Pharmacogenetic association study of cannabis use in chronic pain

Pharmacogenetic association study of cannabis use in chronic pain | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Pharmacogenetic association study of cannabis use in chronic pain William Beauchesne, Jordan Turcotte, Philippe Mercier, Flore Lavoie, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6957614/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Feb, 2026 Read the published version in Journal of Cannabis Research → Version 1 posted 9 You are reading this latest preprint version Abstract Background Pain is one of the leading causes of disability worldwide. Despite the various pharmacological treatments available, patients with chronic pain often remain with significant disabilities and unsatisfactory pain control. Cannabis and cannabinoids are sometimes used in the treatment of chronic pain as they have been shown to be useful in a subset of patients. Some of the adverse effects associated with cannabis use, such as cannabis use disorder (CUD) and cannabis-induced psychosis, have been associated with several genetic variants. Despite this, the paucity of the data or the contradictory results for reported variants limits our ability to use them as genetic markers to personalize cannabis treatment tailored to patients’ genetic background. The aim of this genetic association study was to investigate the link between previously reported genes and cannabinoid response in terms of pain relief, CUD and risk of psychotic adverse events in patients with chronic pain. Methods Phone or in person interviews were conducted to document participants’ characteristics, cannabis use and effects, concurrent pharmacotherapy and comorbid conditions. Screening for CUD was performed using the Cannabis Use Disorders Identification Test – Revised. Blood or saliva samples were collected for the genotyping of 18 variants in 11 genes ( BDNF , CNR1 , CNR2 , COMT , CYP2C9 , FAAH , GABRA2 , HES7 , KAT2B , NRG1 and OPMR1 ). Results One hundred participants were recruited, with blood or saliva samples collected from 77 of them. Two single-nucleotide polymorphisms (SNP) in cannabinoid receptor 1 ( CNR1 ) could be linked with psychotic adverse events. Namely, T allele carriage of the CNR1 rs1049353 C > T variant increased the odds of having psychotic adverse events (OR = 6.1, 95% CI 1.7–27.9, p-value = 0,009) and C allele carriage of the CNR1 rs2023239 T > C intronic variant also increased these odds (OR 3.5, 95% CI 1.5–9.4, p-value = 0,033). These findings were not significant after adjustment for multiple SNPs testing and none of the variants were associated with CUD or pain relief. Conclusions These results suggest alternative allele carriers of rs1049353 and rs2023239 could be at an increased risk of psychotic adverse events related to cannabis use. Cannabis Chronic pain Pharmacogenetics Psychosis Cannabis use disorder 1. Background Chronic pain ranks among the top causes of disability-adjusted life years in the world ( 1 ). In Canada, almost one in five adults (7.6 million) lives with such pain ( 2 ). Despite its high prevalence and substantial impact on patient lives, the management of chronic pain remains particularly challenging ( 3 ). Access to cannabis for medical purposes, such as in the treatment of chronic pain, has been available in Canada since 2001, while non-medical use of cannabis was legalized in 2018. A recent post-legalization study reported that 30.1% of adults living with chronic pain had used cannabis in the past year in the management of their condition ( 4 ). While the evidence regarding the efficacy of cannabis and cannabinoids in the treatment of chronic pain is limited, the latest meta-analysis has demonstrated significant – albeit small to very small – improvements in pain relief among patients with chronic pain ( 5 ). However, there remains a significant proportion of patients (up to 70%) who do not achieve adequate pain relief and no factor has reliably been identified as a predictor of this response ( 6 , 7 ). Even though its accessibility is growing and it use relatively widespread ( 8 ), including using as a means of self-management or as prescribed through health professionals, it is well documented that cannabis is often discontinued due to adverse events ( 9 , 10 ). Two significant adverse events, cannabis use disorder (CUD) and psychotic adverse events related to cannabis use, have been associated with multiple genetic variants ( 11 , 12 ). These variants could ultimately be used as genetic markers to personalize cannabis treatment and offer treatment tailored to the genetic background of patients, thereby reducing the potential harms when cannabis is used. Genetic variants could also be employed to identify patients who are more likely to benefit from cannabis prior to treatment initiation. Despite this, the paucity of the data on some of the previously reported variants and inconsistent results regarding some of them limit our ability to use them as genetic markers at the moment ( 11 , 13 ). The aim of this retrospective genetic association study was to characterize the phenotypes of patients with chronic pain who had used cannabis or cannabinoids in the past and to investigate the effects of different genetic variants. This paper presents the relationship between three main response phenotypes (i.e., pain relief, CUD and psychotic adverse events) and 28 genetic variants located in 17 genes. 2. Methods 2.1. Study population and inclusion criteria This multicentric retrospective genetic association study was conducted at the CIUSSS-SLSJ and CIUSSS de l'Estrie – CHUS University Hospitals in Quebec, Canada. Most of participants (67%) were recruited with an online form distributed by local chronic pain associations, either via social media or online advertisements. The remaining participants were recruited from the “Consortium québecois sur la douleur au dos” recruitment platform (25%) and from participant lists included in previous studies conducted at the CR-CHUS (8%). Inclusion criteria included : 1) having chronic pain (pain lasting longer than 3 months) 2) using or having used cannabis as a mean to reduce pain associated with a chronic pain condition (either prescribed by a physician or in the context of self-management); 3) to be of legal age to use cannabis according to local regulations at the time of the study (≥ 18 years old if prescribed by a physician and ≥ 21 years old if used in self-treatment). The only exclusion criterion was having had a strictly recreative use of cannabis or never having used cannabis to get relief from a chronic pain condition. 2.2. Data collection Timeline Data collection was performed from October 2020 to July 2021. After obtaining free and informed consent, participants completed a primary survey either via telephone or during an in-person visit at one of the participating research centres. This first survey collected data on demographic characteristics, cannabis use, health status, medical history, and current pharmacologic therapies. Subsequently, biological samples – either blood (~ 10 ml) or saliva (~ 4 ml) – were collected on participant preference for DNA extraction. Participants were then invited to complete an online follow-up survey during a subsequent episode of cannabis use to evaluate its effect on pain. This assessment was made using the numerical pain rating scale (NRS) from 0 to 10 (“no pain” to “worst pain imaginable”) ( 14 , 15 ) before and after cannabis use. Assessments Three main phenotypes were assessed during this study: pain response, CUD and psychotic adverse events. Demographics included age, sex, life habits (tobacco use, alcohol use, and drugs), exercise, anthropometry and perception of their health using the EQ-5D-5L instrument all collected in the primary survey ( 16 ). Health Index scores, which represents a combined score for the 5 dimensions and levels of health assessed by the EQ-5D-5L instrument, were calculated using the Canadian value set of the EQ-5D-5L( 17 ). Characteristics on participants’ cannabis use (e.g., age at first use, duration of use, frequency of use, routes of administration, quantities, delta9-tetrahydrocannabinol [THC] and cannabidiol [CBD] content of the products used) were also thoroughly assessed using an in-house questionnaire in the primary survey. Pain characteristics (e.g., pain intensity, impact of pain on physical function, neuropathic component) were documented using the Brief Pain Inventory (BPI) and “Douleur Neuropathique 4” (DN4) questionnaire ( 18 , 19 ). Current cannabis use was defined as cannabis used in the past 6 months. The online follow-up survey was composed of questions regarding the presence of somnolence or pain before and after their cannabis use. These elements were first evaluated before the consumption event and were reassessed 30 minutes to 4 hours after use, at the onset of maximum effect according to the participants. The effect of cannabis on pain was assessed using the NRS from 0 to 10 ( 14 , 15 ), and somnolence was assessed using the French version of the Stanford Sleepiness Scale (SSS) ( 20 ). To assess if a participant had a positive response to cannabis with regards to pain relief, they were asked to rate their average pain relief with a percentage improvement in pain they typically experienced on a 0-100 (“no pain relief” to “complete pain relief") numerical rating scale. Adequate pain relief with the online survey was defined as a reduction of two points or ≥ 30% reduction of pain based on the NRS values before and after cannabis use. Data from the online survey were then used to assess the validity of the adequate pain relief phenotype using the main questionnaire. Screening for the presence of CUD was performed using the Cannabis Use Disorder Identification Test – Revised (CUDIT-R) ( 21 ). The CUDIT-R requires participants to answer 8 multiple-choice questions about their cannabis use, which can be translated to a global score to assess the presence of a cannabis use disorder ( 21 ). Scores ≥ 13 points were considered as having a positive screening test result. Psychotic adverse events, collected in the primary survey, included the presence of hallucinations (visual, auditory or tactile) or delusions, and participants were classified as having had a psychotic adverse event if they had experienced at least one of those adverse reactions. Study data was collected and managed using REDCap electronic data capture tools hosted at Université de Sherbrooke ( 22 , 23 ). Further details on study data collection are given in the Additional file. 2.3. SNP selection and genotyping Genetic variants (single-nucleotide polymorphism, SNP) in candidate genes were identified through a literature review using PubMed database and PharmGKB ( 24 ). SNPs reported in the literature that had at least one positive association with either response to cannabis (e.g., psychiatric adverse events or CUD) or that could have an impact on the pharmacokinetics or pharmacodynamics of cannabis were selected for the study. This literature review identified 28 variants in 17 genes (ATP Binding Cassette Subfamily B Member 1 ( ABCB1 ), AKT Serine/Threonine Kinase 1 ( AKT1 ), Brain Derived Neurotrophic Factor ( BDNF ), Cholinergic Receptor Muscarinic 3 ( CHRM3 ), Cholinergic Receptor Nicotinic Alpha 2 Subunit ( CHRNA2 ), Cannabinoid receptor 1( CNR1 ), Cannabinoid receptor 2 ( CNR2 ), Catechol-O-Methyltransferase ( COMT ), Cytochrome P450 Family 2 Subfamily C Member 9 ( CYP2C9 ), Cytochrome P450 Family 3 Subfamily A Member 5 ( CYP3A5 ), Cytochrome P450 Family 3 Subfamily A Member 5 ( FAAH ), Gamma-Aminobutyric Acid Type A Receptor Subunit Alpha2 ( GABRA2 ), Hes Family BHLH Transcription Factor 7 ( HES7 ), Lysine Acetyltransferase 2B ( KAT2B ), Neuregulin 1 ( NRG1 ), Opioid Receptor Mu 1 ( OPRM1 ), Purinergic Receptor P2X 7 ( P2RX7 )). Blood samples were collected in EDTA tubes and the buffy coat was isolated in the 24 hours following specimen collection. DNA extraction of buffy coat was performed using the Puregene Blood Kit (QIAGEN, Germany). DNA extraction from saliva samples was done using the prepIT-L2P extraction kit (DNAgenoteck, Ottawa, Canada) directly from the sample we received by postal mail from participants using GenoTech® saliva sample collection kit OG-500 (DNAgenoteck, Ottawa, Canada). DNA samples were genotyped by standard TaqMan® method ( 25 ) at the Université de Sherbrooke RNomics platform lab. Details on genotyping, including the probe and primer designs used can be found in the Additional file . 2.4. Statistical analysis Hardy-Weinberg Equilibrium (HWE) was tested for each variant. Assessment of the validity of genotyping was made based on HWE results, genotyping call rate and minor allele frequency (MAF). Variants were excluded from subsequent analysis using the following criteria: 1) genotyping call rate inferior to 95%; 2) statistically significant departure from HWE (after multiple testing correction); 3) MAF inferior to 5%; 4) more than one alternative allele observed. Categorical variables were compared using the Chi-square or Fisher’s exact tests (if > 20% of cells had expected frequencies < 5 or if a cell had an expected frequency of < 1). Normality of data was assessed by the Shapiro-Wilk Test. Comparisons between groups for continuous variables were made using independent samples t-test or Wilcoxon rank sum test (if the variable had a non-normal distribution). Statistical tests were performed for each variant to identify potential statistical association with the three phenotypes assessed. Univariable logistic regression analyses using an additive genetic model were performed for variants with statistically significant associations with the studied phenotypes before multiple testing correction. Multiple testing corrections were performed according to the method proposed by Li, J. & Ji, L. (2005) for adjusting multilocus analyses by calculating the effective number of variants analyzed ( 26 ). Specifically, Bonferroni correction for genetic analyses was conducted for an effective number of 15 variants. Statistical significance threshold was set at p < 0.05. Further details on the statistical analysis are given in the Additional file . All analyses were performed using R Statistical Software (v4.2.1) ( 27 ). 3. Results 3.1. Participants’ description A total of 100 participants were included in the present study and the characteristics of the studied sample are presented in Table 1 . Participants were aged between 22 and 77 years old, and 67% identified as females. Most participants were current cannabis users at the time of the study (92%). Among the health conditions and comorbidities of the participants, musculoskeletal disorders were the most common, present in almost all participants (97%). Psychiatric and gastrointestinal comorbidities were also frequent, being present in more than half of the participants (70% and 53%, respectively). The most frequent chronic pain-related diagnoses were back pain (69%), fibromyalgia (45%) and osteoarthritis (34%). Neuropathic pain was present in almost two thirds of the participants (65%). Patients reported having chronic pain for a median duration of 12.0 years (interquartile range (IQR) = 7.0-21.8 years) with an average severity of moderate pain (BPI pain score median (IQR) = 5.25 (3.50, 6.12)) and mild interference with daily life (BPI interference score median (IQR) = 3.88 (1.67, 5.50)). The main methods of consumption used by the participants were oral (45%) and inhalation (44%). Among participants with inhaled use, the average quantity of inhaled cannabis was 1.22 grams per day of use. Daily use was frequent, with 81% using cannabis at least once per day. Most users with inhalation as their main method of use did so using cannabis with products containing at least twice the amount of THC compared to CBD (71%). The opposite was observed for participants consuming cannabis orally. Indeed, these participants were using products containing at least twice the amount of CBD compared to THC (69%). However, the information regarding THC and CBD content of the products used was missing for many participants. The most frequent concurrent pharmacological treatments were antidepressants (55%) followed by acetaminophen (33%), nonsteroidal anti-inflammatory drugs (NSAIDs) (32%) and opioids (32%). Participants with current cannabis use reported higher BPI interference score with a median value of 3.88 (interquartile range [IQR] 2.00–5.62) compared to past users with a median value of 1.65 (IQR 0.67–2.94) (p = 0.045). Current cannabis use was also associated with a greater proportion of participants with a adequate pain relief phenotype (current use: 82.8% vs. past use: 28.6%, p = 0.004). Table 1 Participant characteristics Overall (N = 100) 1 Current use (N = 92) 1 Past use (N = 8) 1 p-value 2 Demographics Female sex 67 (67.0%) 61 (66.3%) 6 (75.0%) > 0.99 Age (years) 48.0 (13.1) 47.2 (12.7) 57.5 (14.7) 0.053 Ethnicity 3 > 0.99 North American 88 (88.9%) 80 (87.9%) 8 (100.0%) European 7 (7.1%) 7 (7.7%) 0 (0.0%) Latin, Central and South American 2 (2.0%) 2 (2.2%) 0 (0.0%) Other 2 (2.0%) 2 (2.2%) 0 (0.0%) Chronic pain and health status DN4 score (≥ 4) 65 (65.0%) 59 (64.1%) 6 (75.0%) 0.71 Pain duration (years) 4 12.0 (7.0, 21.8) 12.0 (7.0, 21.8) 15.0 (4.5, 22.0) 0.82 BPI pain severity 3 5.25 (3.50, 6.12) 5.25 (3.50, 6.25) 5.25 (3.38, 6.00) 0.75 BPI pain interference 5 3.88 (1.67, 5.50) 3.88 (2.00, 5.62) 1.65 (0.67, 2.94) 0.045 EQ-5D-5L index 3 0.70 (0.50, 0.82) 0.68 (0.47, 0.81) 0.78 (0.67, 0.83) 0.15 EQ VAS 61.8 (20.9) 60.9 (21.3) 72.5 (13.9) 0.13 BMI (kg/m2) 4 27.8 (6.2) 27.5 (6.0) 30.7 (7.5) 0.27 Cannabis use characteristics Main method of use 0.10 Inhaled 44 (44.0%) 43 (46.7%) 1 (12.5%) Oral 45 (45.0%) 40 (43.5%) 5 (62.5%) Other or more than one 11 (11.0%) 9 (9.8%) 2 (25.0%) Frequency of use 0.25 ≤ Weekly 4 (4.0%) 3 (3.3%) 1 (12.5%) More than once per week 15 (15.0%) 13 (14.1%) 2 (25.0%) Daily 27 (27.0%) 26 (28.3%) 1 (12.5%) More than once daily 54 (54.0%) 50 (54.3%) 4 (50.0%) Age at first cannabis use (years) 23.7 (15.4) 23.4 (15.0) 27.2 (20.6) 0.57 Past medical history Musculoskeletal 97 (97.0%) 89 (96.7%) 8 (100.0%) > 0.99 Psychiatric 70 (70.0%) 66 (71.7%) 4 (50.0%) 0.24 Gastrointestinal 53 (53.0%) 50 (54.3%) 3 (37.5%) 0.47 Neurologic 40 (40.0%) 36 (39.1%) 4 (50.0%) 0.71 Cardiovascular 39 (39.0%) 36 (39.1%) 3 (37.5%) > 0.99 Respiratory 36 (36.0%) 34 (37.0%) 2 (25.0%) 0.71 Metabolic 26 (26.0%) 24 (26.1%) 2 (25.0%) > 0.99 Cancer 10 (10.0%) 9 (9.8%) 1 (12.5%) 0.58 Concurrent pharmacotherapy Antidepressants 55 (55.0%) 50 (54.3%) 5 (62.5%) 0.73 Acetaminophen 33 (33.0%) 31 (33.7%) 2 (25.0%) > 0.99 NSAIDs 32 (32.0%) 29 (31.5%) 3 (37.5%) 0.71 Opioids 32 (32.0%) 28 (30.4%) 4 (50.0%) 0.26 Antiepileptics 28 (28.0%) 27 (29.3%) 1 (12.5%) 0.44 Muscle relaxants 19 (19.0%) 17 (18.5%) 2 (25.0%) 0.64 Benzodiazepines 15 (15.0%) 12 (13.0%) 3 (37.5%) 0.10 Stimulants 9 (9.0%) 9 (9.8%) 0 (0.0%) > 0.99 Z drugs/benzodiazepine like 7 (7.0%) 7 (7.6%) 0 (0.0%) > 0.99 Biologics/DMARDs 3 (3.0%) 2 (2.2%) 1 (12.5%) 0.22 Phenotypes Pain relief (≥ 30%) 6 74 (78.7%) 72 (82.8%) 2 (28.6%) 0.004 Psychotic adverse events 6 (6.0%) 6 (6.5%) 0 (0.0%) > 0.99 CUDIT-R ≥ 13 25 (25.0%) 25 (27.2%) 0 (0.0%) 0.20 1 n (%); Median (IQR) for pain duration, BPI pain severity, BPI pain interference and EQ-5D-5L index; otherwise Mean (SD); DMARDs = Disease-modifying antirheumatic drugs; NSAIDs = Non Steroidal Anti-Inflammatory Drugs; 2 Fisher's exact test; Wilcoxon rank sum test 3 Data available N = 99; 4 Data available N = 98; 5 Data available N = 97; 6 Data available N = 94; No statistically significant differences were observed between past and current users concerning demographic characteristics, health status, past medical history or concurrent pharmacotherapy. However, participants with current cannabis use reported higher BPI interference score and a greater proportion of current users had an adequate pain relief phenotype. The complete characteristics of participants’ cannabis use are presented in the Additional file (Supplementary Table S2). 3.2. Phenotypes An adequate pain relief phenotype was observed in 74 of the 100 participants; 25 had a positive screening test for possible CUD and 6 had at least one psychotic adverse event. The characteristics of participants according to each phenotype were investigated ( Additional file) . The data to establish pain response phenotype was missing for 6 participants who were consequently excluded from these analyses. Adequate pain relief was not associated with any demographic characteristics, health status, comorbidities, concurrent pharmacotherapy or with the presence of neuropathic pain. Current use was noted in 72 (97.3%) participants with a positive response phenotype and in 15 (75.0%) of non-responders (p = 0.004). Among participants with a defined pain response phenotype who completed the online survey (n = 43), an adequate pain relief phenotype using the main questionnaire had a sensitivity of 89.1% (95% CI 74.6% – 97.0%) and specificity of 33.3% (4.3% – 77.7%) for adequate pain relief based on the NRS values before and after cannabis use. Some differences were noted among participants according to CUD screening test result. Participants with positive screening test result main method of use was inhaled in 76.0%, followed by oral in 20% and others or more than one in 4.0%. The main method of use for participants with a negative result was oral in 53.3% followed by inhaled at 33.3% and others or more than one in 13.3%. The main method of use differed between participants according to screening result (p < 0.001). A lower prevalence of cardiovascular comorbidities was noted in participants with a positive CUD screening (20% vs. 45%, p = 0.025) as well as a lower prevalence of metabolic comorbidities (8.0% vs. 32%, p = 0.018) and lower body mass index (BMI) (25.1 vs. 28.7 kg / m 2 , p = 0.010). The only difference present regarding concomitant pharmacotherapy was lower benzodiazepine use in participants with positive screening for CUD (0 vs. 20.0%, p = 0.019). Participants with a positive screening test for CUD were younger, had first used cannabis at a younger age, had a lower pain duration and differed in terms of their main method of use. Lower benzodiazepine use, a decreased prevalence of cardiovascular and metabolic comorbidities as well as a BMI were also noted in participants with positive screening result. Psychotic adverse events were not associated with any differences in demographic characteristics, or concurrent pharmacotherapy in the study participants. Metabolic comorbidities were more common among participants with psychotic adverse events. Notably, hallucinations were the only psychotic adverse event reported by the participants. 3.3. Genetic association study Saliva or blood sample was obtained for 77 participants. Statistically significant differences were observed between participants for whom DNA samples were obtained compared to participants without DNA samples ( Additional file ). No differences were noted in the proportions of the three phenotypes studied. Participants with DNA samples were older than participants without DNA samples (50.4 vs. 40.1 years old, p < 0.001) and had longer chronic pain duration (median duration in years (IQR): 15.0 (7.9–23.6) vs. 8.5(4.2–18.0), p = 0.022). No differences were noted in the proportions of the three phenotypes studied. Genotype validity assessment led to the exclusions of 10 variants from 9 different genes ( ABCB1 , AKT1 , CHRM3 , CHRNA2 , CNR2 , CYP2C9 , CYP3A5 , FAAH , P2RX7 ). The 18 remaining variants from 11 different genes ( BDNF , CNR1 , CNR2 , COMT , CYP2C9 , FAAH , GABRA2 , HES7 , KAT2B , NRG1 and OPMR1 ) were at HWE following Holm-Bonferroni correction. HWE p-values, genotyping call rate of all variants (including those with call rate < 95%) and alternative allele frequency of the biallelic markers are presented in the Additional file . The three studied phenotypes according to the participant’s genotype for the different variants investigated are presented in Table 2 . None of the variants investigated were associated with pain response phenotype or with CUD screening result. Two variants in the CNR1 gene were associated with a statistically significant difference in the proportions of psychotic adverse events (before adjustment for multiple SNPs testing). Regarding the CNR1 rs1049353 C > T variant, each additional T allele increased by sixfold the odds of having psychotic adverse events (odds ratio [OR] 6.1, 95% CI 1.7–27.9). Each additional C allele of the CNR1 rs2023239 T > C intronic variant increased by threefold the odds of having psychotic adverse events (OR 3.5, 95% CI 1.5–9.4). These findings were not significant after adjustment for multiple SNPs testing. Table 2 Response phenotype according to participant’s genotype Pain response CUDIT-R Psychotic adverse events Non-responder (N = 16) 1 Responder (N = 57) 1 p-value 2 Negative (< 13) (N = 60) 1 Positive (≥ 13) (N = 17) 1 p-value 3 Absence (N = 71) 1 Presence (N = 6) 1 p-value 4 Demographics Female sex 12 (75.0%) 39 (68.4%) 0.84 46 (76.7%) 9 (52.9%) 0.072 50 (70.4%) 5 (83.3%) 0.67 Age (years) 55.29 (43.85, 64.29) 49.90 (38.10, 58.72) 0.21 51.2 (44.1, 62.5) 48.3 (34.1, 50.9) 0.007 50.4 (42.1, 59.9) 51.5 (40.1, 53.9) 0.79 SNPs BDNF (rs6265) 0.82 0.82 0.46 ref 10 (62%) 37 (65%) 37 (61.7%) 12 (70.6%) 44 (62.0%) 5 (83.3%) ref/alt 6 (38%) 19 (33%) 22 (36.7%) 5 (29.4%) 26 (36.6%) 1 (16.7%) alt 0 (0%) 1 (1.8%) 1 (1.7%) 0 (0.0%) 1 (1.4%) 0 (0.0%) CNR1 (rs806374) 0.79 > 0.99 0.38 ref 4 (25%) 18 (32%) 18 (30.0%) 5 (29.4%) 20 (28.2%) 3 (50.0%) ref/alt 9 (56%) 32 (56%) 34 (56.7%) 10 (58.8%) 42 (59.2%) 2 (33.3%) alt 3 (19%) 7 (12%) 8 (13.3%) 2 (11.8%) 9 (12.7%) 1 (16.7%) CNR1 (rs2023239) 0.15 > 0.99 0.033 ref 13 (81%) 38 (67%) 41 (68.3%) 12 (70.6%) 51 (71.8%) 2 (33.3%) ref/alt 2 (12%) 18 (32%) 17 (28.3%) 5 (29.4%) 19 (26.8%) 3 (50.0%) alt 1 (6.2%) 1 (1.8%) 2 (3.3%) 0 (0.0%) 1 (1.4%) 1 (16.7%) CNR1 (rs1049353) 0.45 0.92 0.009 ref 10 (62%) 30 (53%) 31 (51.7%) 10 (58.8%) 40 (56.3%) 1 (16.7%) ref/alt 6 (38%) 20 (35%) 23 (38.3%) 6 (35.3%) 27 (38.0%) 2 (33.3%) alt 0 (0%) 7 (12%) 6 (10.0%) 1 (5.9%) 4 (5.6%) 3 (50.0%) CNR1 (rs6454674) 0.81 0.74 0.78 ref 9 (56%) 30 (53%) 31 (51.7%) 10 (58.8%) 37 (52.1%) 4 (66.7%) ref/alt 7 (44%) 23 (40%) 25 (41.7%) 7 (41.2%) 30 (42.3%) 2 (33.3%) alt 0 (0%) 4 (7.0%) 4 (6.7%) 0 (0.0%) 4 (5.6%) 0 (0.0%) CNR1 (rs806368) 0.26 0.70 0.41 ref 4 (25%) 26 (46%) 26 (43.3%) 6 (35.3%) 29 (40.8%) 3 (50.0%) ref/alt 11 (69%) 26 (46%) 30 (50.0%) 9 (52.9%) 37 (52.1%) 2 (33.3%) alt 1 (6.2%) 5 (8.8%) 4 (6.7%) 2 (11.8%) 5 (7.0%) 1 (16.7%) CNR1 (rs806378) 0.68 0.52 > 0.99 ref 10 (62%) 30 (53%) 31 (51.7%) 11 (64.7%) 38 (53.5%) 4 (66.7%) ref/alt 6 (38%) 22 (39%) 24 (40.0%) 6 (35.3%) 28 (39.4%) 2 (33.3%) alt 0 (0%) 5 (8.8%) 5 (8.3%) 0 (0.0%) 5 (7.0%) 0 (0.0%) CNR1 (rs806380) 0.42 0.26 0.82 ref 9 (56%) 25 (44%) 25 (41.7%) 11 (64.7%) 32 (45.1%) 4 (66.7%) ref/alt 7 (44%) 25 (44%) 29 (48.3%) 5 (29.4%) 32 (45.1%) 2 (33.3%) alt 0 (0%) 7 (12%) 6 (10.0%) 1 (5.9%) 7 (9.9%) 0 (0.0%) CNR2 (rs2229579) 5 > 0.99 0.082 > 0.99 ref 13 (81%) 43 (80%) 49 (86.0%) 11 (64.7%) 55 (80.9%) 5 (83.3%) ref/GtoA 3 (19%) 10 (19%) 7 (12.3%) 6 (35.3%) 12 (17.6%) 1 (16.7%) alt GtoA 0 (0%) 1 (1.9%) 1 (1.8%) 0 (0.0%) 1 (1.5%) 0 (0.0%) COMT (rs4680) 0.53 0.73 0.87 ref 2 (12%) 13 (23%) 16 (26.7%) 3 (17.6%) 17 (23.9%) 2 (33.3%) ref/alt 10 (62%) 26 (46%) 27 (45.0%) 9 (52.9%) 33 (46.5%) 3 (50.0%) alt 4 (25%) 18 (32%) 17 (28.3%) 5 (29.4%) 21 (29.6%) 1 (16.7%) CYP2C9 (rs1799853) 0.78 0.36 0.17 ref 14 (88%) 44 (77%) 47 (78.3%) 13 (76.5%) 57 (80.3%) 3 (50.0%) ref/alt 2 (12%) 12 (21%) 13 (21.7%) 3 (17.6%) 13 (18.3%) 3 (50.0%) alt 0 (0%) 1 (1.8%) 0 (0.0%) 1 (5.9%) 1 (1.4%) 0 (0.0%) FAAH (rs324420) 0.63 0.81 0.68 ref 11 (69%) 43 (75%) 44 (73.3%) 12 (70.6%) 52 (73.2%) 4 (66.7%) ref/alt 5 (31%) 13 (23%) 15 (25.0%) 5 (29.4%) 18 (25.4%) 2 (33.3%) alt 0 (0%) 1 (1.8%) 1 (1.7%) 0 (0.0%) 1 (1.4%) 0 (0.0%) GABRA2 (rs279858) 6 0.46 0.20 0.82 ref 1 (6.2%) 11 (20%) 11 (18.6%) 2 (11.8%) 13 (18.6%) 0 (0.0%) ref/alt 11 (69%) 35 (62%) 35 (59.3%) 14 (82.4%) 44 (62.9%) 5 (83.3%) alt 4 (25%) 10 (18%) 13 (22.0%) 1 (5.9%) 13 (18.6%) 1 (16.7%) HES7 (rs1442849) 0.76 0.50 0.060 ref 7 (44%) 26 (46%) 25 (41.7%) 10 (58.8%) 32 (45.1%) 3 (50.0%) ref/alt 7 (44%) 27 (47%) 30 (50.0%) 6 (35.3%) 35 (49.3%) 1 (16.7%) alt 2 (12%) 4 (7.0%) 5 (8.3%) 1 (5.9%) 4 (5.6%) 2 (33.3%) KAT2B (rs9829896) 0.16 0.40 > 0.99 ref 4 (25%) 6 (11%) 9 (15.0%) 1 (5.9%) 9 (12.7%) 1 (16.7%) ref/alt 9 (56%) 28 (49%) 32 (53.3%) 8 (47.1%) 37 (52.1%) 3 (50.0%) alt 3 (19%) 23 (40%) 19 (31.7%) 8 (47.1%) 25 (35.2%) 2 (33.3%) NRG1 (rs17664708) > 0.99 0.78 0.61 ref 13 (81%) 46 (81%) 48 (80.0%) 15 (88.2%) 57 (80.3%) 6 (100.0%) ref/alt 3 (19%) 10 (18%) 11 (18.3%) 2 (11.8%) 13 (18.3%) 0 (0.0%) alt 0 (0%) 1 (1.8%) 1 (1.7%) 0 (0.0%) 1 (1.4%) 0 (0.0%) OPRM1 (rs510769) 0.29 0.35 0.76 ref 11 (69%) 30 (53%) 31 (51.7%) 12 (70.6%) 40 (56.3%) 3 (50.0%) ref/alt 4 (25%) 25 (44%) 26 (43.3%) 5 (29.4%) 28 (39.4%) 3 (50.0%) alt 1 (6.2%) 2 (3.5%) 3 (5.0%) 0 (0.0%) 3 (4.2%) 0 (0.0%) OPRM1 (rs1799971) 0.89 0.45 0.52 ref 10 (62%) 38 (67%) 40 (66.7%) 10 (58.8%) 47 (66.2%) 3 (50.0%) ref/alt 5 (31%) 17 (30%) 17 (28.3%) 7 (41.2%) 21 (29.6%) 3 (50.0%) alt 1 (6.2%) 2 (3.5%) 3 (5.0%) 0 (0.0%) 3 (4.2%) 0 (0.0%) 1 n (%); median (IQR) for Age 2 Pearson's Chi-squared test; Two Sample t-test 3 Fisher's exact test; Wilcoxon rank sum test 4 Fisher's exact test; Two Sample t-test 5 Data available N = 74; 6 Data available N = 76; None of the p-values displayed are significant after Bonferroni correction for adjusting multilocus analyses with an effective number of 15 variants. Alt = alternative allele; ref = reference allele. 4. Discussion This retrospective genetic association study in patients with chronic pain who used cannabis or cannabinoids, describes the relationship between previously reported genetic variants and three main response phenotypes. More precisely, we assessed the relationship between different genetic variants and cannabis response in terms of pain relief, CUD and psychotic adverse events. Our findings suggest that two variants of the CNR1 gene (rs1049353 and rs2023239) could be associated with an increased rate of psychotic adverse events although these associations were not significant after adjustment for multiple SNPs testing. None of the studied variants were associated with CUD or pain relief. Previous studies have highlighted the significant inter-individual variability associated with THC use, both in terms of physiologic effects and pharmacokinetics parameters ( 28 , 29 ). This variability, which applies to adverse events but also to pain response, underscores the importance of identifying genetic markers to personalize cannabis treatment. In 2022, an open-label non-randomized observational study by Poli et al. recruited 600 participants who received different cannabis preparations and reported for the first time variants associated with pain response ( 6 ). One of these variants, ABCB1 rs1045642, was included as a candidate gene for this study but was unfortunately discarded due to insufficient call rate. The other two variants, TRPV rs8065080 and UGT2B7 rs7438135, although both were in genes of potential interests due to their role in the pharmacodynamics and pharmacokinetics of cannabis, were not included in our study due to lack of clinical studies investigating their impact on cannabis use and the studied phenotypes. However, Poli et al. identified the CNR1 rs1049353 variant as a treatment discontinuation risk factor. Previous literature highlights the role of genes implicated in the dopaminergic system (e.g., COMT ) and psychosis induced by cannabis ( 12 ), and offers possible insight into the mechanisms underlying the increased psychotic adverse events seen with CNR1 alternative allele carriers. CNR1 encodes one of the two main cannabinoid receptors, cannabinoid receptor 1 (CB 1 ), that is part of the G protein-coupled receptors (GPCRs) family of membrane proteins. CB 1 is ubiquitous in the central nervous system and is distributed at a greater concentration in regions playing a key role in reward, cognition and emotions, such as the limbic areas, hippocampus and amygdala ( 30 ). THC exhibits partial agonist activity of CB 1 and is thought to be at the origin of most of the cannabis observed psychotropic effects ( 31 ). Notably, THC could be responsible for the transient positive psychotic symptoms (e.g., hallucinations) that can result from cannabis use even in the absence of an underlying psychiatric disorder ( 30 ). Data from animal studies suggests exogenous cannabinoids such as THC facilitate dopamine release from dopaminergic neurons via mechanisms involving CB 1 ( 32 ). While the data in humans is unclear, increased expression of CB 1 on peripheral immune cells was documented in patients with multiple episodes of psychosis compared to healthy controls ( 33 ). CNR1 rs1049353 polymorphism in exon 4 produces a synonymous variant in codon 453 (Thr453Thr). However, this synonymous SNP may impact mRNA stability and, consequently, affect CB1 receptor expression. Alteration in CNR1 mRNA stability could therefore affect dopamine release in key dopaminergic regions associated with cannabis-induced psychosis. Moreover, CNR1 rs1049353 associated with psychotic adverse events could reflect an indirect association via linkage disequilibrium, as multiple CNR1 haplotype blocks were documented in rs1049353 region( 34 ). Similarly, evidence also suggests variable expression of CB 1 receptor in presence of the CNR1 rs2023239 polymorphism, also an intronic variant ( 35 ). Greater CB 1 receptor density in peripheral lymphocytes for carriers of the alternative C allele was described in long-term daily cannabis users, like most of the participants in this study ( 36 ). Interestingly, results from a pilot study using data from a placebo-controlled clinical trial investigating the impact of cannabis on driving performance, suggested that the CNR1 rs1049353 and rs2023239 variants could increase subjective effects of acute cannabis intoxication ( 37 ). Surprisingly, despite most of the previous associations in the literature being with CUD (i.e., CNR1 (rs806380, rs806378, rs806374, rs806368, rs2023239, rs1049353 and rs6454674) ( 38 – 43 ), FAAH (rs324420) ( 39 , 44 , 45 ), GABRA2 (rs279858)( 46 ), HES7 (rs1442849) ( 47 ), KAT2B (rs9829896) ( 48 ), NRG1 (rs17664708) ( 49 ) and OPRM1 (rs1799971) ( 50 )), none of the 18 variants included were associated with CUD in our study. An explanation for this discrepancy could be the studied population and the method employed to identify possible CUD among participants. In contrast to the previous studies that were conducted in adolescent or adult populations with non-medical use of cannabis, individuals included in this study used cannabis as means of self-management or as prescribed through health professionals. Limited evidence in the literature points towards altered test characteristics of the CUDIT-R in individuals with cannabis use for medical purposes ( 51 , 52 ). Similarly, Myers et al . recently reported that the CUDIT-R had worse performance among individuals who possessed a medical cannabis card compared to non-card holders ( 53 ). Higher frequency of use among medical users, like the majority of this study participants, could also have contributed to the decreased specificity of the CUDIT-R scale as many of its items are dependent on the frequency or intensity of use ( 51 ). This study has several limitations, primarily stemming from its retrospective design and the relatively small sample size, making the need to interpret the results with caution even more important. Despite the selection of candidate genes with previous positive association or based on our current understanding of the pharmacokinetics or pharmacodynamics of cannabinoids, the multiple testing involved in this study comes with the important risk of type I error. The retrospective nature of the study is obviously prone to recall bias. Furthermore, the small number of participants without active cannabis use implies significant selection bias and could have contributed to the low prevalence of both psychotic adverse events and negative pain response phenotypes observed since both of those could be motives to forgo cannabis use. The small sample size of this study, combined with the modest effect size of some of the previously reported variants, could also have contributed to our study being insufficiently powered to detect these associations. 5. Conclusion In summary, this retrospective genetic association study in patients with chronic pain reports two CNR1 variants (rs1049353 and rs2023239) that could possibly contribute to an increased rate of psychotic adverse events related to cannabis use in patients with chronic pain. This study did not replicate numerous previous findings as none of the variants studied were associated with possible CUD. The adequacy of the available screening tools for CUD in subpopulations of cannabis users remains uncertain and deserves greater attention considering the growing access to cannabis in chronic pain treatment. Finally, the role of these two CNR1 variants should be further studied in an independent prospective cohort. Abbreviations ABCB1 ATP Binding Cassette Subfamily B Member 1 AKT1 AKT Serine/Threonine Kinase 1 BDNF Brain Derived Neurotrophic Factor BMI body mass index BPI Brief Pain Inventory CBD cannabidiol CHRM3 Cholinergic Receptor Muscarinic 3 CHRNA2 Cholinergic Receptor Nicotinic Alpha 2 Subunit CNR1 Cannabinoid receptor 1 CNR2 Cannabinoid receptor 2 COMT Catechol-O-Methyltransferase CUD cannabis use disorder CUDIT-R Cannabis Use Disorder Identification Test – Revised CYP2C9 Cytochrome P450 Family 2 Subfamily C Member 9 CYP3A5 Cytochrome P450 Family 3 Subfamily A Member 5 DN4 Douleur Neuropathique 4 FAAH Cytochrome P450 Family 3 Subfamily A Member 5 GABRA2 Gamma-Aminobutyric Acid Type A Receptor Subunit Alpha2 HES7 Hes Family BHLH Transcription Factor 7 HWE Hardy-Weinberg Equilibrium IQR interquartile range KAT2B Lysine Acetyltransferase 2B NRG1 Neuregulin 1 NRS numerical pain rating scale OPRM1 Opioid Receptor Mu 1 P2RX7 Purinergic Receptor P2X 7 SNP single-nucleotide polymorphism THC delta9-tetrahydrocannabinol Declarations Ethics approval and consent to participate The project was approved by the Institutional Ethics Review Board of the CIUSSS du Saguenay-Lac-Saint-Jean (#2020-038) and realized in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants at recruitment. Availability of data and materials The minimal dataset that would be necessary to interpret or replicate findings of the current study may be available from the corresponding author upon request and according to local access policies. Competing interests The authors declare no financial or other competing interests Funding This research did not receive any specific grant from funding agencies in the public, commercial, or non-profit sectors. Authors’ contribution WB: Writing – original draft (lead), investigation (equal), formal analysis (lead); JT: Investigation (equal), writing – original draft (supporting), data curation (supporting); PM: Writing – original draft (supporting), formal analysis (supporting); LT: DNA extraction, writing – review and editing (supporting); FL: DNA extraction, writing – review and editing (supporting); AG: Methodology (supporting), project administration (supporting); CA: Formal analysis (supporting), validation (lead); EF: Writing - review and editing (lead); GL: Writing – review and editing (equal), supervision (supporting); LG: Writing – review and editing (equal) , supervision (supporting); KT: Supervision (lead), conceptualization (equal), writing – review and editing (equal). All authors read and approved the final manuscript. Acknowledgements The authors would like to thank all the participants as they are essential to successful research. 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Assessing Cannabis Use Disorder in Medical Cannabis Patients: Interim Analyses from an Observational, Longitudinal Study. Cannabis 4(2):47–59. Myers MG, Ganoczy D, Walters HM, Pfeiffer PN, Ilgen MA, Bohnert KM. Assessing the diagnostic utility of the Cannabis Use Disorder Identification Test - Revised (CUDIT-R) among veterans with medical and non-medical cannabis use. Drug Alcohol Depend. 2023;247:109876. Additional Declarations No competing interests reported. Supplementary Files pgxcannabissupplementarydata20250623.docx Cite Share Download PDF Status: Published Journal Publication published 14 Feb, 2026 Read the published version in Journal of Cannabis Research → Version 1 posted Editorial decision: Revision requested 07 Jan, 2026 Reviews received at journal 07 Jan, 2026 Reviewers agreed at journal 05 Jan, 2026 Reviews received at journal 02 Jul, 2025 Reviewers agreed at journal 29 Jun, 2025 Reviewers invited by journal 24 Jun, 2025 Editor assigned by journal 24 Jun, 2025 Submission checks completed at journal 24 Jun, 2025 First submitted to journal 23 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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sociaux (CIUSSS) du Saguenay-Lac-Saint-Jean","correspondingAuthor":false,"prefix":"","firstName":"Jordan","middleName":"","lastName":"Turcotte","suffix":""},{"id":477033519,"identity":"f2b6dd1c-ddac-412a-8aaf-9aeecd586f42","order_by":2,"name":"Philippe Mercier","email":"","orcid":"","institution":"Centre intégré universitaire de santé et de services sociaux (CIUSSS) du Saguenay-Lac-Saint-Jean","correspondingAuthor":false,"prefix":"","firstName":"Philippe","middleName":"","lastName":"Mercier","suffix":""},{"id":477033521,"identity":"d67ae3a3-6f37-4540-976b-08f0000ed0a0","order_by":3,"name":"Flore Lavoie","email":"","orcid":"","institution":"Université de Sherbrooke","correspondingAuthor":false,"prefix":"","firstName":"Flore","middleName":"","lastName":"Lavoie","suffix":""},{"id":477033523,"identity":"6285b473-25a7-438e-ada9-b9118edd81b6","order_by":4,"name":"Laurence Tessier","email":"","orcid":"","institution":"Université de Sherbrooke","correspondingAuthor":false,"prefix":"","firstName":"Laurence","middleName":"","lastName":"Tessier","suffix":""},{"id":477033525,"identity":"4a590ee0-7ac4-4ec7-93e6-f23cb3cd993e","order_by":5,"name":"Ann-Lorie Gagnon","email":"","orcid":"","institution":"Centre intégré universitaire de santé et de services sociaux (CIUSSS) du Saguenay-Lac-Saint-Jean","correspondingAuthor":false,"prefix":"","firstName":"Ann-Lorie","middleName":"","lastName":"Gagnon","suffix":""},{"id":477033527,"identity":"51acc56f-5cc8-4339-a667-6909e6d659bd","order_by":6,"name":"Catherine Allard","email":"","orcid":"","institution":"Centre de recherche du Centre hospitalier universitaire de Sherbrooke (CR-CHUS)","correspondingAuthor":false,"prefix":"","firstName":"Catherine","middleName":"","lastName":"Allard","suffix":""},{"id":477033530,"identity":"fed50599-51b3-4025-b8aa-0cb195641aac","order_by":7,"name":"Elliot Fortin","email":"","orcid":"","institution":"Université de Sherbrooke","correspondingAuthor":false,"prefix":"","firstName":"Elliot","middleName":"","lastName":"Fortin","suffix":""},{"id":477033533,"identity":"c42cc025-2d78-4413-831a-20c5f030c55a","order_by":8,"name":"Guillaume Léonard","email":"","orcid":"","institution":"Université de Sherbrooke","correspondingAuthor":false,"prefix":"","firstName":"Guillaume","middleName":"","lastName":"Léonard","suffix":""},{"id":477033535,"identity":"95b51f5c-d85e-4f9e-a8e8-2f371de03c21","order_by":9,"name":"Louis Gendron","email":"","orcid":"","institution":"Université de Sherbrooke","correspondingAuthor":false,"prefix":"","firstName":"Louis","middleName":"","lastName":"Gendron","suffix":""},{"id":477033537,"identity":"8590e7c8-dfd6-47fe-9a0c-46fc79332e54","order_by":10,"name":"Karine Tremblay","email":"data:image/png;base64,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","orcid":"","institution":"Université de Sherbrooke","correspondingAuthor":true,"prefix":"","firstName":"Karine","middleName":"","lastName":"Tremblay","suffix":""}],"badges":[],"createdAt":"2025-06-23 14:08:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6957614/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6957614/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s42238-026-00408-w","type":"published","date":"2026-02-14T15:57:34+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":102786373,"identity":"32b509ca-8661-4d00-a9cd-e14f4cff16ab","added_by":"auto","created_at":"2026-02-16 16:13:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1569184,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6957614/v1/b987614a-2f99-4732-8d3f-bd03f7df5a0b.pdf"},{"id":85576598,"identity":"629e6a88-318a-445f-bfb3-989f87c8bcef","added_by":"auto","created_at":"2025-06-27 19:05:59","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":109774,"visible":true,"origin":"","legend":"","description":"","filename":"pgxcannabissupplementarydata20250623.docx","url":"https://assets-eu.researchsquare.com/files/rs-6957614/v1/7ef3133f411a1a7331e2418c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pharmacogenetic association study of cannabis use in chronic pain","fulltext":[{"header":"1. Background","content":"\u003cp\u003eChronic pain ranks among the top causes of disability-adjusted life years in the world (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In Canada, almost one in five adults (7.6\u0026nbsp;million) lives with such pain (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Despite its high prevalence and substantial impact on patient lives, the management of chronic pain remains particularly challenging (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Access to cannabis for medical purposes, such as in the treatment of chronic pain, has been available in Canada since 2001, while non-medical use of cannabis was legalized in 2018. A recent post-legalization study reported that 30.1% of adults living with chronic pain had used cannabis in the past year in the management of their condition (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile the evidence regarding the efficacy of cannabis and cannabinoids in the treatment of chronic pain is limited, the latest meta-analysis has demonstrated significant \u0026ndash; albeit small to very small \u0026ndash; improvements in pain relief among patients with chronic pain (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). However, there remains a significant proportion of patients (up to 70%) who do not achieve adequate pain relief and no factor has reliably been identified as a predictor of this response (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEven though its accessibility is growing and it use relatively widespread (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), including using as a means of self-management or as prescribed through health professionals, it is well documented that cannabis is often discontinued due to adverse events (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Two significant adverse events, cannabis use disorder (CUD) and psychotic adverse events related to cannabis use, have been associated with multiple genetic variants (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). These variants could ultimately be used as genetic markers to personalize cannabis treatment and offer treatment tailored to the genetic background of patients, thereby reducing the potential harms when cannabis is used. Genetic variants could also be employed to identify patients who are more likely to benefit from cannabis prior to treatment initiation. Despite this, the paucity of the data on some of the previously reported variants and inconsistent results regarding some of them limit our ability to use them as genetic markers at the moment (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe aim of this retrospective genetic association study was to characterize the phenotypes of patients with chronic pain who had used cannabis or cannabinoids in the past and to investigate the effects of different genetic variants. This paper presents the relationship between three main response phenotypes (i.e., pain relief, CUD and psychotic adverse events) and 28 genetic variants located in 17 genes.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study population and inclusion criteria\u003c/h2\u003e \u003cp\u003eThis multicentric retrospective genetic association study was conducted at the CIUSSS-SLSJ and CIUSSS de l'Estrie \u0026ndash; CHUS University Hospitals in Quebec, Canada. Most of participants (67%) were recruited with an online form distributed by local chronic pain associations, either via social media or online advertisements. The remaining participants were recruited from the \u0026ldquo;Consortium qu\u0026eacute;becois sur la douleur au dos\u0026rdquo; recruitment platform (25%) and from participant lists included in previous studies conducted at the CR-CHUS (8%).\u003c/p\u003e \u003cp\u003eInclusion criteria included : 1) having chronic pain (pain lasting longer than 3 months) 2) using or having used cannabis as a mean to reduce pain associated with a chronic pain condition (either prescribed by a physician or in the context of self-management); 3) to be of legal age to use cannabis according to local regulations at the time of the study (\u0026ge;\u0026thinsp;18 years old if prescribed by a physician and \u0026ge;\u0026thinsp;21 years old if used in self-treatment).\u003c/p\u003e \u003cp\u003eThe only exclusion criterion was having had a strictly recreative use of cannabis or never having used cannabis to get relief from a chronic pain condition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data collection\u003c/h2\u003e \u003cp\u003e \u003cb\u003eTimeline\u003c/b\u003e \u003c/p\u003e \u003cp\u003eData collection was performed from October 2020 to July 2021. After obtaining free and informed consent, participants completed a primary survey either via telephone or during an in-person visit at one of the participating research centres. This first survey collected data on demographic characteristics, cannabis use, health status, medical history, and current pharmacologic therapies. Subsequently, biological samples \u0026ndash; either blood (~\u0026thinsp;10 ml) or saliva (~\u0026thinsp;4 ml) \u0026ndash; were collected on participant preference for DNA extraction. Participants were then invited to complete an online follow-up survey during a subsequent episode of cannabis use to evaluate its effect on pain. This assessment was made using the numerical pain rating scale (NRS) from 0 to 10 (\u0026ldquo;no pain\u0026rdquo; to \u0026ldquo;worst pain imaginable\u0026rdquo;) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) before and after cannabis use.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAssessments\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThree main phenotypes were assessed during this study: pain response, CUD and psychotic adverse events.\u003c/p\u003e \u003cp\u003eDemographics included age, sex, life habits (tobacco use, alcohol use, and drugs), exercise, anthropometry and perception of their health using the EQ-5D-5L instrument all collected in the primary survey (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Health Index scores, which represents a combined score for the 5 dimensions and levels of health assessed by the EQ-5D-5L instrument, were calculated using the Canadian value set of the EQ-5D-5L(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCharacteristics on participants\u0026rsquo; cannabis use (e.g., age at first use, duration of use, frequency of use, routes of administration, quantities, delta9-tetrahydrocannabinol [THC] and cannabidiol [CBD] content of the products used) were also thoroughly assessed using an in-house questionnaire in the primary survey. Pain characteristics (e.g., pain intensity, impact of pain on physical function, neuropathic component) were documented using the Brief Pain Inventory (BPI) and \u0026ldquo;Douleur Neuropathique 4\u0026rdquo; (DN4) questionnaire (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Current cannabis use was defined as cannabis used in the past 6 months.\u003c/p\u003e \u003cp\u003eThe online follow-up survey was composed of questions regarding the presence of somnolence or pain before and after their cannabis use. These elements were first evaluated before the consumption event and were reassessed 30 minutes to 4 hours after use, at the onset of maximum effect according to the participants. The effect of cannabis on pain was assessed using the NRS from 0 to 10 (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), and somnolence was assessed using the French version of the Stanford Sleepiness Scale (SSS) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). To assess if a participant had a positive response to cannabis with regards to pain relief, they were asked to rate their average pain relief with a percentage improvement in pain they typically experienced on a 0-100 (\u0026ldquo;no pain relief\u0026rdquo; to \u0026ldquo;complete pain relief\") numerical rating scale. Adequate pain relief with the online survey was defined as a reduction of two points or \u0026ge;\u0026thinsp;30% reduction of pain based on the NRS values before and after cannabis use. Data from the online survey were then used to assess the validity of the adequate pain relief phenotype using the main questionnaire.\u003c/p\u003e \u003cp\u003eScreening for the presence of CUD was performed using the Cannabis Use Disorder Identification Test \u0026ndash; Revised (CUDIT-R) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). The CUDIT-R requires participants to answer 8 multiple-choice questions about their cannabis use, which can be translated to a global score to assess the presence of a cannabis use disorder (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Scores\u0026thinsp;\u0026ge;\u0026thinsp;13 points were considered as having a positive screening test result.\u003c/p\u003e \u003cp\u003ePsychotic adverse events, collected in the primary survey, included the presence of hallucinations (visual, auditory or tactile) or delusions, and participants were classified as having had a psychotic adverse event if they had experienced at least one of those adverse reactions.\u003c/p\u003e \u003cp\u003eStudy data was collected and managed using REDCap electronic data capture tools hosted at Universit\u0026eacute; de Sherbrooke (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Further details on study data collection are given in the \u003cb\u003eAdditional file.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. SNP selection and genotyping\u003c/h2\u003e \u003cp\u003eGenetic variants (single-nucleotide polymorphism, SNP) in candidate genes were identified through a literature review using PubMed database and PharmGKB (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). SNPs reported in the literature that had at least one positive association with either response to cannabis (e.g., psychiatric adverse events or CUD) or that could have an impact on the pharmacokinetics or pharmacodynamics of cannabis were selected for the study. This literature review identified 28 variants in 17 genes (ATP Binding Cassette Subfamily B Member 1 (\u003cem\u003eABCB1\u003c/em\u003e), AKT Serine/Threonine Kinase 1 (\u003cem\u003eAKT1\u003c/em\u003e), Brain Derived Neurotrophic Factor (\u003cem\u003eBDNF\u003c/em\u003e), Cholinergic Receptor Muscarinic 3 (\u003cem\u003eCHRM3\u003c/em\u003e), Cholinergic Receptor Nicotinic Alpha 2 Subunit (\u003cem\u003eCHRNA2\u003c/em\u003e), Cannabinoid receptor 1(\u003cem\u003eCNR1\u003c/em\u003e), Cannabinoid receptor 2 (\u003cem\u003eCNR2\u003c/em\u003e), Catechol-O-Methyltransferase (\u003cem\u003eCOMT\u003c/em\u003e), Cytochrome P450 Family 2 Subfamily C Member 9 (\u003cem\u003eCYP2C9\u003c/em\u003e), Cytochrome P450 Family 3 Subfamily A Member 5 (\u003cem\u003eCYP3A5\u003c/em\u003e), Cytochrome P450 Family 3 Subfamily A Member 5 (\u003cem\u003eFAAH\u003c/em\u003e), Gamma-Aminobutyric Acid Type A Receptor Subunit Alpha2 (\u003cem\u003eGABRA2\u003c/em\u003e), Hes Family BHLH Transcription Factor 7 (\u003cem\u003eHES7\u003c/em\u003e), Lysine Acetyltransferase 2B (\u003cem\u003eKAT2B\u003c/em\u003e), Neuregulin 1 (\u003cem\u003eNRG1\u003c/em\u003e), Opioid Receptor Mu 1 (\u003cem\u003eOPRM1\u003c/em\u003e), Purinergic Receptor P2X 7 (\u003cem\u003eP2RX7\u003c/em\u003e)).\u003c/p\u003e \u003cp\u003eBlood samples were collected in EDTA tubes and the buffy coat was isolated in the 24 hours following specimen collection. DNA extraction of buffy coat was performed using the Puregene Blood Kit (QIAGEN, Germany). DNA extraction from saliva samples was done using the prepIT-L2P extraction kit (DNAgenoteck, Ottawa, Canada) directly from the sample we received by postal mail from participants using GenoTech\u0026reg; saliva sample collection kit OG-500 (DNAgenoteck, Ottawa, Canada).\u003c/p\u003e \u003cp\u003eDNA samples were genotyped by standard TaqMan\u0026reg; method (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) at the Universit\u0026eacute; de Sherbrooke RNomics platform lab. Details on genotyping, including the probe and primer designs used can be found in the \u003cb\u003eAdditional file\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical analysis\u003c/h2\u003e \u003cp\u003eHardy-Weinberg Equilibrium (HWE) was tested for each variant. Assessment of the validity of genotyping was made based on HWE results, genotyping call rate and minor allele frequency (MAF). Variants were excluded from subsequent analysis using the following criteria: 1) genotyping call rate inferior to 95%; 2) statistically significant departure from HWE (after multiple testing correction); 3) MAF inferior to 5%; 4) more than one alternative allele observed.\u003c/p\u003e \u003cp\u003eCategorical variables were compared using the Chi-square or Fisher\u0026rsquo;s exact tests (if\u0026thinsp;\u0026gt;\u0026thinsp;20% of cells had expected frequencies\u0026thinsp;\u0026lt;\u0026thinsp;5 or if a cell had an expected frequency of \u0026lt;\u0026thinsp;1). Normality of data was assessed by the Shapiro-Wilk Test. Comparisons between groups for continuous variables were made using independent samples t-test or Wilcoxon rank sum test (if the variable had a non-normal distribution).\u003c/p\u003e \u003cp\u003eStatistical tests were performed for each variant to identify potential statistical association with the three phenotypes assessed. Univariable logistic regression analyses using an additive genetic model were performed for variants with statistically significant associations with the studied phenotypes before multiple testing correction. Multiple testing corrections were performed according to the method proposed by Li, J. \u0026amp; Ji, L. (2005) for adjusting multilocus analyses by calculating the effective number of variants analyzed (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Specifically, Bonferroni correction for genetic analyses was conducted for an effective number of 15 variants. Statistical significance threshold was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Further details on the statistical analysis are given in the \u003cb\u003eAdditional file\u003c/b\u003e. All analyses were performed using R Statistical Software (v4.2.1) (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Participants\u0026rsquo; description\u003c/h2\u003e \u003cp\u003eA total of 100 participants were included in the present study and the characteristics of the studied sample are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Participants were aged between 22 and 77 years old, and 67% identified as females. Most participants were current cannabis users at the time of the study (92%).\u003c/p\u003e \u003cp\u003eAmong the health conditions and comorbidities of the participants, musculoskeletal disorders were the most common, present in almost all participants (97%). Psychiatric and gastrointestinal comorbidities were also frequent, being present in more than half of the participants (70% and 53%, respectively). The most frequent chronic pain-related diagnoses were back pain (69%), fibromyalgia (45%) and osteoarthritis (34%). Neuropathic pain was present in almost two thirds of the participants (65%). Patients reported having chronic pain for a median duration of 12.0 years (interquartile range (IQR)\u0026thinsp;=\u0026thinsp;7.0-21.8 years) with an average severity of moderate pain (BPI pain score median (IQR)\u0026thinsp;=\u0026thinsp;5.25 (3.50, 6.12)) and mild interference with daily life (BPI interference score median (IQR)\u0026thinsp;=\u0026thinsp;3.88 (1.67, 5.50)).\u003c/p\u003e \u003cp\u003e The main methods of consumption used by the participants were oral (45%) and inhalation (44%). Among participants with inhaled use, the average quantity of inhaled cannabis was 1.22 grams per day of use. Daily use was frequent, with 81% using cannabis at least once per day. Most users with inhalation as their main method of use did so using cannabis with products containing at least twice the amount of THC compared to CBD (71%). The opposite was observed for participants consuming cannabis orally. Indeed, these participants were using products containing at least twice the amount of CBD compared to THC (69%). However, the information regarding THC and CBD content of the products used was missing for many participants.\u003c/p\u003e \u003cp\u003eThe most frequent concurrent pharmacological treatments were antidepressants (55%) followed by acetaminophen (33%), nonsteroidal anti-inflammatory drugs (NSAIDs) (32%) and opioids (32%).\u003c/p\u003e \u003cp\u003eParticipants with current cannabis use reported higher BPI interference score with a median value of 3.88 (interquartile range [IQR] 2.00\u0026ndash;5.62) compared to past users with a median value of 1.65 (IQR 0.67\u0026ndash;2.94) (p\u0026thinsp;=\u0026thinsp;0.045). Current cannabis use was also associated with a greater proportion of participants with a adequate pain relief phenotype (current use: 82.8% vs. past use: 28.6%, p\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;100)\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCurrent use\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;92)\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePast use\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;8)\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (67.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (66.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.0 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.2 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.5 (14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88 (88.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (87.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatin, Central and South American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eChronic pain and health status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDN4 score (\u0026ge;\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (65.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (64.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain duration (years)\u003csup\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.0 (7.0, 21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.0 (7.0, 21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.0 (4.5, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBPI pain severity\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.25 (3.50, 6.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.25 (3.50, 6.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.25 (3.38, 6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBPI pain interference\u003csup\u003e\u003cem\u003e5\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.88 (1.67, 5.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.88 (2.00, 5.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.65 (0.67, 2.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEQ-5D-5L index\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70 (0.50, 0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68 (0.47, 0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78 (0.67, 0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEQ VAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.8 (20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.9 (21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.5 (13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m2)\u003csup\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.8 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.5 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.7 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eCannabis use characteristics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain method of use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInhaled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (44.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (46.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (45.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (43.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther or more than one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (11.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le; Weekly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than once per week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (15.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (14.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (27.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (28.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than once daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (54.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (54.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at first cannabis use (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.7 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.4 (15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.2 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003ePast medical history\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMusculoskeletal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97 (97.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89 (96.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychiatric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (70.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (71.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrointestinal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (53.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (54.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurologic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (39.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (39.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (39.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (36.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (37.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (26.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (26.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (10.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eConcurrent pharmacotherapy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntidepressants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (55.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (54.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcetaminophen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (33.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (33.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSAIDs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (32.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (31.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpioids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (32.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (30.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiepileptics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (28.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (29.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscle relaxants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (19.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (18.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenzodiazepines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (15.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (13.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStimulants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (9.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ drugs/benzodiazepine like\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (7.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiologics/DMARDs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003ePhenotypes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain relief (\u0026ge;\u0026thinsp;30%)\u003csup\u003e\u003cem\u003e6\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 (78.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (82.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychotic adverse events\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCUDIT-R\u0026thinsp;\u0026ge;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (27.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u0026nbsp;n (%); Median (IQR) for pain duration, BPI pain severity, BPI pain interference and EQ-5D-5L index; otherwise Mean (SD); DMARDs\u0026thinsp;=\u0026thinsp;Disease-modifying antirheumatic drugs; NSAIDs\u0026thinsp;=\u0026thinsp;Non Steroidal Anti-Inflammatory Drugs;\u0026nbsp;\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026nbsp;Fisher's exact test; Wilcoxon rank sum test\u0026nbsp;\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e\u0026nbsp;Data available N\u0026thinsp;=\u0026thinsp;99;\u0026nbsp;\u003csup\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sup\u003e\u0026nbsp;Data available N\u0026thinsp;=\u0026thinsp;98;\u0026nbsp;\u003csup\u003e\u003cem\u003e5\u003c/em\u003e\u003c/sup\u003e\u0026nbsp;Data available N\u0026thinsp;=\u0026thinsp;97;\u0026nbsp;\u003csup\u003e\u003cem\u003e6\u003c/em\u003e\u003c/sup\u003e\u0026nbsp;Data available N\u0026thinsp;=\u0026thinsp;94;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNo statistically significant differences were observed between past and current users concerning demographic characteristics, health status, past medical history or concurrent pharmacotherapy. However, participants with current cannabis use reported higher BPI interference score and a greater proportion of current users had an adequate pain relief phenotype. The complete characteristics of participants\u0026rsquo; cannabis use are presented in the \u003cb\u003eAdditional file (Supplementary Table S2).\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Phenotypes\u003c/h2\u003e \u003cp\u003eAn adequate pain relief phenotype was observed in 74 of the 100 participants; 25 had a positive screening test for possible CUD and 6 had at least one psychotic adverse event. The characteristics of participants according to each phenotype were investigated (\u003cb\u003eAdditional file)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThe data to establish pain response phenotype was missing for 6 participants who were consequently excluded from these analyses. Adequate pain relief was not associated with any demographic characteristics, health status, comorbidities, concurrent pharmacotherapy or with the presence of neuropathic pain. Current use was noted in 72 (97.3%) participants with a positive response phenotype and in 15 (75.0%) of non-responders (p\u0026thinsp;=\u0026thinsp;0.004). Among participants with a defined pain response phenotype who completed the online survey (n\u0026thinsp;=\u0026thinsp;43), an adequate pain relief phenotype using the main questionnaire had a sensitivity of 89.1% (95% CI 74.6% \u003cb\u003e\u0026ndash;\u003c/b\u003e 97.0%) and specificity of 33.3% (4.3% \u003cb\u003e\u0026ndash;\u003c/b\u003e 77.7%) for adequate pain relief based on the NRS values before and after cannabis use.\u003c/p\u003e \u003cp\u003eSome differences were noted among participants according to CUD screening test result. Participants with positive screening test result main method of use was inhaled in 76.0%, followed by oral in 20% and others or more than one in 4.0%. The main method of use for participants with a negative result was oral in 53.3% followed by inhaled at 33.3% and others or more than one in 13.3%. The main method of use differed between participants according to screening result (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A lower prevalence of cardiovascular comorbidities was noted in participants with a positive CUD screening (20% vs. 45%, p\u0026thinsp;=\u0026thinsp;0.025) as well as a lower prevalence of metabolic comorbidities (8.0% vs. 32%, p\u0026thinsp;=\u0026thinsp;0.018) and lower body mass index (BMI) (25.1 vs. 28.7 kg / m\u003csup\u003e2\u003c/sup\u003e, p\u0026thinsp;=\u0026thinsp;0.010). The only difference present regarding concomitant pharmacotherapy was lower benzodiazepine use in participants with positive screening for CUD (0 vs. 20.0%, p\u0026thinsp;=\u0026thinsp;0.019).\u003c/p\u003e \u003cp\u003eParticipants with a positive screening test for CUD were younger, had first used cannabis at a younger age, had a lower pain duration and differed in terms of their main method of use. Lower benzodiazepine use, a decreased prevalence of cardiovascular and metabolic comorbidities as well as a BMI were also noted in participants with positive screening result.\u003c/p\u003e \u003cp\u003ePsychotic adverse events were not associated with any differences in demographic characteristics, or concurrent pharmacotherapy in the study participants. Metabolic comorbidities were more common among participants with psychotic adverse events. Notably, hallucinations were the only psychotic adverse event reported by the participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Genetic association study\u003c/h2\u003e \u003cp\u003e Saliva or blood sample was obtained for 77 participants. Statistically significant differences were observed between participants for whom DNA samples were obtained compared to participants without DNA samples (\u003cb\u003eAdditional file\u003c/b\u003e). No differences were noted in the proportions of the three phenotypes studied. Participants with DNA samples were older than participants without DNA samples (50.4 vs. 40.1 years old, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and had longer chronic pain duration (median duration in years (IQR): 15.0 (7.9\u0026ndash;23.6) vs. 8.5(4.2\u0026ndash;18.0), p\u0026thinsp;=\u0026thinsp;0.022). No differences were noted in the proportions of the three phenotypes studied.\u003c/p\u003e \u003cp\u003eGenotype validity assessment led to the exclusions of 10 variants from 9 different genes (\u003cem\u003eABCB1\u003c/em\u003e, \u003cem\u003eAKT1\u003c/em\u003e, \u003cem\u003eCHRM3\u003c/em\u003e, \u003cem\u003eCHRNA2\u003c/em\u003e, \u003cem\u003eCNR2\u003c/em\u003e, \u003cem\u003eCYP2C9\u003c/em\u003e, \u003cem\u003eCYP3A5\u003c/em\u003e, \u003cem\u003eFAAH\u003c/em\u003e, \u003cem\u003eP2RX7\u003c/em\u003e). The 18 remaining variants from 11 different genes (\u003cem\u003eBDNF\u003c/em\u003e, \u003cem\u003eCNR1\u003c/em\u003e, \u003cem\u003eCNR2\u003c/em\u003e, \u003cem\u003eCOMT\u003c/em\u003e, \u003cem\u003eCYP2C9\u003c/em\u003e, \u003cem\u003eFAAH\u003c/em\u003e, \u003cem\u003eGABRA2\u003c/em\u003e, \u003cem\u003eHES7\u003c/em\u003e, \u003cem\u003eKAT2B\u003c/em\u003e, \u003cem\u003eNRG1\u003c/em\u003e and \u003cem\u003eOPMR1\u003c/em\u003e) were at HWE following Holm-Bonferroni correction. HWE p-values, genotyping call rate of all variants (including those with call rate\u0026thinsp;\u0026lt;\u0026thinsp;95%) and alternative allele frequency of the biallelic markers are presented in the \u003cb\u003eAdditional file\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThe three studied phenotypes according to the participant\u0026rsquo;s genotype for the different variants investigated are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. None of the variants investigated were associated with pain response phenotype or with CUD screening result. Two variants in the CNR1 gene were associated with a statistically significant difference in the proportions of psychotic adverse events (before adjustment for multiple SNPs testing). Regarding the CNR1 rs1049353 C\u0026thinsp;\u0026gt;\u0026thinsp;T variant, each additional T allele increased by sixfold the odds of having psychotic adverse events (odds ratio [OR] 6.1, 95% CI 1.7\u0026ndash;27.9). Each additional C allele of the CNR1 rs2023239 T\u0026thinsp;\u0026gt;\u0026thinsp;C intronic variant increased by threefold the odds of having psychotic adverse events (OR 3.5, 95% CI 1.5\u0026ndash;9.4). These findings were not significant after adjustment for multiple SNPs testing.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResponse phenotype according to participant\u0026rsquo;s genotype\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePain response\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eCUDIT-R\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003ePsychotic adverse events\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-responder\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;16)\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResponder\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;57)\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNegative (\u0026lt;\u0026thinsp;13)\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;60)\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePositive (\u0026ge;\u0026thinsp;13)\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;17)\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbsence\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;71)\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;6)\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep-value\u003csup\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (68.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46 (76.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (52.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50 (70.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5 (83.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.29 (43.85, 64.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.90 (38.10, 58.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.2 (44.1, 62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.3 (34.1, 50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.4 (42.1, 59.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e51.5 (40.1, 53.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eSNPs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBDNF (rs6265)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37 (61.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 (70.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e44 (62.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5 (83.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref/alt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (36.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26 (36.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNR1 (rs806374)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (30.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20 (28.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref/alt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 (56.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (58.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e42 (59.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (11.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9 (12.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNR1 (rs2023239)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41 (68.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 (70.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e51 (71.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref/alt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (28.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19 (26.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNR1 (rs1049353)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 (51.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (58.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40 (56.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref/alt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (38.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (35.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27 (38.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (10.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNR1 (rs6454674)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 (51.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (58.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37 (52.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref/alt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (41.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30 (42.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNR1 (rs806368)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26 (43.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (35.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29 (40.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref/alt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (52.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37 (52.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (11.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNR1 (rs806378)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 (51.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11 (64.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38 (53.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref/alt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (35.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28 (39.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNR1 (rs806380)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (41.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11 (64.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32 (45.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref/alt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29 (48.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32 (45.