Estimation of non-null SNP effect size distributions enables the detection of enriched genes underlying complex traits.

ABSTRACT: Traditional univariate genome-wide association studies generate false positives and negatives due to difficulties distinguishing associated variants from variants with spurious nonzero effects that do not directly influence the trait. Recent efforts have been directed at identifying genes or signaling pathways enriched for mutations in quantitative traits or case-control studies, but these can be computationally costly and hampered by strict model assumptions. Here, we present gene-ε, a new approach for identifying statistical associations between sets of variants and quantitative traits. Our key insight is that enrichment studies on the gene-level are improved when we reformulate the genome-wide SNP-level null hypothesis to identify spurious small-to-intermediate SNP effects and classify

SUBMITTER: Cheng W 

PROVIDER: S-EPMC7316356 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature