Genome-Environmental Risk Assessment of Cocaine Dependence

Abstract

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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.

Keywords

• cocaine dependence
• genome-environmental risk prediction
• childhood abuse
• GBE1 gene
• tree-assembling ROC