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

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 li...

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Bibliographic Details
Published in:Genetic epidemiology 1999, Vol.17 (S1), p.S67-S72
Main Authors: Blangero, John, Williams, Jeff T., Iturria, Stephen J., Almasy, Laura
Format: Article
Language:English
Subjects:
Alcoholism - genetics
Bayes factor
Bayes Theorem
Event-Related Potentials, P300 - genetics
Genetic Linkage
Genetic Testing
Humans
hypothesis testing
likelihood
Models, Genetic
Models, Statistical
Quantitative Trait, Heritable
statistical genetics
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Summary: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 non‐nested 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.
ISSN:0741-0395
1098-2272
DOI:10.1002/gepi.1370170712