TY  - JOUR
AU  - Su, Yu-Ru
AU  - Di, Chongzhi
AU  - Bien, Stephanie
AU  - Huang, Licai
AU  - Dong, Xinyuan
AU  - Abecasis, Goncalo
AU  - Berndt, Sonja
AU  - Bezieau, Stephane
AU  - Brenner, Hermann
AU  - Caan, Bette
AU  - Casey, Graham
AU  - Chang-Claude, Jenny
AU  - Chanock, Stephen
AU  - Chen, Sai
AU  - Connolly, Charles
AU  - Curtis, Keith
AU  - Figueiredo, Jane
AU  - Gala, Manish
AU  - Gallinger, Steven
AU  - Harrison, Tabitha
AU  - Hoffmeister, Michael
AU  - Hopper, John
AU  - Huyghe, Jeroen R
AU  - Jenkins, Mark
AU  - Joshi, Amit
AU  - Le Marchand, Loic
AU  - Newcomb, Polly
AU  - Nickerson, Deborah
AU  - Potter, John
AU  - Schoen, Robert
AU  - Slattery, Martha
AU  - White, Emily
AU  - Zanke, Brent
AU  - Peters, Ulrike
AU  - Hsu, Li
TI  - A Mixed-Effects Model for Powerful Association Tests in Integrative Functional Genomics.
JO  - The American journal of human genetics
VL  - 102
IS  - 5
SN  - 0002-9297
CY  - New York, NY [u.a.]
PB  - Cell Press
M1  - DKFZ-2018-00586
SP  - 904 - 919
PY  - 2018
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - pmid:29727690
DO  - DOI:10.1016/j.ajhg.2018.03.019
UR  - https://inrepo02.dkfz.de/record/132947
ER  -