Department of Statistics, Florida State University, Tallahassee, FL, USA.
Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA.
Bioinformatics. 2019 Oct 1;35(19):3576-3583. doi: 10.1093/bioinformatics/btz161.
Most trait-associated genetic variants identified in genome-wide association studies (GWASs) are located in non-coding regions of the genome and thought to act through their regulatory roles.
To account for enriched association signals in DNA regulatory elements, we propose a novel and general gene-based association testing strategy that integrates enhancer-target gene pairs and methylation quantitative trait locus data with GWAS summary results; it aims to both boost statistical power for new discoveries and enhance mechanistic interpretability of any new discovery. By reanalyzing two large-scale schizophrenia GWAS summary datasets, we demonstrate that the proposed method could identify some significant and novel genes (containing no genome-wide significant SNPs nearby) that would have been missed by other competing approaches, including the standard and some integrative gene-based association methods, such as one incorporating enhancer-target gene pairs and one integrating expression quantitative trait loci.
Software: wuchong.org/egmethyl.html.
Supplementary data are available at Bioinformatics online.
在全基因组关联研究(GWAS)中发现的大多数与特征相关的遗传变异位于基因组的非编码区域,被认为通过其调节作用发挥作用。
为了解释 DNA 调节元件中富集的关联信号,我们提出了一种新颖而通用的基于基因的关联测试策略,该策略整合了增强子-靶基因对和甲基化数量性状基因座数据与 GWAS 汇总结果;其旨在提高新发现的统计能力,并增强任何新发现的机制解释能力。通过重新分析两个大型精神分裂症 GWAS 汇总数据集,我们证明了所提出的方法可以识别一些重要的新基因(附近没有全基因组显著 SNP),而其他竞争方法(包括标准和一些整合基因的关联方法,例如整合增强子-靶基因对的方法和整合表达数量性状基因座的方法)可能会错过这些基因。
软件:wuchong.org/egmethyl.html。
补充数据可在“Bioinformatics”在线获取。