Premium
Sequence Kernel Association Test of Multiple Continuous Phenotypes
Author(s) -
Wu Baolin,
Pankow James S.
Publication year - 2016
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.21945
Subject(s) - association test , genetic association , trait , biology , computational biology , kernel (algebra) , genome wide association study , atherosclerosis risk in communities , multiple comparisons problem , genetics , gene , computer science , genotype , statistics , single nucleotide polymorphism , mathematics , diabetes mellitus , programming language , endocrinology , combinatorics
Genetic studies often collect multiple correlated traits, which could be analyzed jointly to increase power by aggregating multiple weak effects and provide additional insights into the etiology of complex human diseases. Existing methods for multiple trait association tests have primarily focused on common variants. There is a surprising dearth of published methods for testing the association of rare variants with multiple correlated traits. In this paper, we extend the commonly used sequence kernel association test (SKAT) for single‐trait analysis to test for the joint association of rare variant sets with multiple traits. We investigate the performance of the proposed method through extensive simulation studies. We further illustrate its usefulness with application to the analysis of diabetes‐related traits in the Atherosclerosis Risk in Communities (ARIC) Study. We identified an exome‐wide significant rare variant set in the gene YAP1 worthy of further investigations.