Comprehensive statistical study of 452 BRCA1 missense substitutions with classification of eight recurrent substitutions as neutral

By Sean V. Tavtigian, Amie Deffenbaugh, L. Yin, T. Judkins, T. Scholl, P. Samollow, D. Silva, A. Zharkikh, Alun Thomas. Journal of Medical Genetics, 2005

687 citations recorded by the source corpus.

Background: Genetic testing for hereditary cancer syndromes contributes to the medical management of patients who may be at increased risk of one or more cancers. BRCA1 and BRCA2 testing for hereditary breast and ovarian cancer is one such widely used test. However, clinical testing methods with high sensitivity for deleterious mutations in these genes also detect many unclassified variants, primarily missense substitutions. Methods: We developed an extension of the Grantham difference, called A-GVGD, to score missense substitutions against the range of variation present at their position in a multiple sequence alignment. Combining two methods, co-occurrence of unclassified variants with clearly deleterious mutations and A-GVGD, we analysed most of the missense substitutions observed in BRCA1. Results: A-GVGD was able to resolve known neutral and deleterious missense substitutions into distinct sets. Additionally, eight previously unclassified BRCA1 missense substitutions observed in trans with one or more deleterious mutations, and within the cross-species range of variation observed at their position in the protein, are now classified as neutral. Discussion: The methods combined here can classify as neutral about 50% of missense substitutions that have been observed with two or more clearly deleterious mutations. Furthermore, odds ratios estimated for sets of substitutions grouped by A-GVGD scores are consistent with the hypothesis that most unclassified substitutions that are within the cross-species range of variation at their position in BRCA1 are also neutral. For most of these, clinical reclassification will require integrated application of other methods such as pooled family histories, segregation analysis, or validated functional assay.

Subfields: Genetics, Pathology and Forensic Medicine

View Paper: https://www.semanticscholar.org/paper/9fe5be8ed66c820f649c8e66e4941e528af72ee5

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Citations in the corpus

Massively Parallel Functional Analysis of BRCA1 RING Domain Variants (2015, methodology): “For every possible amino acid substitution in BRCA1 (2-103), we obtained Grantham chemical difference values from Grantham(1974), and GVGD values from the A-GVGD BRCA1 web-tool (http://agvgd.iarc.fr/agvgd_input.php).”
Using bioinformatics to predict the functional impact of SNVs (2011, background): “Finally, some researchers use all available information about possible deleterious/neutral amino acid substitutions (human disease databases, systematic functional assays of mutation in single proteins) to construct large benchmark sets (Bromberg and Rost, 2007).”
Assessment of computational methods for predicting the effects of missense mutations in human cancers (2013, background): “morphism) [19,20] and Align-GVGD [21], for example, combine both evolutionary conservation and physiochemical information.”
A systematic approach to assessing the clinical significance of genetic variants (2013, background): “Useful online resources for variant assessment Usage Online tools Url Computational prediction for missense variants Align GVGD (42) http://agvgd.iarc.fr/agvgd_input.php/ CONDEL http://bg.upf.edu/condel/analysis/ MutationAssessor http://mutationassessor.org/ MutationTaster http://www.mutationtaster.”
Predicting the Functional, Molecular, and Phenotypic Consequences of Amino Acid Substitutions using Hidden Markov Models (2012, methodology): “Adetailedanalysisoffourcancer-associatedgenes Adetailedanalysisoffourcancer-associatedgenes(Hicks; BRCA1 , MSH2 , MLH1 , and TP53 ) shows Align-GVGD [Tavtigian et al., 2006] to be the best performing prediction method.”
Computational approaches for predicting the biological effect of p53 missense mutations: a comparison of three sequence analysis based methods (2006, methodology): “This extension of the Grantham Difference, called Align-GVGD, has previously been applied to BRCA1 and contributed to the clinical categorization of eight previously unclassiﬁed missense mutations (21).”
Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines (2017, methodology): “The performance of SIFT and other algorithms within our analyses and others such as Align-GVGD [34] could potentially be improved if gene-specific curated alignments are provided to the classification pipeline.”