Studying the Utility of Using Genetics to Predict Smoking-Related Outcomes in a Population-Based Study and a Selected Cohort.

INTRODUCTION: The purpose of this study is to examine the predictive utility of polygenic risk scores (PRSs) for smoking behaviors. AIMS AND METHODS: Using summary statistics from the Sequencing Consortium of Alcohol and Nicotine use consortium, we generated PRSs of ever smoking, age of smoking initiation, cigarettes smoked per day, and smoking cessation for participants in the population-based Atherosclerosis Risk in Communities (ARIC) study (N = 8638), and the Collaborative Genetic Study of Nicotine Dependence (COGEND) (N = 1935). The outcomes were ever smoking, age of smoking initiation, heaviness of smoking, and smoking cessation. RESULTS: In the European ancestry cohorts, each PRS was significantly associated with the corresponding smoking behavior outcome. In the ARIC cohort, the PRS z-score for ever smoking predicted smoking (odds ratio [OR]: 1.37; 95% confidence interval [CI]: 1.31, 1.43); the PRS z-score for age of smoking initiation was associated with age of smoking initiation (OR: 0.87; 95% CI: 0.82, 0.92); the PRS z-score for cigarettes per day was associated with heavier smoking (OR: 1.17; 95% CI: 1.11, 1.25); and the PRS z-score for smoking cessation predicted successful cessation (OR: 1.24; 95% CI: 1.17, 1.32). In the African ancestry cohort, the PRSs did not predict smoking behaviors. CONCLUSIONS: Smoking-related PRSs were associated with smoking-related behaviors in European ancestry populations. This improvement in prediction is greatest in the lowest and highest genetic risk categories. The lack of prediction in African ancestry populations highlights the urgent need to increase diversity in research so that scientific advances can be applied to populations other than those of European ancestry. IMPLICATIONS: This study shows that including both genetic ancestry and PRSs in a single model increases the ability to predict smoking behaviors compared with the model including only demographic characteristics. This finding is observed for every smoking-related outcome. Even though adding genetics is more predictive, the demographics alone confer substantial and meaningful predictive power. However, with increasing work in PRSs, the predictive ability will continue to improve.

A cascade of care for alcohol use disorder: Using 2015-2019 National Survey on Drug Use and Health data to identify gaps in past 12-month care.

BACKGROUND: Although effective treatments exist, alcohol use disorder (AUD) is undertreated. We used a cascade of care framework to understand gaps in care for persons with AUD. METHODS: Using 2015-2019 National Survey on Drug Use and Health data, we evaluated the following steps in the cascade of care: (1) adult prevalence of AUD; (2) proportion of adults with AUD who utilized health care in the past 12 months; (3) proportion with AUD screened about their alcohol use; (4) proportion with AUD who received a brief intervention about their alcohol misuse; (5) proportion with AUD who received information about treatment for alcohol misuse; and (6) proportion with AUD who received treatment. Analyses were stratified by AUD severity. RESULTS: Of the 214,505 persons included in the sample, the weighted prevalence of AUD was 7.8% (95% CI 7.6-8.0%). Cascades of care showed the majority of individuals with AUD utilized health care in the past 12 months [81.4% (95% CI 80.7-82.1%)] and were screened about alcohol use [69.9% (95% CI 68.9-70.8%)]. However, only a minority of individuals received subsequent steps of care, including 11.6% (95% CI 11.0-12.2%) who reported receiving a brief intervention, 5.1% (95% CI 4.6-5.6%) who were referred to treatment, and 5.8% (95% CI 5.4-6.3%) who received treatment. Similar patterns were observed when cascades of care were stratified by AUD severity. CONCLUSIONS: Persons with AUD commonly utilize health care and are often screened about alcohol use, but few receive treatment. Healthcare settings-particularly primary care settings-represent a prime opportunity to implement AUD treatment to improve outcomes in this high-risk population.

Prolactin and Estrogen Levels in Postmenopausal Women Receiving Aripiprazole Augmentation Treatment for Depression.

