Cited by 595Genomics and Rare DiseasesBRCA gene mutations in cancerGenetic Associations and Epidemiology

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework

Open access

Tavtigian, Sean V., Greenblatt, Marc S., Harrison, Steven M., Nussbaum, Robert L., Prabhu, Snehit A., Boucher, Kenneth M., Biesecker, Leslie G.·2018·Genetics in Medicine

DOI 10.1038/gim.2017.210PMC PMC6336098

Open this paper in CoChat — free account Open PDF

Abstract

Purpose We evaluated the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) variant pathogenicity guidelines for internal consistency and compatibility with Bayesian statistical reasoning.Methods The ACMG/AMP criteria were translated into a naive Bayesian classifier, assuming four levels of evidence and exponentially scaled odds of pathogenicity. We tested this framework with a range of prior probabilities and odds of pathogenicity.Results We modeled the ACMG/AMP guidelines using biologically plausible assumptions. Most ACMG/AMP combining criteria were compatible. One ACMG/AMP likely pathogenic combination was mathematically equivalent to pathogenic and one ACMG/AMP pathogenic combination was actually likely pathogenic. We modeled combinations that include evidence for and against pathogenicity, showing that our approach scored some combinations as pathogenic or likely pathogenic that ACMG/AMP would designate as variant of uncertain significance (VUS).Conclusion By transforming the ACMG/AMP guidelines into a Bayesian framework, we provide a mathematical foundation for what was a qualitative heuristic. Only 2 of the 18 existing ACMG/AMP evidence combinations were mathematically inconsistent with the overall framework. Mixed combinations of pathogenic and benign evidence could yield a likely pathogenic, likely benign, or VUS result. This quantitative framework validates the approach adopted by the ACMG/AMP, provides opportunities to further refine evidence categories and combining rules, and supports efforts to automate components of variant pathogenicity assessments.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1038/gim.2017.210

Cites

10 of 10 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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
Richards, S., Aziz, N., Bale, S., Bick, D., Das, S., Gastier-Foster, J., Grody, W. W., Hegde, M., Lyon, E., Spector, E., et al. (2015) · Genetics in Medicine · doi:10.1038/gim.2015.30 · Cited by 32,594
Sequence variant classification and reporting: recommendations for improving the interpretation of cancer susceptibility genetic test results
Plon, S. E., Eccles, D. M., Easton, D., Foulkes, W. D., Genuardi, M., Greenblatt, M. S., Hogervorst, F. B. L., Hoogerbrugge, N., Spurdle, A. B., Tavtigian, S. V., et al. (2008) · Human Mutation · doi:10.1002/humu.20880 · Cited by 912
Self-citation
Application of a 5-tiered scheme for standardized classification of 2,360 unique mismatch repair gene variants in the InSiGHT locus-specific database
InSiGHT, O. B. O., Thompson, B. A., Spurdle, A. B., Plazzer, J., Greenblatt, M. S., Akagi, K., Al‐Mulla, F., Bapat, B. V., Bernstein, I., Capellá, G., et al. (2013) · Nature Genetics · doi:10.1038/ng.2854 · Cited by 468
Self-citation
A Systematic Genetic Assessment of 1,433 Sequence Variants of Unknown Clinical Significance in the BRCA1 and BRCA2 Breast Cancer–Predisposition Genes
Easton, D. F., Deffenbaugh, A. M., Pruss, D., Frye, C., Wenstrup, R. J., Allen-Brady, K., Tavtigian, S. V., Monteiro, A. N. A., Iversen, E. S., Couch, F. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/521032 · Cited by 455
Self-citation
Integrated Evaluation of DNA Sequence Variants of Unknown Clinical Significance: Application to BRCA1 and BRCA2
Goldgar, D. E., Easton, D. F., Deffenbaugh, A. M., Monteiro, Á. N. A., Tavtigian, S. V., & Couch, F. J. (2004) · The American Journal of Human Genetics · doi:10.1086/424388 · Cited by 388
Self-citation
Classification of rare missense substitutions, using risk surfaces, with genetic- and molecular-epidemiology applications
Tavtigian, S. V., Byrnes, G. B., Goldgar, D. E., & Thomas, A. (2008) · Human Mutation · doi:10.1002/humu.20896 · Cited by 233
Self-citation
Analysis of missense variation in human BRCA1 in the context of interspecific sequence variation
Abkevich, V., Zharkikh, A., Deffenbaugh, A. M., Frank, D., Chen, Y., Shattuck, D., Skolnick, M. H., Gutin, A., & Tavtigian, S. V. (2004) · Journal of Medical Genetics · doi:10.1136/jmg.2003.015867 · Cited by 157
Self-citation
A Full-Likelihood Method for the Evaluation of Causality of Sequence Variants from Family Data
Thompson, D. J., Easton, D. F., & Goldgar, D. E. (2003) · The American Journal of Human Genetics · doi:10.1086/378100 · Cited by 141
A Multifactorial Likelihood Model for MMR Gene Variant Classification Incorporating Probabilities Based on Sequence Bioinformatics and Tumor Characteristics: A Report from the Colon Cancer Family Registry
Thompson, B. A., Goldgar, D. E., Paterson, C. A., Clendenning, M., Walters, R., Arnold, S., Parsons, M. T., Walsh, M. D., Gallinger, S., Haile, R. W., et al. (2012) · Human Mutation · doi:10.1002/humu.22213 · Cited by 95
Self-citation
Calibration of Multiple In Silico Tools for Predicting Pathogenicity of Mismatch Repair Gene Missense Substitutions
Thompson, B. A., Greenblatt, M. S., Vallee, M. P., Herkert, J. C., Tessereau, C., Young, E. L., Adzhubey, I. A., Li, B., Bell, R., Feng, B., et al. (2013) · Human Mutation · doi:10.1002/humu.22214 · Cited by 95
Self-citation

Show all 10 references

Sorted by citations

Technical standards for the interpretation and reporting of constitutional copy-number variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen)
Riggs, E. R., Andersen, E., Cherry, A. M., Kantarci, S., Kearney, H. M., Patel, A., Raca, G., Ritter, D., South, S. T., Thorland, E. C., et al. (2019) · Genetics in Medicine · doi:10.1038/s41436-019-0686-8 · Cited by 1,864
Recommendations for interpreting the loss of function PVS1 ACMG/AMP variant criterion
Tayoun, A. N. A., Pesaran, T. F., DiStefano, M. T., Oza, A. M., Rehm, H. L., Biesecker, L. G., Harrison, S. M., & ClinGen Sequence Variant Interpretation Working Group (ClinGen SVI) (2018) · Human Mutation · doi:10.1002/humu.23626 · Cited by 923
Self-citation
Recommendations for application of the functional evidence PS3/BS3 criterion using the ACMG/AMP sequence variant interpretation framework
Brnich, S. E., Tayoun, A. N. A., Couch, F. J., Cutting, G. R., Greenblatt, M. S., Heinen, C. D., Kanavy, D., Luo, X., McNulty, S. M., Starita, L. M., et al. (2019) · Genome Medicine · doi:10.1186/s13073-019-0690-2 · Cited by 645
Self-citation
Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria
Pejaver, V., Byrne, A. B., Feng, B.-J., Pagel, K. A., Mooney, S. D., Karchin, R., O’Donnell-Luria, A., Harrison, S. M., Tavtigian, S. V., Greenblatt, M. S., et al. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.10.013 · Cited by 589
Self-citation
Expert specification of the ACMG/AMP variant interpretation guidelines for genetic hearing loss
Oza, A. M., DiStefano, M. T., Hemphill, S. E., Cushman, B. J., Grant, A. R., Siegert, R. K., Shen, J., Chapin, A., Boczek, N. J., Schimmenti, L. A., et al. (2018) · Human Mutation · doi:10.1002/humu.23630 · Cited by 487
Genome-wide prediction of disease variant effects with a deep protein language model
Brandes, N., Goldman, G., Wang, C. H., Ye, C., & Ntranos, V. (2023) · Nature Genetics · doi:10.1038/s41588-023-01465-0 · Cited by 489
Adaptation and validation of the ACMG/AMP variant classification framework for MYH7-associated inherited cardiomyopathies: recommendations by ClinGen’s Inherited Cardiomyopathy Expert Panel
Kelly, M., Caleshu, C. A., Morales, A., Buchan, J. G., Wolf, Z. T., Harrison, S. M., Cook, S. A., Dillon, M. W., Garcia, J., Haverfield, E. V., et al. (2018) · Genetics in Medicine · doi:10.1038/gim.2017.218 · Cited by 373
Self-citation
Fitting a naturally scaled point system to the ACMG/AMP variant classification guidelines
Tavtigian, S. V., Harrison, S. M., Boucher, K. M., & Biesecker, L. G. (2020) · Human Mutation · doi:10.1002/humu.24088 · Cited by 365
Self-citation
Using the ACMG/AMP framework to capture evidence related to predicted and observed impact on splicing: Recommendations from the ClinGen SVI Splicing Subgroup
Walker, L. C., de la Hoya, M., Wiggins, G. A. R., Lindy, A. S., Vincent, L. M., Parsons, M. T., Canson, D. M., Bis‐Brewer, D. M., Cass, A. A., Tchourbanov, A. Y., et al. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.06.002 · Cited by 394
Self-citation
Rates and Classification of Variants of Uncertain Significance in Hereditary Disease Genetic Testing
Chen, E. Y., Facio, F. M., Aradhya, K., Rojahn, S., Hatchell, K. E., Aguilar, S. J., Ouyang, K., Saitta, S. C., Hanson‐Kahn, A., Nakousi‐Capurro, N., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.39571 · Cited by 253
GestaltMatcher facilitates rare disease matching using facial phenotype descriptors
Hsieh, T.-C., Bar-Haim, A., Moosa, S., Ehmke, N., Gripp, K. W., Pantel, J. T., Danyel, M., Mensah, M. A., Horn, D., Rosnev, S., et al. (2022) · Nature Genetics · doi:10.1038/s41588-021-01010-x · Cited by 233
Overview of Specifications to the ACMG/AMP Variant Interpretation Guidelines
Harrison, S. M., Biesecker, L. G., & Rehm, H. L. (2019) · Current Protocols in Human Genetics · doi:10.1002/cphg.93 · Cited by 245
Self-citation
Genomic Diagnosis of Rare Pediatric Disease in the United Kingdom and Ireland
Wright, C. F., Campbell, P. H., Eberhardt, R. Y., Aitken, S., Perrett, D., Brent, S., Danecek, P., Gardner, E. J., Chundru, V. K., Lindsay, S., et al. (2023) · New England Journal of Medicine · doi:10.1056/nejmoa2209046 · Cited by 224
Updated recommendation for the benign stand‐alone ACMG/AMP criterion
Ghosh, R., Harrison, S. M., Rehm, H. L., Plon, S. E., Biesecker, L. G., & on behalf of ClinGen Sequence Variant Interpretation Working Group (2018) · Human Mutation · doi:10.1002/humu.23642 · Cited by 179
Self-citation
Diagnostic yield and clinical utility of whole exome sequencing using an automated variant prioritization system, EVIDENCE
Seo, G. H., Kim, T. H., Park, J.-Y., Lee, J., Kim, S., Won, D., Oh, A., Lee, Y., Choi, I. H., Choi, J., et al. (2020) · Clinical Genetics · doi:10.1111/cge.13848 · Cited by 174
Specifications of the ACMG/AMP variant interpretation guidelines for germline TP53 variants
Fortuño, C., Lee, K., Olivier, M., Pesaran, T. F., Mai, P. L., de Andrade, K. C., Attardi, L. D., Crowley, S. B., Evans, D. G., Feng, B., et al. (2020) · Human Mutation · doi:10.1002/humu.24152 · Cited by 173
Gene‐specific criteria for PTEN variant curation: Recommendations from the ClinGen PTEN Expert Panel
Mester, J. L., Ghosh, R., Pesaran, T. F., Huether, R., Karam, R., Hruska, K. S., Costa, H. A., Lachlan, K. L., Ngeow, J. Y. Y., Barnholtz‐Sloan, J. S., et al. (2018) · Human Mutation · doi:10.1002/humu.23636 · Cited by 158
ClinGen Myeloid Malignancy Variant Curation Expert Panel recommendations for germline RUNX1 variants
Luo, X., Feurstein, S. K., Mohan, S., Porter, C. C., Jackson, S. A., Keel, S., Chicka, M. C., Brown, A. L., Kesserwan, C. A., Agarwal, A., et al. (2019) · Blood Advances · doi:10.1182/bloodadvances.2019000644 · Cited by 158
Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification
Parsons, M. T., Tudini, E., Li, H., Hahnen, E., Wappenschmidt, B., Feliubadaló, L., Aalfs, C. M., Agata, S., Aittomäki, K., Alducci, E., et al. (2019) · Human Mutation · doi:10.1002/humu.23818 · Cited by 144
Implementing Whole Genome Sequencing (WGS) in Clinical Practice: Advantages, Challenges, and Future Perspectives
Brlek, P., Bulić, L., Bračić, M., Projić, P., Škaro, V., Shah, N. D., Shah, P. S., & Primorac, D. (2024) · Cells · doi:10.3390/cells13060504 · Cited by 141
Massively parallel functional testing of MSH2 missense variants conferring Lynch syndrome risk
Jia, X., Burugula, B. B., Chen, V., Lemons, R. M., Jayakody, S., Maksutova, M., & Kitzman, J. O. (2020) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2020.12.003 · Cited by 141
The Molecular Tumor Board Portal supports clinical decisions and automated reporting for precision oncology
Tamborero, D., Dienstmann, R., Rachid, M. H., Boekel, J., López‐Fernández, A., Jonsson, M., Razzak, A., Braña, I., Petris, L. D., Yachnin, J., et al. (2022) · Nature Cancer · doi:10.1038/s43018-022-00332-x · Cited by 131
Quantitative approaches to variant classification increase the yield and precision of genetic testing in Mendelian diseases: the case of hypertrophic cardiomyopathy
Walsh, R., Mazzarotto, F., Whiffin, N., Buchan, R. J., Midwinter, W., Wilk, A., Li, N., Felkin, L. E., Ingold, N., Govind, R., et al. (2019) · Genome Medicine · doi:10.1186/s13073-019-0616-z · Cited by 126
Closing the gap: Systematic integration of multiplexed functional data resolves variants of uncertain significance in BRCA1, TP53, and PTEN
Fayer, S., Horton, C., Dines, J. N., Rubin, A. F., Richardson, M. E., McGoldrick, K., Hernandez, F., Pesaran, T. F., Karam, R., Shirts, B. H., et al. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.11.001 · Cited by 116
ClinGen guidance for use of the PP1/BS4 co-segregation and PP4 phenotype specificity criteria for sequence variant pathogenicity classification
Biesecker, L. G., Byrne, A. B., Harrison, S. M., Pesaran, T. F., Schäffer, A. A., Shirts, B. H., Tavtigian, S. V., & Rehm, H. L. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.11.009 · Cited by 107
Self-citation
Diagnostic interpretation of genetic studies in patients with primary immunodeficiency diseases: A working group report of the Primary Immunodeficiency Diseases Committee of the American Academy of Allergy, Asthma & Immunology
Chinn, I. K., Chan, A. Y. K., Chen, K., Chou, J. S., Dorsey, M. J., Hajjar, J., Jongco, A. M., Keller, M. D., Kobrynski, L. J., Kumánovics, A., et al. (2019) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2019.09.009 · Cited by 112
Variant Classification Concordance using the ACMG-AMP Variant Interpretation Guidelines across Nine Genomic Implementation Research Studies
Amendola, L. M., Muenzen, K., Biesecker, L. G., Bowling, K. M., Cooper, G. M., Dorschner, M. O., Driscoll, C. C., Foreman, A. K. M., Golden‐Grant, K. L., Greally, J. M., et al. (2020) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2020.09.011 · Cited by 106
Self-citation
Evidence-based recommendations for gene-specific ACMG/AMP variant classification from the ClinGen ENIGMA BRCA1 and BRCA2 Variant Curation Expert Panel
Parsons, M. T., de la Hoya, M., Richardson, M. E., Tudini, E., Anderson, M. G., Berkofsky‐Fessler, W., Caputo, S. M., Chan, R. C., Cline, M., Feng, B., et al. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2024.07.013 · Cited by 112
Self-citation
Enhancing rare variant interpretation in inherited arrhythmias through quantitative analysis of consortium disease cohorts and population controls
Walsh, R., Lahrouchi, N., Tadros, R., Kyndt, F., Glinge, C., Postema, P. G., Amin, A. S., Nannenberg, E. A., Ware, J. S., Whiffin, N., et al. (2020) · Genetics in Medicine · doi:10.1038/s41436-020-00946-5 · Cited by 99
Germ line predisposition variants occur in myelodysplastic syndrome patients of all ages
Feurstein, S. K., Trottier, A. M., Estrada‐Merly, N., Pozsgai, M. J., McNeely, K. E., Drazer, M. W., Ruhle, B. C., Sadera, K., Koppayi, A. L., Scott, B. L., et al. (2022) · Blood · doi:10.1182/blood.2022015790 · Cited by 96
The genetics and pathogenesis of CAKUT
Kolvenbach, C. M., Shril, S., & Hildebrandt, F. (2023) · Nature Reviews Nephrology · doi:10.1038/s41581-023-00742-9 · Cited by 102
Stepwise ABC system for classification of any type of genetic variant
Houge, G., Laner, A., Çırak, S., de Leeuw, N., Scheffer, H., & den Dunnen, J. T. (2021) · European Journal of Human Genetics · doi:10.1038/s41431-021-00903-z · Cited by 92
Rapid genomic sequencing for genetic disease diagnosis and therapy in intensive care units: a review
Kingsmore, S. F., Nofsinger, R. R., & Ellsworth, K. (2024) · npj Genomic Medicine · doi:10.1038/s41525-024-00404-0 · Cited by 94
Analysis of missense variants in the human genome reveals widespread gene-specific clustering and improves prediction of pathogenicity
Quinodoz, M., Peter, V. G., Cisarova, K. V., Royer‐Bertrand, B., Stenson, P. D., Cooper, D. N., Unger, S., Superti‐Furga, A., & Rivolta, C. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.01.006 · Cited by 94
PEDIA: prioritization of exome data by image analysis
Hsieh, T., Mensah, M. A., Pantel, J. T., Aguilar, D., Bar, O., Bayat, A., Becerra-Solano, L. E., Bentzen, H. B., Biskup, S., Borisov, O., et al. (2019) · Genetics in Medicine · doi:10.1038/s41436-019-0566-2 · Cited by 82
A machine learning approach based on ACMG/AMP guidelines for genomic variant classification and prioritization
Nicora, G., Zucca, S., Limongelli, I., Bellazzi, R., & Magni, P. (2022) · Scientific Reports · doi:10.1038/s41598-022-06547-3 · Cited by 59
Deep CRISPR mutagenesis characterizes the functional diversity of TP53 mutations
Funk, J. S., Klimovich, M., Drangenstein, D., Pielhoop, O., Hunold, P., Borowek, A., Noeparast, A., Pavlakis, E., Neumann, M. M., Balourdas, D.-I., et al. (2025) · Nature Genetics · doi:10.1038/s41588-024-02039-4 · Cited by 94
Calibration of additional computational tools expands ClinGen recommendation options for variant classification with PP3/BP4 criteria
Bergquist, T., Stenton, S. L., Nadeau, E. A. W., Byrne, A. B., Greenblatt, M. S., Harrison, S. M., Tavtigian, S. V., O’Donnell‐Luria, A., Biesecker, L. G., Predrag Radivojac, A., et al. (2025) · Genetics in Medicine · doi:10.1016/j.gim.2025.101402 · Cited by 92
Self-citation
Recommendations for the collection and use of multiplexed functional data for clinical variant interpretation
On behalf of the Brotman Baty Institute Mutational Scanning Working Group, Gelman, H., Dines, J. N., Berg, J., Berger, A. H., Brnich, S., Hisama, F. M., James, R. G., Rubin, A. F., Shendure, J., et al. (2019) · Genome Medicine · doi:10.1186/s13073-019-0698-7 · Cited by 81
Clinical Interpretation of Sequence Variants
Zhang, J., Yao, Y., He, H., & Shen, J. (2020) · Current Protocols in Human Genetics · doi:10.1002/cphg.98 · Cited by 81
Variant curation expert panel recommendations for RYR1 pathogenicity classifications in malignant hyperthermia susceptibility
Johnston, J. J., Dirksen, R. T., Girard, T., Gonsalves, S. G., Hopkins, P. M., Riazi, S., Saddic, L., Sambuughin, N., Saxena, R., Stowell, K. M., et al. (2021) · Genetics in Medicine · doi:10.1038/s41436-021-01125-w · Cited by 81
Self-citation
ClinGen Variant Curation Interface: a variant classification platform for the application of evidence criteria from ACMG/AMP guidelines
Preston, C. G., Wright, M. W., Madhavrao, R., Harrison, S. M., Goldstein, J., Luo, X., Wand, H., Wulf, B., Cheung, G., Mandell, M. E., et al. (2022) · Genome Medicine · doi:10.1186/s13073-021-01004-8 · Cited by 86
Self-citation
Identification of clinically actionable variants from genome sequencing of families with congenital heart disease
Alankarage, D., Ip, E., Szot, J. O., Munro, J. E., Blue, G. M., Harrison, K., Cuny, H., Enriquez, A., Troup, M., Humphreys, D. T., et al. (2018) · Genetics in Medicine · doi:10.1038/s41436-018-0296-x · Cited by 82
Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes
Çubuk, C., Garrett, A., Choi, S., King, L., Loveday, C., Torr, B., Burghel, G. J., Durkie, M., Callaway, A., Robinson, R., et al. (2021) · Genetics in Medicine · doi:10.1038/s41436-021-01265-z · Cited by 82
Quantifying the potential of functional evidence to reclassify variants of uncertain significance in the categorical and Bayesian interpretation frameworks
Brnich, S. E., Rivera-Munoz, E. A., & Berg, J. S. (2018) · Human Mutation · doi:10.1002/humu.23609 · Cited by 80
Identification of pathogenic variant enriched regions across genes and gene familiesPreprint
Pérez‐Palma, E., May, P., Iqbal, S., Niestroj, L., Du, J., Heyne, H., Lal, J. C., O’Donnell‐Luria, A., Nürnberg, P., Palotie, A. V., et al. (2019) · Genome Research · doi:10.1101/gr.252601.119 · Cited by 76
Contemporary Insights Into the Genetics of Hypertrophic Cardiomyopathy: Toward a New Era in Clinical Testing?
Mazzarotto, F., Olivotto, I., Boschi, B., Girolami, F., Poggesi, C., Barton, P. J. R., & Walsh, R. (2020) · Journal of the American Heart Association · doi:10.1161/jaha.119.015473 · Cited by 63
AutoPVS1: An automatic classification tool for PVS1 interpretation of null variants
Xiang, J., Peng, J., Baxter, S. M., & Peng, Z. (2020) · Human Mutation · doi:10.1002/humu.24051 · Cited by 72
The Clinical Genome Resource (ClinGen): Advancing genomic knowledge through global curation
Andersen, E., Azzariti, D. R., Babb, L., Berg, J. S., Biesecker, L. G., Bly, Z., Buchanan, A. H., DiStefano, M. T., Gong, L., Harrison, S. M., et al. (2024) · Genetics in Medicine · doi:10.1016/j.gim.2024.101228 · Cited by 79
Self-citation
DECIPHER: Improving Genetic Diagnosis Through Dynamic Integration of Genomic and Clinical Data
Foreman, J., Perrett, D., Mazaika, E., Hunt, S. E., Ware, J. S., & Firth, H. V. (2023) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-102822-100509 · Cited by 76
Shifting landscapes of human MTHFR missense-variant effects
Weile, J., Kishore, N., Sun, S., Maaieh, R., Verby, M., Li, R., Fotiadou, I., Kitaygorodsky, J., Wu, Y., Holenstein, A., et al. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.05.009 · Cited by 73
Self-citation
Comparison of in silico strategies to prioritize rare genomic variants impacting RNA splicing for the diagnosis of genomic disorders
Rowlands, C. F., Thomas, H. B., Lord, J., Wai, H. A., Arno, G., Beaman, G. M., Sergouniotis, P. I., Gomes-Silva, B., Campbell, C., Gossan, N., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-99747-2 · Cited by 64
Functional evaluation and clinical classification of BRCA2 variants
Huang, H., Hu, C., Na, J., Hart, S. N., Gnanaolivu, R. D., Abozaid, M., Rao, T., Tecleab, Y. A., Ambrosone, C. B., Yao, S., et al. (2025) · Nature · doi:10.1038/s41586-024-08388-8 · Cited by 67
StrVCTVRE: A supervised learning method to predict the pathogenicity of human genome structural variants
Sharo, A. G., Hu, Z., Sunyaev, S. R., & Brenner, S. E. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.12.007 · Cited by 65
Genebe.net: Implementation and validation of an automatic ACMG variant pathogenicity criteria assignment
Stawiński, P., & Płoski, R. (2024) · Clinical Genetics · doi:10.1111/cge.14516 · Cited by 71
Integrating somatic variant data and biomarkers for germline variant classification in cancer predisposition genes
Walsh, M. F., Ritter, D., Kesserwan, C. A., Sonkin, D., Chakravarty, D., Chao, E., Ghosh, R., Kemel, Y. M., Wu, G., Lee, K., et al. (2018) · Human Mutation · doi:10.1002/humu.23640 · Cited by 63
Outcomes of 92 patient-driven family studies for reclassification of variants of uncertain significance
Tsai, G. J., Rañola, J. M. O., Smith, C., Garrett, L. T., Bergquist, T. R., Casadei, S., Bowen, D. J., & Shirts, B. H. (2018) · Genetics in Medicine · doi:10.1038/s41436-018-0335-7 · Cited by 65
High-throughput functional evaluation of BRCA2 variants of unknown significance
Ikegami, M., Kohsaka, S., Ueno, T., Momozawa, Y., Inoue, S., Tamura, K., Shimomura, A., Hosoya, N., Kobayashi, H., Tanaka, S., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-16141-8 · Cited by 67
CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods
Jain, S., Bakolitsa, C., Brenner, S. E., Predrag Radivojac, A., Moult, J., Repo, S., Hoskins, R. A., Andreoletti, G., Barsky, D., Chellapan, A., et al. (2024) · Genome biology · doi:10.1186/s13059-023-03113-6 · Cited by 75
Management of Secondary Genomic Findings
Katz, A. E., Nussbaum, R. L., Solomon, B. D., Rehm, H. L., Williams, M. S., & Biesecker, L. G. (2020) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2020.05.002 · Cited by 67
Self-citation
Updated variant curation expert panel criteria and pathogenicity classifications for 251 variants for RYR1 -related malignant hyperthermia susceptibility
Johnston, J. J., Dirksen, R. T., Girard, T., Hopkins, P. M., Kraeva, N., Mungunsukh, O., Radenbaugh, K. B., Riazi, S., Robinson, R. L., Saddic, L., et al. (2022) · Human Molecular Genetics · doi:10.1093/hmg/ddac145 · Cited by 60
Self-citation
Systematic misclassification of missense variants in BRCA1 and BRCA2 “coldspots”
Dines, J. N., Shirts, B. H., Slavin, T. P., Walsh, T., King, M., Fowler, D. M., & Pritchard, C. C. (2020) · Genetics in Medicine · doi:10.1038/s41436-019-0740-6 · Cited by 60
Combining evidence for and against pathogenicity for variants in cancer susceptibility genes: CanVIG-UK consensus recommendations
Garrett, A., Durkie, M., Callaway, A., Burghel, G. J., Robinson, R., Drummond, J., Torr, B., Çubuk, C., Berry, I. R., Wallace, A. J., et al. (2020) · Journal of Medical Genetics · doi:10.1136/jmedgenet-2020-107248 · Cited by 59
Strong functional data for pathogenicity or neutrality classify BRCA2 DNA-binding-domain variants of uncertain significance
Richardson, M. E., Hu, C., Lee, K. Y., LaDuca, H., Fulk, K., Durda, K. M., Deckman, A., Goldgar, D. E., Monteiro, Á. N. A., Gnanaolivu, R. D., et al. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.02.005 · Cited by 58
Evaluating the impact of in silico predictors on clinical variant classification
Wilcox, E. H., Sarmady, M., Wulf, B., Wright, M. W., Rehm, H. L., Biesecker, L. G., & Tayoun, A. N. A. (2021) · Genetics in Medicine · doi:10.1016/j.gim.2021.11.018 · Cited by 58
Self-citation
Saturation genome editing of BAP1 functionally classifies somatic and germline variants
Waters, A. J., Brendler-Spaeth, T., Smith, D., Offord, V., Tan, H. K., Zhao, Y., Obolenski, S., Nielsen, M., van Doorn, R., Murphy, J.-E., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01799-3 · Cited by 64
A functional assay–based procedure to classify mismatch repair gene variants in Lynch syndrome
Drost, M., Tiersma, Y., Thompson, B. A., Frederiksen, J. H., Keijzers, G., Glubb, D. M., Kathe, S. D., Osinga, J., Westers, H., Pappas, L. M., et al. (2018) · Genetics in Medicine · doi:10.1038/s41436-018-0372-2 · Cited by 58
Self-citation
Artificial Intelligence and Cardiovascular Genetics
Krittanawong, C., Johnson, K. W., Choi, E., Kaplin, S. L., Venner, E., Murugan, M., Wang, Z., Glicksberg, B. S., Amos, C. I., Schatz, M. C., et al. (2022) · Life · doi:10.3390/life12020279 · Cited by 59
Navigating the nuances of clinical sequence variant interpretation in Mendelian disease
Strande, N. T., Brnich, S. E., Roman, T. S., & Berg, J. S. (2018) · Genetics in Medicine · doi:10.1038/s41436-018-0100-y · Cited by 63
Exome sequencing reveals genetic heterogeneity and clinically actionable findings in children with cerebral palsy
Wang, Y., Xu, Y., Zhou, C., Cheng, Y., Qiao, N., Shang, Q., Xia, L., Song, J., Gao, C., Qiao, Y., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02912-z · Cited by 57
Cancer Variant Interpretation Group UK (CanVIG-UK): an exemplar national subspecialty multidisciplinary network
Garrett, A., Callaway, A., Durkie, M., Çubuk, C., Alikian, M., Burghel, G., Robinson, R., Izatt, L., Talukdar, S., Side, L., et al. (2020) · Journal of Medical Genetics · doi:10.1136/jmedgenet-2019-106759 · Cited by 45
Telomere biology disorder prevalence and phenotypes in adults with familial hematologic and/or pulmonary presentations
Feurstein, S. K., Adegunsoye, A., Mojsilovic, D., Vij, R., DePersia, A., Rajagopal, P. S., Osman, A. E. G., Collins, R. H., Kim, R. H., Gore, S. D., et al. (2020) · Blood Advances · doi:10.1182/bloodadvances.2020001721 · Cited by 53
Clinical Variant Reclassification in Hereditary Disease Genetic Testing
Kobayashi, Y., Chen, E. Y., Facio, F. M., Metz, H. C., Poll, S. R., Swartzlander, D. B., Johnson, B. A., & Aradhya, S. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.44526 · Cited by 51
UniProt genomic mapping for deciphering functional effects of missense variants
McGarvey, P. B., Nightingale, A., Luo, J., Huang, H., Martin, M. J., & Wu, C. H. (2019) · Human Mutation · doi:10.1002/humu.23738 · Cited by 50
Comparative analysis of functional assay evidence use by ClinGen Variant Curation Expert Panels
Kanavy, D., McNulty, S. M., Jairath, M. K., Brnich, S. E., Bizon, C., Powell, B. C., & Berg, J. S. (2019) · Genome Medicine · doi:10.1186/s13073-019-0683-1 · Cited by 56
Multiplexed Assays of Variant Effect and Automated Patch Clamping Improve KCNH2 -LQTS Variant Classification and Cardiac Event Risk Stratification
O’Neill, M. J., Ng, C., Aizawa, T., Sala, L., Bains, S., Winbo, A., Ullah, R., Shen, Q., Tan, C.-Y., Kozek, K. A., et al. (2024) · Circulation · doi:10.1161/circulationaha.124.069828 · Cited by 46
TAPES: A tool for assessment and prioritisation in exome studies
Xavier, A., Scott, R. J., & Talseth‐Palmer, B. A. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007453 · Cited by 45
Machine learning-based reclassification of germline variants of unknown significance: The RENOVO algorithm
Favalli, V., Tini, G., Bonetti, E., Vozza, G., Guida, A., Gandini, S., Pelicci, P. G., & Mazzarella, L. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.03.010 · Cited by 49
Classification of variants of uncertain significance in BRCA1 and BRCA2 using personal and family history of cancer from individuals in a large hereditary cancer multigene panel testing cohort
Li, H.-Y., LaDuca, H., Pesaran, T., Chao, E. C., Dolinsky, J. S., Parsons, M., Spurdle, A., Polley, E., Shimelis, H., Hart, S., et al. (2019) · Genetics in Medicine · doi:10.1038/s41436-019-0729-1 · Cited by 45
APOGEE 2: multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants
Bianco, S. D., Parca, L., Petrizzelli, F., Biagini, T., Giovannetti, A., Liorni, N., Napoli, A., Carella, M., Procaccio, V., Lott, M. T., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40797-7 · Cited by 44
Feasibility and limitations of cultured skin fibroblasts for germline genetic testing in hematologic disorders
DeRoin, L., Silva, M. C. D. A., Petras, K., Arndt, K., Phillips, N., Wanjari, P., Subramanian, H. P., Montes, D. M., McElherne, J., Theissen, M., et al. (2022) · Human Mutation · doi:10.1002/humu.24374 · Cited by 43
Improved, ACMG-compliant, in silico prediction of pathogenicity for missense substitutions encoded by TP53 variants
Fortuño, C., James, P. A., Young, E. L., Feng, B., Olivier, M., Pesaran, T. F., Tavtigian, S. V., & Spurdle, A. B. (2018) · Human Mutation · doi:10.1002/humu.23553 · Cited by 43
Self-citation
Genotype–phenotype associations in a large PRPH2 ‐related retinopathy cohort
Reeves, M. J., Goetz, K., Guan, B., Ullah, E., Blain, D., Zein, W. M., Tumminia, S. J., & Hufnagel, R. B. (2020) · Human Mutation · doi:10.1002/humu.24065 · Cited by 43
A fatal case of COQ7‐associated primary coenzyme Q10 deficiency
Kwong, A. K., Chiu, A. T. G., Tsang, M. H., Lun, K., Rodenburg, R. J., Smeitink, J. A. M., Chung, B. H., & Fung, C. (2019) · JIMD Reports · doi:10.1002/jmd2.12032 · Cited by 42
The use of precision diagnostics for monogenic diabetes: a systematic review and expert opinion
Murphy, R., Colclough, K., Pollin, T. I., Iklé, J. M., Svalastoga, P., Maloney, K. A., Saint‐Martin, C., Molnes, J., PMDI, A., Tobias, D. K., et al. (2023) · Communications Medicine · doi:10.1038/s43856-023-00369-8 · Cited by 55
Diagnostic Outcomes of Concurrent DNA and RNA Sequencing in Individuals Undergoing Hereditary Cancer Testing
Horton, C., Hoang, L., Zimmermann, H., Young, C. C., Grzybowski, J., Durda, K. M., Vuong, H., Burks, D. J., Cass, A. A., LaDuca, H., et al. (2023) · JAMA Oncology · doi:10.1001/jamaoncol.2023.5586 · Cited by 41
Self-citation
Allelic prevalence and geographic distribution of cerebrotendinous xanthomatosis
Pramparo, T., Steiner, R. D., Rodems, S., & Jenkinson, C. P. (2023) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-022-02578-1 · Cited by 41
Multiplexed assays of variant effect for clinical variant interpretation
McEwen, A. E., Tejura, M., Fayer, S., Starita, L. M., & Fowler, D. M. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00870-x · Cited by 52
Variants of Uncertain Significance
Morales, A., & Hershberger, R. E. (2018) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.118.002169 · Cited by 40
Using Somatic Mutations from Tumors to Classify Variants in Mismatch Repair Genes
Shirts, B. H., Konnick, E. Q., Upham, S. L., Walsh, T., Rañola, J. M. O., Jacobson, A. L., King, M., Pearlman, R., Hampel, H., & Pritchard, C. C. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.05.001 · Cited by 39
LEAP: Using machine learning to support variant classification in a clinical setting
Lai, C., Zimmer, A. D., O'Connor, R., Kim, S., Chan, R., van den Akker, J., Zhou, A. Y., Topper, S., & Mishne, G. (2020) · Human Mutation · doi:10.1002/humu.24011 · Cited by 38
Adapting the ACMG/AMP variant classification framework: A perspective from the ClinGen Hemoglobinopathy Variant Curation Expert Panel
Kountouris, P., Stephanou, C., Lederer, C. W., Traeger‐Synodinos, J., Bento, C., Harteveld, C. L., Fylaktou, I., Koopmann, T. T., Halim‐Fikri, H., Michailidou, K., et al. (2021) · Human Mutation · doi:10.1002/humu.24280 · Cited by 42
Gene-specific ACMG/AMP classification criteria for germline APC variants: Recommendations from the ClinGen InSiGHT Hereditary Colorectal Cancer/Polyposis Variant Curation Expert Panel
