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Rare‐variant association tests in longitudinal studies, with an application to the Multi‐Ethnic Study of Atherosclerosis (MESA)
Author(s) -
He Zihuai,
Lee Seunggeun,
Zhang Min,
Smith Jennifer A.,
Guo Xiuqing,
Palmas Walter,
Kardia Sharon L.R.,
IonitaLaza Iuliana,
Mukherjee Bhramar
Publication year - 2017
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.22081
Subject(s) - genetic association , trait , heritability , sample size determination , type i and type ii errors , exome , inference , statistical power , covariate , missing heritability problem , exome sequencing , association test , genetics , genome wide association study , biology , statistics , genetic variants , gene , computer science , mutation , genotype , mathematics , single nucleotide polymorphism , programming language , artificial intelligence
Over the past few years, an increasing number of studies have identified rare variants that contribute to trait heritability. Due to the extreme rarity of some individual variants, gene‐based association tests have been proposed to aggregate the genetic variants within a gene, pathway, or specific genomic region as opposed to a one‐at‐a‐time single variant analysis. In addition, in longitudinal studies, statistical power to detect disease susceptibility rare variants can be improved through jointly testing repeatedly measured outcomes, which better describes the temporal development of the trait of interest. However, usual sandwich/model‐based inference for sequencing studies with longitudinal outcomes and rare variants can produce deflated/inflated type I error rate without further corrections. In this paper, we develop a group of tests for rare‐variant association based on outcomes with repeated measures. We propose new perturbation methods such that the type I error rate of the new tests is not only robust to misspecification of within‐subject correlation, but also significantly improved for variants with extreme rarity in a study with small or moderate sample size. Through extensive simulation studies, we illustrate that substantially higher power can be achieved by utilizing longitudinal outcomes and our proposed finite sample adjustment. We illustrate our methods using data from the Multi‐Ethnic Study of Atherosclerosis for exploring association of repeated measures of blood pressure with rare and common variants based on exome sequencing data on 6,361 individuals.