Testing Procedures for Strict Pleiotropy in Genetic Association Studies

Abstract

Pleiotropy, the phenomenon in which a single genetic variant influences multiple traits, is a fundamental feature of genome biology and an important consideration in statistical genetics. Many statistical methods exist that leverage pleiotropy to increase the power of association tests, but they are often designed to identify SNPs which are associated with at least one of the underlying traits. Only a limited number of methods exist for identification of ``strict pleiotropy'', i.e. those associated with more than one trait, but these methods are either restricted to a small number of traits or become computationally challenging as the number of traits increases. In this paper, we develop two testing procedures to identify SNPs under strict pleiotropy and determine the corresponding minimum number of associated traits. Both methods rely only on z-scores available from genome-wide association studies (GWAS) and are applicable to independent as well as correlated traits. The first procedure is frequentist in nature and is designed to control the family-wise-error rate. The second procedure is based on the local false discovery rate and provides improved power while maintaining FDR control. We conduct extensive simulation studies to compare the performance of the proposed methods with alternative methods for testing strict pleiotropy. Finally, we demonstrate an application of the proposed methods to Alzheimer's disease-related biomarkers for the goal of distinguishing SNPs that may represent biomarker-specific variations from those that are related to disease-relevant biology.