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

doi:10.1186/s12864-018-4851-2

CSpace > 应用数学研究所

	LPG: A four-group probabilistic approach to leveraging pleiotropy in genome-wide association studies
	Yang,Yi 1,2; Dai,Mingwei 3,4; Huang,Jian 5; Lin,Xinyi 2; Yang,Can 4; Chen,Min1 ; Liu,Jin 2
	2018-06-28
Source Publication	BMC Genomics
ISSN	1471-2164
Volume	19 Issue:1
Abstract	AbstractBackgroundTo 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.ResultsIn 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.ConclusionsOur 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.
Keyword	Pleiotropy Variational Bayesian expectation-maximization Genome-wide association studies
DOI	10.1186/s12864-018-4851-2
Language	英语
WOS ID	BMC:10.1186/s12864-018-4851-2
Publisher	BioMed Central
Citation statistics
Document Type	期刊论文
Identifier	http://ir.amss.ac.cn/handle/2S8OKBNM/426
Collection	应用数学研究所
Corresponding Author	Liu,Jin
Affiliation	1. 2. 3. 4. 5.
Recommended Citation GB/T 7714	Yang,Yi,Dai,Mingwei,Huang,Jian,et al. LPG: A four-group probabilistic approach to leveraging pleiotropy in genome-wide association studies[J]. BMC Genomics,2018,19(1).
APA	Yang,Yi.,Dai,Mingwei.,Huang,Jian.,Lin,Xinyi.,Yang,Can.,...&Liu,Jin.(2018).LPG: A four-group probabilistic approach to leveraging pleiotropy in genome-wide association studies.BMC Genomics,19(1).
MLA	Yang,Yi,et al."LPG: A four-group probabilistic approach to leveraging pleiotropy in genome-wide association studies".BMC Genomics 19.1(2018).

Files in This Item:
There are no files associated with this item.

If you have any objections to this item, please fill out the form below and the administrator will contact you as soon as possible.
Content:
Email：	*
Institution:
Verification Code:	Refresh

Any comments and suggestions are welcomed.
Title:	*
Content:
Email：	*
Verification Code:	Refresh

中国科学院数学与系统科学研究院机构知识库