Lemaçon Audrey, Scott-Boyer Marie-Pier, Ongaro-Carcy Régis, Soucy Penny, Simard Jacques, Droit Arnaud
Genomics Center, Centre Hospitalier Universitaire de Quebec-Université Laval Research Center, Quebec, QC, Canada.
Front Genet. 2020 Jan 17;10:1349. doi: 10.3389/fgene.2019.01349. eCollection 2019.
One of the most challenging tasks of the post-genome-wide association studies (GWAS) research era is the identification of functional variants among those associated with a trait for an observed GWAS signal. Several methods have been developed to evaluate the potential functional implications of genetic variants. Each of these tools has its own scoring system, which forces users to become acquainted with each approach to interpret their results. From an awareness of the amount of work needed to analyze and integrate results for a single locus, we proposed a flexible and versatile approach designed to help the prioritization of variants by aggregating the predictions of their potential functional implications. This approach has been made available through a graphical user interface called DSNetwork, which acts as a single point of entry to almost 60 reference predictors for both coding and non-coding variants and displays predictions in an easy-to-interpret visualization. We confirmed the usefulness of our methodology by successfully identifying functional variants in four breast cancer and nine schizophrenia susceptibility loci.
后全基因组关联研究（GWAS）时代最具挑战性的任务之一，是在与某个观察到的GWAS信号相关的那些变异中识别出功能变异。已经开发了几种方法来评估遗传变异的潜在功能影响。这些工具中的每一个都有自己的评分系统，这迫使用户熟悉每种方法以解释其结果。鉴于意识到分析和整合单个基因座的结果所需的工作量，我们提出了一种灵活通用的方法，旨在通过汇总对变异潜在功能影响的预测来帮助对变异进行优先级排序。这种方法已通过一个名为DSNetwork的图形用户界面提供，该界面作为一个单一入口点，可用于几乎60个针对编码和非编码变异的参考预测器，并以易于解释的可视化方式显示预测结果。我们通过成功识别四个乳腺癌和九个精神分裂症易感基因座中的功能变异，证实了我们方法的实用性。