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (10.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7 (9.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNR2 (rs2229579)\u003csup\u003e\u003cem\u003e5\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49 (86.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11 (64.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e55 (80.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5 (83.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref/GtoA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (12.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (35.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12 (17.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealt GtoA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOMT (rs4680)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 (26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (17.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17 (23.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref/alt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27 (45.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (52.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33 (46.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (28.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21 (29.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYP2C9 (rs1799853)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47 (78.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 (76.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57 (80.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref/alt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (21.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (17.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAAH (rs324420)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44 (73.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 (70.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e52 (73.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref/alt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18 (25.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGABRA2 (rs279858)\u003csup\u003e\u003cem\u003e6\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (18.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (11.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13 (18.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref/alt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (59.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14 (82.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e44 (62.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5 (83.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (22.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13 (18.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHES7 (rs1442849)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (41.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (58.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32 (45.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref/alt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (35.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35 (49.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKAT2B (rs9829896)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (15.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9 (12.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref/alt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32 (53.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8 (47.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37 (52.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19 (31.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8 (47.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25 (35.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNRG1 (rs17664708)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48 (80.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15 (88.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57 (80.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref/alt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (11.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOPRM1 (rs510769)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 (51.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 (70.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40 (56.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref/alt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26 (43.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28 (39.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOPRM1 (rs1799971)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (58.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e47 (66.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eref/alt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (28.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21 (29.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u0026nbsp;n (%); median (IQR) for Age\u0026nbsp;\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026nbsp;Pearson's Chi-squared test; Two Sample t-test\u0026nbsp;\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e\u0026nbsp;Fisher's exact test; Wilcoxon rank sum test\u0026nbsp;\u003csup\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sup\u003e\u0026nbsp;Fisher's exact test; Two Sample t-test\u0026nbsp;\u003csup\u003e\u003cem\u003e5\u003c/em\u003e\u003c/sup\u003e\u0026nbsp;Data available N\u0026thinsp;=\u0026thinsp;74;\u0026nbsp;\u003csup\u003e\u003cem\u003e6\u003c/em\u003e\u003c/sup\u003e\u0026nbsp;Data available N\u0026thinsp;=\u0026thinsp;76; None of the p-values displayed are significant after Bonferroni correction for adjusting multilocus analyses with an effective number of 15 variants.\u003c/p\u003e \u003cp\u003eAlt\u0026thinsp;=\u0026thinsp;alternative allele; ref\u0026thinsp;=\u0026thinsp;reference allele.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis retrospective genetic association study in patients with chronic pain who used cannabis or cannabinoids, describes the relationship between previously reported genetic variants and three main response phenotypes. More precisely, we assessed the relationship between different genetic variants and cannabis response in terms of pain relief, CUD and psychotic adverse events. Our findings suggest that two variants of the \u003cem\u003eCNR1\u003c/em\u003e gene (rs1049353 and rs2023239) could be associated with an increased rate of psychotic adverse events although these associations were not significant after adjustment for multiple SNPs testing. None of the studied variants were associated with CUD or pain relief.\u003c/p\u003e \u003cp\u003ePrevious studies have highlighted the significant inter-individual variability associated with THC use, both in terms of physiologic effects and pharmacokinetics parameters (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). This variability, which applies to adverse events but also to pain response, underscores the importance of identifying genetic markers to personalize cannabis treatment. In 2022, an open-label non-randomized observational study by Poli \u003cem\u003eet al.\u003c/em\u003e recruited 600 participants who received different cannabis preparations and reported for the first time variants associated with pain response (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). One of these variants, \u003cem\u003eABCB1\u003c/em\u003e rs1045642, was included as a candidate gene for this study but was unfortunately discarded due to insufficient call rate. The other two variants, \u003cem\u003eTRPV\u003c/em\u003e rs8065080 and \u003cem\u003eUGT2B7\u003c/em\u003e rs7438135, although both were in genes of potential interests due to their role in the pharmacodynamics and pharmacokinetics of cannabis, were not included in our study due to lack of clinical studies investigating their impact on cannabis use and the studied phenotypes. However, Poli \u003cem\u003eet al.\u003c/em\u003e identified the \u003cem\u003eCNR1\u003c/em\u003e rs1049353 variant as a treatment discontinuation risk factor.\u003c/p\u003e \u003cp\u003ePrevious literature highlights the role of genes implicated in the dopaminergic system (e.g., \u003cem\u003eCOMT\u003c/em\u003e) and psychosis induced by cannabis (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), and offers possible insight into the mechanisms underlying the increased psychotic adverse events seen with \u003cem\u003eCNR1\u003c/em\u003e alternative allele carriers. \u003cem\u003eCNR1\u003c/em\u003e encodes one of the two main cannabinoid receptors, cannabinoid receptor 1 (CB\u003csub\u003e1\u003c/sub\u003e), that is part of the G protein-coupled receptors (GPCRs) family of membrane proteins. CB\u003csub\u003e1\u003c/sub\u003e is ubiquitous in the central nervous system and is distributed at a greater concentration in regions playing a key role in reward, cognition and emotions, such as the limbic areas, hippocampus and amygdala (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). THC exhibits partial agonist activity of CB\u003csub\u003e1\u003c/sub\u003e and is thought to be at the origin of most of the cannabis observed psychotropic effects (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Notably, THC could be responsible for the transient positive psychotic symptoms (e.g., hallucinations) that can result from cannabis use even in the absence of an underlying psychiatric disorder (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Data from animal studies suggests exogenous cannabinoids such as THC facilitate dopamine release from dopaminergic neurons via mechanisms involving CB\u003csub\u003e1\u003c/sub\u003e (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). While the data in humans is unclear, increased expression of CB\u003csub\u003e1\u003c/sub\u003e on peripheral immune cells was documented in patients with multiple episodes of psychosis compared to healthy controls (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eCNR1\u003c/em\u003e rs1049353 polymorphism in exon 4 produces a synonymous variant in codon 453 (Thr453Thr). However, this synonymous SNP may impact mRNA stability and, consequently, affect CB1 receptor expression. Alteration in \u003cem\u003eCNR1\u003c/em\u003e mRNA stability could therefore affect dopamine release in key dopaminergic regions associated with cannabis-induced psychosis. Moreover, \u003cem\u003eCNR1\u003c/em\u003e rs1049353 associated with psychotic adverse events could reflect an indirect association via linkage disequilibrium, as multiple \u003cem\u003eCNR1\u003c/em\u003e haplotype blocks were documented in rs1049353 region(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Similarly, evidence also suggests variable expression of CB\u003csub\u003e1\u003c/sub\u003e receptor in presence of the \u003cem\u003eCNR1\u003c/em\u003e rs2023239 polymorphism, also an intronic variant (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Greater CB\u003csub\u003e1\u003c/sub\u003e receptor density in peripheral lymphocytes for carriers of the alternative C allele was described in long-term daily cannabis users, like most of the participants in this study (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Interestingly, results from a pilot study using data from a placebo-controlled clinical trial investigating the impact of cannabis on driving performance, suggested that the \u003cem\u003eCNR1\u003c/em\u003e rs1049353 and rs2023239 variants could increase subjective effects of acute cannabis intoxication (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSurprisingly, despite most of the previous associations in the literature being with CUD (i.e., \u003cem\u003eCNR1\u003c/em\u003e (rs806380, rs806378, rs806374, rs806368, rs2023239, rs1049353 and rs6454674) (\u003cspan additionalcitationids=\"CR39 CR40 CR41 CR42\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), \u003cem\u003eFAAH\u003c/em\u003e (rs324420) (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), \u003cem\u003eGABRA2\u003c/em\u003e (rs279858)(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), \u003cem\u003eHES7\u003c/em\u003e (rs1442849) (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e), \u003cem\u003eKAT2B\u003c/em\u003e (rs9829896) (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e), \u003cem\u003eNRG1\u003c/em\u003e (rs17664708) (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e) and \u003cem\u003eOPRM1\u003c/em\u003e (rs1799971) (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e)), none of the 18 variants included were associated with CUD in our study. An explanation for this discrepancy could be the studied population and the method employed to identify possible CUD among participants. In contrast to the previous studies that were conducted in adolescent or adult populations with non-medical use of cannabis, individuals included in this study used cannabis as means of self-management or as prescribed through health professionals. Limited evidence in the literature points towards altered test characteristics of the CUDIT-R in individuals with cannabis use for medical purposes (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Similarly, Myers \u003cem\u003eet al\u003c/em\u003e. recently reported that the CUDIT-R had worse performance among individuals who possessed a medical cannabis card compared to non-card holders (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Higher frequency of use among medical users, like the majority of this study participants, could also have contributed to the decreased specificity of the CUDIT-R scale as many of its items are dependent on the frequency or intensity of use (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study has several limitations, primarily stemming from its retrospective design and the relatively small sample size, making the need to interpret the results with caution even more important. Despite the selection of candidate genes with previous positive association or based on our current understanding of the pharmacokinetics or pharmacodynamics of cannabinoids, the multiple testing involved in this study comes with the important risk of type I error. The retrospective nature of the study is obviously prone to recall bias. Furthermore, the small number of participants without active cannabis use implies significant selection bias and could have contributed to the low prevalence of both psychotic adverse events and negative pain response phenotypes observed since both of those could be motives to forgo cannabis use. The small sample size of this study, combined with the modest effect size of some of the previously reported variants, could also have contributed to our study being insufficiently powered to detect these associations.