Estimating False Discovery Proportion Under Arbitrary Covariance Dependence.

ABSTRACT: Multiple hypothesis testing is a fundamental problem in high dimensional inference, with wide applications in many scientific fields. In genome-wide association studies, tens of thousands of tests are performed simultaneously to find if any SNPs are associated with some traits and those tests are correlated. When test statistics are correlated, false discovery control becomes very challenging under arbitrary dependence. In the current paper, we propose a novel method based on principal factor approximation, which successfully subtracts the common dependence and weakens significantly the correlation structure, to deal with an arbitrary dependence structure. We derive an approximate expression for false discovery proportion (FDP) in large scale multiple testing when a common threshold is use

SUBMITTER: Fan J 

PROVIDER: S-EPMC3983872 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature