Browsing by Author "Anokhin, Andrey P."
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Item Clinical, environmental, and genetic risk factors for substance use disorders: characterizing combined effects across multiple cohorts(Springer, 2022-10-04) Barr, Peter B.; Driver, Morgan N.; Kuo, Sally I-Chun; Stephenson, Mallory; Aliev, Fazil; Linnér, Richard Karlsson; Marks, Jesse; Anokhin, Andrey P.; Bucholz, Kathleen; Chan, Grace; Edenberg, Howard J.; Edwards, Alexis C.; Francis, Meredith W.; Hancock, Dana B.; Harden, K. Paige; Kamarajan, Chella; Kaprio, Jaakko; Kinreich, Sivan; Kramer, John R.; Kuperman, Samuel; Latvala, Antti; Meyers, Jacquelyn L.; Palmer, Abraham A.; Plawecki, Martin H.; Porjesz, Bernice; Rose, Richard J.; Schuckit, Marc A.; Salvatore, Jessica E.; Dick , Danielle M.; Medical and Molecular Genetics, School of MedicineSubstance use disorders (SUDs) incur serious social and personal costs. The risk for SUDs is complex, with risk factors ranging from social conditions to individual genetic variation. We examined whether models that include a clinical/environmental risk index (CERI) and polygenic scores (PGS) are able to identify individuals at increased risk of SUD in young adulthood across four longitudinal cohorts for a combined sample of N = 15,134. Our analyses included participants of European (NEUR = 12,659) and African (NAFR = 2475) ancestries. SUD outcomes included: (1) alcohol dependence, (2) nicotine dependence; (3) drug dependence, and (4) any substance dependence. In the models containing the PGS and CERI, the CERI was associated with all three outcomes (ORs = 01.37-1.67). PGS for problematic alcohol use, externalizing, and smoking quantity were associated with alcohol dependence, drug dependence, and nicotine dependence, respectively (OR = 1.11-1.33). PGS for problematic alcohol use and externalizing were also associated with any substance dependence (ORs = 1.09-1.18). The full model explained 6-13% of the variance in SUDs. Those in the top 10% of CERI and PGS had relative risk ratios of 3.86-8.04 for each SUD relative to the bottom 90%. Overall, the combined measures of clinical, environmental, and genetic risk demonstrated modest ability to distinguish between affected and unaffected individuals in young adulthood. PGS were significant but added little in addition to the clinical/environmental risk index. Results from our analysis demonstrate there is still considerable work to be done before tools such as these are ready for clinical applications.Item Exploring the relationship between polygenic risk for cannabis use, peer cannabis use, and the longitudinal course of cannabis involvement(Wiley, 2019-04) Johnson, Emma C.; Tillman, Rebecca; Aliev, Fazil; Meyers, Jacquelyn L.; Salvatore, Jessica E.; Anokhin, Andrey P.; Dick, Danielle M.; Edenberg, Howard J.; Kramer, John; Kuperman, Samuel; McCutcheon, Vivia V.; Nurnberger, John I., Jr.; Porjesz, Bernice; Schuckit, Marc; Tischfield, Jay; Bucholz, Kathleen K.; Agrawal, Arpana; Biochemistry and Molecular Biology, School of MedicineBackground and aims: Few studies have explored how polygenic propensity to cannabis use unfolds across development, and no studies have yet examined this question in the context of environmental contributions such as peer cannabis use. Outlining the factors that contribute to progression from cannabis initiation to problem use over time may ultimately provide insights into mechanisms for targeted interventions. We sought to examine the relationships between polygenic liability for cannabis use, cannabis use trajectories across ages 12–30, and perceived peer cannabis use at ages 12–17. Design: Mixed effect logistic and linear regressions were used to examine associations between polygenic risk scores, cannabis use trajectory membership, and perceived peer cannabis use. Setting: USA Participants: From the Collaborative Study on the Genetics of Alcoholism (COGA) study, a cohort of 1,167 individuals aged 12–26 years at their baseline (i.e., first) interview. Measurements: Key measurements included lifetime cannabis use (yes/no), frequency of past 12-month cannabis use, maximum lifetime frequency of cannabis use, cannabis use disorder (using DSM-5 criteria), and perceived peer cannabis use. Polygenic risk scores (PRS) were created using summary statistics from a large (N = 162,082) genome-wide association study (GWAS) of cannabis use. Three trajectories reflecting no/low (n=844), moderate (n=137) and high (n=186) use were identified. PRS were significantly associated with trajectory membership (p=0.002 – 0.006, maximum conditional R2 = 0.014, ORs = 1.40 – 1.49). Individuals who reported that most/all of their best friends used cannabis had significantly higher PRS than those who reported that none of their friends were users (OR = 1.35, 95% C.I. = [1.04, 1.75], p = 0.023). Perceived peer use itself explained up to 11.3% of the variance in trajectory class membership (OR: 1.50 – 4.65). When peer cannabis use and the cannabis use PRS were entered into the model simultaneously, both the PRS and peer use continued to be significantly associated with class membership (p < 0.01). Conclusions: Genetic propensity to cannabis use derived from heterogeneous samples appears to correlate with longitudinal increases in cannabis use frequency in young adults.Item Genome-wide association studies of alcohol dependence, DSM-IV criterion count and individual criteria(Wiley, 2019-06-04) Lai, Dongbing; Wetherill, Leah; Bertelsen, Sarah; Carey, Caitlin E.; Kamarajan, Chella; Kapoor, Manav; Meyers, Jacquelyn L.; Anokhin, Andrey P.; Bennett, David A.; Bucholz, Kathleen K.; Chang, Katharine K.; Jager, Philip L. De; Dick, Danielle M.; Hesselbrock, Victor; Kramer, John; Kuperman, Samuel; Nurnberger, John I.; Raj, Towfique; Schuckit, Marc; Scott, Denise M.; Taylor, Robert E.; Tischfield, Jay; Hariri, Ahmad R.; Edenberg, Howard J.; Agrawal, Arpana; Bogdan, Ryan; Porjesz, Bernice; Goate, Alison M.; Foroud, Tatiana; Medical and Molecular Genetics, School of MedicineGenome-wide association studies (GWAS) of alcohol dependence (AD) have reliably identified variation within alcohol metabolizing genes (e.g., ADH1B) but have inconsistently located other signals, which may be partially attributable to symptom heterogeneity underlying the disorder. We conducted GWASs of DSM-IV AD (primary analysis), DSM-IV AD criterion count (secondary analysis), and individual dependence criteria (tertiary analysis) among 7,418 (1,121 families) European American (EA) individuals from the Collaborative Study on the Genetics of Alcoholism (COGA). Trans-ancestral meta-analyses combined these results with data from 3,175 (585 families) African American (AA) individuals from COGA. In the EA GWAS, three loci were genome-wide significant: rs1229984 in ADH1B for AD criterion count (p=4.16E-11) and Desire to cut drinking (p=1.21E-11); rs188227250 (chromosome 8, Drinking more than intended, p=6.72E-09); rs1912461 (chromosome 15, Time spent drinking, p=1.77E-08). In the trans-ancestral meta-analysis, rs1229984 was associated with multiple phenotypes and two additional loci were genome-wide significant: rs61826952 (chromosome 1, DSM-IV AD, p=8.42E-11); rs7597960 (chromosome 2, Time spent drinking, p=1.22E-08). Associations with rs1229984 and rs18822750 were replicated in independent datasets. Polygenic risk scores derived from the EA GWAS of AD predicted AD in two EA datasets (p<0.01; 0.61-1.82% of variance). Identified novel variants (i.e., rs1912461, rs61826952) were associated with differential central evoked theta power (loss minus gain; p=0.0037) and reward-related ventral striatum reactivity (p=0.008), respectively. This study suggests that studying individual criteria may unveil new insights into the genetic etiology of AD liability.Item High Polygenic Risk Scores Are Associated With Early Age of Onset of Alcohol Use Disorder in Adolescents and Young Adults at Risk(Elsevier, 2022-10) Nurnberger, John I., Jr.; Wang, Yumin; Zang, Yong; Lai, Dongbing; Wetherill, Leah; Edenberg, Howard J.; Aliev, Fazil; Plawecki, Martin H.; Chorlian, David; Chan, Grace; Bucholz, Kathleen; Bauer, Lance; Kamarajan, Chella; Salvatore, Jessica E.; Kapoor, Manav; Hesselbrock, Victor; Dick, Danielle; Bierut, Laura; McCutcheon, Vivia; Meyers, Jacquelyn L.; Porjesz, Bernice; Kramer, John; Kuperman, Samuel; Kinreich, Sivan; Anokhin, Andrey P.; Collaborative Study on the Genetics of Alcoholism; Psychiatry, School of MedicineBackground Genome-wide association studies have been conducted in alcohol use disorder (AUD), and they permit the use of polygenic risk scores (PRSs), in combination