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Pathway‐Based Association Study of Multiple Candidate Genes and Multiple Traits Using Structural Equation Models
Author(s) -
Romdhani Hela,
Hwang Heungsun,
Paradis Gilles,
RoyGag MarieHelene,
Labbe Aurelie
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.21872
Subject(s) - structural equation modeling , genetic association , univariate , candidate gene , association test , biology , genetics , gene , association (psychology) , genome wide association study , phenotype , genetic model , quantitative trait locus , computational biology , genotype , statistics , multivariate statistics , psychology , mathematics , single nucleotide polymorphism , psychotherapist
ABSTRACT There is increasing interest in the joint analysis of multiple genetic variants from multiple genes and multiple correlated quantitative traits in association studies. The classical approach involves testing univariate associations between genotypes and phenotypes and correcting for multiple testing that results in loss of power to detect associations. In this paper, we propose modeling complex relationships between genetic variants in candidate genes and measured correlated traits using structural equation models (SEM), taking advantage of prior knowledge on clinical and genetic pathways. We adopt generalized structured component analysis (GSCA) as an approach to SEM and develop a single association test between multiple genetic variants in a gene and a set of correlated traits, taking into account all available data from other genes and other traits. The performance of this test is investigated by simulations. We apply the proposed method to the Quebec Child and Adolescent Health and Social Survey (1999) data to investigate genetic associations with cardiovascular disease‐related traits.