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Pleiotropy Analysis of Quantitative Traits at Gene Level by Multivariate Functional Linear Models
Author(s) -
Wang Yifan,
Liu Aiyi,
Mills James L.,
Boehnke Michael,
Wilson Alexander F.,
BaileyWilson Joan E.,
Xiong Momiao,
Wu Colin O.,
Fan Ruzong
Publication year - 2015
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.21895
Subject(s) - univariate , pleiotropy , multivariate statistics , type i and type ii errors , covariate , statistics , quantitative trait locus , linear model , biology , trait , mathematics , multiple comparisons problem , quantitative genetics , multivariate analysis , genetic model , likelihood ratio test , sample size determination , genetics , genetic variation , phenotype , computer science , gene , programming language
In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F ‐distribution tests based on Pillai–Bartlett trace, Hotelling–Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F ‐distribution tests 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 false positive rates and power performance of the proposed models and tests. We show that the approximate F ‐distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F ‐distribution tests provide much more significant results than those of F ‐tests of univariate analysis and SKAT‐O for the three biochemical traits. The approximate F ‐distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT‐O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT‐O in the univariate case.