A Coalescence-Guided Hierarchical Bayesian Method for Haplotype Inference
نویسندگان
چکیده
منابع مشابه
A coalescence-guided hierarchical Bayesian method for haplotype inference.
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 hapl...
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OBJECTIVE Genetic association studies based on haplotypes are powerful in the discovery and characterization of the genetic basis of complex human diseases. However, statistical methods for detecting haplotype-haplotype and haplotype-environment interactions have not yet been fully developed owing to the difficulties encountered: large numbers of potential haplotypes and unknown haplotype pairs...
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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 ef...
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ژورنال
عنوان ژورنال: The American Journal of Human Genetics
سال: 2006
ISSN: 0002-9297
DOI: 10.1086/506276