A selective inference approach for false discovery rate control using multiomics covariates yields insights into disease risk.

ABSTRACT: To correct for a large number of hypothesis tests, most researchers rely on simple multiple testing corrections. Yet, new methodologies of selective inference could potentially improve power while retaining statistical guarantees, especially those that enable exploration of test statistics using auxiliary information (covariates) to weight hypothesis tests for association. We explore one such method, adaptive P-value thresholding (AdaPT), in the framework of genome-wide association studies (GWAS) and gene expression/coexpression studies, with particular emphasis on schizophrenia (SCZ). Selected SCZ GWAS association P values play the role of the primary data for AdaPT; single-nucleotide polymorphisms (SNPs) are selected because they are gene expression quantitative trait loci

SUBMITTER: Yurko R 

PROVIDER: S-EPMC7334489 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature