Genome-wide Association Analysis for Multiple Continuous Secondary Phenotypes

Abstract

There is increasing interest in the joint analysis of multiple phenotypes in genome-wide association studies (GWASs), especially for the analysis of multiple secondary phenotypes in case-control studies and in detecting pleiotropic effects. Multiple phenotypes often measure the same underlying trait. By taking advantage of similarity across phenotypes, one could potentially gain statistical power in association analysis. Because continuous phenotypes are likely to be measured on different scales, we propose a scaled marginal model for testing and estimating the common effect of single-nucleotide polymorphism (SNP) on multiple secondary phenotypes in case-control studies. This approach improves power in comparison to individual phenotype analysis and traditional multivariate analysis when phenotypes are positively correlated and measure an underlying trait in the same direction (after transformation) by borrowing strength across outcomes with a one degree of freedom (1-DF) test and jointly estimating outcome-specific scales along with the SNP and covariate effects. To account for case-control ascertainment bias for the analysis of multiple secondary phenotypes, we propose weighted estimating equations for fitting scaled marginal models. This weighted estimating equation approach is robust to departures from normality of continuous multiple phenotypes and the misspecification of within-individual correlation among multiple phenotypes. Statistical power improves when the within-individual correlation is correctly specified. We perform simulation studies to show the proposed 1-DF common effect test outperforms several alternative methods. We apply the proposed method to investigate SNP associations with smoking behavior measured with multiple secondary smoking phenotypes in a lung cancer case-control GWAS and identify several SNPs of biological interest.

Identifying Genetic Variants for Addiction via Propensity Score Adjusted Generalized Kendall’s Tau

“…Although this phenomenon has been revealed before, to the best of our knowledge this is the first time to formalize it in the areas of U-statistics and genetic association tests. Moreover, a recently proposed multiple-trait association test called “Scaled Multiple-phenotype Association Test” (SMAT) (Schifano et al, 2013) was brought to our attention by a referee. It is noteworthy that SMAT can only handle continuous phenotypes while our proposed test can take any hybrid of dichotomous, ordinal and quantitative traits.…”

Statistical Methods for Association Tests of Multiple Continuous Traits in Genome‐Wide Association Studies

“…He et al (2013) modeled the marginal distributions of multivariate traits with generalized linear models, and empirically accounted for the dependence via the GEE sandwich variance. A closely related and similar approach is the GEE based scaled marginal association test of Schifano et al (2013), which also works for multiple secondary continuous traits analyses via inverse probability weighting. Dimension reduction methods have also been proposed to linearly combine the multi-traits into a summary score, which is then subject to the traditional likelihood based association testing methods.…”

Integrating Multiple Correlated Phenotypes for Genetic Association Analysis by Maximizing Heritability

“…It is easy to see that using all the MaxH PCs in a multivariate analysis is essentially equivalent to the multivariate analysis using the original traits because both of the PC approaches are full rank linear transformations of the Y (assuming V g and V p are both of full rank), and a multivariate analysis is invariant to linear transformations. However multivariate regression is usually computationally intensive and the power gain compared to other approaches depends upon unknown effects and assumptions (Korte et al, 2012; Schifano et al, 2013). In fact in a simulation study of Suo et al (2013), multivariate analysis of analysis of variance (MANOVA) performs the worst compared to PCA and single phenotype approach.…”

“…A standard approach to analyze multiple phenotypes is to consider each phenotype separately, but many suggestions have been made for combining the phenotypes in some way with the goal of increasing power, or elucidating disease mechanisms. A multivariate regression strategy is straightforward, but computationally intensive and the power of the approach compared to other approaches depends upon unknown effects (Korte et al, 2012; Schifano et al, 2013). Other strategies use linear combinations of the phenotypes for analysis.…”