graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture.
graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture.
复制标题
DOI：
10.1371/journal.pcbi.1005388
复制
发表时间：
2017-02
影响因子：
4.3
通讯作者：
Zhao H
中科院分区：
文献类型：
--
作者：
Chung D;Kim HJ;Zhao H
全基因组关联研究（GWAS）已经确定了与数百种表型和疾病相关的数万种遗传变异，这些变异为患者提供了新的生物标志物和治疗靶点，从而为患者提供了临床和医疗益处。然而，识别与复杂疾病相关的风险变异仍然具有挑战性，因为它们通常受到许多具有小或中等影响的遗传变异的影响。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。最近，已经开发了几种统计方法来提高统计能力，以通过利用多效性对多个GWAS数据集进行联合分析来识别复杂性状的风险变体。虽然这些方法显示出与单独分析相比提高了关联映射的统计功效，但它们仍然限制了可以整合的表型的数量。为了应对这一挑战，在本文中，我们提出了一种新的统计框架，图GPA，使用隐马尔可夫随机场方法集成了大量的GWAS数据集的多个表型。将graph-GPA应用于12种表型的GWAS数据集的联合分析表明，与基于少量GWAS数据集的统计方法相比，graph-GPA提高了识别风险变体的统计能力。此外，graph-GPA还促进了对表型之间共享的遗传机制的更好理解，这可能有助于改进诊断和治疗方法的发展。graph-GPA的R实现目前可在https://dongjunchung.github.io/GGPA/上获得。最近，有越来越多的证据表明多效性，即，多种表型共有的遗传成分。将多效性纳入遗传分析可能会提高统计能力，以确定风险相关的遗传变异。已经提出了几种统计方法来利用多效性进行关联映射，但是它们目前仍然限于相对少量的表型，例如，一对表型这限制了在大量表型之间的关联映射和多效性结构的调查中统计功效的潜在增益。为了应对这一挑战，在本文中，我们提出了图GPA，一种新的统计框架，使用隐马尔可夫随机场架构整合大量的表型。将所提出的统计方法应用于12种表型的GWAS数据集表明，graph-GPA不仅提供了这些表型之间遗传关系的简约表示，而且还识别了大量具有潜在功能的新遗传变异。我们相信，这种新的方法可能有助于调查常见的病因，提高诊断和治疗。
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. However, identification of risk variants associated with complex diseases remains challenging as they are often affected by many genetic variants with small or moderate effects. There has been accumulating evidence suggesting that different complex traits share common risk basis, namely pleiotropy. Recently, several statistical methods have been developed to improve statistical power to identify risk variants for complex traits through a joint analysis of multiple GWAS datasets by leveraging pleiotropy. While these methods were shown to improve statistical power for association mapping compared to separate analyses, they are still limited in the number of phenotypes that can be integrated. In order to address this challenge, in this paper, we propose a novel statistical framework, graph-GPA, to integrate a large number of GWAS datasets for multiple phenotypes using a hidden Markov random field approach. Application of graph-GPA to a joint analysis of GWAS datasets for 12 phenotypes shows that graph-GPA improves statistical power to identify risk variants compared to statistical methods based on smaller number of GWAS datasets. In addition, graph-GPA also promotes better understanding of genetic mechanisms shared among phenotypes, which can potentially be useful for the development of improved diagnosis and therapeutics. The R implementation of graph-GPA is currently available at https://dongjunchung.github.io/GGPA/. Recently, there has been accumulating evidence suggesting pleiotropy, i.e., genetic components shared across multiple phenotypes. Incorporation of pleiotropy in genetic analysis might improve statistical power to identify risk associated genetic variants. Several statistical approaches have been proposed to utilize pleiotropy for association mapping but they are currently still limited to a relatively small number of phenotypes, e.g., a pair of phenotypes. This restricts potential gain in statistical power in association mapping and investigation of pleiotropic structure among a large number of phenotypes. In order to address this challenge, in this paper, we propose graph-GPA, a novel statistical framework to integrate a large number of phenotypes using a hidden Markov random field architecture. Application of the proposed statistical method to GWAS datasets for 12 phenotypes showed that graph-GPA does not only provide a parsimonious representation of genetic relationship among these phenotypes, but also identify significantly larger number of novel genetic variants that are potentially functional. We believe that this novel approach might help investigation of common etiology and improvement of diagnosis and therapeutics.