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J. Mol. Cell Biol. 2017, 9(6): 436-452, DOI: https://doi.org/10.1093/jmcb/mjx059
Simultaneous inference of phenotype-associated genes and relevant tissues from GWAS data via Bayesian integration of multiple tissue-specific gene networks
Mengmeng Wu 1,2,3 , Zhixiang Lin 3 , Shining Ma 3 , Ting Chen 1,2 , Rui Jiang 2,4, *, and Wing Hung Wong 3, *
1 Department of Computer Science, Tsinghua University, Beijing 100084, China

2 Ministry of Education Key Laboratory of Bioinformatics and Bioinformatics Division, Tsinghua National Laboratory for Information Science and Technology,
Beijing 100084, China

3 Department of Statistics, Stanford University, CA 94305, USA

4 Department of Automation, Tsinghua University, Beijing 100084, China
GWAS, tissue-specific gene networks, Markov random field
*Correspondence to
Rui Jiang, E-mail: ruijiang@tsinghua.edu.cn; Wing Hung Wong, E-mail: whwong@stanford.edu

Although genome-wide association studies (GWAS) have successfully identified thousands of genomic loci associated with hundreds of complex traits in the past decade, the debate about such problems as missing heritability and weak interpretability has been appealing for effective computational methods to facilitate the advanced analysis of the vast volume of existing and anticipated genetic data. Towards this goal, gene-level integrative GWAS analysis with the assumption that genes associated with a phenotype tend to be enriched in biological gene sets or gene networks has recently attracted much attention, due to such advantages as straightforward interpretation, less multiple testing burdens, and robustness across studies. However, existing methods in this category usually exploit non-tissue-specific gene networks and thus lack the ability to utilize informative tissue-specific characteristics. To overcome this limitation, we proposed a Bayesian approach called SIGNET (Simultaneously Inference of GeNEs and Tissues) to integrate GWAS data and multiple tissue-specific gene networks for the simultaneous inference of phenotype-associated genes and relevant tissues. Through extensive simulation studies, we showed the effectiveness of our method in finding both associated genes and relevant tissues for a phenotype. In applications to real GWAS data of 14 complex phenotypes, we demonstrated the power of our method in both deciphering genetic basis and discovering biological insights of a phenotype. With this understanding, we expect to see SIGNET as a valuable tool for integrative GWAS analysis, thereby boosting the prevention, diagnosis, and treatment of human inherited diseases and eventually facilitating precision medicine.

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