Model selection based on logistic regression in a highly correlated candidate gene region.
Our aim is to develop methods for identifying a (causal) variant or variants from a dense panel of single-nucleotide polymorphisms (SNPs) that are genotyped on the evidence of previous studies. Because a large number of SNPs are in close proximity to each other, the magnitude of linkage disequilibrium (LD) plays an important role. Namely, highly correlated SNPs may hamper standard methods such as multivariate logistic regression due to multicolinearity between the covariates. Sequences of models with high dimension naturally raise questions about model selection strategies. We investigate three variable selection methods based on logistic regression. The penalties on stepwise selection were imposed using the Akaike's Information Criterion (AIC), and using the lasso penalty. Finally, a Bayesian variable-selection logistic regression model was implemented. The methods are illustrated using the simulated dense SNPs including the causal DR/C locus on chromosome 6. We also evaluate model selection in terms of average prediction error across nine replicates. We conclude that for the Genetic Analysis Workshop 15 (GAW15) data, the newly developed Bayesian selection method performs well.
Uh
Hae-Won
HW
Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands. h.uh@lumc.nl
Mertens
Bart J A
BJ
Jan van der Wijk
Henk
H
Putter
Hein
H
van Houwelingen
Hans C
HC
Houwing-Duistermaat
Jeanine J
JJ