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Analysis of Swedish male breast cancer family data : a simple way to incorporate a common sibling effect

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Author

Summary, in English

Based on a population-based cohort study, Olsson et al. [1993] found significant evidence for elevated incidence of breast and ovarian cancers among female first-degree relatives of men with breast cancer. Using an extension of logistic regressive models we investigate whether, after allowing for multifactorial familial correlations, single locus segregation could be the cause of the elevated incidence in these families. The logit for a given sib in the class D logistic regressive model depends on the order in which affected sibs occur in a sibship. That makes the model less appropriate for the situation where a polygenic component or a common sibling environment may be present, as well as being computationally cumbersome. In this paper, we propose to use the proportion of siblings in a sibship who are affected to quantify a sibling correlation. That not only relaxes the interchangeability problem but also makes the model computationally efficient. We then use this modified class D logistic regressive model for our segregation analysis. Using the proportion of siblings in a sibship who are affected as a covariate resulted in a significantly higher likelihoods in all the models we investigated. We found evidence for a dominant Mendelian gene leading to early age of onset of breast and/or ovarian cancer. This could either be a germline mutation of BRCA2 or a mutation in a gene different from BRCA2.

Publishing year

1998

Language

English

Pages

12-201

Publication/Series

Genetic Epidemiology

Volume

15

Issue

2

Document type

Journal article

Publisher

John Wiley & Sons Inc.

Topic

  • Orthopedics
  • Cancer and Oncology

Keywords

  • Adult
  • Aged
  • Aged, 80 and over
  • BRCA2 Protein
  • Breast Neoplasms
  • Breast Neoplasms, Male
  • Cohort Studies
  • Data Interpretation, Statistical
  • Family Health
  • Female
  • Follow-Up Studies
  • Humans
  • Incidence
  • Male
  • Middle Aged
  • Models, Genetic
  • Models, Statistical
  • Mutation
  • Neoplasm Proteins
  • Prevalence
  • Regression Analysis
  • Risk Assessment
  • Sweden
  • Transcription Factors

Status

Published

ISBN/ISSN/Other

  • ISSN: 0741-0395