Integrated Evaluation of DNA Sequence Variants of Unknown Clinical Significance: Application to BRCA1 and BRCA2

Goldgar, David E.; Easton, Douglas F.; Deffenbaugh, Amie M.; Monteiro, Alvaro N.A.; Tavtigian, Sean V.; Couch, Fergus J.

doi:10.1086/424388

Cited by 350 publications

(479 citation statements)

References 27 publications

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“…However, alterations affecting the exon 3 of the BRCA2 gene should be interpret with caution since the Rad51 binding sites and nuclear localization signals in the 3 0 region of BRCA2 still remain [Diez et al, 2007]. Further research, such as the analysis of cosegregation in the family, frequency of the variant in cases and controls, and loss of heterozygosity (LOH) in tumors will be necessary to fully assess the clinical significance of this variant [Goldgar et al, 2004;Chenevix-Trench et al, 2006]. Given the high lifetime risk for breast cancer and ovarian cancer for BRCA1 and BRCA2 mutation carriers, it is essential that the clinical significance of the intronic variants is elucidated.…”

Section: Discussionmentioning

confidence: 99%

“…Although mutation analysis in these genes has resulted in the identification of a large number of sequence variants, only mutations causing frameshifts and premature stop codons are generally assumed to be pathogenic. However, an increasing number of variants is detected that cannot be readily distinguished as either disease-associated mutations or neutral variants [Goldgar et al, 2004]. In the Breast Cancer Information Core (BIC) database, 27% of all BRCA1 and 41% of all BRCA2 variants are presently of unknown clinical significance (http://research.nhgri.nih.gov/bic).…”

Section: Introductionmentioning

confidence: 99%

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Intronic variants inBRCA1andBRCA2that affect RNA splicing can be reliably selected by splice-site prediction programs

et al. 2009

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A large number of sequence variants identified in BRCA1 and BRCA2 cannot be distinguished as either disease-causing mutations or neutral variants. These so-called unclassified variants (UVs) include variants that are located in the intronic sequences of BRCA1 and BRCA2. The purpose of this study was to assess the use of splice-site prediction programs (SSPPs) to select intronic variants in BRCA1 and BRCA2 that are likely to affect RNA splicing. We performed in vitro molecular characterization of RNA of six intronic variants in BRCA1 and BRCA2. In four cases (BRCA1, c.81-6T4A and c.498615G4T; BRCA2, c.76171 2T4G and c.875415G4A) a deleterious effect on RNA splicing was seen, whereas the c.135-15_-12del variant in BRCA1 showed no effect on RNA splicing. In the case of the BRCA2 c.68-7T4A variant, RNA analysis was not sufficient to establish the clinical significance. Six SSPPs were used to predict whether an effect on RNA splicing was expected for these six variants as well as for 23 intronic variants in BRCA1 for which the effect on RNA splicing has been published. Out of a total of 174 predictions, 161 (93%) were informative (i.e., the wild-type splice-site was recognized). No falsenegative predictions were observed; an effect on RNA splicing was always predicted by these programs. In four cases (2.5%) a false-positive prediction was observed. For DNA diagnostic laboratories, these programs are therefore very useful to select intronic variants that are likely to affect RNA splicing for further analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intronic variants inBRCA1andBRCA2that affect RNA splicing can be reliably selected by splice-site prediction programs

et al. 2009

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“…For example, Parmigiani et al [ 36 ] used a Bayesian hierarchical approach to model the probability that a woman is a carrier of any mutation in the BRCA1 and BRCA2 genes. Goldgar et al [ 37 ] developed a multifactorial likelihood-ratio approach to estimate the odds of causality of a specific genetic variant in the setting of a familial segregation analysis, using information on amino acid conservation, severity of amino acid change and functional data. Neither of these approaches was designed to improve the risk estimation for individual variants.…”

Section: Discussionmentioning

confidence: 99%

The use of hierarchical models for estimating relative risks of individual genetic variants: An application to a study of melanoma

Capanu

Orlow

Berwick

et al. 2008

Statistics in Medicine

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For major genes known to influence the risk of cancer, an important task is to determine the risks conferred by individual variants, so that one can appropriately counsel carriers of these mutations. This is a challenging task, since new mutations are continually being identified, and there is typically relatively little empirical evidence available about each individual mutation. Hierarchical modeling offers a natural strategy to leverage the collective evidence from these rare variants with sparse data. This can be accomplished when there are available higher level covariates that characterize the variants in terms of attributes that could distinguish their association with disease. In this article we explore the use of hierarchical modeling for this purpose using data from a large population-based study of the risks of melanoma conferred by variants in the CDKN2A gene. We employ both a pseudolikelihood approach and a Bayesian approach using Gibbs sampling. The results indicate that relative risk estimates tend to be primarily influenced by the individual case-control frequencies when several cases and/or controls are observed with the variant under study, but that relative risk estimates for variants with very sparse data are more influenced by the higher level covariate values, as one would expect. The analysis offers encouragement that we can draw strength from the aggregating power of hierarchical models to provide guidance to medical geneticists when they offer counseling to patients with rare or even hitherto unobserved variants. However, further research is needed to validate the application of asymptotic methods to such sparse data.

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“…4 -12 It simply represents an initial attempt to evaluate UVs of MMR genes based on a relatively elementary quantitative assessment of several parameters, integrated in a multifactorial likelihood model. 13 …”

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confidence: 99%

Integrated analysis of unclassified variants in mismatch repair genes

Pastrello

Marroni

et al. 2011

Genetics in Medicine

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Purpose: Lynch syndrome is a genetic disease that predisposes to colorectal tumors, caused by mutation in mismatch repair genes. The use of genetic tests to identify mutation carriers does not always give perfectly clear results, as happens when an unclassified variant is found. This study aimed to define the pathogenic role of 35 variants present in MSH2, MLH1, MSH6, and PMS2 genes identified in our 15-year case study. Methods: We collected clinical and molecular data of all carriers, and then we analyzed the variants pathogenic role with web tools and molecular analyses. Using a Bayesian approach, we derived a posterior probability of pathogenicity and classified each variant according to a standardized five-class system. Results: The MSH2 p.Pro349Arg, p.Met688Arg, the MLH1 p.Gly67Arg, p.Thr82Ala, p.Lys618Ala, the MSH6 p.Ala1236Pro, and the PMS2 p.Arg20Gln were classified as pathogenic, and the MSH2 p.Cys697Arg and the PMS2 p.Ser46Ile were classified as likely pathogenic. Seven variants were likely nonpathogenic, 3 were nonpathogenic, and 16 remained uncertain. Conclusion: Quantitative assessment of several parameters and their integration in a multifactorial likelihood model is the method of choice for classifying the variants. As such classifications can be associated with surveillance and testing recommendations, the results and the method developed in our study can be useful for helping laboratory geneticists in evaluation of genetic tests and clinicians in the management of carriers. Genet Med 2011:13(2):115-124.

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Integrated Evaluation of DNA Sequence Variants of Unknown Clinical Significance: Application to BRCA1 and BRCA2

Cited by 350 publications

References 27 publications

Intronic variants inBRCA1andBRCA2that affect RNA splicing can be reliably selected by splice-site prediction programs

Intronic variants inBRCA1andBRCA2that affect RNA splicing can be reliably selected by splice-site prediction programs

The use of hierarchical models for estimating relative risks of individual genetic variants: An application to a study of melanoma

Integrated analysis of unclassified variants in mismatch repair genes

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