Kari J Buck, Lauren C Milner, Deaunne L Denmark, Seth G N Grant, Laura B Kozell
{"title":"Discovering genes involved in alcohol dependence and other alcohol responses: role of animal models.","authors":"Kari J Buck, Lauren C Milner, Deaunne L Denmark, Seth G N Grant, Laura B Kozell","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>The genetic determinants of alcoholism still are largely unknown, hindering effective treatment and prevention. Systematic approaches to gene discovery are critical if novel genes and mechanisms involved in alcohol dependence are to be identified. Although no animal model can duplicate all aspects of alcoholism in humans, robust animal models for specific alcohol-related traits, including physiological alcohol dependence and associated withdrawal, have been invaluable resources. Using a variety of genetic animal models, the identification of regions of chromosomal DNA that contain a gene or genes which affect a complex phenotype (i.e., quantitative trait loci [QTLs]) has allowed unbiased searches for candidate genes. Several QTLs with large effects on alcohol withdrawal severity in mice have been detected, and fine mapping of these QTLs has placed them in small intervals on mouse chromosomes 1 and 4 (which correspond to certain regions on human chromosomes 1 and 9). Subsequent work led to the identification of underlying quantitative trait genes (QTGs) (e.g., Mpdz) and high-quality QTG candidates (e.g., Kcnj9 and genes involved in mitochondrial respiration and oxidative stress) and their plausible mechanisms of action. Human association studies provide supporting evidence that these QTLs and QTGs may be directly relevant to alcohol risk factors in clinical populations.</p>","PeriodicalId":7736,"journal":{"name":"Alcohol Research : Current Reviews","volume":"34 3","pages":"367-74"},"PeriodicalIF":6.8000,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3860408/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Alcohol Research : Current Reviews","FirstCategoryId":"3","ListUrlMain":"","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"SUBSTANCE ABUSE","Score":null,"Total":0}
引用次数: 0
Abstract
The genetic determinants of alcoholism still are largely unknown, hindering effective treatment and prevention. Systematic approaches to gene discovery are critical if novel genes and mechanisms involved in alcohol dependence are to be identified. Although no animal model can duplicate all aspects of alcoholism in humans, robust animal models for specific alcohol-related traits, including physiological alcohol dependence and associated withdrawal, have been invaluable resources. Using a variety of genetic animal models, the identification of regions of chromosomal DNA that contain a gene or genes which affect a complex phenotype (i.e., quantitative trait loci [QTLs]) has allowed unbiased searches for candidate genes. Several QTLs with large effects on alcohol withdrawal severity in mice have been detected, and fine mapping of these QTLs has placed them in small intervals on mouse chromosomes 1 and 4 (which correspond to certain regions on human chromosomes 1 and 9). Subsequent work led to the identification of underlying quantitative trait genes (QTGs) (e.g., Mpdz) and high-quality QTG candidates (e.g., Kcnj9 and genes involved in mitochondrial respiration and oxidative stress) and their plausible mechanisms of action. Human association studies provide supporting evidence that these QTLs and QTGs may be directly relevant to alcohol risk factors in clinical populations.
期刊介绍:
Alcohol Research: Current Reviews (ARCR) is an open-access, peer-reviewed journal published by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) at the National Institutes of Health. Starting from 2020, ARCR follows a continuous, rolling publication model, releasing one virtual issue per yearly volume. The journal offers free online access to its articles without subscription or pay-per-view fees. Readers can explore the content of the current volume, and past volumes are accessible in the journal's archive. ARCR's content, including previous titles, is indexed in PubMed, PsycINFO, Scopus, and Web of Science.