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Am J Hum Genet. 2018 May 3;102(5):904-919. doi: 10.1016/j.ajhg.2018.03.019.

A Mixed-Effects Model for Powerful Association Tests in Integrative Functional Genomics.

Author information

1
Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA. Electronic address: ysu@fredhutch.org.
2
Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
3
Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
4
Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA.
5
Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA.
6
Service de Génétique Médicale Centre Hospitalier Universitaire (CHU) Nantes, Nantes 44093, France.
7
Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany.
8
Division of Research, Kaiser Permanente Medical Care Program of Northern California, Oakland, CA 94612, USA.
9
Public Health Sciences Division, University of Virginia, Charlottesville, VA 22908, USA.
10
Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg 69009, Germany.
11
Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
12
Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
13
Division of Gastroenterology, Massachusetts General Hospital, Boston, MA 02114, USA.
14
Department of Surgery, Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada.
15
Melborne School of Population Health, The University of Melborne, Carlton, VIC 3010, Australia.
16
Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.
17
Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI 96813, USA.
18
Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Department of Epidemiology, University of Washington School of Public Health, Seattle, WA 98109, USA.
19
Genome Sciences, University of Washington, Seattle, WA 98195, USA.
20
Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA.
21
Department of Internal Medicine, University of Utah Health Sciences Center, Salt Lake City, UT 84132, USA.
22
Division of Hematology, Faculty of Medicine, The University of Ottawa, Ottawa, ON K1Y 4E9, USA.
23
Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Department of Biostatistics, University of Washington, Seattle, WA 98195, USA. Electronic address: lih@fredhutch.org.

Abstract

Genome-wide association studies (GWASs) have successfully identified thousands of genetic variants for many complex diseases; however, these variants explain only a small fraction of the heritability. Recently, genetic association studies that leverage external transcriptome data have received much attention and shown promise for discovering novel variants. One such approach, PrediXcan, is to use predicted gene expression through genetic regulation. However, there are limitations in this approach. The predicted gene expression may be biased, resulting from regularized regression applied to moderately sample-sized reference studies. Further, some variants can individually influence disease risk through alternative functional mechanisms besides expression. Thus, testing only the association of predicted gene expression as proposed in PrediXcan will potentially lose power. To tackle these challenges, we consider a unified mixed effects model that formulates the association of intermediate phenotypes such as imputed gene expression through fixed effects, while allowing residual effects of individual variants to be random. We consider a set-based score testing framework, MiST (mixed effects score test), and propose two data-driven combination approaches to jointly test for the fixed and random effects. We establish the asymptotic distributions, which enable rapid calculation of p values for genome-wide analyses, and provide p values for fixed and random effects separately to enhance interpretability over GWASs. Extensive simulations demonstrate that our approaches are more powerful than existing ones. We apply our approach to a large-scale GWAS of colorectal cancer and identify two genes, POU5F1B and ATF1, which would have otherwise been missed by PrediXcan, after adjusting for all known loci.

KEYWORDS:

data-adaptive weight; expression quantitative trait locus; functional annotation; genome-wide association study; mixed-effects score test; set-based association; variance component test

PMID:
29727690
PMCID:
PMC5986723
DOI:
10.1016/j.ajhg.2018.03.019
[Indexed for MEDLINE]
Free PMC Article

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