BACKGROUND: Antipsychotic drugs are well established to alter serum prolactin levels, often resulting in adverse effects including amenorrhea, galactorrhea, osteoporosis, and loss of libido. There is growing preclinical evidence that prolactin-elevating drugs can instigate the progression of precancerous lesions to breast cancer and that genes activated by prolactin are associated with the development and proliferation of breast cancer. Current guides advise a cautious approach (weighing risks and benefits) to the administration of prolactin-elevating antipsychotic drugs in women with a previously detected breast cancer. Aripiprazole is known to be a prolactin-sparing antipsychotic; however, data regarding its effects on prolactin and estrogens in postmenopausal women are lacking. METHODS: We examined serum hormone levels in n = 66 women who participated in a randomized, double-blind, placebo-controlled, multicenter trial of aripiprazole (high and low doses) added to an antidepressant in adults older than 60 years. Aripiprazole or placebo tablets were administered for 12 weeks as an augmentation strategy in venlafaxine-treated women. The primary outcomes were the difference in prolactin and estrogen levels. RESULTS: There was no significant effect of aripiprazole treatment on prolactin or estrogen levels, including in models that divided groups into low and high doses: prolactin (P = 0.075), estrone (P = 0.67), and estradiol (P = 0.96). CONCLUSIONS: Aripiprazole addition to an antidepressant did not alter serum estrogens or prolactin. These findings may be relevant in the treatment of some postmenopausal women with depression.

Association Between Benzodiazepine Use With or Without Opioid Use and All-Cause Mortality in the United States, 1999-2015.

Importance: Although overall rates of opioid use have been plateauing, coprescriptions of benzodiazepines and opioids have increased greatly in recent years. It is unknown whether this combination is an independent risk factor for all-cause mortality as opposed to being more frequently used by persons with a baseline elevated risk of death. Objective: To evaluate whether benzodiazepine use, with or without opioid use, is associated with increased all-cause mortality relative to the use of low-risk antidepressants. Design, Setting, and Participants: This retrospective cohort study used a large, nationally representative US data set (the National Health and Nutrition Examination Surveys [NHANES]) from 1999 to 2015. Eight cycles of NHANES data were used, spanning 37 610 person-years of follow-up time among 5212 individuals. Statistical analysis was performed from August 24, 2019, through May 23, 2020. Exposures: The primary exposure variable was benzodiazepine and opioid coprescriptions. Individuals taking selective serotonin reuptake inhibitors (SSRIs) served as an active comparator reference group. Main Outcomes and Measures: All-cause mortality was obtained via linkage of NHANES to the National Death Index. Propensity scores were calculated from covariates associated with sociodemographic factors, comorbidities, and medication use for more than 1000 prescription types. Propensity score-weighted mortality hazards were calculated from Cox proportional hazards regression models. Results: Of 5212 participants aged 20 years or older (1993 men [38.2%]; mean [SD] age, 54.8 [16.9] years) followed up for a median of 6.7 years (range, 0.2-16.8 years), 101 deaths (33.0 per 1000 person-years) occurred among those receiving cotreatment, 236 deaths (26.5 per 1000 person-years) occurred among those receiving only benzodiazepines, and 227 deaths (20.2 per 1000 person-years) occurred among SSRI recipients taking neither opioids nor benzodiazepines. After propensity score weighting, a significant increase in all-cause mortality was associated with benzodiazepine and opioid cotreatment (hazard ratio, 2.04 [95% CI, 1.65-2.52]) and benzodiazepines without opioids (hazard ratio, 1.60 [95% CI, 1.33-1.92]). Subgroup analyses revealed an increased risk of mortality for individuals receiving cotreatment who were 65 years or younger but not for those older than 65 years; similar findings were observed for those receiving benzodiazepines without opioids. Conclusions and Relevance: This study found a significant increase in all-cause mortality associated with benzodiazepine use with or without opioid use in comparison with SSRI use. Benzodiazepine and opioid cotreatment, in particular, was associated with a 2-fold increase in all-cause mortality even after taking into account medical comorbidities and polypharmacy burden.

A large-scale genome-wide association study meta-analysis of cannabis use disorder.

