Testing a Large Number of Composite Null Hypotheses Using Conditionally Symmetric Multidimensional Gaussian Mixtures in Genome-Wide Studies.

ABSTRACT: Causal mediation, pleiotropy, and replication analyses are three highly popular genetic study designs. Although these analyses address different scientific questions, the underlying statistical inference problems all involve large-scale testing of composite null hypotheses. The goal is to determine whether all null hypotheses - as opposed to at least one - in a set of individual tests should simultaneously be rejected. Recently, various methods have been proposed for each of these situations, including an appealing two-group empirical Bayes approach that calculates local false discovery rates (lfdr). However, lfdr estimation is difficult due to the need for multivariate density estimation. Furthermore, the multiple testing rules for the empirical Bayes lfdr approach can disagree with conve

SUBMITTER: Sun R 

PROVIDER: S-EPMC12176327 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature