Suppr超能文献

ezQTL:一个用于 QTL 和 GWAS 基因座交互可视化和共定位的网络平台。

ezQTL: A Web Platform for Interactive Visualization and Colocalization of QTLs and GWAS Loci.

机构信息

Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.

Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.

出版信息

Genomics Proteomics Bioinformatics. 2022 Jun;20(3):541-548. doi: 10.1016/j.gpb.2022.05.004. Epub 2022 May 25.

Abstract

Genome-wide association studies (GWAS) have identified thousands of genomic loci associated with complex diseases and traits, including cancer. The vast majority of common trait-associated variants identified via GWAS fall in non-coding regions of the genome, posing a challenge in elucidating the causal variants, genes, and mechanisms involved. Expression quantitative trait locus (eQTL) and other molecular QTL studies have been valuable resources in identifying candidate causal genes from GWAS loci through statistical colocalization methods. While QTL colocalization is becoming a standard analysis in post-GWAS investigation, an easy web tool for users to perform formal colocalization analyses with either user-provided or public GWAS and eQTL datasets has been lacking. Here, we present ezQTL, a web-based bioinformatic application to interactively visualize and analyze genetic association data such as GWAS loci and molecular QTLs under different linkage disequilibrium (LD) patterns (1000 Genomes Project, UK Biobank, or user-provided data). This application allows users to perform data quality control for variants matched between different datasets, LD visualization, and two-trait colocalization analyses using two state-of-the-art methodologies (eCAVIAR and HyPrColoc), including batch processing. ezQTL is a free and publicly available cross-platform web tool, which can be accessed online at https://analysistools.cancer.gov/ezqtl.

摘要

全基因组关联研究(GWAS)已经确定了数千个与复杂疾病和特征相关的基因组位点,包括癌症。通过 GWAS 确定的大多数常见性状相关变体都位于基因组的非编码区域,这给阐明涉及的因果变体、基因和机制带来了挑战。表达数量性状基因座(eQTL)和其他分子 QTL 研究一直是通过统计共定位方法从 GWAS 位点识别候选因果基因的有价值的资源。虽然 QTL 共定位在 GWAS 后调查中已成为一种标准分析方法,但用户缺乏一个易于使用的网络工具,用于使用用户提供或公共的 GWAS 和 eQTL 数据集执行正式的共定位分析。在这里,我们介绍了 ezQTL,这是一个基于网络的生物信息学应用程序,可用于在不同连锁不平衡(LD)模式下(1000 基因组计划、英国生物银行或用户提供的数据)交互可视化和分析遗传关联数据,如 GWAS 位点和分子 QTL。该应用程序允许用户对不同数据集之间匹配的变体进行数据质量控制、LD 可视化以及使用两种最先进的方法(eCAVIAR 和 HyPrColoc)进行两性状共定位分析,包括批处理。ezQTL 是一个免费的、公开的跨平台网络工具,可在 https://analysistools.cancer.gov/ezqtl 在线访问。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e3d/9801033/b7f67e1aae32/gr1.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验