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 |
| DOI | 10.1186/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
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