TY - JOUR
T1 - A testable prognostic model of nicotine dependence
AU - Ramoni, Rachel Badovinac
AU - Saccone, Nancy L.
AU - Hatsukami, Dorothy K.
AU - Bierut, Laura J.
AU - Ramoni, Marco F.
N1 - Funding Information:
This study was supported, in part, by a Cutting-Edge Basic Research Award (CEBRA) from NIH/NIDA (R21DA025168), by a Mentored Clinical Scientist Development Award from NIH/NIDCR (5K08DE016956), and by a grant from the National Cancer Institute (CA89392).
PY - 2009/1
Y1 - 2009/1
N2 - Individuals' dependence on nicotine, primarily through cigarette smoking, is a major source of morbidity and mortality worldwide. Many smokers attempt but fail to quit smoking, motivating researchers to identify the origins of this dependence. Because of the known heritability of nicotine-dependence phenotypes, considerable interest has been focused on discovering the genetic factors underpinning the trait. This goal, however, is not easily attained: no single factor is likely to explain any great proportion of dependence because nicotine dependence is thought to be a complex trait (i.e., the result of many interacting factors). Genomewide association studies are powerful tools in the search for the genomic bases of complex traits, and in this context, novel candidate genes have been identified through single nucleotide polymorphism (SNP) association analyses. Beyond association, however, genetic data can be used to generate predictive models of nicotine dependence. As expected in the context of a complex trait, individual SNPs fail to accurately predict nicotine dependence, demanding the use of multivariate models. Standard approaches, such as logistic regression, are unable to consider large numbers of SNPs given existing sample sizes. However, using Bayesian networks, one can overcome these limitations to generate a multivariate predictive model, which has markedly enhanced predictive accuracy on fitted values relative to that of individual SNPs. This approach, combined with the data being generated by genomewide association studies, promises to shed new light on the common, complex trait nicotine dependence.
AB - Individuals' dependence on nicotine, primarily through cigarette smoking, is a major source of morbidity and mortality worldwide. Many smokers attempt but fail to quit smoking, motivating researchers to identify the origins of this dependence. Because of the known heritability of nicotine-dependence phenotypes, considerable interest has been focused on discovering the genetic factors underpinning the trait. This goal, however, is not easily attained: no single factor is likely to explain any great proportion of dependence because nicotine dependence is thought to be a complex trait (i.e., the result of many interacting factors). Genomewide association studies are powerful tools in the search for the genomic bases of complex traits, and in this context, novel candidate genes have been identified through single nucleotide polymorphism (SNP) association analyses. Beyond association, however, genetic data can be used to generate predictive models of nicotine dependence. As expected in the context of a complex trait, individual SNPs fail to accurately predict nicotine dependence, demanding the use of multivariate models. Standard approaches, such as logistic regression, are unable to consider large numbers of SNPs given existing sample sizes. However, using Bayesian networks, one can overcome these limitations to generate a multivariate predictive model, which has markedly enhanced predictive accuracy on fitted values relative to that of individual SNPs. This approach, combined with the data being generated by genomewide association studies, promises to shed new light on the common, complex trait nicotine dependence.
KW - Addiction
KW - Genetics
KW - Nicotine
KW - Prediction
UR - http://www.scopus.com/inward/record.url?scp=68649115066&partnerID=8YFLogxK
U2 - 10.1080/01677060802572911
DO - 10.1080/01677060802572911
M3 - Review article
C2 - 19184766
AN - SCOPUS:68649115066
SN - 0167-7063
VL - 23
SP - 283
EP - 292
JO - Journal of Neurogenetics
JF - Journal of Neurogenetics
IS - 3
ER -