Sequence Kernel Association Test of Multiple Continuous Phenotypes.

Genetic Epidemiology
Baolin Wu, James S Pankow

Abstract

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.

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Citations

Mar 31, 2018·Genetic Epidemiology·Pratyaydipta RudraDebashis Ghosh
Nov 3, 2017·Open Biology·Sophie Hackinger, Eleftheria Zeggini
Oct 5, 2019·European Journal of Human Genetics : EJHG·Debashree Ray, Nilanjan Chatterjee
Nov 17, 2019·Genetic Epidemiology·Stefan KonigorskiTobias Pischon
Jan 10, 2018·Genetic Epidemiology·Arnab MaityJung-Ying Tzeng
Oct 10, 2018·Genetic Epidemiology·Diptavo DuttaSeunggeun Lee
Aug 30, 2017·Genetic Epidemiology·Sneha JadhavQing Lu
Aug 23, 2019·Genetic Epidemiology·Diptavo DuttaSeunggeun Lee
Jan 4, 2019·Genetic Epidemiology·Nicholas B LarsonDaniel J Schaid
Jun 7, 2020·Nature Communications·Lan LuoZheng-Zheng Tang

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