A mixed-effects model for powerful association tests in integrative functional genomics

• Verfasst von: Su, Yu-Ru [VerfasserIn]
• Verfasst von: Brenner, Hermann [VerfasserIn]
• Verfasst von: Chang-Claude, Jenny [VerfasserIn]
• Verfasst von: Hoffmeister, Michael [VerfasserIn]
• Titel: A mixed-effects model for powerful association tests in integrative functional genomics
• Verf.angabe: Yu-Ru Su, Chongzhi Di, Stephanie Bien, Licai Huang, Xinyuan Dong, Goncalo Abecasis, Sonja Berndt, Stephane Bezieau, Hermann Brenner, Bette Caan, Graham Casey, Jenny Chang-Claude, Stephen Chanock, Sai Chen, Charles Connolly, Keith Curtis, Jane Figueiredo, Manish Gala, Steven Gallinger, Tabitha Harrison, Michael Hoffmeister, John Hopper, Jeroen R. Huyghe, Mark Jenkins, Amit Joshi, Loic Le Marchand, Polly Newcomb, Deborah Nickerson, John Potter, Robert Schoen, Martha Slattery, Emily White, Brent Zanke, Ulrike Peters, Li Hsu
• E-Jahr: 2018
• Jahr: 3 May 2018
• Umfang: 16 S.
• Teil: volume:102
• Teil: year:2018
• Teil: number:5
• Teil: pages:904-919
• Teil: extent:16
• Fussnoten: Gesehen am 22.05.2018
• Titel Quelle: Enthalten in: The American journal of human genetics
• Ort Quelle: New York, NY [u.a.] : Cell Press, 1949
• Jahr Quelle: 2018
• Band/Heft Quelle: 102(2018), 5, Seite 904-919
• ISSN Quelle: 1537-6605
• DOI: doi:10.1016/j.ajhg.2018.03.019
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: data-adaptive weight
• Sach-SW: expression quantitative trait locus
• Sach-SW: functional annotation
• Sach-SW: genome-wide association study
• Sach-SW: mixed-effects score test
• Sach-SW: set-based association
• Sach-SW: variance component test
• K10plus-PPN: 1575401738

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.