Display Settings:

Format

Send to:

Choose Destination
See comment in PubMed Commons below
Am J Hum Genet. 2002 Jan;70(1):157-69. Epub 2001 Nov 26.

Bayesian haplotype inference for multiple linked single-nucleotide polymorphisms.

Author information

  • 1Program for Population Genetics, Harvard School of Public Health, Boston, MA, USA.

Erratum in

  • Am J Hum Genet. 2006 Jan;78(1):174.

Abstract

Haplotypes have gained increasing attention in the mapping of complex-disease genes, because of the abundance of single-nucleotide polymorphisms (SNPs) and the limited power of conventional single-locus analyses. It has been shown that haplotype-inference methods such as Clark's algorithm, the expectation-maximization algorithm, and a coalescence-based iterative-sampling algorithm are fairly effective and economical alternatives to molecular-haplotyping methods. To contend with some weaknesses of the existing algorithms, we propose a new Monte Carlo approach. In particular, we first partition the whole haplotype into smaller segments. Then, we use the Gibbs sampler both to construct the partial haplotypes of each segment and to assemble all the segments together. Our algorithm can accurately and rapidly infer haplotypes for a large number of linked SNPs. By using a wide variety of real and simulated data sets, we demonstrate the advantages of our Bayesian algorithm, and we show that it is robust to the violation of Hardy-Weinberg equilibrium, to the presence of missing data, and to occurrences of recombination hotspots.

Comment in

PMID:
11741196
[PubMed - indexed for MEDLINE]
PMCID:
PMC448439
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for PubMed Central
    Loading ...
    Write to the Help Desk