A flexible modeling approach for biomarker-based computation of absolute risk of Alzheimer's disease dementia.

INTRODUCTION: As Alzheimer's disease (AD) biomarkers rapidly develop, tools are needed that accurately and effectively communicate risk of AD dementia. METHODS: We analyzed longitudinal data from >10,000 cognitively unimpaired older adults. Five-year risk of AD dementia was modeled using survival analysis. RESULTS: A demographic model was developed and validated on independent data with area under the receiver operating characteristic curve (AUC) for 5-year prediction of AD dementia of 0.79. Clinical and cognitive variables (AUC = 0.79), and apolipoprotein E genotype (AUC = 0.76) were added to the demographic model. We then incorporated the risk computed from the demographic model with hazard ratios computed from independent data for amyloid positron emission tomography status and magnetic resonance imaging hippocampal volume (AUC = 0.84), and for plasma amyloid beta (Aß)42/Aß40 (AUC = 0.82). DISCUSSION: An adaptive tool was developed and validated to compute absolute risks of AD dementia. This approach allows for improved accuracy and communication of AD risk among cognitively unimpaired older adults.

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.

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.

Leveraging genome-wide data to investigate differences between opioid use vs. opioid dependence in 41,176 individuals from the Psychiatric Genomics Consortium.

To provide insights into the biology of opioid dependence (OD) and opioid use (i.e., exposure, OE), we completed a genome-wide analysis comparing 4503 OD cases, 4173 opioid-exposed controls, and 32,500 opioid-unexposed controls, including participants of European and African descent (EUR and AFR, respectively). Among the variants identified, rs9291211 was associated with OE (exposed vs. unexposed controls; EUR z = -5.39, p = 7.2 × 10-8). This variant regulates the transcriptomic profiles of SLC30A9 and BEND4 in multiple brain tissues and was previously associated with depression, alcohol consumption, and neuroticism. A phenome-wide scan of rs9291211 in the UK Biobank (N > 360,000) found association of this variant with propensity to use dietary supplements (p = 1.68 × 10-8). With respect to the same OE phenotype in the gene-based analysis, we identified SDCCAG8 (EUR + AFR z = 4.69, p = 10-6), which was previously associated with educational attainment, risk-taking behaviors, and schizophrenia. In addition, rs201123820 showed a genome-wide significant difference between OD cases and unexposed controls (AFR z = 5.55, p = 2.9 × 10-8) and a significant association with musculoskeletal disorders in the UK Biobank (p = 4.88 × 10-7). A polygenic risk score (PRS) based on a GWAS of risk-tolerance (n = 466,571) was positively associated with OD (OD vs. unexposed controls, p = 8.1 × 10-5; OD cases vs. exposed controls, p = 0.054) and OE (exposed vs. unexposed controls, p = 3.6 × 10-5). A PRS based on a GWAS of neuroticism (n = 390,278) was positively associated with OD (OD vs. unexposed controls, p = 3.2 × 10-5; OD vs. exposed controls, p = 0.002) but not with OE (p = 0.67). Our analyses highlight the difference between dependence and exposure and the importance of considering the definition of controls in studies of addiction.

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.

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.

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.

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.

The significant impact of education, poverty, and race on Internet-based research participant engagement.

PURPOSE: Internet-based technologies are increasingly being used for research studies. However, it is not known whether Internet-based approaches will effectively engage participants from diverse racial and socioeconomic backgrounds. METHODS: A total of 967 participants were recruited and offered genetic ancestry results. We evaluated viewing Internet-based genetic ancestry results among participants who expressed high interest in obtaining the results. RESULTS: Of the participants, 64% stated that they were very or extremely interested in their genetic ancestry results. Among interested participants, individuals with a high school diploma (n = 473) viewed their results 19% of the time relative to 4% of the 145 participants without a diploma (P < 0.0001). Similarly, 22% of participants with household income above the federal poverty level (n = 286) viewed their results relative to 10% of the 314 participants living below the federal poverty level (P < 0.0001). Among interested participants both with a high school degree and living above the poverty level, self-identified Caucasians were more likely to view results than self-identified African Americans (P < 0.0001), and females were more likely to view results than males (P = 0.0007). CONCLUSION: In an underserved population, engagement in Internet-based research was low despite high reported interest. This suggests that explicit strategies should be developed to increase diversity in Internet-based research.Genet Med 19 2, 240-243.

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.

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.

Distinct loci in the CHRNA5/CHRNA3/CHRNB4 gene cluster are associated with onset of regular smoking.

