Biotechnical development of genetic addiction risk score (GARS) and selective evidence for inclusion of polymorphic allelic risk in substance use disorder (SUD)

Research into the neurogenetic basis of addiction identified and characterized by Reward Deficiency Syndrome (RDS) includes all drug and non-drug addictive, obsessive and compulsive behaviors. We are proposing herein that a new model for the prevention and treatment of Substance Use Disorder (SUD) a subset of RDS behaviors, based on objective biologic evidence, should be given serious consideration in the face of a drug epidemic. The development of the Genetic Addiction Risk Score (GARS) followed seminal research in 1990, whereby, Blum’s group identified the first genetic association with severe alcoholism published in JAMA. While it is true that no one to date has provided adequate RDS free controls there have been many studies using case –controls whereby SUD has been eliminated. We argue that this deficiency needs to be addressed in the field and if adopted appropriately many spurious results would be eliminated reducing confusion regarding the role of genetics in addiction. However, an estimation, based on these previous literature results provided herein, while not representative of all association studies known to date, this sampling of case- control studies displays significant associations between alcohol and drug risk. In fact, we present a total of 110,241 cases and 122,525 controls derived from the current literature. We strongly suggest that while we may take argument concerning many of these so-called controls (e.g. blood donors) it is quite remarkable that there are a plethora of case –control studies indicating selective association of these risk alleles ( measured in GARS) for the most part indicating a hypodopaminergia. The paper presents the detailed methodology of the GARS. Data collection procedures, instrumentation, and the analytical approach used to obtain GARS and subsequent research objectives are described. Can we combat SUD through early genetic risk screening in the addiction field enabling early intervention by the induction of dopamine homeostasis? It is envisaged that GARS type of screening will provide a novel opportunity to help identify causal pathways and associated mechanisms of genetic factors, psychological characteristics, and addictions awaiting additional scientific evidence including a future meta- analysis of all available data –a work in progress.


History of Reward Deficiency Syndrome (RDS) development
Research into the neurogenetic basis of addiction identified and characterized by Reward Deficiency Syndrome (RDS) [1] includes all drug and non-drug addictive, obsessive and compulsive behaviors. We are proposing herein that a new model for the prevention and treatment of RDS behaviors based on objective biologic evidence should be given serious consideration in the face of a drug epidemic [2]. Currently, research directed toward improving treatment for highly drug-dependent patients in underserved populations represents one example of adoption of this bold concept and is under study through a NIH grant [3]. The grant explores utilization of the Genetic Addiction Risk Score (GARS) and the neuronutrient pro-dopamine regulator KB220.
The development of GARS followed seminal research in 1990, whereby, Blum's group identified the first genetic association with severe alcoholism published in JAMA [4]. The non-invasive GARS test identifies and measures the total number of risk alleles of genes and catabolic enzymes affecting an individual's neurochemical hypodopaminergic function and has been associated in hundreds of studies with SUD [5].
According to the American Society of Addiction Medicine (ASAM), addiction is a "primary, chronic disease of brain reward, motivation, memory and related circuitry." This defintion communicates the many effects of addiction, but the factors that can increase the risk of addiction are also varied, including: genetic predisposition, comorbid psychiatric conditions, and certain at-risk environments. Addiction is a broad term that can refer to substance addiction (e.g., opioids, prescription drugs) and non-substance addiction (e.g., thrill seeking, gambling) [6].
The mesolimbic pathway, the "reward pathway," (Figure 1) is a dopaminergic pathway in the brain. The pathway connects the ventral tegmental area (VTA) in the midbrain to the ventral striatum (includes the nucleus accumbens (NAc) and the olfactory tubercle) of the basal ganglia in the forebrain. Release of dopamine (DA) (via signaling involving serotonin, endocannabinoids, enkephalin and GABA) from the mesolimbic pathway to the NAc regulates motivation and desire for reward stimuli. Drug and alcohol use can boost DA, a neurotransmitter that helps produce pleasurable feelings, thus promoting more cravings. As a person continues to abuse substances, the brain adapts by reducing the ability of cells in the reward circuit to respond to it. This reduces the euphoria that the person feels, inducing tolerance. Depletion of DA in this pathway, or lesions at its site of origin, or even serotonin depletion, decrease the extent to which an animal is willing to go to obtain a reward [7]. Brain adaptations often lead the person to become less able to derive pleasure from other things they once enjoyed (the thrill is gone). Continual long-term abuse of substances can cause changes to other brain chemical systems, affecting functions that include: learning, judgement, decision-making, and beahvior.
found to have gene-based association with hypo-dopaminergic function [8]. Among the literature that associate polymorphisms of reward genes with risk of RDS behaviors, the dopamine D2 receptor (DRD2) gene is one of the most widely studied as a receptor type. However, other genes are also involved, and it has been adequately established in association studies and animal research literature that, for example, polymorphisms of the serotonergic-2 A receptor and the catechol-O-methyltransferase genes pre-dispose individuals to aberrant RDS behaviors [9]. RDS, listed as a psychological disorder in the Sage Encyclopedia for Abnormal Psychology (2017), has gained wide acceptance in the scientific community, as a crucial factor in the etiology of all types of addictive, compulsive, and obsessive behaviors, like substance, and non-substance addictive behaviors, like gambling and gaming [10].
There are different strategies for managing addiction. One strategy is prevention of drug use. Principles of prevention of addiction advocates for early intervention (National Institute on Drug Abuse: Lessons from Prevention Research) and identifying risks at an early age [11]. Environmental factors and the induction of epigenetics can increase risk of addiction, so being aware what may be a risk factor for drug use may help individuals for prevention and in developing effective protective strategies [12].
Another strategy for managing addiction is to prevent relapse. People in recovery from substance use disorders are at an increase risk for relapse, even after years of not taking the substance. Relapse for substance use disorder is comparable to other chronic illnesses, such as hypertension and asthma [13]. In spite of over almost three decades of psychiatric genetic research with its 22,961 articles listed in Pubmed (12-1-19), and even after 17 years of addiction directed genetic research, Els [14] suggested that a number country's public thinks of addiction as a moral failing rather than a medical condition, likely exacerbating the guilt and shame that recovering individuals experience.

Rationale for GARS allelic selection
Since 1990 the field related to genetics of addiction has the state as of and Uhls' group [15] provided a snapshot showing the state of the as of 2011, indicating the complex nature of genes linked to addictive behaviors particularly SUD. Figure 2 shows the overall pipeline of a meta-analyses of addiction-associated genetic variations, genome-wide analysis of the molecular mechanisms of implicated SNPs, and the pathways and gene interaction networks that might involve these genetic factors.
Meta-analyses of candidate gene association studies and GWAS were illustrated in detail in STEP 1. In total, 843 vulnerable haplotypes were identified, linked by 12 risk variants and 842 vulnerable SNPs. All data and knowledge were imported to an updated version of the knowledgebase for addiction-related genes (KARG 2.0, marked with a blue box). Haplotypes identified in STEP 1 were annotated with functional and regulatory elements (STEP 2). Taken from Interaction enrichment analyses between the susceptibility genes and addiction-regulated genes previously identified by molecular biology studies (KARG 1.0, marked with a blue box) were performed (STEP 3).
While the entire molecular biological community is interested in genetic risk for alcohol and substance addiction, and personalized medicine, presently, many are not aware of a genetic panel that demonstrates significant predictability to clinical risk. To this aim, we are highlighting this rather new and unique genetic test to provide this community an up-to date knowledge base. A Pubmed search for each gene represented in the GARS on 6-8-19 returned the following results (Table 1).
While it is true that no one to date has provided adequate RDS free controls there have been many studies using case -controls whereby SUD has been eliminated. We argue that this deficiency needs to be addressed in the field and if adopted appropriately many spurious results would be eliminated and as such it will reduce confusion providing a clearer understanding and an overhaul of the current state of the art of genetics underlining all addictive behaviors drug and non-drug or RDS. In Figure 3 we display the current polymorphic risk alleles of the GARS panel.
In addition we have highlighted a number of meta-analyses (if found) for each risk allele selected in GARS to point out a clear association compared to non-SUD controls and experimental SUD probands. Specifically, the genetic panel was selected for polymorphisms of a number of reward genes that have been correlated with chronic dopamine deficiency and drug related reward-seeking behavior.
An estimation based on these results herein, while not representative of all association studies known to date, of case-control studies provides significant associations whereby there are a total of 110,241 cases and 122,525 controls. A review of the related Figure 3 strongly suggest that while we may take argument concerning many of these so-called controls (e.g. blood donors) it is quite remarkable that there are a plethora of case -control studies indicating selective association of these risk alleles (measured in GARS) for the most part indicating a hypodopaminergia. Based on these results, we feel confident that albeit not having RDS free controls, there is sufficient evidence that each risk allele displayed in GARS relative to non-SUD controls associate as risk for prediction for drug and alcohol severity and dependence ( Table 2).
Awareness of biological and environmental factors can impact a person's risk of addiction. Scientists estimate that heritabilities of addictive disorders can range from 30-50% or possibly higher, depending on the substance [16]. Being cognizant of the biological risk may help individuals develop protective factors and help individuals see addiction as a medical condition. Additionally, standard tests, like the Addiction Severity Index [17] or Opioid Risk Tool [18], coupled with genetic polymorphic risk testing can help enhance understanding and achieve a personal medicine approach for each patient

Methods and materials -Device description
The Genetic Addiction Risk Score (GARS) test is a non-diagnostic, DNA genetic testing tool. The Genetic Addiction Risk Score (GARS) is based on a qualitative genetic test for single nucleotide polymorphism detection of Substance Use Disoerder (SUD). We are detailing the methodology of the GARS test to provide the readerdhip with this important information and thereby reduce questions.

Sample collection and processing utilized to obtain data
Buccal cells are collected from each patient using an established minimally invasive collection kit. Sterile Copan 4N6FLOQ Swabs (Regular Size Tip In 109MM Long Dry Tube with Active Drying System) were utilized for sample collection. Individuals collect cells from both cheeks by rubbing the swab at least 25 times on each side of their mouth, and then returned the swab to the specimen tube. For all steps of sample processing, appropriate controls including non-template controls and known DNA standards were included and verified [19].
An index of the genes included in the GARS panel and the specific risk polymorphisms are provided in Figures 4A-4C.
Each polymorphism was selected based on SUD a subset of Reward Deficiency Syndrome (RDS) and a known contribution to a state of low dopaminergic or hypodopaminergic functioning in the brain reward circuitry. Samples were also subject to sex determination using PCR amplification and capillary electrophoresis to detect AMELX and AMELY (AMELX's intron 1 contains a 6 bp deletion relative to intron 1 of AMELY).
DNA was isolated from buccal samples using a Mag-Bind Swab DNA 96 Kit (Custom M6395-01, Omega Bio-Tek, Norcross, GA) with the MagMAX Express-96 Magnetic Particle Processor (Applied Biosystems, Foster City, CA). Extracted DNA was quantified for total human gDNA using the TaqMan RNaseP assay (Life Technologies, Carlsbad, CA) on a QuantStudio 12k Flex (Thermo Fischer Scientific, Waltham, MA).
Testing for genetic variation was performed using 1) Real-Time PCR with TaqMan® allelespecific probes on the QuantStudio 12K Flex, or 2) iPlex reagents on the Agena MassARRAY® system, plus 3) Proflex PCR and size separation using the SeqStudio Genetic Analyzer.
For genotyping the single nucleotide polymorphisms ( Figure 4A) with Real-Time PCR with on the QuantStudio 12K Flex, commercially available or custom TaqMan RT-PCR assays (Thermo Fischer Scientific, Waltham, MA) were used ( Figure 5 for Assay IDs and context sequences). For each reaction, 2.25 μL normalized DNA (10 ng total) was mixed with 2.75 μL assay master mix, and then subjected to RT-PCR amplification and detection. Manufacturer recommended thermal cycling conditions were utilized, and genotypes were called using TaqMan Genotyper Software v1.3 (Life Technologies, Carlsbad, CA).
For genotyping the single nucleotide polymorphisms with the Agena MassARRAY® system, iPlex reagents were used ( Figure 4A for iPlex PCR primer sequences). Primers are multiplex, therefore only one reaction is required for each sample. For each reaction, 2 ul normalized DNA (10 ng total) was mixed with the iPlex Pro PCR cocktail. The reaction was amplified on a Pro Flex thermocycler with the Agena manufacturer PCR conditions. Amplified DNA was then SAP treated, followed by an extension. The iPLEX Extension Reaction Product was then desalted using a Dry Resin Method. Samples were then dispensed onto a 96 well SpectroCHIP Array using the MassARRAY Nanodispenser. Genotypes were called using the MassARRAY Analyzer Software.
For fragment genotyping, two multiplexed PCR reactions (50 μL total volume) were required. Reaction A included 5' fluorescently labeled primers forward primers and nonlabeled reverse primers for AMELOX/Y, DAT1, MAOA, and the GABRB3 dinucleotide repeat (with sets at 150 nM, 120 nM, 120 nM, and 480 nM primer concentrations, respectively). Reaction B included 5' fluorescently labeled forward primers and non-labeled reverse primers for DRD4 and the SLC6A4 HTTLPR, all in 120 nM concentrations. For all PCR reactions, 2 ng DNA was amplified with primers, 25 μL OneTaq HotStart MasterMix (New England Biolabs, Ipswich, MA), and water. For reaction B, 5 μM 7-deaza-dGTP (Thermo Fischer Scientific, Waltham, MA) was added to the above recipe. Primers details are listed in Figure 6.
Amplifications were performed using a touchdown PCR method. An initial 95°C incubation for 10 min was followed by two cycles of 95°C for 30 s, 65°C for 30 s, and 72°C for 60 s. The annealing temperature was decreased every two cycles from 65°C to 55°C in 2°C increments (10 cycles total), followed by 30 cycles of 95°C for 30 s, 55°C for 30 s, and 72°C for 60 s, and a final 30-min incubation at 60°C, then hold at 4°C. A 10 μL aliquot of reaction B amplicon was further subjected to MspI restriction digest (37°C for 1 hr) to interrogate rs25531 (with 1U restriction enzyme and IX Tango Buffer, Thermo Fischer Scientific, Waltham, MA).
For fragment detection by capillary electrophoresis, reactions 1 and 2 were mixed in a 2:1 ratio. 1μL of this amplicon mixture was added to 9.5 μL mixed LIZ1200 size standard/ formamide (Thermo Fischer Scientific, Waltham, MA recommended concentrations). For detection of rs25531, 1 uL of restriction digest mixture was added to 9.5 μL LIZ1200+formamide. Both mixtures were subjected to capillary electrophoresis on the SeqStudio (run time 60 min, voltage 5000 V, 10 sec injection at 1200 V) then analyzed with GeneMapper 5 software (Life Technologies, Carlsbad, CA). The patient/clinician who requests the test receives a personalized report discussing the results. The report provides a GARS Score (based on scale 1-22) that is the sum of all risk alleles for that individual. Various substance and non-substance behaviors are listed as high, moderate, or low risk behavior frequency for that individual. The reports are designed to help users understand the meaning of their results and any appropriate actions that may be taken.

