Wei Wei, Ramos Paula S, Hunt Kelly J, Wolf Bethany J, Hardiman Gary, Chung Dongjun
Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA.
Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA; Department of Medicine, Medical University of South Carolina, Charleston, SC, USA.
Int J Genomics. 2016;2016:6589843. doi: 10.1155/2016/6589843. Epub 2016 Oct 27.
Genome-wide association studies (GWAS) have identified tens of thousands of genetic variants associated with hundreds of phenotypes and diseases, which have provided clinical and medical benefits to patients with novel biomarkers and therapeutic targets. Recently, there has been accumulating evidence suggesting that different complex traits share a common risk basis, namely, pleiotropy. Previously, a statistical method, namely, GPA (Genetic analysis incorporating Pleiotropy and Annotation), was developed to improve identification of risk variants and to investigate pleiotropic structure through a joint analysis of multiple GWAS datasets. While GPA provides a statistically rigorous framework to evaluate pleiotropy between phenotypes, it is still not trivial to investigate genetic relationships among a large number of phenotypes using the GPA framework. In order to address this challenge, in this paper, we propose a novel approach, GPA-MDS, to visualize genetic relationships among phenotypes using the GPA algorithm and multidimensional scaling (MDS). This tool will help researchers to investigate common etiology among diseases, which can potentially lead to development of common treatments across diseases. We evaluate the proposed GPA-MDS framework using a simulation study and apply it to jointly analyze GWAS datasets examining 18 unique phenotypes, which helps reveal the shared genetic architecture of these phenotypes.
全基因组关联研究（GWAS）已经鉴定出数万个与数百种表型和疾病相关的遗传变异，这些研究通过新的生物标志物和治疗靶点为患者带来了临床和医学益处。最近，越来越多的证据表明不同的复杂性状具有共同的风险基础，即多效性。此前，开发了一种统计方法，即GPA（纳入多效性和注释的遗传分析），以通过对多个GWAS数据集进行联合分析来改进风险变异的识别并研究多效性结构。虽然GPA提供了一个统计上严格的框架来评估表型之间的多效性，但使用GPA框架研究大量表型之间的遗传关系仍然并非易事。为了应对这一挑战，在本文中，我们提出了一种新颖的方法，即GPA-MDS，以使用GPA算法和多维缩放（MDS）来可视化表型之间的遗传关系。该工具将帮助研究人员研究疾病之间的共同病因，这可能会推动跨疾病的共同治疗方法的开发。我们使用模拟研究评估了所提出的GPA-MDS框架，并将其应用于联合分析检测18种独特表型的GWAS数据集，这有助于揭示这些表型的共享遗传结构。
