Journal Article

DKFZ-2018-00586

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png A Mixed-Effects Model for Powerful Association Tests in Integrative Functional Genomics.

Su, Y.-R. ; Di, C. ; Bien, S. ; Huang, L. ; Dong, X. ; Abecasis, G. ; Berndt, S. ; Bezieau, S. ; Brenner, H.DKFZ* ; Caan, B. ; Casey, G. ; Chang-Claude, J.DKFZ* ; Chanock, S. ; Chen, S. ; Connolly, C. ; Curtis, K. ; Figueiredo, J. ; Gala, M. ; Gallinger, S. ; Harrison, T. ; Hoffmeister, M.DKFZ* ; Hopper, J. ; Huyghe, J. R. ; Jenkins, M. ; Joshi, A. ; Le Marchand, L. ; Newcomb, P. ; Nickerson, D. ; Potter, J. ; Schoen, R. ; Slattery, M. ; White, E. ; Zanke, B. ; Peters, U. ; Hsu, L.

2018
Cell Press New York, NY [u.a.]

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Please use a persistent id in citations: doi:10.1016/j.ajhg.2018.03.019

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.

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Contributing Institute(s):

1. Klinische Epidemiologie und Alternsforschung (C070)
2. Präventive Onkologie (G110)
3. Epidemiologie von Krebserkrankungen (C020)

Research Program(s):

1. 313 - Cancer risk factors and prevention (POF3-313) (POF3-313)

Appears in the scientific report 2018

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Database coverage:
; BIOSIS Previews ; Current Contents - Clinical Medicine ; Current Contents - Life Sciences ; Ebsco Academic Search ; IF >= 10 ; JCR ; NCBI Molecular Biology Database ; SCOPUS ; Science Citation Index ; Science Citation Index Expanded ; Thomson Reuters Master Journal List ; Web of Science Core Collection

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