Jin Liu1, Xiang Wan2, Shuangge Ma3, Can Yang4. 1. Center of Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore. 2. Department of Computer Science, Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon, Hong Kong. 3. Department of Biostatistics, Yale University, New Heaven, CT, USA. 4. Department of Mathematics, Hong Kong Baptist University, Kowloon, Hong Kong.
Abstract
MOTIVATION: Researchers worldwide have generated a huge volume of genomic data, including thousands of genome-wide association studies (GWAS) and massive amounts of gene expression data from different tissues. How to perform a joint analysis of these data to gain new biological insights has become a critical step in understanding the etiology of complex diseases. Due to the polygenic architecture of complex diseases, the identification of risk genes remains challenging. Motivated by the shared risk genes found in complex diseases and tissue-specific gene expression patterns, we propose as an Empirical Bayes approach to integrating Pleiotropy and Tissue-Specific information (EPS) for prioritizing risk genes. RESULTS: As demonstrated by extensive simulation studies, EPS greatly improves the power of identification for disease-risk genes. EPS enables rigorous hypothesis testing of pleiotropy and tissue-specific risk gene expression patterns. All of the model parameters can be adaptively estimated from the developed expectation-maximization (EM) algorithm. We applied EPS to the bipolar disorder and schizophrenia GWAS from the Psychiatric Genomics Consortium, along with the gene expression data for multiple tissues from the Genotype-Tissue Expression project. The results of the real data analysis demonstrate many advantages of EPS. AVAILABILITY AND IMPLEMENTATION: The EPS software is available on https://sites.google.com/site/liujin810822 CONTACT: eeyang@hkbu.edu.hk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Researchers worldwide have generated a huge volume of genomic data, including thousands of genome-wide association studies (GWAS) and massive amounts of gene expression data from different tissues. How to perform a joint analysis of these data to gain new biological insights has become a critical step in understanding the etiology of complex diseases. Due to the polygenic architecture of complex diseases, the identification of risk genes remains challenging. Motivated by the shared risk genes found in complex diseases and tissue-specific gene expression patterns, we propose as an Empirical Bayes approach to integrating Pleiotropy and Tissue-Specific information (EPS) for prioritizing risk genes. RESULTS: As demonstrated by extensive simulation studies, EPS greatly improves the power of identification for disease-risk genes. EPS enables rigorous hypothesis testing of pleiotropy and tissue-specific risk gene expression patterns. All of the model parameters can be adaptively estimated from the developed expectation-maximization (EM) algorithm. We applied EPS to the bipolar disorder and schizophrenia GWAS from the Psychiatric Genomics Consortium, along with the gene expression data for multiple tissues from the Genotype-Tissue Expression project. The results of the real data analysis demonstrate many advantages of EPS. AVAILABILITY AND IMPLEMENTATION: The EPS software is available on https://sites.google.com/site/liujin810822 CONTACT: eeyang@hkbu.edu.hk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: 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 Journal: Am J Hum Genet Date: 2018-05-03 Impact factor: 11.025
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