Variant interpretation using population databases: Lessons from gnomAD.
Variant interpretation using population databases: Lessons from gnomAD.
复制标题
使用群体数据库进行变异解释：gnomAD 的经验教训。
作者：
参考群体数据库是变异和基因解释的重要工具。它们的使用指导了在每个人类基因组中存在的良性变异海洋中识别致病变异，并支持发现新的疾病-基因关系。基因组聚合数据库（gnomAD）是目前最大和最广泛使用的公开收集的人口变异的协调测序数据。这些数据可通过在线gnomAD浏览器（https：//gnomad.broadinstitute.org/）获得，该浏览器可进行快速直观的变体分析。这篇评论提供了gnomAD浏览器的内容指导，以及它在变异和基因解释中的使用。我们介绍了关键特征，包括等位基因频率，每碱基表达水平，约束评分和变异共现，以及如何在分析中使用这些的指导，重点是罕见疾病中候选变异和新基因的解释。参考人群数据库在解释用于诊断罕见疾病的基因组变异方面至关重要，并支持发现新的疾病-基因关系。本综述为使用基因组聚合数据库（gnomAD）浏览器和关键特征（如等位基因频率、每碱基表达水平、约束评分和变异共现）在临床和研究分析中进行变异和基因解释提供了指导。
Reference population databases are an essential tool in variant and gene interpretation. Their use guides the identification of pathogenic variants amidst the sea of benign variation present in every human genome, and supports the discovery of new disease–gene relationships. The Genome Aggregation Database (gnomAD) is currently the largest and most widely used publicly available collection of population variation from harmonized sequencing data. The data is available through the online gnomAD browser (https://gnomad.broadinstitute.org/) that enables rapid and intuitive variant analysis. This review provides guidance on the content of the gnomAD browser, and its usage for variant and gene interpretation. We introduce key features including allele frequency, per‐base expression levels, constraint scores, and variant co‐occurrence, alongside guidance on how to use these in analysis, with a focus on the interpretation of candidate variants and novel genes in rare disease. Reference population databases are critical in the interpretation of genomic variation for diagnosing rare disease, and supports the discovery of new disease–gene relationships. This review provides guidance for using the Genome Aggregation Database (gnomAD) browser and key features like allele frequency, per‐base expression levels, constraint scores, and variant co‐occurrence, for variant and gene interpretation in clinical and research analysis.
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DOI：
10.1093/bioinformatics/btr209
发表时间：
2011-07-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Lin MF;Jungreis I;Kellis M
通讯作者：
Kellis M
影响因子：
64.8
作者：
Cummings, Beryl B.;Karczewski, Konrad J.;MacArthur, Daniel G.
通讯作者：
MacArthur, Daniel G.
影响因子：
64.8
作者：
Karczewski, Konrad J;Francioli, Laurent C;MacArthur, Daniel G
通讯作者：
MacArthur, Daniel G
DOI：
10.1126/science.1262110
发表时间：
2015-05-08
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
GTEx Consortium
通讯作者：
GTEx Consortium
影响因子：
64.8
作者：
Dickinson ME;Flenniken AM;Ji X;Teboul L;Wong MD;White JK;Meehan TF;Weninger WJ;Westerberg H;Adissu H;Baker CN;Bower L;Brown JM;Caddle LB;Chiani F;Clary D;Cleak J;Daly MJ;Denegre JM;Doe B;Dolan ME;Edie SM;Fuchs H;Gailus-Durner V;Galli A;Gambadoro A;Gallegos J;Guo S;Horner NR;Hsu CW;Johnson SJ;Kalaga S;Keith LC;Lanoue L;Lawson TN;Lek M;Mark M;Marschall S;Mason J;McElwee ML;Newbigging S;Nutter LM;Peterson KA;Ramirez-Solis R;Rowland DJ;Ryder E;Samocha KE;Seavitt JR;Selloum M;Szoke-Kovacs Z;Tamura M;Trainor AG;Tudose I;Wakana S;Warren J;Wendling O;West DB;Wong L;Yoshiki A;International Mouse Phenotyping Consortium;Jackson Laboratory;Infrastructure Nationale PHENOMIN, Institut Clinique de la Souris (ICS);Charles River Laboratories;MRC Harwell;Toronto Centre for Phenogenomics;Wellcome Trust Sanger Institute;RIKEN BioResource Center;MacArthur DG;Tocchini-Valentini GP;Gao X;Flicek P;Bradley A;Skarnes WC;Justice MJ;Parkinson HE;Moore M;Wells S;Braun RE;Svenson KL;de Angelis MH;Herault Y;Mohun T;Mallon AM;Henkelman RM;Brown SD;Adams DJ;Lloyd KC;McKerlie C;Beaudet AL;Bućan M;Murray SA
通讯作者：
Murray SA