Marker selection by Akaike information criterion and Bayesian information criterion

Li, W. & Nyholt, D.R. (2001) Marker selection by Akaike information criterion and Bayesian information criterion. Genetic Epidemiology, 21 Suppl, S272-S277.

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We carried out a discriminant analysis with identity by descent (IBD) at each marker as inputs, and the sib pair type (affected-affected versus affected-unaffected) as the output. Using simple logistic regression for this discriminant analysis, we illustrate the importance of comparing models with different number of parameters. Such model comparisons are best carried out using either the Akaike information criterion (AIC) or the Bayesian information criterion (BIC). When AIC (or BIC) stepwise variable selection was applied to the German Asthma data set, a group of markers were selected which provide the best fit to the data (assuming an additive effect). Interestingly, these 25-26 markers were not identical to those with the highest (in magnitude) single-locus lod scores.

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ID Code: 92179
Item Type: Journal Article
Refereed: Yes
Additional Information: Li, W
Nyholt, D R
Research Support, U.S. Gov't, P.H.S.
2002/01/17 10:00
Genet Epidemiol. 2001;21 Suppl 1:S272-7.
Keywords: Adult, Asthma/epidemiology/*genetics, Bayes Theorem, Child, Discriminant Analysis, Female, Genetic Markers/*genetics, Genetics, Population, Humans, Male, Models, Genetic, Pedigree
ISSN: 0741-0395
Divisions: Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Copyright Owner: Copyright 2001 Wiley
Deposited On: 27 Jan 2016 05:16
Last Modified: 27 Jan 2016 05:41

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