Oligogenic model selection using the Bayesian information criterion: Linkage analysis of the P300 Cz event-related brain potential

John Blangero, Jeff T. Williams, Stephen J. Iturria, Laura Almasy

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

The traditional likelihood-based approach to hypothesis testing may not be an optimal strategy for evaluating oligogenic models of inheritance. Under oligogenic inheritance the number of possible multilocus models can become very large; there may be several competing linkage models having similar likelihoods; and comparisons among nonnested models can be required to determine if a given multilocus model provides a significantly better fit to observed phenotypic variation than an alternative model. We propose an efficient Bayesian approach to oligogenic model selection that makes use of existing model likelihoods, and show how model uncertainty can be incorporated into parameter estimation.

Original languageEnglish (US)
Pages (from-to)S67-S72
JournalGenetic epidemiology
Volume17
Issue numberSUPPL. 1
DOIs
StatePublished - 1999
Externally publishedYes

Keywords

  • Bayes factor
  • Hypothesis testing
  • Likelihood
  • Statistical genetics

ASJC Scopus subject areas

  • Epidemiology
  • Genetics(clinical)

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