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JEM: A joint test to estimate the effect of multiple genetic variants on DNA methylation.

Authors
  • Sarnowski, Chloé1
  • Huan, Tianxiao2, 3
  • Jain, Deepti4
  • Liu, Chunyu1, 2, 3
  • Yao, Chen2, 3
  • Joehanes, Roby2, 3, 5
  • Levy, Daniel2, 3
  • Dupuis, Josée1
  • 1 Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA.
  • 2 Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, USA.
  • 3 The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA.
  • 4 Department of Biostatistics, University of Washington, Seattle, Washington, USA.
  • 5 Hebrew SeniorLife, Harvard Medical School, Boston, Massachusetts, USA.
Type
Published Article
Journal
Genetic Epidemiology
Publisher
Wiley (John Wiley & Sons)
Publication Date
Apr 01, 2021
Volume
45
Issue
3
Pages
280–292
Identifiers
DOI: 10.1002/gepi.22369
PMID: 33038041
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Multiple methods have been proposed to aggregate genetic variants in a gene or a region and jointly test their association with a trait of interest. However, these joint tests do not provide estimates of the individual effect of each variant. Moreover, few methods have evaluated the joint association of multiple variants with DNA methylation. We propose a method based on linear mixed models to estimate the joint and individual effect of multiple genetic variants on DNA methylation leveraging genomic annotations. Our approach is flexible, can incorporate covariates and annotation features, and takes into account relatedness and linkage disequilibrium (LD). Our method had correct Type-I error and overall high power for different simulated scenarios where we varied the number and specificity of functional annotations, number of causal and total genetic variants, frequency of genetic variants, LD, and genetic variant effect. Our method outperformed the family Sequence Kernel Association Test and had more stable estimations of effects than a classical single-variant linear mixed-effect model. Applied genome-wide to the Framingham Heart Study data, our method identified 921 DNA methylation sites influenced by at least one rare or low-frequency genetic variant located within 50 kilobases (kb) of the DNA methylation site. © 2020 Wiley Periodicals LLC.

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