Classification of missense mutations of disease genes

Xi Zhou, Edwin S. Iversen, Giovanni Parmigiani

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)51-60
Number of pages10
JournalJournal of the American Statistical Association
Volume100
Issue number469
DOIs
StatePublished - Mar 2005

Fingerprint

Mutation
Gene
Pedigree
Susceptibility
Amino Acids
Protein
Bayesian Hierarchical Model
Small Sample Size
Substitution
Classify
Predict
Model

Keywords

  • Bayesian analysis
  • Classification
  • Family data
  • Gene characterization
  • Pedigree analysis
  • Penetrance

ASJC Scopus subject areas

  • Mathematics(all)
  • Statistics and Probability

Cite this

Classification of missense mutations of disease genes. / Zhou, Xi; Iversen, Edwin S.; Parmigiani, Giovanni.

In: Journal of the American Statistical Association, Vol. 100, No. 469, 03.2005, p. 51-60.

Research output: Contribution to journalArticle

Zhou, Xi ; Iversen, Edwin S. ; Parmigiani, Giovanni. / Classification of missense mutations of disease genes. In: Journal of the American Statistical Association. 2005 ; Vol. 100, No. 469. pp. 51-60.
@article{c2b0d3932330432fb1f1607d89260049,
title = "Classification of missense mutations of disease genes",
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.",
keywords = "Bayesian analysis, Classification, Family data, Gene characterization, Pedigree analysis, Penetrance",
author = "Xi Zhou and Iversen, {Edwin S.} and Giovanni Parmigiani",
year = "2005",
month = "3",
doi = "10.1198/016214504000001817",
language = "English (US)",
volume = "100",
pages = "51--60",
journal = "Journal of the American Statistical Association",
issn = "0162-1459",
publisher = "Taylor and Francis Ltd.",
number = "469",

}

TY - JOUR

T1 - Classification of missense mutations of disease genes

AU - Zhou, Xi

AU - Iversen, Edwin S.

AU - Parmigiani, Giovanni

PY - 2005/3

Y1 - 2005/3

N2 - 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.

AB - 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.

KW - Bayesian analysis

KW - Classification

KW - Family data

KW - Gene characterization

KW - Pedigree analysis

KW - Penetrance

UR - http://www.scopus.com/inward/record.url?scp=14944367262&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=14944367262&partnerID=8YFLogxK

U2 - 10.1198/016214504000001817

DO - 10.1198/016214504000001817

M3 - Article

C2 - 18418466

AN - SCOPUS:14944367262

VL - 100

SP - 51

EP - 60

JO - Journal of the American Statistical Association

JF - Journal of the American Statistical Association

SN - 0162-1459

IS - 469

ER -