Data sharing to improve concordance in variant interpretation across laboratories: results from the Canadian Open Genetics Repository.
genetic testing
genetics
human genetics
Journal
Journal of medical genetics
ISSN: 1468-6244
Titre abrégé: J Med Genet
Pays: England
ID NLM: 2985087R
Informations de publication
Date de publication:
06 2022
06 2022
Historique:
received:
25
01
2021
revised:
22
03
2021
accepted:
25
03
2021
pubmed:
21
4
2021
medline:
25
5
2022
entrez:
20
4
2021
Statut:
ppublish
Résumé
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.
Sections du résumé
BACKGROUND
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.
METHODS
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.
RESULTS
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.
CONCLUSIONS
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.
Identifiants
pubmed: 33875564
pii: jmedgenet-2021-107738
doi: 10.1136/jmedgenet-2021-107738
pmc: PMC8523590
mid: NIHMS1706059
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
571-578Subventions
Organisme : NHGRI NIH HHS
ID : R01 HG010372
Pays : United States
Investigateurs
Ron Agatep
(R)
Peter Ainsworth
(P)
Mohammad R Akbari
(MR)
Melyssa Aronson
(M)
Raveen Basran
(R)
Andre Blavier
(A)
Andrea Blumenthal
(A)
Yvonne Bombard
(Y)
Ian Bosdet
(I)
Kym Boycott
(K)
Michael Brudno
(M)
Kathleen Buckley
(K)
Jodi Campbell
(J)
Philippe M Campeau
(PM)
Melanie Care
(M)
Nancy Carson
(N)
Martin C Chang
(MC)
Vanessa Di Gioacchino
(V)
Ronald Carter
(R)
George Charames
(G)
David Chitayat
(D)
George Chong
(G)
Edmond Chouinard
(E)
Kathy Chun
(K)
Kenneth J Craddock
(KJ)
Rod Docking
(R)
Andrea Eisen
(A)
Hanna Faghfoury
(H)
Sandra Farrell
(S)
Harriet Feilotter
(H)
Bridget Fernandez
(B)
Marc Fiume
(M)
Cynthia Forster-Gibson
(C)
Jan Friedman
(J)
William Foulkes
(W)
Peter Goodhand
(P)
Robert Hegele
(R)
Spring Holter
(S)
Sheri Horsburgh
(S)
Lauren Hughes
(L)
Stacey Hume
(S)
Olga Jarinova
(O)
Anne Junker
(A)
Aly Karsan
(A)
Sam Khalouei
(S)
Raymond H Kim
(RH)
Joan Knoll
(J)
Elena Kolomietz
(E)
Bartha Knoppers
(B)
Ryan Lamont
(R)
Matthew Lebo
(M)
Jordan Lerner-Ellis
(J)
Georges Maire
(G)
Christian Marshall
(C)
Elizabeth McCready
(E)
Grant Mitchell
(G)
Chantal Morel
(C)
Tanya Nelson
(T)
Abdul Noor
(A)
Brian O'Connor
(B)
Darren O'Rielly
(D)
Francis Ouellette
(F)
Jillian Parboosingh
(J)
Trevor Pugh
(T)
Hilary Racher
(H)
Heidi Rehm
(H)
Christie Riddell
(C)
Jean-Baptiste Riviere
(JB)
David S Rosenblatt
(DS)
Guy Rouleau
(G)
Andrea Ruchon
(A)
Peter Sabatini
(P)
Bekim Sadikovic
(B)
Kara Semotiuk
(K)
Stephen W Scherer
(SW)
Cheryl Shuman
(C)
Josh Silver
(J)
Katherine Siminovitch
(K)
Lesley Solomon-Izsak
(L)
Jean-Francois Soucy
(JF)
Marsha Speevak
(M)
James Stavropoulos
(J)
Lincoln Stein
(L)
Sherryl Taylor
(S)
Deborah Terespolsky
(D)
Robert Tomaszewski
(R)
Tracy Tucker
(T)
Richard F Wintle
(RF)
Nora Wong
(N)
Marina Wang
(M)
Nicholas Watkins
(N)
John S Waye
(JS)
Shana White
(S)
Michael O Woods
(MO)
Philip Wyatt
(P)
Sean Young
(S)
Kathleen-Rose Zakoor
(KR)
Informations de copyright
© Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ.
Déclaration de conflit d'intérêts
Competing interests: None declared.
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