Genome-Environmental Risk Assessment of Cocaine Dependence

• Main Authors: Changshuai eWei, James C Anthony, Qing eLu
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2012-05-01
• Series: Frontiers in Genetics
• Subjects: cocaine dependence; genome-environmental risk prediction; childhood abuse; GBE1 gene; tree-assembling ROC
• Online Access: http://journal.frontiersin.org/Journal/10.3389/fgene.2012.00083/full

Cocaine-associated biomedical and psychosocial problems are substantial 21st century global burdens of disease. This burden is largely driven by a cocaine dependence process that becomes engaged with increasing occasions of cocaine product use. For this reason, the development of a risk prediction model for cocaine dependence may be of special value. Ultimately, success in building such a risk prediction model may help promote personalized cocaine dependence prediction, prevention, and treatment approaches not presently available. As an initial step toward this goal, we conducted a genome-environmental risk prediction study for cocaine dependence, simultaneously considering 948,658 single nucleotide polymorphisms (SNPs), six potentially cocaine-related facets of environment, and three personal characteristics. In this study, a novel statistical approach was applied to 1045 case-control samples from the Family Study of Cocaine Dependence. The results identify 330 low- to medium-effect size SNPs (i.e., those with a single locus p-value of less than 10-4) that made a substantial contribution to cocaine dependence risk prediction (AUC=0.718). Inclusion of six facets of environment and three personal characteristics yielded greater accuracy (AUC=0.809). Of special importance was childhood abuse (CA) among trauma experiences, with a potentially important interaction of CA and the GBE1 gene in cocaine dependence risk prediction. Genome-environmental risk prediction models may become more promising in future risk prediction research, once a more substantial array of environmental facets are taken into account, sometimes with model improvement when gene-by-environment product terms are included as part of these risk predication models.