Two commonly prescribed treatments for opioid addiction are methadone and buprenorphine. While these drugs show some efficacy in treating opioid dependence, treatment response varies among individuals. It is likely that genetic factors play a role in determining treatment outcome. This study analyses the pharmacogenetic association of 6 polymorphisms in OPRD1, the gene encoding the delta-opioid receptor, on treatment outcome in 582 opioid addicted European Americans randomized to either methadone or buprenorphine/naloxone ((Suboxone®) over the course of a 24 week open-label clinical trial. Treatment outcome was assessed as the number of missed or opioid positive urine drug screens over the 24 weeks. In the total sample, no SNPs in OPRD1 were significantly associated with treatment outcome in either treatment arm. However, sex-specific analyses revealed 2 intronic SNPs (rs581111 and rs529520) that predicted treatment outcome in females treated with buprenorphine. Females with the AA or AG genotypes at rs581111 had significantly worse outcomes than those with the GG genotype when treated with buprenorphine (p=0.03, RR=1.67, 95% C.I.[1.06-2.1]). For rs529520, females with the AA genotype had a significantly worse outcome than those with the CC genotype when (p=0.006, RR=2.15, 95%C.I.[1.3-2.29]). No significant associations were detected in males. These findings suggest that rs581111 and rs52920 may be useful when considering treatment options for female opioid addicts, however confirmation in an independent sample is warranted.

Long interspersed element-1 retrotransposons (LINE-1 or L1) are ~6 kb mobile DNA elements implicated in the origins of many Mendelian and complex diseases. The actively retrotransposing L1s are mostly limited to the L1 human specific Ta subfamily. In this manuscript, we present REBELseq as a method for the construction of differentially amplified next-generation sequencing libraries and bioinformatic identification of Ta subfamily long interspersed element-1 human specific elements. REBELseq was performed on DNA isolated from NeuN+ neuronal nuclei from postmortem brain samples of 177 individuals and empirically-driven bioinformatic and experimental cutoffs were established. REBELseq reliably identified both known and novel Ta subfamily L1 insertions distributed throughout the genome. Differences in the proportion of individuals possessing a given reference or non-reference retrotransposon insertion were identified. We conclude that REBELseq is an unbiased, whole genome approach to the amplification and detection of Ta subfamily L1 retrotransposons.