D840–D845 Nucleic Acids Research, 2009, Vol. 37, Database issue doi:10.1093/nar/gkn816 Published online 31 October 2008 ERGR: An ethanol-related gene resource An-Yuan Guo 1 , Bradley T. Webb 2 , Michael F. Miles3,4, Mark P. Zimmerman 4 , Kenneth S. Kendler1,5 and Zhongming Zhao1,4,5,* 1Department of Psychiatry and Virginia Institute for Psychiatric and Behavioral Genetics, 2Department of Pharmacy, 3Department of Pharmacology/Toxicology, Virginia Commonwealth University, Richmond, VA 23298, 4Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA 23284 and 5Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA 23298, USA Received August 12, 2008; Revised September 24, 2008; Accepted October 13, 2008 ABSTRACT Over the last decade rapid progress has been made in the study of ethanol-related traits including alcohol abuse and dependence, and behavioral responses to ethanol in both humans and animal models. To collect, curate, integrate these results so as to make them easily accessible and inter- pretable for researchers, we developed ERGR, a comprehensive ethanol-related gene resource. We collected and curated more than 30 large-scale data sets including linkage, association and micro- array gene expression from the literature and 21 mouse QTLs from public databases. At present, the ERGR deposits ethanol-related information of ~7000 genes from five organisms: human (3311), mouse (2129), rat (679), fly (614) and worm (228). ERGR provides gene annotations and orthologs, detailed gene study information (e.g. fold changes of gene expression, P-values), and both the text and BLAST searches. Moreover, ERGR has data integra- tion tools such as for data union and intersection, and candidate gene selection based on evidence in multiple datasets or organisms. The ERGR database is evolving with new data releases. More functions will also be added. ERGR has a user-friendly web interface with browse and search functions at multi- ple levels. It is freely available at http://bioinfo. vipbg.vcu.edu/ERGR/. INTRODUCTION Alcohol dependence, ethanol response and ethanol-related traits have been extensively studied in both humans and animal models. It is now clear that there are correlations between acute behavioral responses to ethanol and etha- nol consumption or incidence of alcoholism in both animals and humans (1). In humans, alcoholism (alcohol dependence) is a common, genetically inﬂuenced complex disorder across the world. Family, twin and adoption stu- dies demonstrated that genetic factors play a strong role in the etiology of alcoholism, accounting for 50–60% of the population variance in both men and women (2,3). Although genetic factors are important, alcoholism is a complex disease with environmental inﬂuences. Further, the architecture likely involves many genes with small eﬀects along with environmental inﬂuences, as well as potential interactions between them. Therefore, it is a challenge to explore the molecular mechanisms underlying the genetic propensity to excessive alcohol consumption and use these for the development of new treatments for alcoholism. Many experimental strategies [linkage scan, association study, quantitative trait loci (QTLs) and microarray gene expression] have been applied in the stu- dies of alcoholism and ethanol response in order to iden- tify genes or chromosomal regions in both humans and model organisms (4,5). Rapid progress in genetic studies over the past decade has identiﬁed a relatively large number of chromosomal locations or candidate genes that are linked to alcoholism, alcohol-related phenotypes and behavioral responses to ethanol (6–13). Human genetic studies have generally focused on alcohol dependence using linkage studies and association studies. The recent advances of high- throughput molecular technologies such as large-scale genotyping and DNA microarrays have greatly acceler- ated the generation of data used in studies searching for speciﬁc variants contributing to the genetic risk for alco- holism and ethanol response behaviors (14,15). The increasing rate of production for ethanol-related data is expected to accelerate in the near future since the cost of conducting genome-wide association studies (GWAS) is decreasing rapidly. Thus, these data provide us an unpre- cedented opportunity for integrating and making the wealth of results easily accessible and interpretable. So far, a few databases and computational tools, such as *To whom correspondence should be addressed. Tel: +1 804 828 8129; Fax: +1 804 828 1471; Email: zzhao@vcu.edu 2008 The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.