BACKGROUND: Variation in liability to cannabis use disorder has a strong genetic component (estimated twin and family heritability about 50-70%) and is associated with negative outcomes, including increased risk of psychopathology. The aim of the study was to conduct a large genome-wide association study (GWAS) to identify novel genetic variants associated with cannabis use disorder. METHODS: To conduct this GWAS meta-analysis of cannabis use disorder and identify associations with genetic loci, we used samples from the Psychiatric Genomics Consortium Substance Use Disorders working group, iPSYCH, and deCODE (20 916 case samples, 363 116 control samples in total), contrasting cannabis use disorder cases with controls. To examine the genetic overlap between cannabis use disorder and 22 traits of interest (chosen because of previously published phenotypic correlations [eg, psychiatric disorders] or hypothesised associations [eg, chronotype] with cannabis use disorder), we used linkage disequilibrium score regression to calculate genetic correlations. FINDINGS: We identified two genome-wide significant loci: a novel chromosome 7 locus (FOXP2, lead single-nucleotide polymorphism [SNP] rs7783012; odds ratio [OR] 1·11, 95% CI 1·07-1·15, p=1·84 × 10-9) and the previously identified chromosome 8 locus (near CHRNA2 and EPHX2, lead SNP rs4732724; OR 0·89, 95% CI 0·86-0·93, p=6·46 × 10-9). Cannabis use disorder and cannabis use were genetically correlated (rg 0·50, p=1·50 × 10-21), but they showed significantly different genetic correlations with 12 of the 22 traits we tested, suggesting at least partially different genetic underpinnings of cannabis use and cannabis use disorder. Cannabis use disorder was positively genetically correlated with other psychopathology, including ADHD, major depression, and schizophrenia. INTERPRETATION: These findings support the theory that cannabis use disorder has shared genetic liability with other psychopathology, and there is a distinction between genetic liability to cannabis use and cannabis use disorder. FUNDING: National Institute of Mental Health; National Institute on Alcohol Abuse and Alcoholism; National Institute on Drug Abuse; Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing; The European Commission, Horizon 2020; National Institute of Child Health and Human Development; Health Research Council of New Zealand; National Institute on Aging; Wellcome Trust Case Control Consortium; UK Research and Innovation Medical Research Council (UKRI MRC); The Brain & Behavior Research Foundation; National Institute on Deafness and Other Communication Disorders; Substance Abuse and Mental Health Services Administration (SAMHSA); National Institute of Biomedical Imaging and Bioengineering; National Health and Medical Research Council (NHMRC) Australia; Tobacco-Related Disease Research Program of the University of California; Families for Borderline Personality Disorder Research (Beth and Rob Elliott) 2018 NARSAD Young Investigator Grant; The National Child Health Research Foundation (Cure Kids); The Canterbury Medical Research Foundation; The New Zealand Lottery Grants Board; The University of Otago; The Carney Centre for Pharmacogenomics; The James Hume Bequest Fund; National Institutes of Health: Genes, Environment and Health Initiative; National Institutes of Health; National Cancer Institute; The William T Grant Foundation; Australian Research Council; The Virginia Tobacco Settlement Foundation; The VISN 1 and VISN 4 Mental Illness Research, Education, and Clinical Centers of the US Department of Veterans Affairs; The 5th Framework Programme (FP-5) GenomEUtwin Project; The Lundbeck Foundation; NIH-funded Shared Instrumentation Grant S10RR025141; Clinical Translational Sciences Award grants; National Institute of Neurological Disorders and Stroke; National Heart, Lung, and Blood Institute; National Institute of General Medical Sciences.

Dissecting the genetic overlap of smoking behaviors, lung cancer, and chronic obstructive pulmonary disease: A focus on nicotinic receptors and nicotine metabolizing enzyme.

Smoking is a major contributor to lung cancer and chronic obstructive pulmonary disease (COPD). Two of the strongest genetic associations of smoking-related phenotypes are the chromosomal regions 15q25.1, encompassing the nicotinic acetylcholine receptor subunit genes CHRNA5-CHRNA3-CHRNB4, and 19q13.2, encompassing the nicotine metabolizing gene CYP2A6. In this study, we examined genetic relations between cigarettes smoked per day, smoking cessation, lung cancer, and COPD. Data consisted of genome-wide association study summary results. Genetic correlations were estimated using linkage disequilibrium score regression software. For each pair of outcomes, z-score-z-score (ZZ) plots were generated. Overall, heavier smoking and decreased smoking cessation showed positive genetic associations with increased lung cancer and COPD risk. The chromosomal region 19q13.2, however, showed a different correlational pattern. For example, the effect allele-C of the sentinel SNP (rs56113850) within CYP2A6 was associated with an increased risk of heavier smoking (z-score = 19.2; p = 1.10 × 10-81 ), lung cancer (z-score = 8.91; p = 5.02 × 10-19 ), and COPD (z-score = 4.04; p = 5.40 × 10-5 ). Surprisingly, this allele-C (rs56113850) was associated with increased smoking cessation (z-score = -8.17; p = 2.52 × 10-26 ). This inverse relationship highlights the need for additional investigation to determine how CYP2A6 variation could increase smoking cessation while also increasing the risk of lung cancer and COPD likely through increased cigarettes smoked per day.

Identifying blood pressure loci whose effects are modulated by multiple lifestyle exposures.

Although multiple lifestyle exposures simultaneously impact blood pressure (BP) and cardiovascular health, most analysis so far has considered each single lifestyle exposure (e.g., smoking) at a time. Here, we exploit gene-multiple lifestyle exposure interactions to find novel BP loci. For each of 6,254 Framingham Heart Study participants, we computed lifestyle risk score (LRS) value by aggregating the risk of four lifestyle exposures (smoking, alcohol, education, and physical activity) on BP. Using the LRS, we performed genome-wide gene-environment interaction analysis in systolic and diastolic BP using the joint 2 degree of freedom (DF) and 1 DF interaction tests. We identified one genome-wide significant (p < 5 × 10-8 ) and 11 suggestive (p < 1 × 10-6 ) loci. Gene-environment analysis using single lifestyle exposures identified only one of the 12 loci. Nine of the 12 BP loci detected were novel. Loci detected by the LRS were located within or nearby genes with biologically plausible roles in the pathophysiology of hypertension, including KALRN, VIPR2, SNX1, and DAPK2. Our results suggest that simultaneous consideration of multiple lifestyle exposures in gene-environment interaction analysis can identify additional loci missed by single lifestyle approaches.

E-cigarette Usage Is Associated With Increased Past-12-Month Quit Attempts and Successful Smoking Cessation in Two US Population-Based Surveys.

INTRODUCTION: We examined past-12-month quit attempts and smoking cessation from 2006 to 2016 while accounting for demographic shifts in the US population. In addition, we sought to understand whether the current use of electronic cigarettes was associated with a change in past-12-month quit attempts and successful smoking cessation at the population level. METHODS: We analyzed data from 25- to 44-year-olds from the National Health Interview Survey (NHIS) from 2006 to 2016 (N = 26,354) and the Tobacco Use Supplement to the Current Population Survey (TUS-CPS) in 2006-2007, 2010-2011, and 2014-2015 (N = 33,627). Data on e-cigarette use were available in the 2014-2016 NHIS and 2014-2015 TUS-CPS surveys. RESULTS: Past-12-month quit attempts and smoking cessation increased in recent years compared with 2006. Current e-cigarette use was associated with higher quit attempts (adjusted odds ratio [aOR] = 2.29, 95% confidence interval [CI] = 1.87 to 2.81, p < .001) and greater smoking cessation (aOR = 1.64, 95% CI = 1.21 to 2.21, p = .001) in the NHIS. Multivariable logistic regression of the TUS-CPS data showed that current e-cigarette use was similarly significantly associated with increased past-12-month quit attempts and smoking cessation. Significant interactions were found for smoking frequency (everyday and some-day smoking) and current e-cigarette use for both outcomes (p < .0001) with the strongest positive effects seen in everyday smokers. CONCLUSIONS: Compared with 2006, past-12-month quit attempts and smoking cessation increased among adults aged 25-44 in recent years. Current e-cigarette use was associated with increased past-12-month quit attempts and successful smoking cessation among established smokers. These findings are relevant to future tobacco policy decisions. IMPLICATIONS: E-cigarettes were introduced into the US market over the past decade. During this period, past-12-month quit attempts and smoking cessation have increased among US adults aged 25-44. These trends are inconsistent with the hypothesis that e-cigarette use is delaying quit attempts and leading to decreased smoking cessation. In contrast, current e-cigarette use was associated with significantly higher past-12-month quit attempts and past-12-month cessation. These findings suggest that e-cigarette use contributes to a reduction in combustible cigarette use among established smokers.

