Way Gregory P, Youngstrom Daniel W, Hankenson Kurt D, Greene Casey S, Grant Struan Fa
Genomics and Computational Biology Graduate Program, University of Pennsylvania, Philadelphia, PA, USA.
Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, USA.
Eur J Hum Genet. 2017 Nov;25(11):1286-1289. doi: 10.1038/ejhg.2017.108. Epub 2017 Aug 9.
Genome-wide association studies (GWAS) have contributed significantly to the understanding of complex disease genetics. However, GWAS only report association signals and do not necessarily identify culprit genes. As most signals occur in non-coding regions of the genome, it is often challenging to assign genomic variants to the underlying causal mechanism(s). Topologically associating domains (TADs) are primarily cell-type-independent genomic regions that define interactome boundaries and can aid in the designation of limits within which an association most likely impacts gene function. We describe and validate a computational method that uses the genic content of TADs to prioritize candidate genes. Our method, called 'TAD_Pathways', performs a Gene Ontology (GO) analysis over genes that reside within TAD boundaries corresponding to GWAS signals for a given trait or disease. Applying our pipeline to the bone mineral density (BMD) GWAS catalog, we identify 'Skeletal System Development' (Benjamini-Hochberg adjusted P=1.02x10) as the top-ranked pathway. In many cases, our method implicated a gene other than the nearest gene. Our molecular experiments describe a novel example: ACP2, implicated near the canonical 'ARHGAP1' locus. We found ACP2 to be an important regulator of osteoblast metabolism, whereas ARHGAP1 was not supported. Our results via BMD, for example, demonstrate how basic principles of three-dimensional genome organization can define biologically informed association windows.
全基因组关联研究（GWAS）为理解复杂疾病遗传学做出了重大贡献。然而，GWAS仅报告关联信号，并不一定能确定致病基因。由于大多数信号出现在基因组的非编码区域，将基因组变异与潜在的因果机制联系起来往往具有挑战性。拓扑相关结构域（TADs）主要是不依赖细胞类型的基因组区域，它们定义了相互作用组边界，并有助于确定关联最有可能影响基因功能的范围。我们描述并验证了一种利用TADs的基因内容对候选基因进行优先级排序的计算方法。我们的方法称为“TAD_Pathways”，它对位于与给定性状或疾病的GWAS信号相对应的TAD边界内的基因进行基因本体（GO）分析。将我们的流程应用于骨密度（BMD）GWAS目录，我们确定“骨骼系统发育”（Benjamini-Hochberg校正P = 1.02x10）为排名最高的通路。在许多情况下，我们的方法涉及到的基因并非最邻近的基因。我们的分子实验描述了一个新的例子：在经典的“ARHGAP1”基因座附近涉及到的ACP2。我们发现ACP2是成骨细胞代谢的重要调节因子，而ARHGAP1则未得到支持。例如，我们通过骨密度得出的结果表明，三维基因组组织的基本原理如何能够定义具有生物学意义的关联窗口。
