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Pleiotropic Mapping and Annotation Selection in Genome-wide Association Studies with Penalized Gaussian Mixture Models

Ping Zeng, Xinjie Hao, Xiang Zhou
doi: https://doi.org/10.1101/256461
Ping Zeng
1Department of Epidemiology and Biostatistics, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China,
2Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA,
3Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA
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Xinjie Hao
2Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA,
3Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA
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Xiang Zhou
2Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA,
3Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA
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  • For correspondence: xzhousph@umich.edu
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Abstract

Motivation Genome-wide association studies (GWASs) have identified many genetic loci associated with complex traits. A substantial fraction of these identified loci are associated with multiple traits – a phenomena known as pleiotropy. Identification of pleiotropic associations can help characterize the genetic relationship among complex traits and can facilitate our understanding of disease etiology. Effective pleiotropic association mapping requires the development of statistical methods that can jointly model multiple traits with genome-wide SNPs together.

Results We develop a joint modeling method, which we refer to as the integrative MApping of Pleiotropic association (iMAP). iMAP models summary statistics from GWASs, uses a multivariate Gaussian distribution to account for phenotypic correlation, simultaneously infers genome-wide SNP association pattern using mixture modeling, and has the potential to reveal causal relationship between traits. Importantly, iMAP integrates a large number of SNP functional annotations to substantially improve association mapping power, and, with a sparsity-inducing penalty, is capable of selecting informative annotations from a large, potentially noninformative set. To enable scalable inference of iMAP to association studies with hundreds of thousands of individuals and millions of SNPs, we develop an efficient expectation maximization algorithm based on an approximate penalized regression algorithm. With simulations and comparisons to existing methods, we illustrate the benefits of iMAP both in terms of high association mapping power and in terms of accurate estimation of genome-wide SNP association patterns. Finally, we apply iMAP to perform a joint analysis of 48 traits from 31 GWAS consortia together with 40 tissue-specific SNP annotations generated from the Roadmap Project. iMAP is freely available at www.xzlab.org/software.html.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted January 31, 2018.
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Pleiotropic Mapping and Annotation Selection in Genome-wide Association Studies with Penalized Gaussian Mixture Models
Ping Zeng, Xinjie Hao, Xiang Zhou
bioRxiv 256461; doi: https://doi.org/10.1101/256461
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Pleiotropic Mapping and Annotation Selection in Genome-wide Association Studies with Penalized Gaussian Mixture Models
Ping Zeng, Xinjie Hao, Xiang Zhou
bioRxiv 256461; doi: https://doi.org/10.1101/256461

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