A multi-threaded approach to genotype pattern mining for detecting digenic disease genes.

To locate disease-causing DNA variants on the human gene map, the customary approach has been to carry out a genome-wide association study for one variant after another by testing for genotype frequency differences between individuals affected and unaffected with disease. So-called digenic traits are due to the combined effects of two variants, often on different chromosomes, while individual variants may have little or no effect on disease. Machine learning approaches have been developed to find variant pairs underlying digenic traits. However, many of these methods have large memory requirements so that only small datasets can be analyzed. The increasing availability of desktop computers with large numbers of processors and suitable programming to distribute the workload evenly over all processors in a machine make a new and relatively straightforward approach possible, that is, to evaluate all existing variant and genotype pairs for disease association. We present a prototype of such a method with two components, Vpairs and Gpairs, and demonstrate its advantages over existing implementations of such well-known algorithms as Apriori and FP-growth. We apply these methods to published case-control datasets on age-related macular degeneration and Parkinson disease and construct an ROC curve for a large set of genotype patterns.

Digenic Analysis Finds Highly Interactive Genetic Variants Underlying Polygenic Traits.

We briefly review our recently published approach to mining digenic genotype patterns, which consist of two genotypes each originating in a different DNA variant. We do this for a genetic case-control study by evaluating all possible pairs of genotypes, distributing the workload over numerous CPUs (threads) in a high-performance computing environment and apply our methods to two known datasets, age-related macular degeneration (AMD) and Parkinson Disease (PD). Based on a list of (e.g., 100,000) genotype pairs with largest genotype pair frequency differences between cases and controls, we determine the number N u of unique variants occurring in this list. For each unique variant, we find the number of genotype pairs it participates in, which identifies a set of variants "connected" with the given unique variant. Among the total of variants "connected" with all unique variants, only a subset of variants is unique. The ratio of all connected variants divided by that subset of variants is a measure for the overall density or connectedness of variants interacting with each other. We find that variants for the AMD data are much more interconnected than those for PD, at least based on the 100,000 genotype pairs with largest chi-square we investigated. Further, for each of the N u unique variants, we use the number of variants connected with it as a test statistic, weighted by the inverse of the rank at which the unique variant first occurred in the original list of genotype patterns. This weighing scheme ties the number of connections to the genetics of the trait and allows us to obtain, for each of the N u unique variants, an empirical significance level by permuting ranks. We find 12 and 8 significant, highly connected variants for AMD and PD, respectively, some of which have previously been identified by other machine learning methods, thus providing credence to our approach. Among the 100,000 genotype pairs investigated for each of AMD and PD, significant variants showed connections with up to 7,093 and 3,777 other variants, respectively. Our approach has been implemented in a freely available piece of software, the Digenic Network Test. Thus, our statistical genetics method can provide important information on the genetic architecture of polygenic traits.

Comorbidity of Novel CRHR2 Gene Variants in Type 2 Diabetes and Depression.

The corticotropin-releasing hormone receptor 2 (CRHR2) gene encodes CRHR2, contributing to the hypothalamic-pituitary-adrenal stress response and to hyperglycemia and insulin resistance. CRHR2-/- mice are hypersensitive to stress, and the CRHR2 locus has been linked to type 2 diabetes and depression. While CRHR2 variants confer risk for mood disorders, MDD, and type 2 diabetes, they have not been investigated in familial T2D and MDD. In 212 Italian families with type 2 diabetes and depression, we tested 17 CRHR2 single nucleotide polymorphisms (SNPs), using two-point parametric-linkage and linkage-disequilibrium (i.e., association) analysis (models: dominant-complete-penetrance-D1, dominant-incomplete-penetrance-D2, recessive-complete-penetrance-R1, recessive-incomplete-penetrance-R2). We detected novel linkage/linkage-disequilibrium/association to/with depression (3 SNPs/D1, 2 SNPs/D2, 3 SNPs/R1, 3 SNPs/R2) and type 2 diabetes (3 SNPs/D1, 2 SNPs/D2, 2 SNPs/R1, 1 SNP/R2). All detected risk variants are novel. Two depression-risk variants within one linkage-disequilibrium block replicate each other. Two independent novel SNPs were comorbid while the most significant conferred either depression- or type 2 diabetes-risk. Although the families were primarily ascertained for type 2 diabetes, depression-risk variants showed higher significance than type 2 diabetes-risk variants, implying CRHR2 has a stronger role in depression-risk than type 2 diabetes-risk. In silico analysis predicted variants' dysfunction. CRHR2 is for the first time linked to/in linkage-disequilibrium/association with depression-type 2 diabetes comorbidity and may underlie the shared genetic pathogenesis via pleiotropy.

Implication of Melanocortin Receptor Genes in the Familial Comorbidity of Type 2 Diabetes and Depression.

The melanocortin receptors are G-protein-coupled receptors, which are essential components of the hypothalamic-pituitary-adrenal axis, and they mediate the actions of melanocortins (melanocyte-stimulating hormones: α-MSH, ß-MSH, and γ-MSH) as well as the adrenocorticotropin hormone (ACTH) in skin pigmentation, adrenal steroidogenesis, and stress response. Three melanocortin receptor genes (MC1R, MC2R, and MC5R) contribute to the risk of major depressive disorder (MDD), and one melanocortin receptor gene (MC4R) contributes to the risk of type 2 diabetes (T2D). MDD increases T2D risk in drug-naïve patients; thus, MDD and T2D commonly coexist. The five melanocortin receptor genes might confer risk for both disorders. However, they have never been investigated jointly to evaluate their potential contributing roles in the MDD-T2D comorbidity, specifically within families. In 212 Italian families with T2D and MDD, we tested 11 single nucleotide polymorphisms (SNPs) in the MC1R gene, 9 SNPs in MC2R, 3 SNPs in MC3R, 4 SNPs in MC4R, and 2 SNPs in MC5R. The testing used 2-point parametric linkage and linkage disequilibrium (LD) (i.e., association) analysis with four models (dominant with complete penetrance (D1), dominant with incomplete penetrance (D2), recessive with complete penetrance (R1), and recessive with incomplete penetrance (R2)). We detected significant (p ≤ 0.05) linkage and/or LD (i.e., association) to/with MDD for one SNP in MC2R (rs111734014) and one SNP in MC5R (rs2236700), and to/with T2D for three SNPs in MC1R (rs1805007 and rs201192930, and rs2228479), one SNP in MC2R (rs104894660), two SNPs in MC3R (rs3746619 and rs3827103), and one SNP in MC4R genes (Chr18-60372302). The linkage/LD/association was significant across different linkage patterns and different modes of inheritance. All reported variants are novel in MDD and T2D. This is the first study to report risk variants in MC1R, MC2R, and MC3R genes in T2D. MC2R and MC5R genes are replicated in MDD, with one novel variant each. Within our dataset, only the MC2R gene appears to confer risk for both MDD and T2D, albeit with different risk variants. To further clarity the role of the melanocortin receptor genes in MDD-T2D, these findings should be sought among other ethnicities as well.

Machine learning approaches to explore digenic inheritance.

Some rare genetic disorders, such as retinitis pigmentosa or Alport syndrome, are caused by the co-inheritance of DNA variants at two different genetic loci (digenic inheritance). To capture the effects of these disease-causing variants and their possible interactive effects, various statistical methods have been developed in human genetics. Analogous developments have taken place in the field of machine learning, particularly for the field that is now called Big Data. In the past, these two areas have grown independently and have started to converge only in recent years. We discuss an overview of each of the two fields, paying special attention to machine learning methods for uncovering the combined effects of pairs of variants on human disease.

Analyses of polymorphisms of intron 2 of OPRK1 (kappa-opioid receptor gene) in association with opioid and cocaine dependence diagnoses in an African-American population.

RATIONALE: The dynorphin/kappa-opioid receptor (KOR) system (encoded by PDYN and OPRK1 genes respectively) is highly regulated by repeated exposure to drugs of abuse, including mu-opioid agonists and cocaine. These changes in the dynorphin/KOR system can then influence the rewarding effects of these drugs of abuse. Activation of the dynorphin/KOR system is also thought to have a role in the pro-addictive effects of stress. Recent in vitro assays showed that the OPRK1 intron 2 may function as a genomic enhancer in the regulation KOR expression, and contains a glucocorticiod-responsive sequence site. We hypothesize that SNPs in intron 2 of OPRK1 are associated with categorical opioid or cocaine dependence diagnoses, as well as with dimensional aspects of drug use (i.e., magnitude of drug exposure). METHODS: This study includes 577 subjects ≥ 18 years old, with African ancestry (AA) from the USA. They were divided into three groups: 152 control subjects, 142 persons with lifetime opioid dependence diagnosis (OD), and 283 subjects with lifetime cocaine dependence diagnosis (CD). Five SNPs (rs16918909, rs7016778, rs997917, rs6473797, rs10111937) that span 10 Kb nucleotides in intron 2 of OPRK1 were used for the association analyses. Genotyping was performed with the Smokescreen® array or sequencing of PCR-amplified DNA fragments. Association analyses for OD and CD diagnoses and the OPRK1 intron 2 alleles were carried out with Fisher's exact test. The Kreek-McHugh-Schluger-Kellogg (KMSK) scales were used for dimensional measure of maximum exposure to specific drugs, using Mann-Whitney tests. RESULTS: Two SNPs, rs997917 and rs10111937 showed point-wise significant allelic association (p < 0.05) with CD diagnosis, and rs10111937 showed a point-wise significance in association with OD. None of these single SNP associations with categorical diagnoses were significant after correction for multiple testing (pcorr > 0.05). However, significant associations of several genotype patterns (diplotypes) were found with cocaine dependence, but none for opioid dependence. The most significant genotype pattern with cocaine dependence diagnosis occurred for rs6473797 and rs10111937 (pcorr = 0.036, odds ratio = 1.92, FDR < 0.05), and survived correction for multiple testing. Dimensional analyses with KMSK scores show that persons with either rs997917 or rs10111937 variants had greater exposure to cocaine, compared to those with prototype allele (Mann-Whitney tests, point-wise). CONCLUSIONS: This study provides additional support of potential importance of regulatory regions of intron 2 of the OPRK1 gene in development of cocaine and opioid dependence diagnoses, in a population with African-American ancestry.

Overview of frequent pattern mining.

Various methods of frequent pattern mining have been applied to genetic problems, specifically, to the combined association of two genotypes (a genotype pattern, or diplotype) at different DNA variants with disease. These methods have the ability to come up with a selection of genotype patterns that are more common in affected than unaffected individuals, and the assessment of statistical significance for these selected patterns poses some unique problems, which are briefly outlined here.

Genotype Pattern Mining for Pairs of Interacting Variants Underlying Digenic Traits.

Some genetic diseases ("digenic traits") are due to the interaction between two DNA variants, which presumably reflects biochemical interactions. For example, certain forms of Retinitis Pigmentosa, a type of blindness, occur in the presence of two mutant variants, one each in the ROM1 and RDS genes, while the occurrence of only one such variant results in a normal phenotype. Detecting variant pairs underlying digenic traits by standard genetic methods is difficult and is downright impossible when individual variants alone have minimal effects. Frequent pattern mining (FPM) methods are known to detect patterns of items. We make use of FPM approaches to find pairs of genotypes (from different variants) that can discriminate between cases and controls. Our method is based on genotype patterns of length two, and permutation testing allows assigning p-values to genotype patterns, where the null hypothesis refers to equal pattern frequencies in cases and controls. We compare different interaction search approaches and their properties on the basis of published datasets. Our implementation of FPM to case-control studies is freely available.

Population genetics: past, present, and future.

We present selected topics of population genetics and molecular phylogeny. As several excellent review articles have been published and generally focus on European and American scientists, here, we emphasize contributions by Japanese researchers. Our review may also be seen as a belated 50-year celebration of Motoo Kimura's early seminal paper on the molecular clock, published in 1968.

Polymorphisms in Stress-Related Genes Are Associated with Reduced Cocaine Abuse and Longer Retention in Methadone Maintenance Treatment for Opioid Use Disorder.

BACKGROUND: As CRH-binding protein (CRHBP) SNP rs1500 was associated with reduced cocaine abuse after 1 year in methadone maintenance treatment (MMT) for heroin addiction, we evaluated the association of additional 28 selected SNPs, in 17 stress-related genes, with MMT outcome. METHODS: The distribution of genotypes of each SNP by cocaine abuse after 1 year in MMT was assessed under the dominant and recessive models using χ2. Cumulative retention (up to 26.5 years) was studied using Kaplan-Meier analyses. Logistic regression and Cox model were used for multivariate analyses. RESULTS: Of a nonselective sample of 404 patients, 25 patients with <50% Europeans/Middle Eastern ancestry were excluded. Of the remaining 379 patients, 330 (87.1%) stayed at least 1 year in treatment. Four SNPs were associated with cocaine abuse after 1 year in MMT. A lower proportion of cocaine abusers was found in the groups of subjects with the following genotypes: arginine vasopressin (AVP) SNP rs2282018 CC, CRHBP rs7728378 TT, galanin rs3136541 TT/TC, and neuropeptide Y receptor Y1 (NPY1R) rs4518200 AA. The following independent variables were associated with lack of cocaine in urine after 1 year (multivariate analyses): CRHBP rs7728378 TT, NPY1R rs4518200 AA, no cocaine in urine on admission, as well as opiate and benzodiazepine use after 1 year in MMT. Cumulative retention (n = 379) was longer in carriers of AVP rs2282018 CC (13.7 years, 95% CI 11.1-16.2) versus TT/TC genotypes (10.5, 95% CI 9.4-11.5) (p = 0.0230) Conclusions: The study suggests that a reduction in cocaine abuse and longer retention among MMT patients is mediated in part by variants in stress-related genes and is a step toward precision medicine.

A Novel Locus and Candidate Gene for Familial Developmental Dyslexia on Chromosome 4q.

Objective: Developmental dyslexia is a highly heritable specific reading and writing disability. To identify a possible new locus and candidate gene for this disability, we investigated a four-generation pedigree where transmission of dyslexia is consistent with an autosomal dominant inheritance pattern. Methods: We performed genome wide array-based SNP genotyping and parametric linkage analysis and sequencing analysis of protein-coding exons, exon-intron boundaries and conserved extragenic regions within the haplotype cosegregating with dyslexia in DNA from one affected and one unaffected family member. Cosegregation was confirmed by sequencing all available family members. Additionally, we analyzed 96 dyslexic individuals who had previously shown positive LOD scores on chromosome 4q28 as well as an even larger sample (n = 2591). Results: We found a single prominent linkage interval on chromosome 4q, where sequence analysis revealed a nucleotide variant in the 3' UTR of brain expressed SPRY1 in the dyslexic family member that cosegregated with dyslexia. This sequence alteration might affect the binding efficiency of the IGF2BP1 RNA-binding protein and thus influence the expression level of the SPRY1 gene product. An analysis of 96 individuals from a cohort of dyslexic individuals revealed a second heterozygous variant in this gene, which was absent in the unaffected sister of the proband. An investigation of the region in a much larger sample further found a nominal p-value of 0.0016 for verbal short-term memory (digit span) in 2,591 individuals for a neighboring SNV. After correcting for the local number of analyzed SNVs, and after taking into account linkage disequilibrium, we found this corresponds to a p-value of 0.0678 for this phenotype. Conclusions: We describe a new locus for familial dyslexia and discuss the possibility that SPRY1 might play a role in the etiology of a monogenic form of dyslexia.

Variants of opioid genes and response to treatment of opioid use disorder with buprenorphine-naloxone versus extended-release naltrexone in Caucasians.

Background: Sublingual buprenorphine-naloxone (BUP-NX), an FDA-approved treatment for opioid use disorder (OUD), combines buprenorphine (a partial mu/kappa agonist) with naloxone (a mu/ kappa antagonist). Extended-release injection naltrexone (XR-NTX; a mu receptor antagonist and kappa receptor partial agonist) is also an FDA-approved treatment for OUD. However, while some patients respond well to these medications, many others leave treatment and relapse. Objectives: Determine whether gene variants in the opioid gene system are associated with better or worse treatment response. Methods: In a 24-week, multisite, randomized, comparative effectiveness trial of daily, sublingual self-administration of BUP-NX versus monthly injection of XR-NTX conducted in the National Drug Abuse Clinical Trials Network, DNA was collected and four opioid gene variants were evaluated: (1) mu opioid receptor 118A>G; (2) 68-bp repeat in prodynorphin; (3) prodynorphin SNP rs910080; and (4) kappa opioid receptor SNP rs6473797. In non-Hispanic Caucasians (N = 334), two outcomes measures were assessed: received first dose (yes/no) and received last dose (yes/no). Separate logistic regressions were used to model each outcome measure as a function of treatment (XR-NTX vs BUP-NX), each gene variant, and their interaction. Results: There were no significant main effects of gene variant on receiving first dose or last dose. There were also no significant gene variant by treatment interactions. Conclusions: The outcome of treatment of OUD with medications is likely a complex function of multiple factors, including environmental, psychosocial, and possibly genetic, such that major effects of genetic variants may be unlikely.

Further evidence for the association of GAL, GALR1 and NPY1R variants with opioid dependence.

Aim: Heroin addiction is a chronic, relapsing disease that has genetic and environmental, including drug-induced, contributions. Stress influences the development of addictions. This study was conducted to determine if variants in stress-related genes are associated with opioid dependence (OD). Patients & methods: One hundred and twenty variants in 26 genes were analyzed in 597 Dutch subjects. Patients included 281 OD in methadone maintenance with or without heroin-assisted treatment and 316 controls. Results: Twelve SNPs in seven genes showed a nominally significant association with OD. Experiment-wise significant associations (p < 0.05) were found for three SNP pairs, through an interaction effect: NPY1R/GAL rs4691910/rs1893679, NPY1R/GAL rs4691910/rs3136541 and GALR1/GAL rs9807208/rs3136541. Conclusion: This study lends more evidence to previous reports of association of stress-related variants with heroin dependence.

Maximal Segmental Score Method for Localizing Recessive Disease Variants Based on Sequence Data.

BACKGROUND: Due to the affordability of whole-genome sequencing, the genetic association design can now address rare diseases. However, some common statistical association methods only consider homozygosity mapping and need several criteria, such as sliding windows of a given size and statistical significance threshold setting, such as P-value < 0.05 to achieve good power in rare disease association detection. METHODS: Our region-specific method, called expanded maximal segmental score (eMSS), converts p-values into continuous scores based on the maximal segmental score (MSS) (Lin et al., 2014) for detecting disease-associated segments. Our eMSS considers the whole genome sequence data, not only regions of homozygosity in candidate genes. Unlike sliding window methods of a given size, eMSS does not need predetermined parameters, such as window size or minimum or maximum number of SNPs in a segment. The performance of eMSS was evaluated by simulations and real data analysis for autosomal recessive diseases multiple intestinal atresia (MIA) and osteogenesis imperfecta (OI), where the number of cases is extremely small. For the real data, the results by eMSS were compared with a state-of-the-art method, HDR-del (Imai et al., 2016). RESULTS: Our simulation results show that eMSS had higher power as the number of non-causal haplotype blocks decreased. The type I error for eMSS under different scenarios was well controlled, p < 0.05. For our observed data, the bone morphogenetic protein 1 (BMP1) gene on chromosome 8, the Violaxanthin de-epoxidase-related chloroplast (VDR) gene on chromosome 12 associated with OI, and the tetratricopeptide repeat domain 7A (TTC7A) gene on chromosome 2 associated with MIA have previously been identified as harboring the relevant pathogenic mutations. CONCLUSIONS: When compared to HDR-del, our eMSS is powerful in analyzing even small numbers of recessive cases, and the results show that the method can further reduce numbers of candidate variants to a very small set of susceptibility pathogenic variants underlying OI and MIA. When we conduct whole-genome sequence analysis, eMSS used 3/5 the computation time of HDR-del. Without additional parameters needing to be set in the segment detection, the computational burden for eMSS is lower compared with that in other region-specific approaches.