Summary of initial GARS study
We are briefly summarizing herein the first study of an association between the Genetic Addiction Risk Score (GARS) and the Addiction Severity Index -Media Version (ASI-MV) among patients from treatment facilities (submitted for publication).
The initial sample of 393 subjects who provided saliva for genotyping, was drawn, from eight geographically diverse treatment centers in the United States. The available sample size of 273 (69%) consisted of individuals who had also completed the ASI-MV questionnaire [17]. The alcohol, and drug severity scores in the ASI-MV were determined using a proprietary algorithm developed by Inflexxion. A laboratory located at the Institute for Behavioral Genetics (University of Colorado Boulder) performed standard genotyping for specific polymorphic risk alleles derived from a panel of reward genes. The subjects, participating in the pilot phase of the GARS analysis self-reported their race as White at 88.1% (n = 244) and were 57.8% (n = 160) male. The average age of the of subjects was 35.3 years (S.D. =13.1, maximum age = 70, minimum age = 18). This study is a statistical analysis that compared a number of risk alleles to the ASI-MV alcohol and drug severity score of each subject.
Among the ASI analysis sample the number of risk alleles detected ranged from 3 to 15, and the average was 7.97 (S.D. = 2.34) with a median of 8.0. Preliminary examination of the relationship between GARS genotype panel and the Alcohol Risk Severity Score using the Fishers Exact Test revealed a significant predictive relationship (X 2 = 8.84, df = 1, p = 0.004 2 tailed) which remained significant after controlling for age [Hardy-Weinberg Equilibrium intact]. Both age and genetic addiction risk scores were predictive of higher alcohol severity scores as assessed with the ASI-MV. In fact, a lower ASI-score predicted a lower GARS score. To account for non-normality in the distribution, drug scores were transformed to (Log 10 ) before analysis of the relationship between the GARS panel and ASI-MV Drugs Risk Severity Score. The relationship between the GARS panel and the Drug Risk Severity Score was found to be similar but less robust than the observation for the Alcohol Risk Severity. Preliminary examination revealed a nominally significant relationship (B = −0.122, t = −1.91, p = 0.057 −2 tailed) in this study, following apriori hypothesis of an association of GARS and ASI predictability of risk in which a one-tailed analysis revealed (P=0.028) for the drug severity. The predictive value of GARS was more robust for alcohol risk severity (a score equal or greater that 7) and for drug risk severity (a score equal or greater that 4). A limitation of this study relates to the attempt of matching an objective score (genes) with a score from a subjective self-report (ASI).
These results show the GARS test to be a useful predictor of susceptibility to problematic substance use, especially alcoholism. In future studies using highly screened cohorts eliminating all Reward Deficiency Syndrome (RDS) behaviors, TOD scores will be analyzed for each risk allele to determine weighted associations that could lead to even more accurate predictability of the GARS test.

Population GARS prevalence
PubMed provides frequency data of major and minor allele, but not population prevalence. SNPedia provides population diversity percentages for homozygous SNP; homozygous normal; and heterozygous for all but one of our SNPs, in the following populations (rs4532; rs1800497; rs6280; rs1800955; rs4680 and rs1799971). Unfortunately, currently, there is no population prevalence data on variable number tandem repeats or dinucleotide repeats (Tables 3 and 4).

Sampling of data retrieval
Tables 4 and 5 illustrates the utility of the GARS across various ethnic groups in a population of 293. We are presenting this data as a sampling of types of data collection that could be obtained utilizing the above cited techniques. The prevalence in either N or % of each risk allele is displayed in Tables 4 and 5

Future perspectives
These data and other analyses of GARS have allowed for the current utilization of precise genetic guided therapy coined "Precision Addiction Management" (PAM®) [20]. Simply, "Precision Addiction Management" (PAM®) uses the GARS to customize KB220PAM [21] formulations to deliver putative dopamine homeostasis based on developed algorithms matched to polymorphic results. To date there have been 42 published studies on KB220 related to many RDS behaviors [22]. There is evidence derived from animal and human studies using BOED neuroimaging and behavioral methodologies that support homeostatic activation of brain dopamine in the reward circuitry by KB220 variants, as well as antisubstance seeking and modification of RDS behaviors [23][24][25][26][27][28]. RDS encompasses behaviors like PTSD, ADHD, over-eating, shopping, hoarding and related RDS cognitive insults. Combating the drug crisis requires PBM across ethnic groups, to induce dopamine homeostasis to those born with RDS predisposition [29].
Previously Blum developed a RDS inventory (questionnaire) which has been significantly modified by Demetrovics & Blum to display 29 items. In other work conducted by Demetrovics' group involved in the PGA study, a wide spectrum national study was carried out on approximately 1500 adolescents and young adults. These data will be analyzed for 1) explore the characteristics ofRDS addictions; 2) Analyze the relationship between both drug and non-drug addictive behaviors; 3) explore the possible genetic markers for all types ofRDS behaviors; 4) provide a genetic map using GARS for RDS type addictive behaviors; 5) explore the possible distinct & overlapping common psychological and genetic characteristics of different types of substance use and behavioral addictions and 6) Provide a multidisciplinary approach and test and test possible psychological and genetic interaction effects; 7) develop modifications to the current GARS if necessary based on forthcoming analytic results.
It is agreed that both specific psychological and genetics may play an important role in the development of addiction. For example, studies have indicated that personality traits (e.g. schizoid/avoidance; sensation seeking and impulsivity) are associated with SUD [38] biogenic model, specific genetic variants can cause dysfunctions in the brain reward cascade [39] evoking a hypodopaminergia. The take home message is that the hypodopaminergic brain requires a "dopamine fix" to feel good to subsequently lead to RDS seeking behaviors. With this stated research directed as suggested by the PGA study, could widen the horizon of addiction like phenotypes both drug and non-drug with novel associations that might lead to new perspectives and assist in the identification of yet unknown correlates of these RDS behaviors.
In order to provide a simple schematic portraying our proposal ( Figure 7).

Summary
It is the goal through this novel model that by using PBM the addiction field will have a synergistic tool along with MAT or even alone, to overcome dopamine dysregulation either surfeit (adolescents) or deficit (adults) by the induction of " dopamine homeostasis" to help attenuate SUD by enabling early intervention through genetic testing [23,24].     Provides a simple schematic portraying our proposal 9R allele compared to 10R. Variants in DAT VNTR, showed that the presence of the 9-9 genotype significantly increases the risk of irritability and direct aggressiveness more than six and 10 times with respect to the 9-10 genotype in heroin addicts compared to controls. 9-repeat allele of the DAT polymorphism confers increased susceptibility to antisocialviolent behavior and aggressiveness in heroin addicts.