D844 Nucleic Acids Research, 2009, Vol. 37, Database issue Data union and intersection ERGR supports the union operation of any two datasets from the same organism and the intersection operation of any two datasets. There are three rules for the dataset intersection operation. First, if the two datasets to be com- pared are from the same organism, ERGR performs the intersection operation and outputs the results based on gene ID. Second, if the two datasets are from diﬀerent organisms and one of them is from the human, ERGR uses human genes as the reference, transforms non- human genes to the human orthologs, and then performs the intersection operation based on human gene IDs. Third, if the two datasets are from two diﬀerent non- human organisms, ERGR transforms genes in the both datasets to human orthologs and then performs the inter- section operation based on human gene IDs. An example of data integration is shown in Figure 1. Candidate gene selection The candidate gene selection and prioritization function can be used to select candidate genes for follow up experi- mental replication or bioinformatics analysis. To make the ERGR data more useful and serve the community more eﬀectively, ERGR provides some candidate gene selection results based on the evidence in multiple datasets either in one organism or multiple organisms. At present, ERGR contains four such candidate gene lists for ethanol response or alcoholism-related traits. The ﬁrst one is a candidate gene list generated from the datasets of all organisms using human genes as reference. The non- human genes were mapped to human orthologous genes so that they could be compared systematically. There were 42 human genes or their orthologs that had evidence in more than four datasets. The other three candidate gene lists were selected from all the available datasets in one organism (human, mouse or rat) only. These candidate genes include those that have been well studied for alcohol dependence such as ADH, ALDH, GABA receptors, and NPY (4). However, the candidate gene lists also include genes with multiple lines of evidence but not so well stu- died such as CPE, GFAP, CRYAB, GAD1 and NTRK2, among which two [GAD1 (26) and NTRK2 (27)] had asso- ciation studies reported. We found three genes (KCNJ9, GNB1 and ATP1A2) that had evidence in at least ﬁve datasets from the mouse, rat or ﬂy but no evidence yet in any human datasets. FUTURE PERSPECTIVES ERGR is a unique database for ethanol-related genes and their annotations. It is freely available to public and also serves as a core data management system for the local VCU alcohol research community (VCU ARC). We will continue to collect and curate ethanol-related data, espe- cially the genome-wide association studies. We will develop more tools that allow the users to customize their gene ranking, track their own data, and present the genes or integration results graphically. ACKNOWLEDGEMENTS We regret that numerous ethanol studies, from which the ERGR data were extracted through literature search, have not been cited in this paper because of our focus on data curation and database development. The authors would like to thank Drs. Danielle Dick, Brien Riley and Gursharan Kalsi for their valuable advice and discussions. FUNDING The National Institute on Alcohol Abuse and Alcohol- ism (R21AA017437, R01AA011408, R01AA014717, U01AA016667 and U01AA016662). Funding for open access charge: R21AA017437. Conﬂict of interest statement. None declared. REFERENCES 1. Davies,A.G., Bettinger,J.C., Thiele,T.R., Judy,M.E. and McIntire,S.L. (2004) Natural variation in the npr-1 gene modiﬁes ethanol responses of wild strains of C. elegans. Neuron, 42, 731–743. 2. 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