A novel bayesian graphical model for genome-wide multi-SNP association mapping.

ABSTRACT: Most disease association mapping algorithms are based on hypothesis testing procedures that test one variant at a time. Those methods lose power when the disease mutations are jointly tagged by multiple variants, or when gene-gene interaction exist. Nearby variants are also correlated, for which procedures ignoring the dependence between variants will inevitably produce redundant results. With a large number of variants genotyped in current genome-wide disease association studies, simultaneous multivariant association mapping algorithms are strongly desired. We present a novel Bayesian method for automatic detection of multivariant joint association in genome-wide case-control studies. Our method has improved power and specificity over existing tools. We fit a joint probabilistic model to

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC3337957 | biostudies-literature | 2012 Jan

REPOSITORIES: biostudies-literature