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The United States is experiencing an epidemic of opioid use disorder (OUD) and overdose-related deaths. However, the genetic basis for the ability to discontinue opioid use has not been investigated. We performed a genome-wide association study (GWAS) of opioid cessation (defined as abstinence from illicit opioids for >1 year or <6 months before the interview date) in 1130 African American (AA) and 2919 European ancestry (EA) participants recruited for genetic studies of substance use disorders and who met lifetime Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) criteria for OUD. Association tests performed separately within each ethnic group were combined by meta-analysis with results obtained from the Comorbidity and Trauma Study. Although there were no genome-wide significant associations, we found suggestive associations with nine independent loci, including three which are biologically relevant: rs4740988 in PTPRD (pAA + EA = 2.24 × 10−6), rs36098404 in MYOM2 (pEA = 2.24 × 10−6), and rs592026 in SNAP25-AS1 (pEA = 6.53 × 10−6). Significant pathways identified in persons of European ancestry (EA) are related to vitamin D metabolism (p = 3.79 × 10−2) and fibroblast growth factor (FGF) signaling (p = 2.39 × 10−2). UK Biobank traits including smoking and drinking cessation and chronic back pain were significantly associated with opioid cessation using GWAS-derived polygenic risk scores. These results provide evidence for genetic influences on opioid cessation, suggest genetic overlap with other relevant traits, and may indicate potential novel therapeutic targets for OUD.

Background The study of DNA methylation quantitative trait loci (meQTLs) helps dissect regulatory mechanisms underlying genetic associations of human diseases. In this study, we conducted the first genome-wide examination of genetic drivers of methylation variation in response to a triglyceride-lowering treatment with fenofibrate (response-meQTL) by using an efficient analytic approach. Methods Subjects ( n  = 429) from the GAW20 real data set with genotype and both pre- (visit 2) and post- (visit 4) fenofibrate treatment methylation measurements were included. Following the quality control steps of removing certain cytosine-phosphate-guanine (CpG) probes, the post−/premethylation changes (post/pre) were log transformed and the association was performed on 208,449 CpG sites. An additive linear mixed-effects model was used to test the association between each CpG probe and single nucleotide polymorphisms (SNPs) around ±1 Mb region, with age, sex, smoke, batch effect, and principal components included as covariates. Bonferroni correction was applied to define the significance threshold ( p  < 5.6 × 10 − 10 , given a total of 89,217,303 tests). Finally, we integrated our response-meQTL (re-meQTL) findings with the published genome-wide association study (GWAS) catalog of human diseases/traits. Results We identified 1087 SNPs as cis re-meQTLs associated with 610 CpG probes/sites located in 351 unique gene loci. Among these 1087 cis re-meQTL SNPs, 229 were unique and 6 were co-localized at 8 unique disease/trait loci reported in the GWAS catalog (enrichment p  = 1.51 × 10 − 23 ). Specifically, a lipid SNP, rs10903129, located in intron regions of gene TMEM57 , was a re-meQTL ( p  = 3.12 × 10 − 36 ) associated with the CpG probe cg09222892, which is in the upstream region of the gene RHCE, indicating a new target gene for rs10903129. In addition, we found that SNP rs12710728 has a suggestive association with cg17097782 ( p  = 1.77 × 10 − 4 ), and that this SNP is in high linkage disequilibrium (LD) (R 2  > 0.8) with rs7443270, which was previously reported to be associated with fenofibrate response ( p  = 5.00 × 10 − 6 ). Conclusions By using a novel analytic approach, we efficiently identified thousands of cis re-meQTLs that provide a unique resource for further characterizing functional roles and gene targets of the SNPs that are most responsive to fenofibrate treatment. Our efficient analytic approach can be extended to large response quantitative trait locus studies with large sample sizes and multiple time points data.

Stimulant dependence is heritable, but specific genetic factors underlying the trait have not been identified. A genome-wide association study for stimulant dependence was performed in a discovery cohort of African- (AA) and European-ancestry (EA) subjects ascertained for genetic studies of alcohol, opioid, and cocaine use disorders. The sample comprised individuals with DSM-IV stimulant dependence (393 EA cases, 5288 EA controls; 155 AA cases, 5603 AA controls). An independent cohort from the family-based Collaborative Study on the Genetics of Alcoholism (532 EA cases, 7635 EA controls; 53 AA cases, AA 3352 controls) was used for replication. One variant in SLC25A16 (rs2394476, p = 3.42 × 10−10, odds ratio [OR] = 3.70) was GWS in AAs. Four other loci showed suggestive evidence, including KCNA4 in AAs (rs11500237, p = 2.99 × 10−7, OR = 2.31) which encodes one of the potassium voltage-gated channel protein that has been linked to several other substance use disorders, and CPVL in the combined population groups (rs1176440, p = 3.05 × 10−7, OR = 1.35), whose expression was previously shown to be upregulated in the prefrontal cortex from users of cocaine, cannabis, and phencyclidine. Analysis of the top GWAS signals revealed a significant enrichment with nicotinic acetylcholine receptor genes (adjusted p = 0.04) and significant pleiotropy between stimulant dependence and alcohol dependence in EAs (padj = 3.6 × 10−3), an anxiety disorder in EAs (padj = 2.1 × 10−4), and ADHD in both AAs (padj = 3.0 × 10−33) and EAs (padj = 6.7 × 10−35). Our results implicate novel genes and pathways as having roles in the etiology of stimulant dependence.

Opioid use disorder is heritable, yet its genetic etiology is largely unknown. C57BL/6J and C57BL/6NJ mouse substrains exhibit phenotypic diversity in the context of limited genetic diversity which together can facilitate genetic discovery. Here, we found C57BL/6NJ mice were less sensitive to oxycodone (OXY)‐induced locomotor activation versus C57BL/6J mice in a conditioned place preference paradigm. Narrow‐sense heritability of OXY‐induced locomotor activity traits ranged from 0.22 to 0.31, implicating suitability for genetic analysis. Quantitative trait locus (QTL) mapping in an F2 cross identified a chromosome 1 QTL explaining 7%–12% of the variance in OXY locomotion and anxiety‐like withdrawal in the elevated plus maze. A second QTL for EPM withdrawal behavior on chromosome 5 near Gabra2 (alpha‐2 subunit of GABA‐A receptor) explained 9% of the variance. To narrow the chromosome 1 locus, we generated recombinant lines spanning 163–181 Mb, captured the QTL for OXY locomotor traits and withdrawal, and fine‐mapped a 2.45‐Mb region (170.16–172.61 Mb). Transcriptome analysis identified five, localized striatal cis‐eQTL transcripts and two were confirmed at the protein level (KCNJ9, ATP1A2). Kcnj9 codes for a potassium channel (GIRK3) that is a major effector of mu opioid receptor signaling. Atp1a2 codes for a subunit of a Na+/K+ ATPase enzyme that regulates neuronal excitability and shows functional adaptations following chronic opioid administration. To summarize, we identified two candidate genes underlying the physiological and behavioral properties of opioids, with direct preclinical relevance to investigators employing these widely used substrains and clinical relevance to human genetic studies of opioid use disorder. We exploited near‐isogenic C57BL/6 substrains to fine‐map a 2.45‐Mb region containing DNA variants underlying oxycodone behaviors. We mapped a genomic region on distal chromosome 1 and identified genotype‐driven, differentially expressed mRNAs and proteins within this region and DNA variants. Atp1a2 and Kcnj9 as two likely candidate genes underlying oxycodone behavior.

Methamphetamine addiction remains a major public health concern in the United States that has paralleled the opioid epidemic. Psychostimulant use disorders have a heritable genetic component that remains unexplained. Methamphetamine targets membrane and vesicular transporters to increase synaptic dopamine, norepinephrine, and serotonin. We previously identified (heterogeneous nuclear ribonucleoprotein H1) as a quantitative trait gene underlying methamphetamine behavioral sensitivity. encodes the RNA-binding protein hnRNP H1 that is ubiquitously expressed in neurons throughout the brain. Gene-edited mice with a heterozygous frameshift deletion in first coding exon of showed reduced methamphetamine-induced dopamine release and behaviors. To inform the mechanism linking hnRNP H with methamphetamine neurobehavioral effects, we surveyed the mRNA targetome of hnRNP H via cross-linking immunoprecipitation coupled with RNA-sequencing in striatal tissue at baseline and at 30 min post-methamphetamine in wild-type male and female C57BL/6J mice. Methamphetamine induced changes in RNA-binding targets of hnRNP H in mice, including differential binding to 3'UTR targets and multiple enriched mRNAs involved in synaptic plasticity. Targetome, transcriptome, and spliceome analyses triangulated on as a suggestive target, with differences in hnRNP H binding, gene expression and splicing following methamphetamine treatment (2 mg/kg, i.p.). Furthermore, pre-treatment with pregabalin, an inhibitor of α2δ2 and α2δ1 voltage-gated calcium channel subunits, attenuated methamphetamine-induced locomotor activity in male and female mice, supporting a role for Cacna2d1/d2 in methamphetamine locomotor stimulant sensitivity. Our study identifies a dynamic hnRNP H RNA targetome that can rapidly and adaptively respond to methamphetamine to regulate gene expression and likely synaptic plasticity and behavior.

The United States is currently experiencing an epidemic of opioid use, use disorder, and overdose-related deaths. While studies have identified several loci that are associated with opioid use disorder (OUD) risk, the genetic basis for the ability to discontinue opioid use has not been investigated. Furthermore, very few studies have investigated the non-genetic factors that are predictive of opioid cessation or their predictive ability.In this thesis, I studied a novel phenotype–opioid cessation, defined as the time since last use of illicit opioids (< 6 months ago as not cease, > 1 year ago as cease) among persons meeting lifetime DSM-5 criteria for opioid use disorder (OUD).In chapter two, I identified novel genetic variants and biological pathways that potentially regulate opioid cessation success through a genome wide study, as well as genetic overlap between opioid cessation and other substance cessation traits.In chapter three, I identified multiple non-genetic risk factors specific to each racial group that are predictive of opioid cessation from the same individuals analyzed in chapter two by applying several linear and non-linear machine learning techniques to a set of more than 3,000 variables assessed by a structured psychiatric interview. Factors identified from this atheoretical approach can be grouped into opioid use activities, other drug use, health conditions, and demographics, while the predictive accuracy as high as nearly 80% was achieved. The findings from this research generated more hypotheses for future studies to reference.In chapter four, I performed differential gene expression and network analysis on mice with different oxycodone (an opioid receptor agonist)-induced behaviors and compared the significantly associated genes and network modules with top-ranked genes identified in humans. The pathway cross-talks and gene homologs identified from both species illuminate the potential molecular mechanism of opioid behaviors.In summary, this thesis utilized statistical genetics, machine learning, and a computational biology framework to address factors that are associative with opioid cessation in humans, and cross-referenced the genetic findings in a mouse model. These findings serve as references for future studies and provide a framework for personalizing the treatment of OUD.

People with opioid use disorder (OUD) can stop using opioids on their own, with help from groups and with treatment, but there is limited research on the factors that influence opioid cessation. We employed multiple machine learning prediction algorithms (LASSO, random forest, deep neural network, and support vector machine) to assess factors associated with ceasing opioid use in a sample comprised of African Americans (AAs) and European Americans (EAs) who met DSM-5 criteria for mild to severe OUD. Values for several thousand demographic, alcohol and other drug use, general health, and behavioral variables, as well as diagnoses for other psychiatric disorders, were obtained for each participant from a detailed semi-structured interview. Support vector machine models performed marginally better on average than those derived using other machine learning methods with maximum prediction accuracies of 75.4% in AAs and 79.4% in EAs. Subsequent stepwise regression analyses that considered the 83 most highly ranked variables across all methods and models identified less recent cocaine use (p<5e-8), a shorter duration of opioid use (p<5e-6), and older age (p<5e-9) as the strongest independent predictors of opioid cessation. Factors related to drug use comprised about half of the significant independent predictors, with other predictors related to non-drug use behaviors, psychiatric disorders, overall health, and demographics. These proof-of-concept findings provide information that can help develop strategies for improving OUD management and the methods we applied provide a framework for personalizing OUD treatment.

Opioid use disorder is heritable, yet its genetic etiology is largely unknown. C57BL/6J and C57BL/6NJ mouse substrains exhibit phenotypic diversity in the context of limited genetic diversity which together can facilitate genetic discovery. Here, we found C57BL/6NJ mice were less sensitive to oxycodone (OXY)-induced locomotor activation versus C57BL/6J mice in a conditioned place preference paradigm. Narrow-sense heritability was estimated at 0.22-0.31, implicating suitability for genetic analysis. Quantitative trait locus (QTL) mapping in an F2 cross identified a chromosome 1 QTL explaining 7-12% of the variance in OXY locomotion and anxiety-like withdrawal in the elevated plus maze. A second QTL for EPM withdrawal behavior on chromosome 5 near (alpha-2 subunit of GABA-A receptor) explained 9% of the variance. To narrow the chromosome 1 locus, we generated recombinant lines spanning 163-181 Mb, captured the QTL for OXY locomotor traits and withdrawal, and fine-mapped a 2.45-Mb region (170.16-172.61 Mb). Transcriptome analysis identified five, localized striatal cis-eQTL transcripts and two were confirmed at the protein level (KCNJ9, ATP1A2). codes for a potassium channel (GIRK3) that is a major effector of mu opioid receptor signaling. codes for a subunit of a Na+/K+ ATPase enzyme that regulates neuronal excitability and shows functional adaptations following chronic opioid administration. To summarize, we identified two candidate genes underlying the physiological and behavioral properties of opioids, with direct preclinical relevance to investigators employing these widely used substrains and clinical relevance to human genetic studies of opioid use disorder.

Psychostimulant (methamphetamine, cocaine) use disorders have a genetic component that remains mostly unknown. Here, we conducted genome-wide quantitative trait locus (QTL) analysis of methamphetamine stimulant sensitivity. To facilitate gene identification, we employed a Reduced Complexity Cross between closely related C57BL/6 mouse substrains and examined maximum speed and distance traveled over 30 min following methamphetamine (2 mg/kg, i.p.). For maximum methamphetamine-induced speed following the second and third administration, we identified a single genome-wide significant QTL on chromosome 11 that peaked near the Cyfip2 locus [LOD = 3.5, 4.2; peak = 21 cM (36 Mb)]. For methamphetamine-induced distance traveled, we identified a single genome-wide significant QTL on chromosome 5 that peaked near a functional intronic indel in Gabra2 that codes for the alpha-2 subunit of the GABA-A receptor [LOD = 5.2; peak = 35 cM (67 Mb)]. Striatal cis-expression QTL mapping corroborated Gabra2 as a functional candidate gene underlying methamphetamine-induced distance traveled. CRISPR/Cas9-mediated correction of the mutant intronic deletion on the C57BL/6J background to the wild-type C57BL/6NJ allele was sufficient to reduce methamphetamine-induced locomotor activity toward the wild-type C57BL/6NJ-like level, thus validating the quantitative trait variant (QTV). These studies demonstrate the power and efficiency of Reduced Complexity Crosses in identifying causal genes and variants underlying complex traits. Functionally restoring Gabra2 expression decreased methamphetamine stimulant sensitivity and supports preclinical and human genetic studies implicating the GABA-A receptor in psychostimulant addiction-relevant traits. Importantly, our findings have major implications for investigators studying psychostimulants in the C57BL/6J strain - the gold standard strain in biomedical research.

Purpose This study aims to analyse the correlation between the incidence rate of hand injuries and various major economic indicators in Singapore. We hypothesise that the number of hand injuries is correlated to activity in the construction and manufacturing industries in Singapore. Methods Twenty thousand seven hundred sixty-four patients who underwent hand surgeries in a tertiary institution between 2012 to 2018 were reviewed. Two independent, blinded observers extracted the frequency of hand surgeries performed from Electronic Medical Records. Economic indicators pertinent to Singapore’s economic activity were collected and smoothed by simple moving average of the prior 3 months. Results were analysed using IBM SPSS v25.0. Results Significant independent univariate variables were Purchasing-Manager-Index and Industrial-Production-Index. Multiple linear regression of quarterly reported figures showed that Total-Livestock-Slaughtered, Total-Seafood-Handled, Purchasing-Manger-Index, Industrial-Production-Index, Gas-Tariffs, Construction-Index, Consumer-Price-Index, Total-Air-Cargo-Handled, Total-Container-Throughput, Total-Road-Traffic-Accident-Casualties, Food-&-Beverage-Services-Index were significantly correlated ( p  < 0.05) with hand injuries, with R 2  = 62.3%. Conclusion Quarterly economic indicators from major economic industries can be used to predict the incidence of hand injuries with a 62.3% correlation. These findings may be useful for anticipating healthcare resource allocation to treat hand injuries. Type of study and level of evidence Economic and decision, Level II.