Neuronal nicotinic acetylcholine receptor (nAChR) genes (CHRNA5/CHRNA3/CHRNB4) have been reproducibly associated with nicotine dependence, smoking behaviors, and lung cancer risk. Of the few reports that have focused on early smoking behaviors, association results have been mixed. This meta-analysis examines early smoking phenotypes and SNPs in the gene cluster to determine: (1) whether the most robust association signal in this region (rs16969968) for other smoking behaviors is also associated with early behaviors, and/or (2) if additional statistically independent signals are important in early smoking. We focused on two phenotypes: age of tobacco initiation (AOI) and age of first regular tobacco use (AOS). This study included 56,034 subjects (41 groups) spanning nine countries and evaluated five SNPs including rs1948, rs16969968, rs578776, rs588765, and rs684513. Each dataset was analyzed using a centrally generated script. Meta-analyses were conducted from summary statistics. AOS yielded significant associations with SNPs rs578776 (beta = 0.02, P = 0.004), rs1948 (beta = 0.023, P = 0.018), and rs684513 (beta = 0.032, P = 0.017), indicating protective effects. There were no significant associations for the AOI phenotype. Importantly, rs16969968, the most replicated signal in this region for nicotine dependence, cigarettes per day, and cotinine levels, was not associated with AOI (P = 0.59) or AOS (P = 0.92). These results provide important insight into the complexity of smoking behavior phenotypes, and suggest that association signals in the CHRNA5/A3/B4 gene cluster affecting early smoking behaviors may be different from those affecting the mature nicotine dependence phenotype.

Hippocampal volumes in UK Biobank are associated with APOE only in older adults.

INTRODUCTION: The hippocampus atrophies with age and is implicated in neurodegenerative disorders including Alzheimer's disease (AD). We examined the interplay between age and APOE genotype on total hippocampal volume. METHODS: Utilizing neuroimaging data from 37,463 UK Biobank participants, we applied linear regression to quantify the association of age and APOE with hippocampal volume and identified the age when volumes of ε2/ε3, ε3/ε4, and ε4/ε4 carriers significantly deviated from ε3/ε3 using generalized additive modeling. RESULTS: Total hippocampal volume declined with age, with significant differences by APOE genotype emerging after age 60. ε3/ε4 and ε4/ε4 carriers displayed reduced volumes from ages 69 and 61, respectively, while ε2/ε3 showed delayed decline starting at age 76. DISCUSSION: The association of APOE and hippocampal volume is age-dependent, with differences in volumes of ε4/ε4 carriers detected as early as age 61. This work underscores the importance of APOE genotype in determining when to begin screening for AD.

Demographic differences in the cascade of care for unhealthy alcohol use: A cross-sectional analysis of data from the 2015-2019 National Survey on Drug Use and Health.

BACKGROUND: The screening, brief intervention, and referral to treatment (SBIRT) model is recommended by the U.S. Preventive Services Task Force to improve recognition of and intervention for unhealthy alcohol use. How SBIRT implementation differs by demographic characteristics is poorly understood. METHODS: We analyzed data from the 2015-2019 National Survey on Drug Use and Health from respondents ≥18 years old who used an outpatient clinic and had at least one alcoholic drink within the past year. Respondents were grouped into one of three mutually exclusive groups: "no binge drinking or alcohol use disorder (AUD)," "binge drinking without AUD," or "AUD." Outcome variables were likelihood of screening, brief intervention (BI), referral to treatment (RT), and AUD treatment. The demographic predictors on which outcomes were regressed included gender, age, race and ethnicity, sexual orientation, insurance status, and history of military involvement. Consistent with SBIRT guidelines, the entire sample was included in the screening model; screened persons with either binge drinking without AUD or with AUD were included in the BI model; screened persons with AUD were included in the RT model, and persons referred to treatment with AUD were included in the AUD treatment model. RESULTS: Analyses included 120,804 respondents. Women were more likely than men to be screened, but less likely to receive BI or RT. When referred to treatment, women were more likely than men to receive it. Persons aged ≥50 were least likely to be screened about alcohol, but most likely to receive BI, while persons aged 18-25 were least likely to receive BI or AUD treatment. Racial and ethnic minorities were less likely than White persons to be screened; Asians were less likely to receive RT, and Black persons were less likely to receive treatment than White persons. Persons identifying as gay, lesbian, or bisexual were equally as likely or more likely to receive SBIRT or AUD treatment as those identifying as heterosexual. Persons without insurance were less likely to be screened than those with insurance. Persons with a history of military involvement were more likely to be screened and receive BI and RT than persons who had not served in the military. CONCLUSIONS: Demographic disparities in SBIRT implementation exist. Addressing the sources of these disparities and minimizing attrition from care could improve outcomes for persons with unhealthy alcohol use.

Copy number variations in 6q14.1 and 5q13.2 are associated with alcohol dependence.

BACKGROUND: Excessive alcohol use is the third leading cause of preventable death and is highly correlated with alcohol dependence, a heritable phenotype. Many genetic factors for alcohol dependence have been found, but many remain unknown. In search of additional genetic factors, we examined the association between Diagnostic and StatisticalManual of Mental Disorders, Fourth Edition (DSM-IV) alcohol dependence and all common copy number variations (CNVs) with good reliability in the Study of Addiction: Genetics and Environment (SAGE). METHODS: All participants in SAGE were interviewed using the Semi-Structured Assessment for the Genetics of Alcoholism, as a part of 3 contributing studies. A total of 2,610 non-Hispanic European American samples were genotyped on the Illumina Human 1M array. We performed CNV calling by CNVPartition, PennCNV, and QuantiSNP, and only CNVs identified by all 3 software programs were examined. Association was conducted with the CNV (as a deletion/duplication) as well as with probes in the CNV region. Quantitative polymerase chain reaction (qPCR) was used to validate the CNVs in the laboratory. RESULTS: CNVs in 6q14.1 (p = 1.04 × 10(-6)) and 5q13.2 (p = 3.37 × 10(-4)) were significantly associated with alcohol dependence after adjusting multiple tests. On chromosome 5q13.2, there were multiple candidate genes previously associated with various neurological disorders. The region on chromosome 6q14.1 is a gene desert that has been associated with mental retardation and language delay. The CNV in 5q13.2 was validated, whereas only a component of the CNV on 6q14.1 was validated by qPCR. Thus, the CNV on 6q14.1 should be viewed with caution. CONCLUSIONS: This is the first study to show an association between DSM-IV alcohol dependence and CNVs. CNVs in regions previously associated with neurological disorders may be associated with alcohol dependence.

Copy number variation accuracy in genome-wide association studies.

BACKGROUND/AIM: Copy number variations (CNVs) are a major source of alterations among individuals and are a potential risk factor in many diseases. Numerous diseases have been linked to deletions and duplications of these chromosomal segments. Data from genome-wide association studies and other microarrays may be used to identify CNVs by several different computer programs, but the reliability of the results has been questioned. METHODS: To help researchers reduce the number of false-positive CNVs that need to be followed up with laboratory testing, we evaluated the relative performance of CNVPartition, PennCNV and QuantiSNP, and developed a statistical method for estimating sensitivity and positive predictive values of CNV calls and tested it on 96 duplicate samples in our dataset. RESULTS: We found that the positive predictive rate increases with the number of probes in the CNV and the size of the CNV, with the highest positive predicted rates in CNVs of at least 500 kb and at least 100 probes. Our analysis also indicates that identifying CNVs reported by multiple programs can greatly improve the reproducibility rate and the positive predicted rate. CONCLUSION: Our methods can be used by investigators to identify CNVs in genome-wide data with greater reliability.

Association Between Workplace Absenteeism and Alcohol Use Disorder From the National Survey on Drug Use and Health, 2015-2019.

Importance: Alcohol use disorder (AUD) is common and associated with increased morbidity. The degree to which AUD currently factors into workplace absenteeism needs further characterization in the US. Objective: To examine the association between AUD and workplace absenteeism in a nationally representative sample. Design, Setting, and Participants: This cross-sectional study used data from a nationally representative sample of noninstitutionalized US residents from the 2015-2019 National Survey on Drug Use and Health to examine the association of AUD with workplace absenteeism. Eligible respondents were aged 18 years and older who reported full-time employment. Data were analyzed from March to September 2021. Main Outcomes and Measures: Primary outcomes were markers of workplace absenteeism as defined by the number of days missed from work because of illness or injury and days skipped from work in the last 30 days. Descriptive statistics, prevalence ratios, and logistic regression analyses were performed to assess the association between AUD and absenteeism. Results: A total of 110 701 adults aged 18 years and older reported current full-time employment (58 948 [53.2%] men, 51 753 [46.8%] women; 12 776 [11.5%] Black, 18 096 [16.3%] Hispanic, and 69 506 [62.8%] White respondents). Weighted prevalence of AUD in this sample of working adults was 9.3% (95% CI, 9.0%-9.5%); 6.2% (95% CI, 6.0%-6.4%) of respondents met criteria for mild AUD, 1.9% (95% CI, 1.7%-2.0%) for moderate AUD, and 1.2% (95% CI, 1.1%-1.3%) for severe AUD. Mean days missed from work annually increased in a stepwise fashion with increasing AUD severity (no AUD, 13.0 days; 95% CI, 12.7-13.2 days; mild AUD, 17.7 days; 95% CI, 16.4-19.1 days; moderate AUD, 23.6 days; 95% CI, 21.5-25.7 days; severe AUD, 32.3 days; 95% CI, 27.5-37.0 days). People with AUD represented 9.3% of the full-time workforce and contributed to 14.1% of total reported workplace absences. Conclusions and Relevance: In this cross-sectional study, AUD was disproportionately associated with an increased prevalence of workplace absenteeism, with individuals with AUD contributing over 232 million missed workdays annually. These results provide economic incentive for increased investment in AUD prevention and treatment, both for employers and policy makers.