Claussnitzer, M. et al. A brief history of human disease genetics. Nature 577, 179–189 (2020).
Dorling, L. et al. Breast cancer risks associated with missense variants in breast cancer susceptibility genes. Genome Med. 14, 51 (2022).
Breast Cancer Association Consortium et al. Breast Cancer Risk Genes—association analysis in more than 113,000 women. N. Engl. J. Med. 384, 428–439 (2021).
Nik-Zainal, S. et al. Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature 534, 47–54 (2016).
Cooper, G. M. & Shendure, J. Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data. Nat. Rev. Genet. 12, 628–640 (2011).
Tabet, D., Parikh, V., Mali, P., Roth, F. P. & Claussnitzer, M. Scalable functional assays for the interpretation of human genetic variation. Annu. Rev. Genet. 56, 19.1–19.25 (2022).
Starita, L. M. et al. Variant interpretation: functional assays to the rescue. Am. J. Hum. Genet. 101, 315–325 (2017).
Weile, J. & Roth, F. P. Multiplexed assays of variant effects contribute to a growing genotype-phenotype atlas. Hum. Genet. 137, 665–678 (2018).
Fowler, D. M. & Fields, S. Deep mutational scanning: a new style of protein science. Nat. Methods 11, 801–807 (2014).
Findlay, G. M. et al. Accurate classification of BRCA1 variants with saturation genome editing. Nature 562, 217–222 (2018).
Findlay, G. M., Boyle, E. A., Hause, R. J., Klein, J. C. & Shendure, J. Saturation editing of genomic regions by multiplex homology-directed repair. Nature 513, 120–123 (2014).
Li, H. et al. Functional annotation of variants of the BRCA2 gene via locally haploid human pluripotent stem cells. Nat. Biomed. Eng. 8, 165–176 (2024).
Hanna, R. et al. Massively parallel assessment of human variants with base editor screens. Cell 184, 1064–1080.e20 (2020).
Cuella-Martin, R. et al. Functional interrogation of DNA damage response variants with base editing screens. Cell 184, 1081–1097.e19 (2021).
Huang, C., Li, G., Wu, J., Liang, J. & Wang, X. Identification of pathogenic variants in cancer genes using base editing screens with editing efficiency correction. Genome Biol. 22, 80 (2021).
Kweon, J. et al. A CRISPR-based base-editing screen for the functional assessment of BRCA1 variants. Oncogene 39, 30–35 (2020).
Kim, Y. et al. High-throughput functional evaluation of human cancer-associated mutations using base editors. Nat. Biotechnol. 40, 874–884 (2022).
Erwood, S. et al. Saturation variant interpretation using CRISPR prime editing. Nat. Biotechnol. 40, 885–895 (2022).
Monteiro, A. N. et al. Variants of uncertain clinical significance in hereditary breast and ovarian cancer genes: best practices in functional analysis for clinical annotation. J. Med. Genet. 57, 509–518 (2020).
Parsons, M. T. et al. Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: an ENIGMA resource to support clinical variant classification. Hum. Mutat. 40, 1557–1578 (2019).
Guidugli, L. et al. Assessment of the clinical relevance of BRCA2 missense variants by functional and computational approaches. Am. J. Hum. Genet. 102, 233–248 (2018).
Richardson, M. E. et al. Strong functional data for pathogenicity or neutrality classify BRCA2 DNA-binding-domain variants of uncertain significance. Am. J. Hum. Genet. 108, 458–468 (2021).
Biswas, K. et al. A computational model for classification of BRCA2 variants using mouse embryonic stem cell-based functional assays. NPJ Genom. Med. 5, 52 (2020).
Ikegami, M. et al. High-throughput functional evaluation of BRCA2 variants of unknown significance. Nat. Commun. 11, 2573 (2020).
Biswas, K. et al. Sequencing-based functional assays for classification of BRCA2 variants in mouse ESCs. Cell Rep. Methods 3, 100628 (2023).
Mishra, A. P. et al. BRCA2–DSS1 interaction is dispensable for RAD51 recruitment at replication-induced and meiotic DNA double strand breaks. Nat. Commun. 13, 1751 (2022).
Mishra, A. P. et al. Characterization of BRCA2 R3052Q variant in mice supports its functional impact as a low-risk variant. Cell Death Dis. 14, 753 (2023).
Hartford, S. A. et al. Interaction with PALB2 is essential for maintenance of genomic integrity by BRCA2. PLoS Genet. 12, e1006236 (2016).
Roy, R., Chun, J. & Powell, S. N. BRCA1 and BRCA2: different roles in a common pathway of genome protection. Nat. Rev. Cancer 12, 68–78 (2011).
Kuznetsov, S. G., Liu, P. & Sharan, S. K. Mouse embryonic stem cell-based functional assay to evaluate mutations in BRCA2. Nat. Med. 14, 875–881 (2008).
Sharan, S. K. BRCA2 deficiency in mice leads to meiotic impairment and infertility. Development 131, 131–142 (2003).
Sahu, S. et al. Saturation genome editing of 11 codons and exon 13 of BRCA2 coupled with chemotherapeutic drug response accurately determines pathogenicity of variants. PLoS Genet. 19, e1010940 (2023).
Landrum, M. J. et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 42, D980–D985 (2014).
Cline, M. S. et al. BRCA challenge: BRCA exchange as a global resource for variants in BRCA1 and BRCA2. PLoS Genet. 14, e1007752 (2018).
Lord, C. J. & Ashworth, A. PARP inhibitors: synthetic lethality in the clinic. Science 355, 1152–1158 (2017).
Clark, K. A. et al. Comprehensive evaluation and efficient classification of BRCA1 RING domain missense substitutions. Am. J. Hum. Genet. 109, 1153–1174 (2022).
Pejaver, V. et al. Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria. Am. J. Hum. Genet. 109, 2163–2177 (2022).
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. 17, 405–424 (2015).
Brnich, S. E. et al. Recommendations for application of the functional evidence PS3/BS3 criterion using the ACMG/AMP sequence variant interpretation framework. Genome Med. 12, 3 (2019).
Tavtigian, S. V., Harrison, S. M., Boucher, K. M. & Biesecker, L. G. Fitting a naturally scaled point system to the ACMG/AMP variant classification guidelines. Hum. Mutat. 41, 1734–1737 (2020).
Sahu, S. et al. Protocol for the saturation and multiplexing of genetic variants using CRISPR–Cas9. STAR Protoc. 4, 102702 (2023).
Easton, D. F. et al. A systematic genetic assessment of 1,433 sequence variants of unknown clinical significance in the BRCA1 and BRCA2 breast cancer-predisposition genes. Am. J. Hum. Genet. 81, 873–883 (2007).
Sirisena, N. et al. Functional evaluation of five BRCA2 unclassified variants identified in a Sri Lankan cohort with inherited cancer syndromes using a mouse embryonic stem cell-based assay. Breast Cancer Res. 22, 43 (2020).
Biswas, K. et al. A comprehensive functional characterization of BRCA2 variants associated with Fanconi anemia using mouse ES cell-based assay. Blood 118, 2430–2442 (2011).
Arnaudi, M. et al. MAVISp: multi-layered assessment of variants by structure for proteins. Preprint at bioRxiv https://doi.org/10.1101/2022.10.22.513328 (2023).
Yang, H. et al. BRCA2 function in DNA binding and recombination from a BRCA2–DSS1–ssDNA structure. Science 297, 1837–1848 (2002).
Li, H. et al. Risks of breast and ovarian cancer for women harboring pathogenic missense variants in BRCA1 and BRCA2 compared with those harboring protein truncating variants. Genet Med. 24, 119–129 (2022).
Spurdle, A. B. et al. Towards controlled terminology for reporting germline cancer susceptibility variants: an ENIGMA report. J. Med. Genet. 56, 347–357 (2019).
Hu, C. et al. Functional analysis and clinical classification of 462 germline BRCA2 missense variants affecting the DNA binding domain. Am. J. Hum. Genet. 111, 584–593 (2024).
Shimelis, H. et al. BRCA2 hypomorphic missense variants confer moderate risks of breast cancer. Cancer Res. 77, 2789–2799 (2017).
Huang, H. et al. Functional evaluation and clinical classification of BRCA2 variants. Nature https://doi.org/10.1038/s41586-024-08388-8 (2024).
Ran, F. A. et al. Genome engineering using the CRISPR–Cas9 system. Nat. Protoc. 8, 2281–2308 (2013).
Walker, L. C. et al. Using the ACMG/AMP framework to capture evidence related to predicted and observed impact on splicing: recommendations from the ClinGen SVI Splicing Subgroup. Am. J. Hum. Genet. 110, 1046–1067 (2023).
Drost, M. et al. A functional assay-based procedure to classify mismatch repair gene variants in Lynch syndrome. Genet. Med. 21, 1486–1496 (2019).
Sorrentino, E. et al. Integration of VarSome API in an existing bioinformatic pipeline for automated ACMG interpretation of clinical variants. Eur. Rev. Med. Pharmacol. Sci. 25, 1–6 (2021).
Kopanos, C. et al. VarSome: the human genomic variant search engine. Bioinformatics 35, 1978–1980 (2019).
Tate, J. G. et al. COSMIC: the Catalogue Of Somatic Mutations In Cancer. Nucleic Acids Res. 47, D941–D947 (2019).
de Bruijn, I. et al. Analysis and visualization of longitudinal genomic and clinical data from the AACR Project GENIE Biopharma Collaborative in cBioPortal. Cancer Res. 83, 3861–3867 (2023).
Tiberti, M. et al. MutateX: an automated pipeline for in silico saturation mutagenesis of protein structures and structural ensembles. Brief. Bioinformatics 23, bbac074 (2022).
Sora, V. et al. RosettaDDGPrediction for high-throughput mutational scans: from stability to binding. Protein Sci. 32, e4527 (2023).
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Cheng, J. et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science 381, eadg7492 (2023).
Frazer, J. et al. Disease variant prediction with deep generative models of evolutionary data. Nature 599, 91–95 (2021).
Rentzsch, P., Schubach, M., Shendure, J. & Kircher, M. CADD-Splice-improving genome-wide variant effect prediction using deep learning-derived splice scores. Genome Med. 13, 31 (2021).
Feng, B.-J. PERCH: a unified framework for disease gene prioritization. Hum. Mutat. 38, 243–251 (2017).
Ioannidis, N. M. et al. REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am. J. Hum. Genet. 99, 877–885 (2016).
Tavtigian, S. V., Deffenbaugh, A. M., Yin, L., Judkins, T., Scholl, T., Samollow, P. B., de Silva, D., Zharkikh, A. & Thomas, A. Comprehensive statistical study of 452 BRCA1 missense substitutions with classification of eight recurrent substitutions as neutral. J. Med. Genet. 43, 295–305 (2005).