Use of polygenic risk scores of nicotine metabolism in predicting smoking behaviors.

AIM: This study tests whether polygenic risk scores (PRSs) for nicotine metabolism predict smoking behaviors in independent data. MATERIALS & METHODS: Linear regression, logistic regression and survival analyses were used to analyze nicotine metabolism PRSs and nicotine metabolism, smoking quantity and smoking cessation. RESULTS: Nicotine metabolism PRSs based on two genome wide association studies (GWAS) meta-analyses significantly predicted nicotine metabolism biomarkers (R2 range: 9.2-16%; minimum p = 7.6 × 10-8). The GWAS top hit variant rs56113850 significantly predicted nicotine metabolism biomarkers (R2 range: 14-17%; minimum p = 4.4 × 10-8). There was insufficient evidence for these PRSs predicting smoking quantity and smoking cessation. CONCLUSION: Results suggest that nicotine metabolism PRSs based on GWAS meta-analyses predict an individual's nicotine metabolism, so does use of the top hit variant. We anticipate that PRSs will enter clinical medicine, but additional research is needed to develop a more comprehensive genetic score to predict smoking behaviors.

Daily Drinking Is Associated with Increased Mortality.

BACKGROUND: There is evidence that low-level alcohol use, drinking 1 to 2 drinks on occasion, is protective for cardiovascular disease, but increases the risk of cancer. Synthesizing the overall impact of low-level alcohol use on health is therefore complex. The objective of this paper was to examine the association between frequency of low-level drinking and mortality. METHODS: Two data sets with self-reported alcohol use and mortality follow-up were analyzed: 340,668 individuals from the National Health Interview Survey (NHIS) and 93,653 individuals from the Veterans Health Administration (VA) outpatient medical records. Survival analyses were conducted to evaluate the association between low-level drinking frequency and mortality. RESULTS: The minimum risk drinking frequency among those who drink 1 to 2 drinks per occasion was found to be 3.2 times weekly in the NHIS data, based on a continuous measure of drinking frequency, and 2 to 3 times weekly in the VA data. Relative to these individuals with minimum risk, individuals who drink 7 times weekly had an adjusted hazard ratio (HR) of all-cause mortality of 1.23 (p < 0.0001) in the NHIS data, and individuals who drink 4 to 7 times weekly in the VA data also had an adjusted HR of 1.23 (p = 0.01). Secondary analyses in the NHIS data showed that the minimum risk was drinking 4 times weekly for cardiovascular mortality, and drinking monthly or less for cancer mortality. The associations were consistent in stratified analyses of men, women, and never smokers. CONCLUSIONS: The minimum risk of low-level drinking frequency for all-cause mortality appears to be approximately 3 occasions weekly. The robustness of this finding is highlighted in 2 distinctly different data sets: a large epidemiological data set and a data set of veterans sampled from an outpatient clinic. Daily drinking, even at low levels, is detrimental to one's health.

From genes to treatments: a systematic review of the pharmacogenetics in smoking cessation.

Smoking cessation treatment outcomes may be heavily influenced by genetic variations among smokers. Therefore, identifying specific variants that affect response to different pharmacotherapies is of major interest to the field. In the current study, we systematically review all studies published in or after the year 1990 which examined one or more gene-drug interactions for smoking cessation treatment. Out of 644 citations, 46 articles met the inclusion criteria for the systematic review. We summarize evidence on several genetic polymorphisms (CHRNA5-A3-B4, CYP2A6, DBH, CHRNA4, COMT, DRD2, DRD4 and CYP2B6) and their potential moderating pharamacotherarpy effects on patient cessation efficacy rates. These findings are promising and call for further research to demonstrate the effectiveness of genetic testing in personalizing treatment decision-making and improving outcome.

Toward the implementation of genomic applications for smoking cessation and smoking-related diseases.

The incorporation of genomic information into routine care settings is a burgeoning area for investigation in behavioral medicine. The past decade has witnessed rapid advancements in knowledge of genetic biomarkers associated with smoking behaviors and tobacco-related morbidity and mortality, providing the basis for promising genomic applications in clinical and community settings. We assessed the current state of readiness for implementing genomic applications involving variation in the α5 nicotinic cholinergic receptor subunit gene CHRNA5 and smoking outcomes (behaviors and related diseases) using a process that could be translatable to a wide range of genomic applications in behavioral medicine. We reviewed the scientific literature involving CHRNA5 genetic variation and smoking cessation, and then summarized and synthesized a chain of evidence according to analytic validity, clinical validity, clinical utility, and ethical, legal, and social implications (ACCE), a well-established set of criteria used to evaluate genomic applications. Our review identified at least three specific genomic applications for which implementation may be considered, including the use of CHRNA5 genetic test results for informing disease risk, optimizing smoking cessation treatment, and motivating smoking behavior change. For these genomic applications, we rated analytic validity as convincing, clinical validity as adequate, and clinical utility and ethical, legal, and social implications as inadequate. For clinical genomic applications involving CHRNA5 variation and smoking outcomes, research efforts now need to focus on establishing clinical utility. This approach is compatible with pre-implementation research, which is also needed to accelerate translation, improve innovation design, and understand and refine system processes involved in implementation. This study informs the readiness to incorporate smoking-related genomic applications in real-world settings and facilitates cross-disciplinary collaboration to accelerate the integration of evidence-based genomics in behavioral medicine.

Genetic correlation between smoking behaviors and schizophrenia.

Nicotine dependence is highly comorbid with schizophrenia, and the etiology of the comorbidity is unknown. To determine whether there is a genetic correlation of smoking behavior with schizophrenia, genome-wide association study (GWAS) meta-analysis results from five smoking phenotypes (ever/never smoker (N=74,035), age of onset of smoking (N=28,647), cigarettes smoked per day (CPD, N=38,860), nicotine dependence (N=10,666), and current/former smoker (N=40,562)) were compared to GWAS meta-analysis results from schizophrenia (N=79,845) using linkage disequilibrium (LD) score regression. First, the SNP heritability (h2g) of each of the smoking phenotypes was computed using LD score regression (ever/never smoker h2g=0.08, age of onset of smoking h2g=0.06, CPD h2g=0.06, nicotine dependence h2g=0.15, current/former smoker h2g=0.07, p<0.001 for all phenotypes). The SNP heritability for nicotine dependence was statistically higher than the SNP heritability for the other smoking phenotypes (p<0.0005 for all two-way comparisons). Next, a statistically significant (p<0.05) genetic correlation was observed between schizophrenia and three of the five smoking phenotypes (nicotine dependence rg=0.14, CPD rg=0.12, and ever/never smoking rg=0.10). These results suggest that there is a component of common genetic variation that is shared between smoking behaviors and schizophrenia.

Association Between Substance Use Disorder and Polygenic Liability to Schizophrenia.

BACKGROUND: There are high levels of comorbidity between schizophrenia and substance use disorder, but little is known about the genetic etiology of this comorbidity. METHODS: We tested the hypothesis that shared genetic liability contributes to the high rates of comorbidity between schizophrenia and substance use disorder. To do this, polygenic risk scores for schizophrenia derived from a large meta-analysis by the Psychiatric Genomics Consortium were computed in three substance use disorder datasets: the Collaborative Genetic Study of Nicotine Dependence (ascertained for tobacco use disorder; n = 918 cases; 988 control subjects), the Collaborative Study on the Genetics of Alcoholism (ascertained for alcohol use disorder; n = 643 cases; 384 control subjects), and the Family Study of Cocaine Dependence (ascertained for cocaine use disorder; n = 210 cases; 317 control subjects). Phenotypes were harmonized across the three datasets and standardized analyses were performed. Genome-wide genotypes were imputed to the 1000 Genomes reference panel. RESULTS: In each individual dataset and in the mega-analysis, strong associations were observed between any substance use disorder diagnosis and the polygenic risk score for schizophrenia (mega-analysis pseudo-R2 range 0.8-3.7%; minimum p = 4 × 10-23). CONCLUSIONS: These results suggest that comorbidity between schizophrenia and substance use disorder is partially attributable to shared polygenic liability. This shared liability is most consistent with a general risk for substance use disorder rather than specific risks for individual substance use disorders and adds to increasing evidence of a blurred boundary between schizophrenia and substance use disorder.

