Bayesian mixture models for the incorporation of prior knowledge to inform genetic association studies.

ABSTRACT: In the last decade, numerous genome-wide linkage and association studies of complex diseases have been completed. The critical question remains of how to best use this potentially valuable information to improve study design and statistical analysis in current and future genetic association studies. With genetic effect size for complex diseases being relatively small, the use of all available information is essential to untangle the genetic architecture of complex diseases. One promising approach to incorporating prior knowledge from linkage scans, or other information, is to up- or down-weight P-values resulting from genetic association study in either a frequentist or Bayesian manner. As an alternative to these methods, we propose a fully Bayesian mixture model to incorporate previous kn

SUBMITTER: Fridley BL 

PROVIDER: S-EPMC2910528 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature