Journal article
A Mixed-Effects Model for Powerful Association Tests in Integrative Functional Genomics
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 Show all
The American Journal of Human Genetics | CELL PRESS | Published : 2018
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 ri..
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Grants
Awarded by NIH
Awarded by U.S. Department of Health and Human Services
Awarded by National Cancer Institute
Funding Acknowledgements
This work is supported by NIH grants R01 CA189532, R01 CA195789, and P01 CA53996 (to L.H.). GECCO is supported by National Cancer Institute, National Institutes of Health, and U.S. Department of Health and Human Services (U01 CA164930; U01 CA137088; R01 CA059045). CCFR was supported by National Cancer Institute (UM1 CA167551). Funding for the studies in the GECCO and CCFR is listed in the Supplemental Data.