Rationale: GWAS (Genome-Wide Association Studies) have identified hundreds of genetic loci associated with atrial fibrillation (AF). However, these loci explain only a small proportion of AF heritability. Objective: To develop an approach to identify additional AF-related genes by integrating multiple omics data. Methods and Results: Three types of omics data were integrated: (1) summary statistics from the AFGen 2017 GWAS; (2) a whole blood EWAS (Epigenome-Wide Association Study) of AF; and (3) a whole blood TWAS (Transcriptome-Wide Association Study) of AF. The variant-level GWAS results were collapsed into gene-level associations using fast set-based association analysis. The CpG-level EWAS results were also collapsed into gene-level associations by an adapted SNP-set Kernel Association Test approach. Both GWAS and EWAS gene-based associations were then meta-analyzed with TWAS using a fixed-effects model weighted by the sample size of each data set. A tissue-specific network was subsequently constructed using the NetWAS (Network-Wide Association Study). The identified genes were then compared with the AFGen 2018 GWAS that contained more than triple the number of AF cases compared with AFGen 2017 GWAS. We observed that the multiomics approach identified many more relevant AF-related genes than using AFGen 2018 GWAS alone (1931 versus 206 genes). Many of these genes are involved in the development and regulation of heart- and muscle-related biological processes. Moreover, the gene set identified by multiomics approach explained much more AF variance than those identified by GWAS alone (10.4% versus 3.5%). Conclusions: We developed a strategy to integrate multiple omics data to identify AF-related genes. Our integrative approach may be useful to improve the power of traditional GWAS, which might be particularly useful for rare traits and diseases with limited sample size.

中文翻译：

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整合组学方法来鉴定与心房颤动相关的基因。

理由：GWAS（全基因组关联研究）已鉴定出数百种与心房颤动（AF）相关的遗传基因座。然而，这些基因座仅解释了AF遗传力的一小部分。目的：通过整合多种组学数据，开发一种鉴定其他与AF相关基因的方法。方法和结果：整合了三种类型的组学数据：（1）来自AFGen 2017 GWAS的摘要统计数据; （2）AF的全血EWAS（表观基因组关联研究）；（3）AF的全血TWAS（转录组-全关联研究）。使用基于快速集的关联分析，将变体水平的GWAS结果分解为基因水平的关联。CpG级EWAS结果也通过一种经过改进的SNP-set内核关联测试方法分解为基因级关联。然后，使用固定效应模型对GWAS和基于EWAS基因的关联进行TWAS荟萃分析，该模型以每个数据集的样本大小加权。随后使用NetWAS（网络范围的关联研究）来构建组织特定的网络。然后将鉴定出的基因与AFGen 2018 GWAS进行比较，该结果包含的AF病例数是AFGen 2017 GWAS的三倍以上。我们观察到，与仅使用AFGen 2018 GWAS相比，多组学方法识别出更多与AF相关的基因（1931对206基因）。这些基因中的许多都参与了与心脏和肌肉相关的生物过程的发展和调控。而且，通过多组学方法鉴定的基因组比仅通过GWAS鉴定的基因组解释了更多的AF变异（10.4％对3.5％）。结论：我们制定了整合多种组学数据以鉴定与AF相关的基因的策略。我们的综合方法可能有助于提高传统GWAS的功能，这对于样本量有限的稀有性状和疾病尤其有用。