Large-scale multiple testing in genome-wide association studies via region-specific hidden Markov models.

ABSTRACT:

Background

Identifying genetic variants associated with complex human diseases is a great challenge in genome-wide association studies (GWAS). Single nucleotide polymorphisms (SNPs) arising from genetic background are often dependent. The existing methods, i.e., local index of significance (LIS) and pooled local index of significance (PLIS), were both proposed for modeling SNP dependence and assumed that the whole chromosome follows a hidden Markov model (HMM). However, the fact that SNP data are often collected from separate heterogeneous regions of a single chromosome encourages different chromosomal regions to follow different HMMs. In this research, we developed a data-driven penalized criterion combined with a dynamic programming algorithm to find change points that divide the

SUBMITTER: Xiao J 

PROVIDER: S-EPMC3850654 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature