Genome-environmental risk assessment of cocaine dependence.
Cocaine-associated biomedical and psychosocial problems are substantial twenty-first 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 the joint effect of childhood abuse (CA) among trauma experiences 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.
Department of Epidemiology and Biostatistics, Michigan State University East Lansing, MI, USA.
Changshuai
C
Wei
James C
JC
Anthony
Qing
Q
Lu