ArticleAmerican journal of human genetics1998
Missense mutations in disease genes: a Bayesian approach to evaluate causality.
Article in American journal of human genetics, 1998. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 47 citations in OpenAlex.
- BICEP: Bayesian inference for rare genomic variant causality evaluation in pedigrees.Briefings in bioinformatics · 2024Article
- shinyseg: a web application for flexible cosegregation and sensitivity analysis.Bioinformatics (Oxford, England) · 2024Article
- Novel JAG1 variants leading to Alagille syndrome in two Chinese cases.Scientific reports · 2024Article
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- Detection of activating mutations in liquid biopsy of Egyptian breast cancer patients using targeted next-generation sequencing: a pilot study.Journal of the Egyptian National Cancer Institute · 2021Article
- Article
- Identification of rare causal variants in sequence-based studies: methods and applications to VPS13B, a gene involved in Cohen syndrome and autism.PLoS genetics · 2014Article
- The implications of familial incidental findings from exome sequencing: the NIH Undiagnosed Diseases Program experience.Genetics in medicine : official journal of the American College of Medical Genetics · 2014Article
- Large numbers of individuals are required to classify and define risk for rare variants in known cancer risk genes.Genetics in medicine : official journal of the American College of Medical Genetics · 2014Article
- Two-stage family-based designs for sequencing studies.BMC proceedings · 2014Article
- Two-phase and family-based designs for next-generation sequencing studies.Frontiers in genetics · 2013Review
- A simple method for co-segregation analysis to evaluate the pathogenicity of unclassified variants; BRCA1 and BRCA2 as an example.BMC cancer · 2009Article
- 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
- Integrated evaluation of DNA sequence variants of unknown clinical significance: application to BRCA1 and BRCA2.American journal of human genetics · 2004Article
- A full-likelihood method for the evaluation of causality of sequence variants from family data.American journal of human genetics · 2003Article
- Understanding missense mutations in the BRCA1 gene: an evolutionary approach.Proceedings of the National Academy of Sciences of the United States of America · 2003Article
- Interpretation of genetic association studies in complex disease.The pharmacogenomics journal · 2002Review
- Inherited colorectal polyposis and cancer risk of the APC I1307K polymorphism.American journal of human genetics · 1999Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
Abstract
The problem of interpreting missense mutations of disease-causing genes is an increasingly important one. Because these point mutations result in alteration of only a single amino acid of the protein product, it is often unclear whether this change alone is sufficient to cause disease. We propose a Bayesian approach that utilizes genetic information on affected relatives in families ascertained through known missense-mutation carriers. This method is useful in evaluating known disease genes for common disease phenotypes, such as breast cancer or colorectal cancer. The posterior probability that a missense mutation is disease causing is conditioned on the relationship of the relatives to the proband, the population frequency of the mutation, and the phenocopy rate of the disease. The approach is demonstrated in two cancer data sets: BRCA1 R841W and APC I1307K. In both examples, this method helps establish that these mutations are likely to be disease causing, with Bayes factors in favor of causality of 5.09 and 66.97, respectively, and posterior probabilities of .836 and .985. We also develop a simple approximation for rare alleles and consider the case of unknown penetrance and allele frequency.
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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.