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Meta-analysis and genome-wide interpretation of genetic susceptibility to drug addiction

Chuan-Yun Li12*, Wei-Zhen Zhou2, Ping-Wu Zhang3, Catherine Johnson4, Liping Wei2 and George R Uhl4*

Author Affiliations

1 Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, Peking University, Beijing, China

2 Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University. Beijing, China

3 Department of Neurology, the Johns Hopkins School of Medicine, Baltimore, Maryland, USA

4 Molecular Neurobiology Branch, NIH-IRP (NIDA), Suite 3510, 333 Cassell Drive Baltimore, Maryland, USA

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BMC Genomics 2011, 12:508  doi:10.1186/1471-2164-12-508

Published: 15 October 2011



Classical genetic studies provide strong evidence for heritable contributions to susceptibility to developing dependence on addictive substances. Candidate gene and genome-wide association studies (GWAS) have sought genes, chromosomal regions and allelic variants likely to contribute to susceptibility to drug addiction.


Here, we performed a meta-analysis of addiction candidate gene association studies and GWAS to investigate possible functional mechanisms associated with addiction susceptibility. From meta-data retrieved from 212 publications on candidate gene association studies and 5 GWAS reports, we linked a total of 843 haplotypes to addiction susceptibility. We mapped the SNPs in these haplotypes to functional and regulatory elements in the genome and estimated the magnitude of the contributions of different molecular mechanisms to their effects on addiction susceptibility. In addition to SNPs in coding regions, these data suggest that haplotypes in gene regulatory regions may also contribute to addiction susceptibility. When we compared the lists of genes identified by association studies and those identified by molecular biological studies of drug-regulated genes, we observed significantly higher participation in the same gene interaction networks than expected by chance, despite little overlap between the two gene lists.


These results appear to offer new insights into the genetic factors underlying drug addiction.