Table 1 Summary of sources used to develop the Hospital Israelita Albert Einstein Standards for Constitutional Sequence Variants Classification
Criteria | Document | Source | Year |
|---|---|---|---|
All | Richards S, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17:405–424 | PMID: 25741868 [2] | 2015 |
Harrison SM, et al. Overview of Specifications to the ACMG/AMP Variant Interpretation Guidelines. Curr Protoc Hum Genet. 2019;103:e93 | PMID: 31479589 [3] | 2019 | |
Biesecker LG, Harrison SM. ClinGen Sequence Variant Interpretation Working Group. The ACMG/AMP reputable source criteria for the interpretation of sequence variants. Genet Med. 2018 Dec;20(12):1687–1688 | PMID: 29543229 [7] | 2018 | |
Tavtigian SV, et al. Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework. Genet Med. 2018;20:1054–1060 | PMID: 29300386 [5] | 2018 | |
Tavtigian SV, et al. Fitting a naturally scaled point system to the ACMG/AMP variant classification guidelines. Hum Mutat. 2020;41:1734–1737 | PMID: 32720330 [6] | 2020 | |
ClinGen General Sequence Variant Curation Process | clinicalgenome.org/site/assets/files/3677/clingen_variant-curation_sopv1.pdf | 2019 | |
ACGS Best Practice Guidelines for Variant Classification in Rare Disease 2020 | www.acgs.uk.com/media/11631/uk-practice-guidelines-for-variant-classification-v4-01-2020.pdf | 2020 | |
Tavtigian SV, et al. Fitting a naturally scaled point system to the ACMG/AMP variant classification guidelines. Hum Mutat. 2020;41:1734–1737 | PMID: 32720330 [6] | 2020 | |
ClinGen Variant Curation Standard Operating Procedure, Version 2 | clinicalgenome.org/docs/variant-curation-standard-operating-procedure-version-2/ | 2021 | |
ClinGen Variant Curation Standard Operating Procedure, Version 3 | clinicalgenome.org/docs/variant-curation-standard-operating-procedure-version-3/ | 2022 | |
ClinGen Sequence Variant Interpretation Working Group | clinicalgenome.org/working-groups/sequence-variant-interpretation/ | Dynamic | |
PVS1 | Abou Tayoun AN, et al. Recommendations for interpreting the loss of function PVS1 ACMG/AMP variant criterion. Hum Mutat. 2018;39(11):1517–1524 | PMID: 30192042 [8] | 2018 |
PS3/BS3 | Brnich SE, et al. Recommendations for application of the functional evidence PS3/BS3 criterion using the ACMG/AMP sequence variant interpretation framework. Genome Med. 2019;12(1):3 | PMID: 31892348 [9] | 2019 |
PP3/BP4/BP7 | Cooper GM, et al. Distribution and intensity of constraint in mammalian genomic sequence. Genome Res. 2005;15(7):901–913 | PMID: 15965027 [10] | 2005 |
Jian X, et al. In silico prediction of splice-altering single nucleotide variants in the human genome. Nucleic Acids Res. 2014;42:13,534–13,544 | PMID: 25416802 [11] | 2014 | |
Ghosh R, et al. Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines. Genome Biol. 2017;18(1):225 | PMID: 29179779 [12] | 2017 | |
Tian Y, et al. REVEL and BayesDel outperform other in silico meta-predictors for clinical variant classification. Sci Rep. 2019;9:12,752 | PMID: 31484976 [13] | 2019 | |
Jaganathan K, et al. Predicting Splicing from Primary Sequence with Deep Learning. Cell. 2019;176(3):535–548.e24 | PMID: 30661751 [14] | 2019 | |
Pejaver V, et al. Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria. Am J Hum Genet. 2022;109:2163–2177 | PMID: 36413997 [15] | 2022 | |
BA1 | Ghosh R, et al. Updated recommendation for the benign stand-alone ACMG/AMP criterion.Hum Mutat. 2018;39:1525–1530 | PMID: 30311383 [16] | 2018 |
PP2/PM1 | Walsh R, et al. Quantitative approaches to variant classification increase the yield and precision of genetic testing in Mendelian diseases: the case of hypertrophic cardiomyopathy. Genome Med. 2019;11:5 | PMID: 30696458 [17] | 2019 |