Spier, I., Yin, X., Richardson, M. E., Pineda, M., Laner, A., Ritter, D., Boyle, J., Mur, P., Hansen, T. V. O., Shi, X., et al. (2023) · Genetics in Medicine · doi:10.1016/j.gim.2023.100992 · Cited by 40
Self-citation
DECIPHER: Supporting the interpretation and sharing of rare disease phenotype‐linked variant data to advance diagnosis and research
Foreman, J., Brent, S., Perrett, D., Bevan, A. P., Hunt, S. E., Cunningham, F. M., Hurles, M. E., & Firth, H. V. (2022) · Human Mutation · doi:10.1002/humu.24340 · Cited by 36
Considerations in assessing germline variant pathogenicity using cosegregation analysis
Belman, S., Parsons, M. T., Spurdle, A. B., Goldgar, D. E., & Feng, B. (2020) · Genetics in Medicine · doi:10.1038/s41436-020-0920-4 · Cited by 37
Integration of functional assay data results provides strong evidence for classification of hundreds of BRCA1 variants of uncertain significance
Junior, P. C. M. L., Nepomuceno, T. C., de Souza, M. L. M., Machado, G. F., Veloso, M. D., Henriques, T. B., dos Santos, D. Z., Ribeiro, I. G., Ribeiro, R. S., Rangel, L. B. A., et al. (2020) · Genetics in Medicine · doi:10.1038/s41436-020-00991-0 · Cited by 37
Functional interpretation, cataloging, and analysis of 1,341 glucose-6-phosphate dehydrogenase variants
Geck, R. C., Powell, N. R., & Dunham, M. J. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.01.003 · Cited by 37
Specifications of the variant curation guidelines for ITGA2B/ITGB3: ClinGen Platelet Disorder Variant Curation Panel
Ross, J. E., Zhang, B. M., Lee, K., Mohan, S., Branchford, B. R., Bray, P. F., Dugan, S. N., Freson, K., Heller, P. G., Kahr, W. H. A., et al. (2021) · Blood Advances · doi:10.1182/bloodadvances.2020003712 · Cited by 36
Saturation-scale functional evidence supports clinical variant interpretation in Lynch syndrome
Scott, A. J., Hernandez, F., Chamberlin, A. C., Smith, C., Karam, R., & Kitzman, J. O. (2022) · Genome biology · doi:10.1186/s13059-022-02839-z · Cited by 36
Critical assessment of missense variant effect predictors on disease-relevant variant data
Rastogi, R., Chung, R., Li, S., Li, C., Lee, K., Woo, J., Kim, D., Keum, C., Babbi, G., Martelli, P. L., et al. (2025) · Human Genetics · doi:10.1007/s00439-025-02732-2 · Cited by 39

Authors: Tavtigian, S. V., Greenblatt, M. S., Harrison, S. M., Nussbaum, R. L., Prabhu, S. A., Boucher, K. M., & Biesecker, L. G.
Affiliations: University of Utah (US)
University of Vermont (US)
Harvard University (US)
Invitae (United States) (US)
Stanford University (US)
National Institutes of Health (US)
Corresponding author: Tavtigian, Sean V.
ORCID: Tavtigian, Sean V.
Greenblatt, Marc S.
Harrison, Steven M.
Nussbaum, Robert L.
Prabhu, Snehit A.
Boucher, Kenneth M.
Biesecker, Leslie G.
Venue: Genetics in Medicine
Publisher: Elsevier BV
Published: 2018-09
Volume: 20
Issue: 9
Pages: 1054-1060
ISSN: 1098-3600 (ISSN-L)
DOI: 10.1038/gim.2017.210
PMC: PMC6336098
OpenAlex: W2781534540
Cited by: 590
References: 10
Citations by year: 2026: 113 · 2025: 81 · 2024: 77 · 2023: 72 · 2022: 84 · 2021: 57 · 2020: 48 · 2019: 37 · 2018: 18
FWCI: 24.53
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 1236 d)
Locations: Genetics in Medicine (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: University of Connecticut
Johns Hopkins University
Wellcome Trust
Huntsman Cancer Institute (P30CA042014)
Invitae
University of Cambridge
National Institutes of Health
University of North Carolina at Chapel Hill
School of Medicine
National Human Genome Research Institute (HG200388 03, U41HG009650, U41HG006834, U01HG007436, HG200387, HG200388, U01HG007437, U41HG009649, ZIAHG200388, U24HG009650, ZIAHG200387)
National Cancer Institute (R01CA164944, R01CA121245, P30CA042014)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › BRCA gene mutations in cancer
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Keywords: variant pathogenicity assessment, Bayesian classification, naive Bayesian classifier, variants of uncertain significance, combining rules, prior probability
MeSH terms: Bayes Theorem, Genetic Testing, Humans, Software, Genetic Variation, Genome, Human, Sequence Analysis, DNA, Computational Biology, Genomics, High-Throughput Nucleotide Sequencing

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks