Pleiotropy Analysis of Quantitative Traits at Gene Level by Multivariate Functional Linear Models

• Activity Number: 289
• Type: Contributed
• Date/Time: Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
• Sponsor: ENAR
• Author(s): Yifan Wang*+ and Ruzong Fan and James L. Mills and Alexander F. Wilson and Joan E. Bailey-Wilson and Momiao Xiong
• Companies: NICHD and NICHD and NIH/NICHD and NIH/NHGRI and NIH/NHGRI and University of Texas Health Science Center at Houston
• Keywords: functional data analysis ; quantitative trait loci ; association mapping ; pleiotropy analysis ; rare variants ; complex traits

Abstract

In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. One way to analyze the phenotypic traits caused by pleiotropy is to analyze the traits one by one. This approach may lead to low power. In this article, multivariate functional linear models are developed to connect genetic data to quantitative traits adjusting for covariates. Three types of approximate F-distributions based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test association between multiple quantitative traits and multiple genetic variants in one gene region. The proposed methods were applied to analyze three biochemical traits in data from the Trinity Students Study. The approximate F-distributions provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the robustness and power performance of the proposed models and tests. We show that the approximate F-distributions control the type I error very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an ind