Shared genomic segment analysis with equivalence testing.

An important aspect of disease gene mapping is replication, that is, a putative finding in one group of individuals is confirmed in another set of individuals. As it can happen by chance that individuals share an estimated disease position, we developed a statistical approach to determine the p-value for multiple individuals or families to share a possibly small number of candidate susceptibility variants. Here, we focus on candidate variants for dominant traits that have been obtained by our previously developed heterozygosity analysis, and we are testing the sharing of candidate variants obtained for different individuals. Our approach allows for multiple pathogenic variants in a gene to contribute to disease, and for estimated disease variant positions to be imprecise. Statistically, the method developed here falls into the category of equivalence testing, where the classical null and alternative hypotheses of homogeneity and heterogeneity are reversed. The null hypothesis situation is created by permuting genomic locations of variants for one individual after another. We applied our methodology to the ALSPAC data set of 1,927 whole-genome sequenced individuals, where some individuals carry a pathogenic variant for the BRCA1 gene, but no two individuals carry the same variant. Our shared genomic segment analysis found significant evidence for BRCA1 pathogenic variants within ±5 kb of a given DNA variant.

Are trait impulsivity and exposure to cannabis or alcohol associated with the age of trajectory of cocaine use? A gender-specific dimensional analysis in humans with cocaine dependence diagnosis.

Cocaine use disorders (CUD) cause major morbidity and optimized prevention efforts are critical. It is unclear if trait impulsivity and exposure to cannabis or alcohol are associated with age trajectory of cocaine use (e.g., age of onset of heaviest use, or time of escalation), or with vulnerability to develop a CUD. This is an observational study with volunteers (≥ 18 years old), from a metropolitan area. The sample (n = 1,010) included: n = 360 normal volunteers, n = 438 with cocaine dependence (CD) diagnoses, and n = 212 with other addictive diseases. Trait impulsivity was examined with BIS-11 scores. Maximal self-exposure to cannabis, alcohol, and cocaine were characterized dimensionally with Kreek-McHugh-Schluger-Kellogg (KMSK) scales. Time of escalation was defined as the interval between age of first use and age of onset of heaviest use. Onset of maximal use of cannabis (median age = 17) and alcohol (median age = 21) preceded that of cocaine (median age = 27), in volunteers with CD. Multivariate Cox regressions in volunteers with CD show that increasing self-exposure to cannabis was a predictor of earlier onset of heaviest use of cocaine. Also, more rapid time of escalation of alcohol was a predictor of more rapid time of escalation of cocaine. A multiple logistic regression shows that increasing self-exposure to cannabis or alcohol was a positive predictor of odds of CD diagnosis. Trait impulsivity and gender were not significant predictors in these multivariate analyses. This study shows that aspects of adolescent exposure to nonmedical cannabis and alcohol are predictors of early onset of CUD, and may be potentially targeted for prevention efforts. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

A 3' UTR SNP rs885863, a cis-eQTL for the circadian gene VIPR2 and lincRNA 689, is associated with opioid addiction.

There is a reciprocal relationship between the circadian and the reward systems. Polymorphisms in several circadian rhythm-related (clock) genes were associated with drug addiction. This study aims to search for associations between 895 variants in 39 circadian rhythm-related genes and opioid addiction (OUD). Genotyping was performed with the Smokescreen® array. Ancestry was verified by principal/MDS component analysis and the sample was limited to European Americans (EA) (OUD; n = 435, controls; n = 138). Nominally significant associations (p < 0.01) were detected for several variants in genes encoding vasoactive intestinal peptide receptor 2 (VIPR2), period circadian regulator 2 (PER2), casein kinase 1 epsilon (CSNK1E), and activator of transcription and developmental regulator (AUTS2), but no signal survived correction for multiple testing. There was intriguing association signal for the untranslated region (3' UTR) variant rs885863 in VIPR2, (p = .0065; OR = 0.51; 95% CI 0.31-0.51). The result was corroborated in an independent EA OUD sample (n = 398, p = 0.0036; for the combined samples). Notably, this SNP is an expression quantitative trait locus (cis-eQTL) for VIPR2 and a long intergenic non-coding RNA, lincRNA 689, in a tissue-specific manner, based on the Genotype-Tissue Expression (GTEx) project. Vasoactive intestinal peptide (VIP) is an important peptide of light-activated suprachiasmatic nucleus cells. It regulates diverse physiological processes including circadian rhythms, learning and memory, and stress response. This is the first report of an association of a VIPR2 variant and OUD. Additionally, analysis of combinations of single nucleotide polymorphisms (SNPs) genotypes revealed an association of PER2 SNP rs80136044, and SNP rs4128839, located 41.6 kb downstream of neuropeptide Y receptor type 1 gene, NPY1R (p = 3.4 × 10-6, OR = 11.4, 95% CI 2.7-48.2). The study provides preliminary insight into the relationship between genetic variants in circadian rhythm genes and long non-coding RNA (lncRNAs) in their vicinity, and opioid addiction.

VMAT2 gene (SLC18A2) variants associated with a greater risk for developing opioid dependence.

Aim: To determine if selected serotonergic and noradrenergic gene variants are associated with heroin addiction. Subjects & methods: A total of 126 variants in 19 genes in subjects with Dutch European ancestry from The Netherlands. Subjects included 281 opioid-dependent volunteers in methadone maintenance or heroin-assisted treatment, 163 opioid-exposed but not opioid-dependent volunteers who have been using illicit opioids but never became opioid-dependent and 153 healthy controls. Results: Nominal associations were indicated for 20 variants in six genes including an experiment-wise significant association from the combined effect of three SLC18A2 SNPs (rs363332, rs363334 and rs363338) with heroin dependence (pfinal = 0.047). Conclusion: Further studies are warranted to confirm and elucidate the role of these variants in the vulnerability to opioid addiction.

Association of variants of prodynorphin promoter 68-bp repeats in caucasians with opioid dependence diagnosis: Effect on age trajectory of heroin use.

The dynorphin/kappa opioid receptor (Dyn/KOR) system is involved in reward processing and dysphoria/anhedonia. Exposure to mu-opioid receptor agonists such as heroin increases expression of the prodynorphin gene (PDYN) in the brain. In this study in a Caucasian cohort, we examined the association of the functional PDYN 68-bp repeat polymorphism with opioid use disorders. In this case-control study, 554 subjects with Caucasian ancestry (142 healthy controls, 153 opioid-exposed, but never opioid dependent, NOD, and 259 with an opioid dependence diagnosis, OD) were examined for association of the PDYN 68-bp repeats with the diagnosis of opioid dependence (DSM-IV criteria), with a dimensional measure of heroin exposure (KMSK scale), and age trajectory parameters of heroin use (age of heroin first use, and age of onset of heaviest use). The PDYN 68-bp repeat genotype (classified as: "short-short" [SS], "long-long" [LL], and "short-long" [SL], based on the number of repeats) was not associated with categorical opioid dependence diagnoses. However, the LL genotype was associated with later age of first heroin use than the SS + SL genotype (19 versus 18 years; p < 0.01). This was also confirmed by a significant positive correlation between the number of repeats and the age of first use of heroin, in volunteers with OD (Spearman r = 0.16; p = 0.01). This suggests that the functional PDYN 68-bp repeat genotype is associated with the age of first use of heroin in Caucasians diagnosed with opioid dependence.