There exist few, if any, practical guidelines for predictive and falsifiable multi-omic data integration that systematically integrate existing knowledge. Disease modules are popular concepts for interpreting genome-wide studies in medicine but have so far not been systematically evaluated and may lead to corroborating multi-omic modules. We assessed eight module identification methods in 57 previously published expression and methylation studies of 19 diseases using GWAS enrichment analysis. Next, we applied the same strategy for multi-omic integration of 20 datasets of multiple sclerosis (MS), and further validated the resulting module using both GWAS and risk-factor-associated genes from several independent cohorts. Our benchmark of modules showed that in immune-associated diseases modules inferred from clique-based methods were the most enriched for GWAS genes. The multi-omic case study using MS data revealed the robust identification of a module of 220 genes. Strikingly, most genes of the module were differentially methylated upon the action of one or several environmental risk factors in MS (n = 217, P = 10− 47) and were also independently validated for association with five different risk factors of MS, which further stressed the high genetic and epigenetic relevance of the module for MS. We believe our analysis provides a workflow for selecting modules and our benchmark study may help further improvement of disease module methods. Moreover, we also stress that our methodology is generally applicable for combining and assessing the performance of multi-omic approaches for complex diseases.

中文翻译：

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使用 GWAS 对疾病模块进行系统评估的经过验证的普遍适用方法揭示了与多发性硬化症危险因素密切相关的多组学模块

对于系统地整合现有知识的预测性和可证伪的多组学数据集成，几乎没有实用的指南（如果有的话）。疾病模块是解释医学全基因组研究的流行概念，但迄今为止尚未经过系统评估，可能会导致证实多组学模块。我们使用 GWAS 富集分析评估了先前发表的 19 种疾病的 57 项表达和甲基化研究中的 8 种模块识别方法。接下来，我们应用相同的策略对 20 个多发性硬化症 (MS) 数据集进行多组学整合，并使用 GWAS 和来自几个独立队列的风险因素相关基因进一步验证所得模块。我们的模块基准表明，在免疫相关疾病中，从基于派系的方法推断出的模块的 GWAS 基因最丰富。使用 MS 数据的多组学案例研究揭示了 220 个基因模块的可靠识别。引人注目的是，该模块的大多数基因在 MS 中一种或多种环境风险因素的作用下发生差异甲基化（n = 217，P = 10−47），并且还独立验证了与 MS 的五种不同风险因素的关联，这进一步强调了 MS 模块的高度遗传和表观遗传相关性。我们相信我们的分析提供了选择模块的工作流程，并且我们的基准研究可能有助于进一步改进疾病模块方法。此外，我们还强调，我们的方法通常适用于组合和评估复杂疾病的多组学方法的性能。