We can improve understanding of human methamphetamine dependence, and possibly our abilities to prevent and treat this devastating disorder, by identifying genes whose allelic variants predispose to methamphetamine dependence.
To find “methamphetamine dependence” genes identified by each of 2 genome-wide association (GWA) studies of independent samples of methamphetamine-dependent individuals and matched controls.
Replicated GWA results in each of 2 case-control studies.
Japan and Taiwan.
Individuals with methamphetamine dependence and matched control subjects free from psychiatric, substance abuse, or substance dependence diagnoses (N = 580).
Main Outcome Measures
“Methamphetamine dependence” genes that were reproducibly identified by clusters of nominally positive single-nucleotide polymorphisms (SNPs) in both samples in ways that were unlikely to represent chance observations, based on Monte Carlo simulations that corrected for multiple comparisons, and subsets of “methamphetamine dependence”
genes that were also identified by GWA studies of dependence on other addictive substances, success in quitting smoking, and memory.
Genes identified by clustered nominally positive SNPs from both samples were unlikely to represent chance observations (Monte Carlo P < .00001). Variants in these “methamphetamine dependence” genes are likely to alter cell adhesion, enzymatic functions, transcription, cell structure, and DNA, RNA, and/or protein handling or modification. Cell adhesion genes CSMD1 and CDH13 displayed the largest numbers of clustered nominally positive SNPs. “Methamphetamine dependence”
genes overlapped, to extents much greater than chance, with genes identified in GWA studies of dependence on other addictive substances, success in quitting smoking, and memory (Monte Carlo P range < .04 to < .00001).
These data support polygenic contributions to methamphetamine dependence from genes that include those whose variants contribute to dependence on several addictive substances, success in quitting smoking, and mnemonic processes.