Classification of Missense Mutations of Disease Genes. Academic Article uri icon

Overview

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.

publication date

  • January 1, 2005

Identity

PubMed Central ID

  • PMC2311507

Scopus Document Identifier

  • 14944367262

Digital Object Identifier (DOI)

  • 10.1198/016214504000001817

PubMed ID

  • 18418466

Additional Document Info

volume

  • 100

issue

  • 469