Literature DB >> 33875564

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

Chloe Mighton1,2,3,4, Amanda C Smith5, Justin Mayers2, Robert Tomaszewski6, Sherryl Taylor6,7, Stacey Hume6,7, Ron Agatep8,9, Elizabeth Spriggs8,9, Harriet E Feilotter10,11, Laura Semenuk10, Henry Wong10, Lorena Lazo de la Vega12,13, Christian R Marshall14,15, Michelle M Axford14,15, Talia Silver14, George S Charames2,4,15, Vanessa Di Gioacchino2, Nicholas Watkins2,16, William D Foulkes17,18, Marcos Clavier18, Nancy Hamel19, George Chong18,20, Ryan E Lamont21,22, Jillian Parboosingh21,22, Aly Karsan23, Ian Bosdet23,24, Sean S Young23,24, Tracy Tucker23,24, Mohammad Reza Akbari25,26, Marsha D Speevak27, Andrea K Vaags27, Matthew S Lebo12,13, Jordan Lerner-Ellis28,4,15.   

Abstract

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. © Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ.

Entities:  

Keywords:  genetic testing; genetics; human genetics

Mesh:

Year:  2021        PMID: 33875564      PMCID: PMC8523590          DOI: 10.1136/jmedgenet-2021-107738

Source DB:  PubMed          Journal:  J Med Genet        ISSN: 0022-2593            Impact factor:   5.941


  22 in total

1.  Systematic reanalysis of genomic data improves quality of variant interpretation.

Authors:  S M Hiatt; M D Amaral; K M Bowling; C R Finnila; M L Thompson; D E Gray; J M J Lawlor; J N Cochran; E M Bebin; K B Brothers; K M East; W V Kelley; N E Lamb; S E Levy; E J Lose; M B Neu; C A Rich; S Simmons; R M Myers; G S Barsh; G M Cooper
Journal:  Clin Genet       Date:  2018-05-10       Impact factor: 4.438

2.  Recommendations for interpreting the loss of function PVS1 ACMG/AMP variant criterion.

Authors:  Ahmad N Abou Tayoun; Tina Pesaran; Marina T DiStefano; Andrea Oza; Heidi L Rehm; Leslie G Biesecker; Steven M Harrison
Journal:  Hum Mutat       Date:  2018-09-07       Impact factor: 4.878

3.  Prevalence of Variant Reclassification Following Hereditary Cancer Genetic Testing.

Authors:  Jacqueline Mersch; Nichole Brown; Sara Pirzadeh-Miller; Erin Mundt; Hannah C Cox; Krystal Brown; Melissa Aston; Lisa Esterling; Susan Manley; Theodora Ross
Journal:  JAMA       Date:  2018-09-25       Impact factor: 56.272

4.  A new era in the interpretation of human genomic variation.

Authors:  Heidi L Rehm
Journal:  Genet Med       Date:  2017-07-13       Impact factor: 8.822

5.  Points to consider in the reevaluation and reanalysis of genomic test results: a statement of the American College of Medical Genetics and Genomics (ACMG).

Authors:  Joshua L Deignan; Wendy K Chung; Hutton M Kearney; Kristin G Monaghan; Catherine W Rehder; Elizabeth C Chao
Journal:  Genet Med       Date:  2019-04-24       Impact factor: 8.822

6.  Canadian Open Genetics Repository (COGR): a unified clinical genomics database as a community resource for standardising and sharing genetic interpretations.

Authors:  Jordan Lerner-Ellis; Marina Wang; Shana White; Matthew S Lebo
Journal:  J Med Genet       Date:  2015-04-22       Impact factor: 6.318

7.  Points to consider for sharing variant-level information from clinical genetic testing with ClinVar.

Authors:  Danielle R Azzariti; Erin Rooney Riggs; Christa L Martin; Heidi L Rehm; Annie Niehaus; Laura Lyman Rodriguez; Erin M Ramos; Brandi Kattman; Melissa J Landrum
Journal:  Cold Spring Harb Mol Case Stud       Date:  2018-02-01

8.  The impact of variant classification on the clinical management of hereditary cancer syndromes.

Authors:  Scott A Turner; Smita K Rao; R Hayes Morgan; Cindy L Vnencak-Jones; Georgia L Wiesner
Journal:  Genet Med       Date:  2018-06-06       Impact factor: 8.822

9.  Clinical laboratories collaborate to resolve differences in variant interpretations submitted to ClinVar.

Authors:  Steven M Harrison; Jill S Dolinsky; Amy E Knight Johnson; Tina Pesaran; Danielle R Azzariti; Sherri Bale; Elizabeth C Chao; Soma Das; Lisa Vincent; Heidi L Rehm
Journal:  Genet Med       Date:  2017-03-16       Impact factor: 8.822

10.  Genomic variant sharing: a position statement.

Authors:  Caroline F Wright; James S Ware; Anneke M Lucassen; Alison Hall; Anna Middleton; Nazneen Rahman; Sian Ellard; Helen V Firth
Journal:  Wellcome Open Res       Date:  2019-02-05
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  2 in total

Review 1.  Methods to Improve Molecular Diagnosis in Genomic Cold Cases in Pediatric Neurology.

Authors:  Magda K Kadlubowska; Isabelle Schrauwen
Journal:  Genes (Basel)       Date:  2022-02-11       Impact factor: 4.096

2.  Frequency of Parkinson's Disease Genes and Role of PARK2 in Amyotrophic Lateral Sclerosis: An NGS Study.

Authors:  Veria Vacchiano; Anna Bartoletti-Stella; Giovanni Rizzo; Patrizia Avoni; Piero Parchi; Fabrizio Salvi; Rocco Liguori; Sabina Capellari
Journal:  Genes (Basel)       Date:  2022-07-22       Impact factor: 4.141

  2 in total

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