Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK.
Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester, UK.
Genet Epidemiol. 2022 Dec;46(8):629-643. doi: 10.1002/gepi.22496. Epub 2022 Aug 5.
As popularised by PrediXcan (and related methods), transcriptome-wide association studies (TWAS), in which gene expression is imputed from single-nucleotide polymorphism (SNP) genotypes and tested for association with a phenotype, are a popular approach for investigating the role of gene expression in complex traits. Like gene expression, DNA methylation is an important biological process and, being under genetic regulation, may be imputable from SNP genotypes. Here, we investigate prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits. We start by examining how well CpG methylation can be predicted from SNP genotypes, comparing three penalised regression approaches and examining whether changing the window size improves prediction accuracy. Although methylation at most CpG sites cannot be accurately predicted from SNP genotypes, for a subset it can be predicted well. We next apply our methylation prediction models (trained using the optimal method and window size) to carry out a methylome-wide association study (MWAS) of primary biliary cholangitis. We intersect the regions identified via MWAS with those identified via TWAS, providing insight into the interplay between CpG methylation, gene expression and disease status. We conclude that MWAS has the potential to improve understanding of biological mechanisms in complex traits.
正如 PrediXcan（和相关方法）所推广的那样，转录组全基因组关联研究（TWAS），其中基因表达从单核苷酸多态性（SNP）基因型推断，并测试与表型的关联，是研究基因表达在复杂性状中的作用的一种流行方法。与基因表达一样，DNA 甲基化是一个重要的生物学过程，并且受遗传调控，可能可以从 SNP 基因型推断出来。在这里，我们研究了从 SNP 基因型数据预测 CpG 甲基化水平，以帮助阐明甲基化、基因表达和复杂性状之间的关系。我们首先检查从 SNP 基因型预测 CpG 甲基化的效果如何，比较了三种惩罚回归方法，并检查改变窗口大小是否可以提高预测准确性。尽管大多数 CpG 位点的甲基化不能从 SNP 基因型准确预测，但对于一部分 CpG 位点可以很好地预测。接下来，我们应用我们的甲基化预测模型（使用最佳方法和窗口大小进行训练）对原发性胆汁性胆管炎进行全基因组甲基化关联研究（MWAS）。我们将通过 MWAS 识别的区域与通过 TWAS 识别的区域相交，深入了解 CpG 甲基化、基因表达和疾病状态之间的相互作用。我们的结论是，MWAS 有可能提高对复杂性状中生物机制的理解。