Association of the OPRM1 Variant rs1799971 (A118G) with Non-Specific Liability to Substance Dependence in a Collaborative de novo Meta-Analysis of European-Ancestry Cohorts.

The mu1 opioid receptor gene, OPRM1, has long been a high-priority candidate for human genetic studies of addiction. Because of its potential functional significance, the non-synonymous variant rs1799971 (A118G, Asn40Asp) in OPRM1 has been extensively studied, yet its role in addiction has remained unclear, with conflicting association findings. To resolve the question of what effect, if any, rs1799971 has on substance dependence risk, we conducted collaborative meta-analyses of 25 datasets with over 28,000 European-ancestry subjects. We investigated non-specific risk for "general" substance dependence, comparing cases dependent on any substance to controls who were non-dependent on all assessed substances. We also examined five specific substance dependence diagnoses: DSM-IV alcohol, opioid, cannabis, and cocaine dependence, and nicotine dependence defined by the proxy of heavy/light smoking (cigarettes-per-day >20 vs. ≤ 10). The G allele showed a modest protective effect on general substance dependence (OR = 0.90, 95% C.I. [0.83-0.97], p value = 0.0095, N = 16,908). We observed similar effects for each individual substance, although these were not statistically significant, likely because of reduced sample sizes. We conclude that rs1799971 contributes to mechanisms of addiction liability that are shared across different addictive substances. This project highlights the benefits of examining addictive behaviors collectively and the power of collaborative data sharing and meta-analyses.

Nicotine dependence and psychosis in Bipolar disorder and Schizoaffective disorder, Bipolar type.

Patients with Bipolar disorder smoke more than the general population. Smoking negatively impacts mortality and clinical course in Bipolar disorder patients. Prior studies have shown contradictory results regarding the impact of psychosis on smoking behavior in Bipolar disorder. We analyzed a large sample of Bipolar disorder and Schizoaffective disorder, Bipolar Type patients and predicted those with a history of psychosis would be more likely to be nicotine dependent. Data from subjects and controls were collected from the Genomic Psychiatry Cohort (GPC). Subjects were diagnosed with Bipolar disorder without psychosis (N = 610), Bipolar disorder with psychosis (N = 1544). Participants were classified with or without nicotine dependence. Diagnostic groups were compared to controls (N = 10065) using logistic regression. Among smokers (N = 6157), those with Bipolar disorder had an increased risk of nicotine dependence (OR = 2.5; P < 0.0001). Patients with Bipolar disorder with psychosis were more likely to be dependent than Bipolar disorder patients without psychosis (OR = 1.3; P = 0.03). Schizoaffective disorder, Bipolar Type patients had more risk of nicotine dependence when compared to Bipolar disorder patients with or without psychosis (OR = 1.2; P = 0.02). Bipolar disorder patients experiencing more severity of psychosis have more risk of nicotine dependence. © 2015 Wiley Periodicals, Inc.

When Does Choice of Accuracy Measure Alter Imputation Accuracy Assessments?

Imputation, the process of inferring genotypes for untyped variants, is used to identify and refine genetic association findings. Inaccuracies in imputed data can distort the observed association between variants and a disease. Many statistics are used to assess accuracy; some compare imputed to genotyped data and others are calculated without reference to true genotypes. Prior work has shown that the Imputation Quality Score (IQS), which is based on Cohen's kappa statistic and compares imputed genotype probabilities to true genotypes, appropriately adjusts for chance agreement; however, it is not commonly used. To identify differences in accuracy assessment, we compared IQS with concordance rate, squared correlation, and accuracy measures built into imputation programs. Genotypes from the 1000 Genomes reference populations (AFR N = 246 and EUR N = 379) were masked to match the typed single nucleotide polymorphism (SNP) coverage of several SNP arrays and were imputed with BEAGLE 3.3.2 and IMPUTE2 in regions associated with smoking behaviors. Additional masking and imputation was conducted for sequenced subjects from the Collaborative Genetic Study of Nicotine Dependence and the Genetic Study of Nicotine Dependence in African Americans (N = 1,481 African Americans and N = 1,480 European Americans). Our results offer further evidence that concordance rate inflates accuracy estimates, particularly for rare and low frequency variants. For common variants, squared correlation, BEAGLE R2, IMPUTE2 INFO, and IQS produce similar assessments of imputation accuracy. However, for rare and low frequency variants, compared to IQS, the other statistics tend to be more liberal in their assessment of accuracy. IQS is important to consider when evaluating imputation accuracy, particularly for rare and low frequency variants.

