabatti,2 Jun Teng,3 Bronya J.B. Keats,4 and Neil Risch5

1 Department of Statistics, Harvard University, Cambridge, Massachusetts 02138, USA; 2 Department of Statistics, University of California, Los Angeles, California 90095, USA; 3 JP Morgan, New York, New York 10036, USA; 4 Louisiana State University, Department of Genetics, Health Science Center, New Orleans, Louisiana 70112, USA; 5 Department of Genetics, Stanford University, Stanford, California 94305, USA

Haplotype analysis of disease chromosomes can help identify probable historical recombination events and localize disease mutations. Most available analyses use only marginal and pairwise allele frequency information. We have developed a Bayesian framework that utilizes full haplotype information to overcome various complications such as multiple founders, unphased chromosomes, data contamination, and incomplete marker data. A stochastic model is used to describe the dependence structure among several variables characterizing the observed haplotypes, for example, the ancestral haplotypes and their ages, mutation rate, recombination events, and the location of the disease mutation. An efficient Markov chain Monte Carlo algorithm was developed for computing the estimates of the quantities of interest. The method is shown to perform well in both real data sets (cystic fibrosis data and Friedreich ataxia data) and simulated data sets. The program that implements the proposed method, BLADE, as well as the two real datasets, can be obtained from http://www.fas.harvard.edu/~junliu/TechRept/01folder/diseq_prog.tar.gz.

6 Corresponding author.

11:1716-1724 ©2001 by Cold Spring Harbor Laboratory Press  ISSN 1088-9051/01 $5.00
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