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Data sharing to improve concordance in variant interpretation across laboratories: results from the Canadian Open Genetics Repository

Authors :
Mighton, Chloe
Smith, Amanda C
Mayers, Justin
Tomaszewski, Robert
Taylor, Sherryl
Hume, Stacey
Agatep, Ron
Spriggs, Elizabeth
Feilotter, Harriet E
Semenuk, Laura
Wong, Henry
Lazo de la Vega, Lorena
Marshall, Christian R
Axford, Michelle M
Silver, Talia
Charames, George S
Di Gioacchino, Vanessa
Watkins, Nicholas
Foulkes, William D
Clavier, Marcos
Hamel, Nancy
Chong, George
Lamont, Ryan E
Parboosingh, Jillian
Karsan, Aly
Bosdet, Ian
Young, Sean S
Tucker, Tracy
Akbari, Mohammad Reza
Speevak, Marsha D
Vaags, Andrea K
Lebo, Matthew S
Lerner-Ellis, Jordan
Source :
Journal of Medical Genetics (JMG); 2022, Vol. 59 Issue: 6 p571-578, 8p
Publication Year :
2022

Abstract

BackgroundThis 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.MethodsLaboratories 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.ResultsTwelve 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.ConclusionsThe 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.

Details

Language :
English
ISSN :
00222593 and 14686244
Volume :
59
Issue :
6
Database :
Supplemental Index
Journal :
Journal of Medical Genetics (JMG)
Publication Type :
Periodical
Accession number :
ejs59711711
Full Text :
https://doi.org/10.1136/jmedgenet-2021-107738