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About the Authors:
Arunabha Majumdar
Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editing
Affiliation: Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America
Tanushree Haldar
Roles Data curation, Formal analysis, Software, Visualization, Writing - original draft, Writing - review & editing
Affiliation: Institute for Human Genetics, University of California, San Francisco, California, United States of America
Sourabh Bhattacharya
Roles Conceptualization, Methodology, Supervision
Affiliation: Interdisciplinary Statistical Research Unit, Indian Statistical Institute, Kolkata, India
John S. Witte
Roles Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Writing - original draft, Writing - review & editing
* E-mail: [email protected]
Affiliations Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America, Institute for Human Genetics, University of California, San Francisco, California, United States of AmericaAbstract
Simultaneous analysis of genetic associations with multiple phenotypes may reveal shared genetic susceptibility across traits (pleiotropy). For a locus exhibiting overall pleiotropy, it is important to identify which specific traits underlie this association. We propose a Bayesian meta-analysis approach (termed CPBayes) that uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. This method uses a unified Bayesian statistical framework based on a spike and slab prior. CPBayes performs a fully Bayesian analysis by employing the Markov Chain Monte Carlo (MCMC) technique Gibbs sampling. It takes into account heterogeneity in the size and direction of the genetic effects across traits. It can be applied to both cohort data and separate studies of multiple traits having overlapping or non-overlapping subjects. Simulations show that CPBayes can produce higher accuracy in the selection of associated traits underlying a pleiotropic signal than the subset-based meta-analysis ASSET. We used CPBayes to undertake a genome-wide pleiotropic association study of 22 traits in the large Kaiser GERA cohort and detected six independent pleiotropic loci associated with at least two phenotypes. This includes a locus at chromosomal region 1q24.2 which exhibits an association simultaneously with the risk of five different diseases: Dermatophytosis, Hemorrhoids, Iron Deficiency, Osteoporosis and Peripheral Vascular Disease. We provide an R-package ‘CPBayes’ implementing the proposed method.
Author summary
Genome-wide...