Classification Tree Analysis as a Method for Uncovering Relations Between CHRNA5A3B4 and CHRNB3A6 in Predicting Smoking Progression in Adolescent Smokers.
BACKGROUND
Prior research suggests the CHRNA5A3B4 and CHRNB3A6 gene clusters have independent effects on smoking progression in young smokers. Here classification tree analysis uncovers conditional relations between these genes.


METHODS
Conditional classification tree and random forest analyses were employed to predict daily smoking at 6-year follow-up in a longitudinal sample of young smokers (N = 480) who had smoked at least one puff at baseline and were of European ancestry. Potential predictors included gender, lifetime smoking, Nicotine Dependence Syndrome Scale (NDSS), and five single nucleotide polymorphisms (SNPs) tagging CHRNB3A6 and CHRNA5A3B4 Haplotypes A, B, and C. Conditional random forest analysis was used to calculate variable importance.


RESULTS
The classification tree identified NDSS, the CHRNB3A6 SNP rs2304297, and the CHRNA5A3B4 Haplotype C SNP rs6495308 as predictive of year 6 daily smoking with the baseline NDSS identified as the strongest predictor. The CHRNB3A6 protective effect was contingent on a lower level of baseline NDSS, whereas the CHRNA5A3B4 Haplotype C protective effect was seen at a higher level of baseline NDSS. A CHRNA5A3B4 Haplotype C protective effect also was observed in participants with low baseline NDSS who had no CHRNB3A6 rs2304297 minor allele.


CONCLUSIONS
The protective effects of CHRNA5A3B4 Haplotype C and CHRNB3A6 on smoking progression are conditional on different levels of baseline cigarette use. Also, duplicate dominant epistasis between SNPs indicated the minor allele of either SNP afforded comparable protective effects in the absence of a minor allele at the other locus. Possible mechanisms underlying these conditional relations are discussed.


CONCLUSIONS
The substantive contributions of this paper are the demonstration of a difference in the protective effects of CHRNB3A6 and CHRNA5A3B4 Haplotype C in young smokers attributable to level of cigarette use, as well as observation of duplicate dominant epistasis between the two markers. The methodological contribution is demonstrating that classification tree and random forest statistical methods can uncover conditional relations among genetic effects not detected with more common regression methods.
Institute for Health Research and Policy, University of Illinois at Chicago, Chicago, IL.
Oksana
O
Pugach
Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT.
Dale S
DS
Cannon
Department of Human Genetics, University of Utah School of Medicine, Salt Lake City, UT.
Robert B
RB
Weiss
Department of Public Health Sciences and Department of Medicine, University of Chicago Biological Sciences, Chicago, IL.
Donald
D
Hedeker
Institute for Health Research and Policy and Psychology Department, University of Illinois, Chicago, IL.
Robin J
RJ
Mermelstein
D000293:Adolescent; D003663:Decision Trees; D006801:Humans; D015233:Models, Statistical; D009419:Nerve Tissue Proteins / Q000235:genetics*; D011978:Receptors, Nicotinic / Q000235:genetics*; D012907:Smoking / Q000453:epidemiology* / Q000235:genetics*
C447158:CHRNA5 protein, human; C539371:CHRNB3 protein, human; D009419:Nerve Tissue Proteins; D011978:Receptors, Nicotinic