Return of individual genetic results in a high-risk sample: enthusiasm and positive behavioral change.

PURPOSE: The goal of this study was to examine participant responses to disclosure of genetic results in a minority population at high risk for depression and anxiety. METHODS: Eighty-two subjects in a genetic study of nicotine dependence were offered personalized genetic results. All were nicotine-dependent and 64% self-identified as African American. Pathway Genomics was used to evaluate genetic risks for five complex diseases. Participants returned 4-8 weeks after enrollment for in-person genetic counseling interviews and evaluation of baseline measures. A telephone follow-up was performed 4-8 weeks later to assess responses to results. RESULTS: Fifty of the 82 subjects (61%) were interested in receiving genetic results. These participants had multiple risk factors, including high baseline measures of depression (66%) and anxiety (32%), as well as low rates of employment (46%), adequate health literacy (46%), and health insurance (45%). Pathway Genomics reported "increased risk" for at least one disease in 77% of subjects. Ninety-five percent of participants reported that they appreciated the genetic results, and receiving these results was not associated with changes in symptoms of depression or anxiety. Furthermore, after return of genetic results, smoking cessation attempts increased (P = 0.003). CONCLUSION: Even in an underserved population at high risk for adverse psychological reactions, subjects responded positively to personalized genetic results.

Beyond cigarettes per day. A genome-wide association study of the biomarker carbon monoxide.

RATIONALE: The CHRNA5-CHRNA3-CHRNB4 locus is associated with self-reported smoking behavior and also harbors the strongest genetic associations with chronic obstructive pulmonary disease (COPD) and lung cancer. Because the associations with lung disease remain after adjustment for self-reported smoking behaviors, it has been asserted that CHRNA5-CHRNA3-CHRNB4 variants increase COPD and lung cancer susceptibility independently of their effects on smoking. OBJECTIVES: To compare the genetic associations of exhaled carbon monoxide (CO), a biomarker of current cigarette exposure, with self-reported smoking behaviors. METHODS: A total of 1,521 European American and 247 African American current smokers recruited into smoking cessation studies were assessed for CO at intake before smoking cessation. DNA samples were genotyped using the Illumina Omni2.5 microarray. Genetic associations with CO and smoking behaviors (cigarettes smoked per day, Fagerstrom test for nicotine dependence) were studied. MEASUREMENTS AND MAIN RESULTS: Variants in the CHRNA5-CHRNA3-CHRNB4 locus, including rs16969968, a nonsynonymous variant in CHRNA5, are genomewide association study-significantly associated with CO (ß = 2.66; 95% confidence interval [CI], 1.74-3.58; P = 1.65 × 10(-8)), and this association remains strong after adjusting for smoking behavior (ß = 2.18; 95% CI, 1.32-3.04; P = 7.47 × 10(-7)). The correlation between CO and cigarettes per day is statistically significantly lower (z = 3.43; P = 6.07 × 10(-4)) in African Americans (r = 0.14; 95% CI, 0.02-0.26; P = 0.003) than in European-Americans (r = 0.36; 95% CI, 0.31-0.40; P = 0.0001). CONCLUSIONS: Exhaled CO, a biomarker that is simple to measure, captures aspects of cigarette smoke exposure in current smokers beyond the number of cigarettes smoked per day. Behavioral measures of smoking are therefore insufficient indices of cigarette smoke exposure, suggesting that genetic associations with COPD or lung cancer that persist after adjusting for self-reported smoking behavior may still reflect genetic effects on smoking exposure.