Classification of missense mutations of disease genes

• 成果类型: Article
• 署名作者: Zhou, X; Iversen, ES Jr; Parmigiani, G
• 署名单位: Cornell University; Duke University; Johns Hopkins University; Johns Hopkins Medicine; Johns Hopkins University; Johns Hopkins Medicine; Johns Hopkins University; Johns Hopkins Medicine
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214504000001817
• 发表日期: 2005
• 页码: 51-60
• 关键词: ascertainment bias; strong candidate; variable age; breast; brca1; susceptibility; models; onset; frequency; survival

摘要

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.