Manjunath Mohith, Zhang Yi, Zhang Shilu, Roy Sushmita, Perez-Pinera Pablo, Song Jun S
Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL, United States.
Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, United States.
Front Genet. 2020 Jul 20;11:730. doi: 10.3389/fgene.2020.00730. eCollection 2020.
Over the past decade, hundreds of genome-wide association studies (GWAS) have implicated genetic variants in various diseases, including cancer. However, only a few of these variants have been functionally characterized to date, mainly because the majority of the variants reside in non-coding regions of the human genome with unknown function. A comprehensive functional annotation of the candidate variants is thus necessary to fill the gap between the correlative findings of GWAS and the development of therapeutic strategies. By integrating large-scale multi-omics datasets such as the Cancer Genome Atlas (TCGA) and the Encyclopedia of DNA Elements (ENCODE), we performed multivariate linear regression analysis of expression quantitative trait loci, sequence permutation test of transcription factor binding perturbation, and modeling of three-dimensional chromatin interactions to analyze the potential molecular functions of 2,813 single nucleotide variants in 93 genomic loci associated with estrogen receptor-positive breast cancer. To facilitate rapid progress in functional genomics of breast cancer, we have created "Analysis of Breast Cancer GWAS" (ABC-GWAS), an interactive database of functional annotation of estrogen receptor-positive breast cancer GWAS variants. Our resource includes expression quantitative trait loci, long-range chromatin interaction predictions, and transcription factor binding motif analyses to prioritize putative target genes, causal variants, and transcription factors. An embedded genome browser also facilitates convenient visualization of the GWAS loci in genomic and epigenomic context. ABC-GWAS provides an interactive visual summary of comprehensive functional characterization of estrogen receptor-positive breast cancer variants. The web resource will be useful to both computational and experimental biologists who wish to generate and test their hypotheses regarding the genetic susceptibility, etiology, and carcinogenesis of breast cancer. ABC-GWAS can also be used as a user-friendly educational resource for teaching functional genomics. ABC-GWAS is available at http://education.knoweng.org/abc-gwas/.
在过去十年中，数百项全基因组关联研究（GWAS）已表明基因变异与包括癌症在内的各种疾病有关。然而，迄今为止，这些变异中只有少数已在功能上得到表征，主要是因为大多数变异位于人类基因组的非编码区域，其功能未知。因此，对候选变异进行全面的功能注释对于填补GWAS的相关发现与治疗策略开发之间的空白是必要的。通过整合大规模多组学数据集，如癌症基因组图谱（TCGA）和DNA元件百科全书（ENCODE），我们对表达数量性状基因座进行了多元线性回归分析，对转录因子结合扰动进行了序列置换测试，并对三维染色质相互作用进行了建模，以分析与雌激素受体阳性乳腺癌相关的93个基因组位点中2813个单核苷酸变异的潜在分子功能。为了促进乳腺癌功能基因组学的快速发展，我们创建了“乳腺癌GWAS分析”（ABC-GWAS），这是一个雌激素受体阳性乳腺癌GWAS变异功能注释的交互式数据库。我们的资源包括表达数量性状基因座、长程染色质相互作用预测以及转录因子结合基序分析，以便对推定的靶基因、因果变异和转录因子进行优先级排序。一个嵌入式基因组浏览器还便于在基因组和表观基因组背景下方便地可视化GWAS位点。ABC-GWAS提供了雌激素受体阳性乳腺癌变异全面功能表征的交互式可视化总结。该网络资源对于希望生成和测试其关于乳腺癌遗传易感性、病因和致癌作用假设的计算生物学家和实验生物学家都将是有用的。ABC-GWAS也可以用作教授功能基因组学的用户友好型教育资源。ABC-GWAS可在http://education.knoweng.org/abc-gwas/获取。
