Methods for Analyzing Multivariate Phenotypes in Genetic Association Studies

Abstract

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Multivariate phenotypes are frequently encountered in genetic association studies. The purpose of analyzing multivariate phenotypes usually includes discovery of novel genetic variants of pleiotropy effects, that is, affecting multiple phenotypes, and the ultimate goal of uncovering the underlying genetic mechanism. In recent years, there have been new method development and application of existing statistical methods to such phenotypes. In this paper, we provide a review of the available methods for analyzing association between a single marker and a multivariate phenotype consisting of the same type of components (e.g., all continuous or all categorical) or different types of components (e.g., some are continuous and others are categorical). We also reviewed causal inference methods designed to test whether the detected association with the multivariate phenotype is truly pleiotropy or the genetic marker exerts its effects on some phenotypes through affecting the others.

Power Comparisons of Methods for Joint Association Analysis of Multiple Phenotypes

“…In the presence of missing data in the outcomes, dropping individuals with missing data in the analyses may result in power loss [12] . Imputation can be used to impute missing genotype data or phenotype data.…”

“…Existing methods include principal components analysis, where for the first component the coefficients maximize the variance of the multivariate phenotypes [12] , and PCH, where coefficients maximize the total heritability of the phenotypes [21] . Recently, Klei et al [8] have developed a PCH, in which the sample is randomly split in a training set used to construct the optimal linear combination of phenotypes from a heritability point of view and a test set used for association testing between genotype and the optimal linear combination of phenotypes.…”

“…These multivariate analyses of phenotypes offer several advantages over analyzing each phenotype separately [4,[10][11][12][13][14] . First, joint analyses of correlated phenotypes can exploit the correlation among phenotypes [12] . Second, most multivariate procedures can perform a single test for association using a set of phenotypes, which reduces the number of performed tests and alleviates the multiple testing burden compared to analyzing all phenotypes separately [8,15] .…”

“…These methods can be divided into 3 groups: combining test statistics from univariate analysis, variable reduction methods, and regression models [12] . The first group includes the O'Brien method (OB) [6] , cross-validation method (CV) [1] , and Trait-based Association Test that uses Extended Simes procedure (TATES) [7] .…”

Unraveling Genetic Origin of Aging-Related Traits: Evolving Concepts

“…This is a particularly important issue to consider in newly emerging methods of multivariate genotype-phenotype analyses. 99,100 Abundance of antagonism in gene actions on aging-related traits is supported by evidence of antagonistic relationships at the phenotypic level (e.g., see refs. 93, 101-104).…”