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A Versatile Omnibus Test for Detecting Mean and Variance Heterogeneity
Author(s) -
Cao Ying,
Wei Peng,
Bailey Matthew,
Kauwe John S. K.,
Maxwell Taylor J.
Publication year - 2014
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.21778
Subject(s) - covariate , omnibus test , variance (accounting) , linkage disequilibrium , trait , parametric statistics , statistics , biology , analysis of variance , linkage (software) , statistical hypothesis testing , genetics , computational biology , mathematics , allele , computer science , gene , haplotype , accounting , business , programming language
Recent research has revealed loci that display variance heterogeneity through various means such as biological disruption, linkage disequilibrium (LD), gene‐by‐gene (G × G), or gene‐by‐environment interaction. We propose a versatile likelihood ratio test that allows joint testing for mean and variance heterogeneity ( LRT MV ) or either effect alone ( LRT M or LRT V ) in the presence of covariates. Using extensive simulations for our method and others, we found that all parametric tests were sensitive to nonnormality regardless of any trait transformations. Coupling our test with the parametric bootstrap solves this issue. Using simulations and empirical data from a known mean‐only functional variant, we demonstrate how LD can produce variance‐heterogeneity loci (vQTL) in a predictable fashion based on differential allele frequencies, high D′, and relatively low r 2 values. We propose that a joint test for mean and variance heterogeneity is more powerful than a variance‐only test for detecting vQTL. This takes advantage of loci that also have mean effects without sacrificing much power to detect variance only effects. We discuss using vQTL as an approach to detect G × G interactions and also how vQTL are related to relationship loci, and how both can create prior hypothesis for each other and reveal the relationships between traits and possibly between components of a composite trait.