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A genome-wide tree- and forest-based association analysis of comorbidity of alcoholism and smoking

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...

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Autores principales: Ye, Yuanqing, Zhong, Xiaoyun, Zhang, Heping
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2005
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1866801/
https://www.ncbi.nlm.nih.gov/pubmed/16451594
http://dx.doi.org/10.1186/1471-2156-6-S1-S135
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author Ye, Yuanqing
Zhong, Xiaoyun
Zhang, Heping
author_facet Ye, Yuanqing
Zhong, Xiaoyun
Zhang, Heping
author_sort Ye, Yuanqing
collection PubMed
description 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.
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spelling pubmed-18668012007-05-11 A genome-wide tree- and forest-based association analysis of comorbidity of alcoholism and smoking Ye, Yuanqing Zhong, Xiaoyun Zhang, Heping BMC Genet Proceedings 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. BioMed Central 2005-12-30 /pmc/articles/PMC1866801/ /pubmed/16451594 http://dx.doi.org/10.1186/1471-2156-6-S1-S135 Text en Copyright © 2005 Ye et al; licensee BioMed Central Ltd http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Proceedings
Ye, Yuanqing
Zhong, Xiaoyun
Zhang, Heping
A genome-wide tree- and forest-based association analysis of comorbidity of alcoholism and smoking
title A genome-wide tree- and forest-based association analysis of comorbidity of alcoholism and smoking
title_full A genome-wide tree- and forest-based association analysis of comorbidity of alcoholism and smoking
title_fullStr A genome-wide tree- and forest-based association analysis of comorbidity of alcoholism and smoking
title_full_unstemmed A genome-wide tree- and forest-based association analysis of comorbidity of alcoholism and smoking
title_short A genome-wide tree- and forest-based association analysis of comorbidity of alcoholism and smoking
title_sort genome-wide tree- and forest-based association analysis of comorbidity of alcoholism and smoking
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1866801/
https://www.ncbi.nlm.nih.gov/pubmed/16451594
http://dx.doi.org/10.1186/1471-2156-6-S1-S135
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