title: A genome-wide tree- and forest-based association analysis of comorbidity of alcoholism and smoking.
abstract: Genetic mechanisms underlying alcoholism are complex. Understanding the etiology of alcohol dependence and its comorbid conditions such as smoking is important because of the significant health concerns. In this report, we describe a method based on classification trees and deterministic forests for association studies to perform a genome-wide joint association analysis of alcoholism and smoking. This approach is used to analyze the single-nucleotide polymorphism data from the Collaborative Study on the Genetics of Alcoholism in the Genetic Analysis Workshop 14. Our analysis reaffirmed the importance of sex difference in alcoholism. Our analysis also identified genes that were reported in other studies of alcoholism and identified new genes or single-nucleotide polymorphisms that can be useful candidates for future studies.
authors.affiliation: Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520-8034, USA. yye@masal.med.yale.edu
authors.forename: Yuanqing
authors.initials: Y
authors.lastname: Ye
authors.forename: Xiaoyun
authors.initials: X
authors.lastname: Zhong
authors.forename: Heping
authors.initials: H
authors.lastname: Zhang
mesh_terms: D000437:Alcoholism / Q000453:epidemiology* / Q000235:genetics*; D030541:Databases, Genetic; D003663:Decision Trees; D005260:Female; D055106:Genome-Wide Association Study; D006801:Humans; D008297:Male; D020411:Oligonucleotide Array Sequence Analysis; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D012907:Smoking / Q000453:epidemiology* / Q000235:genetics*