easyGWAS:比较全基因组关联研究结果的云平台。

Deutsche Zeitschrift fur Nervenheilkunde Pub Date : 2017-01-01 Epub Date: 2016-12-16 DOI:10.1105/tpc.16.00551
Dominik G Grimm, Damian Roqueiro, Patrice A Salomé, Stefan Kleeberger, Bastian Greshake, Wangsheng Zhu, Chang Liu, Christoph Lippert, Oliver Stegle, Bernhard Schölkopf, Detlef Weigel, Karsten M Borgwardt
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引用次数: 0

摘要

随着大量物种高质量基因型的不断增加,研究人员能够利用全基因组关联研究(GWAS)以前所未有的详细程度探索复杂表型的潜在遗传结构。对不同性状的全基因组关联研究(GWAS)结果进行系统比较,为分析多效应等开辟了新的可能性。整合多个全基因组关联研究的其他优势还包括:能够复制全基因组关联研究的信号,并通过荟萃分析提高检测此类信号的统计能力。为了便于对 GWAS 结果进行简单比较,我们推出了 easyGWAS,这是一个功能强大、独立于物种的在线资源,可用于计算、存储、共享、注释和比较 GWAS。easyGWAS 工具支持多物种、上传私有基因型数据和现有 GWAS 的摘要统计,以及在交互式用户友好界面中比较不同实验和数据集的 GWAS 结果的高级方法。easyGWAS 也是 GWAS 数据和摘要统计的公共数据存储库,已经包含了几个主要 GWAS 的已发表数据和结果。我们以模式生物拟南芥的开花和生长相关性状为案例,展示了 easyGWAS 的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
easyGWAS: A Cloud-Based Platform for Comparing the Results of Genome-Wide Association Studies.

The ever-growing availability of high-quality genotypes for a multitude of species has enabled researchers to explore the underlying genetic architecture of complex phenotypes at an unprecedented level of detail using genome-wide association studies (GWAS). The systematic comparison of results obtained from GWAS of different traits opens up new possibilities, including the analysis of pleiotropic effects. Other advantages that result from the integration of multiple GWAS are the ability to replicate GWAS signals and to increase statistical power to detect such signals through meta-analyses. In order to facilitate the simple comparison of GWAS results, we present easyGWAS, a powerful, species-independent online resource for computing, storing, sharing, annotating, and comparing GWAS. The easyGWAS tool supports multiple species, the uploading of private genotype data and summary statistics of existing GWAS, as well as advanced methods for comparing GWAS results across different experiments and data sets in an interactive and user-friendly interface. easyGWAS is also a public data repository for GWAS data and summary statistics and already includes published data and results from several major GWAS. We demonstrate the potential of easyGWAS with a case study of the model organism Arabidopsis thaliana, using flowering and growth-related traits.

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