ArticleJournal of the American Statistical Association2005
Classification of Missense Mutations of Disease Genes.
Article in Journal of the American Statistical Association, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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Who cites it
15 citing papers in PubMed, 25 citations in OpenAlex.
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- Genomic-Analysis-Oriented Drug Repurposing in the Search for Novel Antidepressants.Biomedicines · 2022Article
- An integrative model for the comprehensive classification of BRCA1 and BRCA2 variants of uncertain clinical significance.NPJ genomic medicine · 2022Article
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- Prior robust empirical Bayes inference for large-scale data by conditioning on rank with application to microarray data.Biostatistics (Oxford, England) · 2014Article
- A computational method to classify variants of uncertain significance using functional assay data with application to BRCA1.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2011Article
- Hierarchical modeling for estimating relative risks of rare genetic variants: properties of the pseudo-likelihood method.Biometrics · 2011Article
- Genetic evidence and integration of various data sources for classifying uncertain variants into a single model.Human mutation · 2008Article
- A systematic genetic assessment of 1,433 sequence variants of unknown clinical significance in the BRCA1 and BRCA2 breast cancer-predisposition genes.American journal of human genetics · 2007Article
- Classification of Missense Mutations of Disease Genes.Journal of the American Statistical Association · 2005Article
- Incorporating molecular and functional context into the analysis and prioritization of human variants associated with cancer.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Clinical management of individuals found to harbor a mutation at a known disease-susceptibility gene depends on accurate assessment of mutation-specific disease risk. For missense mutations (MMs)-mutations that lead to a single amino acid change in the protein coded by the gene-this poses a particularly challenging problem. Because it is not possible to predict the structural and functional changes to the protein product for a given amino acid substitution, and because functional assays are often not available, disease association must be inferred from data on individuals with the mutation. Inference is complicated by small sample sizes and by sampling mechanisms that bias toward individuals at high familial risk of disease. We propose a Bayesian hierarchical model to classify the disease association of MMs given pedigree data collected in the high-risk setting. The model's structure allows simultaneous characterization of multiple MMs. It uses a group of pedigrees identified through probands tested positive for known disease associated mutations and a group of test-negative pedigrees, both obtained from the same clinic, to calibrate classification and control for potential ascertainment bias. We apply this model to study MMs of breast-ovarian susceptibility genes BRCA1 and BRCA2, using data collected at the Duke University Medical Center in Durham, North Carolina.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.