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Inferring Gene-Disease Association by an Integrative Analysis of eQTL Genome-Wide Association Study and Protein-Protein Interaction Data.
Human Heredity ( IF 1.8 ) Pub Date : 2019-01-23 , DOI: 10.1159/000489761
Jun Wang 1 , Jiashun Zheng 2 , Zengmiao Wang 3 , Hao Li 4, 5 , Minghua Deng 1, 6, 7
Affiliation  

OBJECTIVES Genome-wide association studies (GWASs) have revealed many candidate SNPs, but the mechanisms by which these SNPs influence diseases are largely unknown. In order to decipher the underlying mechanisms, several methods have been developed to predict disease-associated genes based on the integration of GWAS and eQTL data (e.g., Sherlock and COLOC). A number of studies have also incorporated information from gene networks into GWAS analysis to reprioritize candidate genes. METHODS Motivated by these two different approaches, we have developed a statistical framework to integrate information from GWAS, eQTL, and protein-protein interaction (PPI) data to predict disease-associated genes. Our approach is based on a hidden Markov random field (HMRF) model, and we called the resulting computational algorithm GeP-HMRF (a GWAS-eQTL-PPI-based HMRF). RESULTS We compared the performance of GeP-HMRF with Sherlock, COLOC, and NetWAS methods on 9 GWAS datasets, using the disease-related genes in the MalaCards database as the standard, and found that GeP-HMRF significantly improves the prediction accuracy. We also applied GeP-HMRF to an age-related macular degeneration disease (AMD) dataset. Among the top 50 genes predicted by GeP-HMRF, 7 are reported by the MalaCards database to be AMD-related with an enrichment p value of 3.61 × 10-119. Among the top 20 genes predicted by GeP-HMRF, CFHR1, CGHR3, HTRA1, and CFH are AMD-related in the MalaCards database, and another 9 genes are supported by the literature. CONCLUSIONS We built a unified statistical model to predict disease-related genes by integrating GWAS, eQTL, and PPI data. Our approach outperforms Sherlock, COLOC, and NetWAS in simulation studies and 9 GWAS datasets. Our approach can be generalized to incorporate other molecular trait data beyond eQTL and other interaction data beyond PPI.

中文翻译:

通过对eQTL全基因组关联研究和蛋白质-蛋白质相互作用数据的综合分析推断基因-疾病关联。

目的全基因组关联研究(GWAS)已揭示了许多候选SNP,但这些SNP影响疾病的机制尚不清楚。为了解释潜在的机制,已经开发了几种方法来基于GWAS和eQTL数据的整合来预测与疾病相关的基因(例如,Sherlock和COLOC)。多项研究还将来自基因网络的信息纳入GWAS分析,以重新确定候选基因的优先级。方法基于这两种不同的方法,我们开发了一个统计框架,以整合来自GWAS,eQTL和蛋白质-蛋白质相互作用(PPI)数据的信息,以预测与疾病相关的基因。我们的方法基于隐马尔可夫随机场(HMRF)模型,我们将得到的计算算法称为GeP-HMRF(基于GWAS-eQTL-PPI的HMRF)。结果我们以MalaCards数据库中与疾病相关的基因为标准,在9个GWAS数据集上比较了Sherlock,COLOC和NetWAS方法与GeP-HMRF的性能,发现GeP-HMRF显着提高了预测准确性。我们还将GeP-HMRF应用于与年龄相关的黄斑变性疾病(AMD)数据集。GeP-HMRF预测的前50个基因中,MalaCards数据库报告了7个与AMD相关,其富集p值为3.61×10-119。GeP-HMRF预测的前20个基因中,CFHR1,CGHR3,HTRA1和CFH在MalaCards数据库中与AMD相关,文献中还支持另外9个基因。结论我们建立了统一的统计模型,通过整合GWAS预测疾病相关基因,eQTL和PPI数据。在仿真研究和9个GWAS数据集中,我们的方法优于Sherlock,COLOC和NetWAS。我们的方法可以推广到包括除eQTL之外的其他分子特征数据和除PPI之外的其他相互作用数据。
更新日期:2019-11-01
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