with clinical variables, to predict the onset of AUD in vulnerable populations. Methods A total of 2794 adolescent/young adult subjects from the Collaborative Study on the Genetics of Alcoholism were followed, with clinical assessments every 2 years. Subjects were genotyped using a genome-wide chip. Separate PRS analyses were performed for subjects of European ancestry and African ancestry. Age of onset of DSM-5 AUD was evaluated using the Cox proportional hazard model. Predictive power was assessed using receiver operating characteristic curves and by analysis of the distribution of PRS. Results European ancestry subjects with higher than median PRSs were at greater risk for onset of AUD than subjects with lower than median PRSs (p = 3 × 10–7). Area under the curve for the receiver operating characteristic analysis peaked at 0.88 to 0.95 using PRS plus sex, family history, comorbid disorders, age at first drink, and peer drinking; predictive power was primarily driven by clinical variables. In this high-risk sample, European ancestry subjects with a PRS score in the highest quartile showed a 72% risk for developing AUD and a 35% risk of developing severe AUD (compared with risks of 54% and 16%, respectively, in the lowest quartile). Conclusions Predictive power for PRSs in the extremes of the distribution suggests that these may have future clinical utility. Uncertainties in interpretation at the individual level still preclude current application.Item Polygenic Contributions to Suicidal Thoughts and Behaviors in a Sample Ascertained for Alcohol Use Disorders(Karger, 2023-01-18) Colbert, Sarah M. C.; Mullins, Niamh; Chan, Grace; Meyers, Jacquelyn L.; Schulman, Jessica; Kuperman, Samuel; Lai, Dongbing; Nurnberger, John; Plawecki, Martin H.; Kamarajan, Chella; Anokhin, Andrey P.; Bucholz, Kathleen K.; Hesselbrock, Victor; Edenberg, Howard J.; Kramer, John; Dick, Danielle M.; Porjesz, Bernice; Agrawal, Arpana; Johnson, Emma C.; Medical and Molecular Genetics, School of MedicineIntroduction: Suicidal thoughts and behaviors have partially distinct genetic etiologies. Methods: We used PRS-CS to create polygenic risk scores (PRSs) from GWAS of non-suicidal self-injury, broad-sense self-harm ideation, nonfatal suicide attempt, death by suicide, and depression. Using mixed-effect models, we estimated whether these PRSs were associated with a range of suicidal thoughts and behaviors in the Collaborative Study on the Genetics of Alcoholism (N = 7,526). Results: All PRSs were significantly associated with suicidal ideation and suicide attempt (betas = 0.08-0.44, false discovery rate [FDR] <0.023). All PRSs except non-suicidal self-injury PRS were associated with active suicidal ideation (betas = 0.14-0.22, FDR <0.003). Several associations remained significant in models where all significant PRSs were included as simultaneous predictors, and when all PRSs predicted suicide attempt, the PRS together explained 6.2% of the variance in suicide attempt. Significant associations were also observed between some PRSs and persistent suicidal ideation, non-suicidal self-injury, compounded suicide attempt, and desire to die. Conclusion: Our findings suggest that PRS for depression does not explain the entirety of the variance in suicidal thoughts and behaviors, with PRS specifically for suicidal thoughts and behaviors making additional and sometimes unique contributions.Item Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach(Springer Nature, 2021-03-15) Kinreich, Sivan; McCutcheon, Vivia V.; Aliev, Fazil; Meyers, Jacquelyn L.; Kamarajan, Chella; Pandey, Ashwini K.; Chorlian, David B.; Zhang, Jian; Kuang, Weipeng; Pandey, Gayathri; Subbie-Saenz de. Viteri, Stacey; Francis, Meredith W.; Chan, Grace; Bourdon, Jessica L.; Dick, Danielle M.; Anokhin, Andrey P.; Bauer, Lance; Hesselbrock, Victor; Schuckit, Marc A.; Nurnberger, John I., Jr.; Foroud, Tatiana M.; Salvatore, Jessica E.; Bucholz, Kathleen K.; Porjesz, Bernice; Psychiatry, School of MedicinePredictive models for recovering from alcohol use disorder (AUD) and identifying related predisposition biomarkers can have a tremendous impact on addiction treatment outcomes and cost reduction. Our sample (N = 1376) included individuals of European (EA) and African (AA) ancestry from the Collaborative Study on the Genetics of Alcoholism (COGA) who were initially assessed as having AUD (DSM-5) and reassessed years later as either having AUD or in remission. To predict this difference in AUD recovery status, we analyzed the initial data using multimodal, multi-features machine learning applications including EEG source-level functional brain connectivity, Polygenic Risk Scores (PRS), medications, and demographic information. Sex and ancestry age-matched stratified analyses were performed with supervised linear Support Vector Machine application and were calculated twice, once when the ancestry was defined by self-report and once defined by genetic data. Multifeatured prediction models achieved higher accuracy scores than models based on a single domain and higher scores in male models when the ancestry was based on genetic data. The AA male group model with PRS, EEG functional connectivity, marital and employment status features achieved the highest accuracy of 86.04%. Several discriminative features were identified, including collections of PRS related to neuroticism, depression, aggression, years of education, and alcohol consumption phenotypes. Other discriminated features included being married, employed, medication, lower default mode network and fusiform connectivity, and higher insula connectivity. Results highlight the importance of increasing genetic homogeneity of analyzed groups, identifying sex, and ancestry-specific features to increase prediction scores revealing biomarkers related to AUD remission.Item Predicting Alcohol-Related Memory Problems in Older Adults: A Machine Learning Study with Multi-Domain Features(MDPI, 2023-05-18) Kamarajan, Chella; Pandey, Ashwini K.; Chorlian, David B.; Meyers, Jacquelyn L.; Kinreich, Sivan; Pandey, Gayathri; Subbie-Saenz de Viteri, Stacey; Zhang, Jian; Kuang, Weipeng; Barr, Peter B.; Aliev, Fazil; Anokhin, Andrey P.; Plawecki, Martin H.; Kuperman, Samuel; Almasy, Laura; Merikangas, Alison; Brislin, Sarah J.; Bauer, Lance; Hesselbrock, Victor; Chan, Grace; Kramer, John; Lai, Dongbing; Hartz, Sarah; Bierut, Laura J.; McCutcheon, Vivia V.; Bucholz, Kathleen K.; Dick, Danielle M.; Schuckit, Marc A.; Edenberg, Howard J.; Porjesz, Bernice; Psychiatry, School of MedicineMemory problems are common among older adults with a history of alcohol use disorder (AUD). Employing a machine learning framework, the current study investigates the use of multi-domain features to classify individuals with and without alcohol-induced memory problems. A group of 94 individuals (ages 50–81 years) with alcohol-induced memory problems (the memory group) were compared with a matched control group who did not have memory problems. The random forests model identified specific features from each domain that contributed to the classification of the memory group vs. the control group (AUC = 88.29%). Specifically, individuals from the memory group manifested a predominant pattern of hyperconnectivity across the default mode network regions except for some connections involving the anterior cingulate cortex, which were predominantly hypoconnected. Other significant contributing features were: (i) polygenic risk scores for AUD, (ii) alcohol consumption and related health consequences during the past five years, such as health problems, past negative experiences, withdrawal symptoms, and the largest number of drinks in a day during the past twelve months, and (iii) elevated neuroticism and increased harm avoidance, and fewer positive “uplift” life events. At the neural systems level, hyperconnectivity across the default mode network regions, including the connections across the hippocampal hub regions, in individuals with memory problems may indicate dysregulation in neural information processing. Overall, the study outlines the importance of utilizing multidomain features, consisting of resting-state brain connectivity data collected ~18 years ago, together with personality, life experiences, polygenic risk, and alcohol consumption and related consequences, to predict the alcohol-related memory problems that arise in later life.Item Predicting risk for Alcohol Use Disorder using longitudinal data with multimodal biomarkers and family history: a machine learning study(Springer Nature, 2021) Kinreich, Sivan; Meyers, Jacquelyn L.; Maron-Katz, Adi; Kamarajan, Chella; Pandey, Ashwini K.; Chorlian, David B.; Zhang, Jian; Pandey, Gayathri; de Viteri, Stacey Subbie-Saenz; Pitti, Dan; Anokhin, Andrey P.; Bauer, Lance; Hesselbrock, Victor; Schuckit, Marc A.; Edenberg, Howard J.; Porjesz, Bernice; Medical and Molecular Genetics, School of MedicinePredictive models have succeeded in distinguishing between individuals with Alcohol use Disorder (AUD) and controls. However, predictive models identifying who is prone to develop AUD and the biomarkers indicating a predisposition to AUD are still unclear. Our sample (n = 656) included offspring and non-offspring of European American (EA) and African American (AA) ancestry from the Collaborative Study of the Genetics of Alcoholism (COGA) who were recruited as early as age 12 and were unaffected at first assessment and reassessed years later as AUD (DSM-5) (n = 328) or unaffected (n = 328). Machine learning analysis was performed for 220 EEG measures, 149 alcohol-related single nucleotide polymorphisms (SNPs) from a recent large Genome-wide Association Study (GWAS) of alcohol use/misuse and two family history (mother DSM-5 AUD and father DSM-5 AUD) features using supervised, Linear Support Vector Machine (SVM) classifier to test which features assessed before developing AUD predict those who go on to develop AUD. Age, gender, and ancestry stratified analyses were performed. Results indicate significant and higher accuracy rates for the AA compared with the EA prediction models and a higher model accuracy trend among females compared with males for both ancestries. Combined EEG and SNP features model outperformed models based on only EEG features or only SNP features for both EA and AA samples. This multidimensional superiority was confirmed in a follow-up analysis in the AA age groups (12-15, 16-19, 20-30) and EA age group (16-19). In both ancestry samples, the youngest age group achieved higher accuracy score than the two other older age groups. Maternal AUD increased the model's accuracy in both ancestries' samples. Several discriminative EEG measures and SNPs features were identified, including lower posterior gamma, higher slow wave connectivity (delta, theta, alpha), higher frontal gamma ratio, higher beta correlation in the parietal area, and 5 SNPs: rs4780836, rs2605140, rs11690265, rs692854, and rs13380649. Results highlight the significance of sampling uniformity followed by stratified (e.g., ancestry, gender, developmental period) analysis, and wider selection of features, to generate better prediction scores allowing a more accurate estimation of AUD development.Item Trauma Exposure and Post-Traumatic Stress Disorder Among Youth in a High-Risk Family Study: Associations with Maternal and Paternal Alcohol Use Disorder(Taylor & Francis, 2020) Bender, Annah K.; Bucholz, Kathleen K.; Edenberg, Howard J.; Kramer, John R.; Anokhin, Andrey P.; Meyers, Jacquelyn L.; Kuperman, Samuel; Hesselbrock, Victor; Hesselbrock, Michie; McCutcheon, Vivia V.; Biochemistry and Molecular Biology, School of MedicineThis study presents findings regarding the prevalence of trauma exposure and Posttraumatic Stress Disorder (PTSD) based on discrete types of trauma (physical, sexual, witnessed violence, and non-assaultive trauma) among 3404 youth in a family study of Alcohol Use Disorder (AUD). Data from the Collaborative Study on the Genetics of Alcoholism (COGA) were used to examine associations of parent AUD with offspring's childhood trauma exposure, and with lifetime diagnosis of DSM-IV PTSD among White and Black participants aged 12-35. Of 3404 youth, 59.7% had parents affected by AUD and 78% experienced ≤1 traumatic events before age 18. AUD in one or both parents was associated with physical, sexual, and witnessed violence among Whites. Among African Americans, maternal AUD was associated with sexual assault. The lifetime PTSD rate among youth exposed to childhood trauma was 8.6%, and mother-only AUD was significantly associated with lifetime PTSD among participants in both groups. PTSD among youth in this study were somewhat higher (7.9% to 8.83%) than those found in general population studies of the same demographic (5% to 6.8%). Maternal AUD appears to be a salient risk factor for sexual assault before age 18 among Black and development of lifetime PTSD among White youth.