Topic:

A Unified Framework for Modeling Multiple Correlated Traits in Genome-wide Association Studies

Abstract:

Most common diseases are complex genetic traits, with multiple genetic and environmental components contributing to the disease susceptibility. Genome-wide Association Studies (GWASs) offer a powerful approach to identify the genetic variants (single nucleotide polymorphisms or SNPs) that modulate the susceptibility to these complex diseases. GWASs often collect data on multiple correlated disease-related traits. These traits may share a common set of SNPs influencing them and a joint analysis of these traits may improve the power to detect these SNPs and may provide a better understanding of the underlying disease mechanism. Multivariate analysis of variance (MANOVA) can perform such an association analysis at a GWAS level. In this talk, I will discuss the behavior of MANOVA, both theoretically and using simulations under different genetic association models, and derive the conditions where MANOVA loses power to detect association of a single SNP with multiple traits. Based on these findings, we propose a unified score-based association test (USAT) that can outperform MANOVA in such situations. We will illustrate our novel test through simulation experiments as well as real data analysis and establish the advantages of the test to detect association at a genome-wide level. Our proposed test reports an approximate asymptotic p-value for association and is computationally very efficient to implement at a GWAS level. We have compared through extensive simulation the performance of USAT, MANOVA and other existing approaches and also demonstrated the advantage of using the USAT approach to detect genetic association of multivariate phenotypes to study diabetes-related traits in ARIC cohort.