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Rare-variant association tests in longitudinal studies, with an application to the Multi-Ethnic Study of Atherosclerosis (MESA).

Authors
  • He, Zihuai1
  • Lee, Seunggeun2
  • Zhang, Min2
  • Smith, Jennifer A3
  • Guo, Xiuqing4
  • Palmas, Walter5
  • Kardia, Sharon L R3
  • Ionita-Laza, Iuliana1
  • Mukherjee, Bhramar2
  • 1 Department of Biostatistics, Columbia University, New York, New York, United States of America. , (United States)
  • 2 Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America. , (United States)
  • 3 Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America. , (United States)
  • 4 Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, California, United States of America. , (United States)
  • 5 Department of Medicine, Columbia University, New York, New York, United States of America. , (United States)
Type
Published Article
Journal
Genetic Epidemiology
Publisher
Wiley (John Wiley & Sons)
Publication Date
Dec 01, 2017
Volume
41
Issue
8
Pages
801–810
Identifiers
DOI: 10.1002/gepi.22081
PMID: 29076270
Source
Medline
Keywords
License
Unknown

Abstract

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.

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