中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
LPG: A four-group probabilistic approach to leveraging pleiotropy in genome-wide association studies

文献类型:期刊论文

作者Yang,Yi1,2; Dai,Mingwei3,4; Huang,Jian5; Lin,Xinyi2; Yang,Can4; Chen,Min1; Liu,Jin2
刊名BMC Genomics
出版日期2018-06-28
卷号19期号:1
关键词Pleiotropy Variational Bayesian expectation-maximization Genome-wide association studies
ISSN号1471-2164
DOI10.1186/s12864-018-4851-2
英文摘要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.
语种英语
WOS记录号BMC:10.1186/S12864-018-4851-2
出版者BioMed Central
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/425]  
专题应用数学研究所
通讯作者Liu,Jin
作者单位1.
2.
3.
4.
5.
推荐引用方式
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).

入库方式: OAI收割

来源:数学与系统科学研究院

浏览0
下载0
收藏0
其他版本

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。