LPG: A four-group probabilistic approach to leveraging pleiotropy in genome-wide association studies

Yi Yang; Mingwei Dai; Jian Huang; Xinyi Lin; Can Yang; Min Chen; Jin Liu

doi:10.1186/s12864-018-4851-2

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LPG: A four-group probabilistic approach to leveraging pleiotropy in genome-wide association studies

Journal article

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LPG: A four-group probabilistic approach to leveraging pleiotropy in genome-wide association studies

Yi Yang, Mingwei Dai, Jian Huang, Xinyi Lin, Can Yang, Min Chen and Jin Liu

BMC genomics, Vol.19(1), pp.503-503

06/28/2018

DOI: 10.1186/s12864-018-4851-2

PMCID: PMC6022345

PMID: 29954342

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Abstract

Background: To date, genome-wide association studies (GWAS) have successfully identified tens of thousands of genetic variants among a variety of traits/diseases, shedding light on the genetic architecture of complex disease. The polygenicity of complex diseases is a widely accepted phenomenon through which a vast number of risk variants, each with a modest individual effect, collectively contribute to the heritability of complex diseases. This imposes a major challenge on fully characterizing the genetic bases of complex diseases. An immediate implication of polygenicity is that a much larger sample size is required to detect individual risk variants with weak/moderate effects. Meanwhile, accumulating evidence suggests that different complex diseases can share genetic risk variants, a phenomenon known as pleiotropy. Results: In this study, we propose a statistical framework for Leveraging Pleiotropic effects in large-scale GWAS data (LPG). LPG utilizes a variational Bayesian expectation-maximization (VBEM) algorithm, making it computationally efficient and scalable for genome-wide-scale analysis. To demonstrate the advantages of LPG over existing methods that do not leverage pleiotropy, we conducted extensive simulation studies and applied LPG to analyze two pairs of disorders (Crohn's disease and Type 1 diabetes, as well as rheumatoid arthritis and Type 1 diabetes). The results indicate that by levelaging pleiotropy, LPG can improve the power of prioritization of risk variants and the accuracy of risk prediction. Conclusions: Our methodology provides a novel and efficient tool to detect pleiotropy among GWAS data for multiple traits/diseases collected from different studies. The software is available at https://github.com/Shufeyangyi2015310117/LPG.

Genome-wide association studies

Methodology

Pleiotropy

Variational Bayesian expectation-maximization

Details

Title: Subtitle: LPG: A four-group probabilistic approach to leveraging pleiotropy in genome-wide association studies
Creators: Yi Yang - National University of Singapore
Mingwei Dai - Hong Kong University of Science and Technology
Jian Huang - Hong Kong Polytechnic University
Xinyi Lin - National University of Singapore
Can Yang - Hong Kong University of Science and Technology
Min Chen - Shanghai University of Finance and Economics
Jin Liu - National University of Singapore
Resource Type: Journal article
Publication Details: BMC genomics, Vol.19(1), pp.503-503
DOI: 10.1186/s12864-018-4851-2
PMID: 29954342
PMCID: PMC6022345
NLM abbreviation: BMC Genomics
ISSN: 1471-2164
eISSN: 1471-2164
Publisher: BioMed Central
Grant note: 22302815 / ; 61501389 / ; 12316116 / ; 12301417 / ; MOE2016-T2-2-029 / ; R-913-200-098-263 / ;
Language: English
Date published: 06/28/2018
Academic Unit: Statistics and Actuarial Science
Record Identifier: 9984257621002771

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