Yale University
Department of Statistics
Seminar

Monday, February 3rd, 2003

Steve Qin
Department of Statistics
Harvard University

Bayesian Haplotype Inference for Multiple Linked Single Nucleotide Polymorphisms

Haplotypes have become increasingly popular because of the abundance of
single nucleotide polymorphisms (SNPs) and the limited power of the
single-locus analyses. To contend with some weaknesses of the existing
haplotype inference methods, we propose new algorithms based on the
partition-ligation idea. In particular, we first partition the whole
haplotype into smaller segments. Then, we use either the Gibbs sampler or
the EM algorithm to construct the partial haplotypes of each segment and
to assemble all the segments together. Our algorithm can infer haplotype
frequencies rapidly and accurately for a large number of linked SNPs and
provides a robust estimate of their standard deviations. The algorithms
are robust to the violation of Hardy-Weinberg equilibrium and can handle
missing marker data easily.


Seminar to be held in Room 107, 24 Hillhouse Avenue at 4:15 pm