bioRxiv preprint doi: https://doi.org/10.1101/529297; this version posted January 23, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Leveraging tissue specific gene expression regulation to identify genes associated with complex diseases Wei Liu1,#, Mo Li2,#, Wenfeng Zhang2, Geyu Zhou1, Xing Wu4, Jiawei Wang1, Hongyu Zhao1,2,3* 1 Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA 06510 2 Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA 06510 3 Department of Genetics, Yale School of Medicine, New Haven, CT, USA 06510 4 Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT, USA 06510 # These authors contributed equally to this work * To Whom correspondence should be addressed: Dr. Hongyu Zhao Department of Biostatistics Yale School of Public Health 60 College Street, New Haven, CT, 06520, USA
[email protected] Key words: GWAS; gene expression imputation; Alzheimer’s disease; gene-level association test 1 bioRxiv preprint doi: https://doi.org/10.1101/529297; this version posted January 23, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Abstract To increase statistical power to identify genes associated with complex traits, a number of methods like PrediXcan and FUSION have been developed using gene expression as a mediating trait linking genetic variations and diseases.