Data sharing to improve concordance in variant interpretation across laboratories: results from the Canadian Open Genetics Repository.
Data sharing to improve concordance in variant interpretation across laboratories: results from the Canadian Open Genetics Repository.
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DOI：
10.1136/jmedgenet-2021-107738
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发表时间：
2022-06
影响因子：
4
通讯作者：
Canadian Open Genetics Repository Working Group
中科院分区：
文献类型：
--
作者：
Mighton C;Smith AC;Mayers J;Tomaszewski R;Taylor S;Hume S;Agatep R;Spriggs E;Feilotter HE;Semenuk L;Wong H;Lazo de la Vega L;Marshall CR;Axford MM;Silver T;Charames GS;Di Gioacchino V;Watkins N;Foulkes WD;Clavier M;Hamel N;Chong G;Lamont RE;Parboosingh J;Karsan A;Bosdet I;Young SS;Tucker T;Akbari MR;Speevak MD;Vaags AK;Lebo MS;Lerner-Ellis J;Canadian Open Genetics Repository Working Group
本研究旨在通过加拿大开放遗传学知识库（COGR）（一种用于变体共享和解释的在线协作努力）识别和解决临床分子遗传实验室之间的不一致变体解释。实验室将变异数据上传到富兰克林Genoox平台。向每个实验室发布报告，总结与另一个实验室的分类冲突的变体。然后，实验室可以重新评估变异以解决不一致性。使用五层模型（致病性（P）、可能致病性（LP）、不确定显著性变异（VUS）、可能良性（LB）、良性（B））、三层模型（LP/P为阳性，VUS为不确定性，LB/B为阴性）和两层模型（LP/P具有临床可行性，VUS/LB/B不具有临床可行性）计算不一致性。我们将COGR分类与富兰克林生成的自动分类进行了比较。12个实验室提交了44510个独特变异的分类。2419个变异体（5.4%）被两个或多个实验室分类。从基线到重新评估后，基于五层模型，不一致变异的数量从833（34.4%的变异由两个或两个以上实验室报告）减少到723（29.9%），基于三层模型，从403（16.7%）减少到279（11.5%），基于两层模型，从77（3.2%）减少到37（1.5%）。与COGR分类相比，自动化富兰克林分类对识别可操作（P或LP）变异的敏感性为94.5%，特异性为96.6%。COGR为实验室提供了一个标准化的机制，以识别不一致的变异解释，并减少基因检测结果交付中的不一致性。随着基因检测在临床护理中得到更广泛的应用，这种质量保证方案非常重要。
This study aimed to identify and resolve discordant variant interpretations across clinical molecular genetic laboratories through the Canadian Open Genetics Repository (COGR), an online collaborative effort for variant sharing and interpretation. Laboratories uploaded variant data to the Franklin Genoox platform. Reports were issued to each laboratory, summarising variants where conflicting classifications with another laboratory were noted. Laboratories could then reassess variants to resolve discordances. Discordance was calculated using a five-tier model (pathogenic (P), likely pathogenic (LP), variant of uncertain significance (VUS), likely benign (LB), benign (B)), a three-tier model (LP/P are positive, VUS are inconclusive, LB/B are negative) and a two-tier model (LP/P are clinically actionable, VUS/LB/B are not). We compared the COGR classifications to automated classifications generated by Franklin. Twelve laboratories submitted classifications for 44 510 unique variants. 2419 variants (5.4%) were classified by two or more laboratories. From baseline to after reassessment, the number of discordant variants decreased from 833 (34.4% of variants reported by two or more laboratories) to 723 (29.9%) based on the five-tier model, 403 (16.7%) to 279 (11.5%) based on the three-tier model and 77 (3.2%) to 37 (1.5%) based on the two-tier model. Compared with the COGR classification, the automated Franklin classifications had 94.5% sensitivity and 96.6% specificity for identifying actionable (P or LP) variants. The COGR provides a standardised mechanism for laboratories to identify discordant variant interpretations and reduce discordance in genetic test result delivery. Such quality assurance programmes are important as genetic testing is implemented more widely in clinical care.
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影响因子：
3.9
作者：
Rivera-Muñoz EA;Milko LV;Harrison SM;Azzariti DR;Kurtz CL;Lee K;Mester JL;Weaver MA;Currey E;Craigen W;Eng C;Funke B;Hegde M;Hershberger RE;Mao R;Steiner RD;Vincent LM;Martin CL;Plon SE;Ramos E;Rehm HL;Watson M;Berg JS
通讯作者：
Berg JS
影响因子：
8.8
作者：
Mighton, Chloe;Charames, George S.;Lerner-Ellis, Jordan
通讯作者：
Lerner-Ellis, Jordan
影响因子：
9.8
作者：
Amendola, Laura M.;Muenzen, Kathleen;Jarvik, Gail P.
通讯作者：
Jarvik, Gail P.
影响因子：
9.8
作者：
Garber, Kathryn B.;Vincent, Lisa M.;Hegde, Madhuri
通讯作者：
Hegde, Madhuri
基因组数据的系统重新分析可提高变异解释的质量。
影响因子：
3.5
作者：
Hiatt SM;Amaral MD;Bowling KM;Finnila CR;Thompson ML;Gray DE;Lawlor JMJ;Cochran JN;Bebin EM;Brothers KB;East KM;Kelley WV;Lamb NE;Levy SE;Lose EJ;Neu MB;Rich CA;Simmons S;Myers RM;Barsh GS;Cooper GM
通讯作者：
Cooper GM