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summary, this retrospective genetic association study in patients with chronic pain reports two \u003cem\u003eCNR1\u003c/em\u003e variants (rs1049353 and rs2023239) that could possibly contribute to an increased rate of psychotic adverse events related to cannabis use in patients with chronic pain. This study did not replicate numerous previous findings as none of the variants studied were associated with possible CUD. The adequacy of the available screening tools for CUD in subpopulations of cannabis users remains uncertain and deserves greater attention considering the growing access to cannabis in chronic pain treatment. Finally, the role of these two CNR1 variants should be further studied in an independent prospective cohort.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eABCB1\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eATP Binding Cassette Subfamily B Member 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eAKT1\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAKT Serine/Threonine Kinase 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eBDNF\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBrain Derived Neurotrophic Factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBrief Pain Inventory\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCBD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecannabidiol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCHRM3\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCholinergic Receptor Muscarinic 3\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCHRNA2\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCholinergic Receptor Nicotinic Alpha 2 Subunit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCNR1\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCannabinoid receptor 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCNR2\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCannabinoid receptor 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCOMT\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCatechol-O-Methyltransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCUD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecannabis use disorder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCUDIT-R\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCannabis Use Disorder Identification Test \u0026ndash; Revised\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCYP2C9\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCytochrome P450 Family 2 Subfamily C Member 9\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCYP3A5\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCytochrome P450 Family 3 Subfamily A Member 5\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDN4\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDouleur Neuropathique 4\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eFAAH\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCytochrome P450 Family 3 Subfamily A Member 5\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eGABRA2\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGamma-Aminobutyric Acid Type A Receptor Subunit Alpha2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eHES7\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHes Family BHLH Transcription Factor 7\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHWE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHardy-Weinberg Equilibrium\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eKAT2B\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLysine Acetyltransferase 2B\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eNRG1\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeuregulin 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNRS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enumerical pain rating scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eOPRM1\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOpioid Receptor Mu 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eP2RX7\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePurinergic Receptor P2X 7\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esingle-nucleotide polymorphism\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edelta9-tetrahydrocannabinol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe project was approved by the Institutional Ethics Review Board of the CIUSSS du Saguenay-Lac-Saint-Jean (#2020-038) and realized in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants at recruitment.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe minimal dataset that would be necessary to interpret or replicate findings of the current study may be available from the corresponding author upon request and according to local access policies.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no financial or other competing interests\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or non-profit sectors.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contribution\u003c/p\u003e\n\u003cp\u003eWB: Writing \u0026ndash; original draft (lead), investigation (equal), formal analysis (lead);\u0026nbsp;JT: \u0026nbsp; Investigation (equal), writing \u0026ndash; original draft (supporting), data curation (supporting);\u0026nbsp;PM: Writing \u0026ndash; original draft (supporting), formal analysis (supporting);\u0026nbsp;LT: DNA extraction, writing \u0026ndash; review and editing (supporting);\u0026nbsp;FL: DNA extraction, writing \u0026nbsp;\u0026ndash; review and editing (supporting);\u0026nbsp;AG: Methodology (supporting), project administration (supporting);\u0026nbsp;CA: Formal analysis (supporting), validation (lead);\u0026nbsp;EF: Writing - review and editing (lead);\u0026nbsp;GL: Writing \u0026ndash; review and editing (equal), supervision (supporting);\u0026nbsp;LG: Writing \u0026ndash; review and editing (equal) , supervision (supporting);\u0026nbsp;KT: Supervision (lead), conceptualization (equal), writing \u0026ndash; review and editing (equal). All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all the participants as they are essential to successful research. The authors would also like to thank Antoine Lamontagne, William Gaudreault-Fortin, Alexis Dufour and Audr\u0026eacute;anne Tremblay for their valuable assistance in the good execution of various steps of the project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGBD 2016 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990\u0026ndash;2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Lond Engl. 2017;390(10100):1211\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSant\u0026eacute; C. 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Cannabis 4(2):47\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMyers MG, Ganoczy D, Walters HM, Pfeiffer PN, Ilgen MA, Bohnert KM. Assessing the diagnostic utility of the Cannabis Use Disorder Identification Test - Revised (CUDIT-R) among veterans with medical and non-medical cannabis use. Drug Alcohol Depend. 2023;247:109876.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-cannabis-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcan","sideBox":"Learn more about [Journal of Cannabis Research](https://jcannabisresearch.biomedcentral.com/)","snPcode":"42238","submissionUrl":"https://submission.springernature.com/new-submission/42238/3","title":"Journal of Cannabis Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cannabis, Chronic pain, Pharmacogenetics, Psychosis, Cannabis use disorder","lastPublishedDoi":"10.21203/rs.3.rs-6957614/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6957614/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePain is one of the leading causes of disability worldwide. Despite the various pharmacological treatments available, patients with chronic pain often remain with significant disabilities and unsatisfactory pain control. Cannabis and cannabinoids are sometimes used in the treatment of chronic pain as they have been shown to be useful in a subset of patients. Some of the adverse effects associated with cannabis use, such as cannabis use disorder (CUD) and cannabis-induced psychosis, have been associated with several genetic variants. Despite this, the paucity of the data or the contradictory results for reported variants limits our ability to use them as genetic markers to personalize cannabis treatment tailored to patients\u0026rsquo; genetic background. The aim of this genetic association study was to investigate the link between previously reported genes and cannabinoid response in terms of pain relief, CUD and risk of psychotic adverse events in patients with chronic pain.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e Phone or in person interviews were conducted to document participants\u0026rsquo; characteristics, cannabis use and effects, concurrent pharmacotherapy and comorbid conditions. Screening for CUD was performed using the Cannabis Use Disorders Identification Test \u0026ndash; Revised. Blood or saliva samples were collected for the genotyping of 18 variants in 11 genes (\u003cem\u003eBDNF\u003c/em\u003e, \u003cem\u003eCNR1\u003c/em\u003e, \u003cem\u003eCNR2\u003c/em\u003e, \u003cem\u003eCOMT\u003c/em\u003e, \u003cem\u003eCYP2C9\u003c/em\u003e, \u003cem\u003eFAAH\u003c/em\u003e, \u003cem\u003eGABRA2\u003c/em\u003e, \u003cem\u003eHES7\u003c/em\u003e, \u003cem\u003eKAT2B\u003c/em\u003e, \u003cem\u003eNRG1\u003c/em\u003e and \u003cem\u003eOPMR1\u003c/em\u003e).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e One hundred participants were recruited, with blood or saliva samples collected from 77 of them. Two single-nucleotide polymorphisms (SNP) in cannabinoid receptor 1 (\u003cem\u003eCNR1\u003c/em\u003e) could be linked with psychotic adverse events. Namely, T allele carriage of the \u003cem\u003eCNR1\u003c/em\u003e rs1049353 C\u0026thinsp;\u0026gt;\u0026thinsp;T variant increased the odds of having psychotic adverse events (OR\u0026thinsp;=\u0026thinsp;6.1, 95% CI 1.7\u0026ndash;27.9, p-value\u0026thinsp;=\u0026thinsp;0,009) and C allele carriage of the \u003cem\u003eCNR1\u003c/em\u003e rs2023239 T\u0026thinsp;\u0026gt;\u0026thinsp;C intronic variant also increased these odds (OR 3.5, 95% CI 1.5\u0026ndash;9.4, p-value\u0026thinsp;=\u0026thinsp;0,033). These findings were not significant after adjustment for multiple SNPs testing and none of the variants were associated with CUD or pain relief.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese results suggest alternative allele carriers of rs1049353 and rs2023239 could be at an increased risk of psychotic adverse events related to cannabis use.\u003c/p\u003e","manuscriptTitle":"Pharmacogenetic association study of cannabis use in chronic pain","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-27 18:57:54","doi":"10.21203/rs.3.rs-6957614/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-07T20:04:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-07T18:18:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"76771900961234430317647289286318464397","date":"2026-01-05T18:45:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-02T10:09:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"258215266809917974572171170428941343614","date":"2025-06-29T14:48:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-24T14:08:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-24T11:06:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-24T11:05:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cannabis Research","date":"2025-06-23T14:02:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cannabis-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcan","sideBox":"Learn more about [Journal of Cannabis Research](https://jcannabisresearch.biomedcentral.com/)","snPcode":"42238","submissionUrl":"https://submission.springernature.com/new-submission/42238/3","title":"Journal of Cannabis Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"360bb6da-37ed-435f-b800-80a1c0122156","owner":[],"postedDate":"June 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T16:10:42+00:00","versionOfRecord":{"articleIdentity":"rs-6957614","link":"https://doi.org/10.1186/s42238-026-00408-w","journal":{"identity":"journal-of-cannabis-research","isVorOnly":false,"title":"Journal of Cannabis Research"},"publishedOn":"2026-02-14 15:57:34","publishedOnDateReadable":"February 14th, 2026"},"versionCreatedAt":"2025-06-27 18:57:54","video":"","vorDoi":"10.1186/s42238-026-00408-w","vorDoiUrl":"https://doi.org/10.1186/s42238-026-00408-w","workflowStages":[]},"version":"v1","identity":"rs-6957614","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6957614","identity":"rs-6957614","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}