ArticleBMC cancer2009
A simple method for co-segregation analysis to evaluate the pathogenicity of unclassified variants; BRCA1 and BRCA2 as an example.
Article in BMC cancer, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- A comprehensive and accessible model for co-segregation analysis in BRCA1, BRCA2, and PALB2 variant classification.Human genetics · 2026Article
- BICEP: an extension to indels and copy number variants for rare variant prioritisation in pedigree analysis.bioRxiv : the preprint server for biology · 2026Article
- Article
- BICEP: Bayesian inference for rare genomic variant causality evaluation in pedigrees.Briefings in bioinformatics · 2024Article
- Germline DNA Damage Repair Gene Alterations in Patients with Metachronous Breast and Colorectal Cancer.International journal of molecular sciences · 2024Article
- shinyseg: a web application for flexible cosegregation and sensitivity analysis.Bioinformatics (Oxford, England) · 2024Article
- Genetic determinants of global developmental delay and intellectual disability in Ukrainian children.Journal of neurodevelopmental disorders · 2024Article
- Classification of MLH1 Missense VUS Using Protein Structure-Based Deep Learning-Ramachandran Plot-Molecular Dynamics Simulations Method.International journal of molecular sciences · 2024Article
- ClinGen guidance for use of the PP1/BS4 co-segregation and PP4 phenotype specificity criteria for sequence variant pathogenicity classification.American journal of human genetics · 2024Article
- Characterization of BRCA2 R3052Q variant in mice supports its functional impact as a low-risk variant.Cell death & disease · 2023Article
- Integration of deep learning with Ramachandran plot molecular dynamics simulation for genetic variant classification.iScience · 2023Article
- Article
- Clinical, splicing, and functional analysis to classify BRCA2 exon 3 variants: Application of a points-based ACMG/AMP approach.Human mutation · 2022Article
- An algorithm for optimal testing in co-segregation analysis.Human mutation · 2022Article
- Assessing the pathogenicity ofFrontiers in oncology · 2022Article
- The Pathogenic R3052W BRCA2 Variant Disrupts Homology-Directed Repair by Failing to Localize to the Nucleus.Frontiers in genetics · 2022Article
- Comprehensive Identification of DeleteriousInternational journal of molecular sciences · 2021Article
- Review
- Considerations in assessing germline variant pathogenicity using cosegregation analysis.Genetics in medicine : official journal of the American College of Medical Genetics · 2020Article
- Outcomes of 92 patient-driven family studies for reclassification of variants of uncertain significance.Genetics in medicine : official journal of the American College of Medical Genetics · 2019Article
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22 authors.
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Abstract
backgroundAssessment of the clinical significance of unclassified variants (UVs) identified in BRCA1 and BRCA2 is very important for genetic counselling. The analysis of co-segregation of the variant with the disease in families is a powerful tool for the classification of these variants. Statistical methods have been described in literature but these methods are not always easy to apply in a diagnostic setting.
methodsWe have developed an easy to use method which calculates the likelihood ratio (LR) of an UV being deleterious, with penetrance as a function of age of onset, thereby avoiding the use of liability classes. The application of this algorithm is publicly available http://www.msbi.nl/cosegregation. It can easily be used in a diagnostic setting since it requires only information on gender, genotype, present age and/or age of onset for breast and/or ovarian cancer.
resultsWe have used the algorithm to calculate the likelihood ratio in favour of causality for 3 UVs in BRCA1 (p.M18T, p.S1655F and p.R1699Q) and 5 in BRCA2 (p.E462G p.Y2660D, p.R2784Q, p.R3052W and p.R3052Q). Likelihood ratios varied from 0.097 (BRCA2, p.E462G) to 230.69 (BRCA2, p.Y2660D). Typing distantly related individuals with extreme phenotypes (i.e. very early onset cancer or old healthy individuals) are most informative and give the strongest likelihood ratios for or against causality.
conclusionAlthough co-segregation analysis on itself is in most cases insufficient to prove pathogenicity of an UV, this method simplifies the use of co-segregation as one of the key features in a multifactorial approach considerably.
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