- Browse by Author
Browsing by Author "Agrawal, Arpana"
Now showing 1 - 10 of 65
Results Per Page
Sort Options
Item 5. Collaborative Study on the Genetics of Alcoholism: Functional genomics(Wiley, 2023) Gameiro-Ros, Isabel; Popova, Dina; Prytkova, Iya; Pang, Zhiping P.; Liu, Yunlong; Dick, Danielle; Bucholz, Kathleen K.; Agrawal, Arpana; Porjesz, Bernice; Goate, Alison M.; Xuei, Xiaoling; Kamarajan, Chella; COGA Collaborators; Tischfield, Jay A.; Edenberg, Howard J.; Slesinger, Paul A.; Hart, Ronald P.; Medical and Molecular Genetics, School of MedicineAlcohol Use Disorder is a complex genetic disorder, involving genetic, neural, and environmental factors, and their interactions. The Collaborative Study on the Genetics of Alcoholism (COGA) has been investigating these factors and identified putative alcohol use disorder risk genes through genome-wide association studies. In this review, we describe advances made by COGA in elucidating the functional changes induced by alcohol use disorder risk genes using multimodal approaches with human cell lines and brain tissue. These studies involve investigating gene regulation in lymphoblastoid cells from COGA participants and in post-mortem brain tissues. High throughput reporter assays are being used to identify single nucleotide polymorphisms in which alternate alleles differ in driving gene expression. Specific single nucleotide polymorphisms (both coding or noncoding) have been modeled using induced pluripotent stem cells derived from COGA participants to evaluate the effects of genetic variants on transcriptomics, neuronal excitability, synaptic physiology, and the response to ethanol in human neurons from individuals with and without alcohol use disorder. We provide a perspective on future studies, such as using polygenic risk scores and populations of induced pluripotent stem cell-derived neurons to identify signaling pathways related with responses to alcohol. Starting with genes or loci associated with alcohol use disorder, COGA has demonstrated that integration of multimodal data within COGA participants and functional studies can reveal mechanisms linking genomic variants with alcohol use disorder, and potential targets for future treatments.Item An ADH1B variant and peer drinking in progression to adolescent drinking milestones: Evidence of a gene-by-environment interaction(Wiley Online Library, 2014-10) Olfson, Emily; Edenberg, Howard J.; Nurnberger Jr., John; Agrawal, Arpana; Bucholz, Kathleen K.; Almasy, Laura A.; Chorlian, David; Dick, Danielle M.; Hesselbrock, Victor M.; Kramer, John R.; Kuperman, Samuel; Porjesz, Bernice; Schuckit, Marc A.; Tischfield, Jay A.; Wang, Jen-Chyong; Wetherill, Leah; Foroud, Tatiana M.; Rice, John; Goate, Alison; Bierut, Laura J.; Department of Biochemistry & Molecular Biology, IU School of MedicineBACKGROUND: Adolescent drinking is an important public health concern, one that is influenced by both genetic and environmental factors. The functional variant rs1229984 in alcohol dehydrogenase 1B (ADH1B) has been associated at a genome-wide level with alcohol use disorders in diverse adult populations. However, few data are available regarding whether this variant influences early drinking behaviors and whether social context moderates this effect. This study examines the interplay between rs1229984 and peer drinking in the development of adolescent drinking milestones. METHODS: One thousand five hundred and fifty European and African American individuals who had a full drink of alcohol before age 18 were selected from a longitudinal study of youth as part of the Collaborative Study on the Genetics of Alcoholism (COGA). Cox proportional hazards regression, with G × E product terms in the final models, was used to study 2 primary outcomes during adolescence: age of first intoxication and age of first DSM-5 alcohol use disorder symptom. RESULTS: The minor A allele of rs1229984 was associated with a protective effect for first intoxication (HR = 0.56, 95% CI 0.41 to 0.76) and first DSM-5 symptom (HR = 0.45, 95% CI 0.26 to 0.77) in the final models. Reporting that most or all best friends drink was associated with a hazardous effect for first intoxication (HR = 1.81, 95% CI 1.62 to 2.01) and first DSM-5 symptom (HR = 2.17, 95% 1.88 to 2.50) in the final models. Furthermore, there was a significant G × E interaction for first intoxication (p = 0.002) and first DSM-5 symptom (p = 0.01). Among individuals reporting none or few best friends drinking, the ADH1B variant had a protective effect for adolescent drinking milestones, but for those reporting most or all best friends drinking, this effect was greatly reduced. CONCLUSIONS: Our results suggest that the risk factor of best friends drinking attenuates the protective effect of a well-established ADH1B variant for 2 adolescent drinking behaviors. These findings illustrate the interplay between genetic and environmental factors in the development of drinking milestones during adolescence.Item Analysis of whole genome-transcriptomic organization in brain to identify genes associated with alcoholism(Springer Nature, 2019-02-14) Kapoor, Manav; Wang, Jen-Chyong; Farris, Sean P.; Liu, Yunlong; McClintick, Jeanette; Gupta, Ishaan; Meyers, Jacquelyn L.; Bertelsen, Sarah; Chao, Michael; Nurnberger, John; Tischfield, Jay; Harari, Oscar; Zeran, Li; Hesselbrock, Victor; Bauer, Lance; Raj, Towfique; Porjesz, Bernice; Agrawal, Arpana; Foroud, Tatiana; Edenberg, Howard J.; Mayfield, R. Dayne; Goate, Alison; Medical and Molecular Genetics, School of MedicineAlcohol exposure triggers changes in gene expression and biological pathways in human brain. We explored alterations in gene expression in the Pre-Frontal Cortex (PFC) of 65 alcoholics and 73 controls of European descent, and identified 129 genes that showed altered expression (FDR < 0.05) in subjects with alcohol dependence. Differentially expressed genes were enriched for pathways related to interferon signaling and Growth Arrest and DNA Damage-inducible 45 (GADD45) signaling. A coexpression module (thistle2) identified by weighted gene co-expression network analysis (WGCNA) was significantly correlated with alcohol dependence, alcohol consumption, and AUDIT scores. Genes in the thistle2 module were enriched with genes related to calcium signaling pathways and showed significant downregulation of these pathways, as well as enrichment for biological processes related to nicotine response and opioid signaling. A second module (brown4) showed significant upregulation of pathways related to immune signaling. Expression quantitative trait loci (eQTLs) for genes in the brown4 module were also enriched for genetic associations with alcohol dependence and alcohol consumption in large genome-wide studies included in the Psychiatric Genetic Consortium and the UK Biobank's alcohol consumption dataset. By leveraging multi-omics data, this transcriptome analysis has identified genes and biological pathways that could provide insight for identifying therapeutic targets for alcohol dependence.Item Ancestry May Confound Genetic Machine Learning: Candidate-Gene Prediction of Opioid Use Disorder as an Example(Elsevier, 2021) Hatoum, Alexander S.; Wendt, Frank R.; Galimberti, Marco; Polimanti, Renato; Neale, Benjamin; Kranzler, Henry R.; Gelernter, Joel; Edenberg, Howard J.; Agrawal, Arpana; Medical and Molecular Genetics, School of MedicineBackground: Machine learning (ML) models are beginning to proliferate in psychiatry, however machine learning models in psychiatric genetics have not always accounted for ancestry. Using an empirical example of a proposed genetic test for OUD, and exploring a similar test for tobacco dependence and a simulated binary phenotype, we show that genetic prediction using ML is vulnerable to ancestral confounding. Methods: We utilize five ML algorithms trained with 16 brain reward-derived "candidate" SNPs proposed for commercial use and examine their ability to predict OUD vs. ancestry in an out-of-sample test set (N = 1000, stratified into equal groups of n = 250 cases and controls each of European and African ancestry). We rerun analyses with 8 random sets of allele-frequency matched SNPs. We contrast findings with 11 genome-wide significant variants for tobacco smoking. To document generalizability, we generate and test a random phenotype. Results: None of the 5 ML algorithms predict OUD better than chance when ancestry was balanced but were confounded with ancestry in an out-of-sample test. In addition, the algorithms preferentially predicted admixed subpopulations. Random sets of variants matched to the candidate SNPs by allele frequency produced similar bias. Genome-wide significant tobacco smoking variants were also confounded by ancestry. Finally, random SNPs predicting a random simulated phenotype show that the bias attributable to ancestral confounding could impact any ML-based genetic prediction. Conclusions: Researchers and clinicians are encouraged to be skeptical of claims of high prediction accuracy from ML-derived genetic algorithms for polygenic traits like addiction, particularly when using candidate variants.Item Association of Polygenic Liability for Alcohol Dependence and EEG Connectivity in Adolescence and Young Adulthood(MDPI, 2019-10-17) Meyers, Jacquelyn L.; Chorlian, David B.; Johnson, Emma C.; Pandey, Ashwini K.; Kamarajan, Chella; Salvatore, Jessica E.; Aliev, Fazil; Subbie-Saenz de Viteri, Stacey; Zhang, Jian; Chao, Michael; Kapoor, Manav; Hesselbrock, Victor; Kramer, John; Kuperman, Samuel; Nurnberger, John; Tischfield, Jay; Goate, Alison; Foroud, Tatiana; Dick, Danielle M.; Edenberg, Howard J.; Agrawal, Arpana; Porjesz, Bernice; Medical and Molecular Genetics, School of MedicineDifferences in the connectivity of large-scale functional brain networks among individuals with alcohol use disorders (AUD), as well as those at risk for AUD, point to dysfunctional neural communication and related cognitive impairments. In this study, we examined how polygenic risk scores (PRS), derived from a recent GWAS of DSM-IV Alcohol Dependence (AD) conducted by the Psychiatric Genomics Consortium, relate to longitudinal measures of interhemispheric and intrahemispheric EEG connectivity (alpha, theta, and beta frequencies) in adolescent and young adult offspring from the Collaborative Study on the Genetics of Alcoholism (COGA) assessed between ages 12 and 31. Our findings indicate that AD PRS (p-threshold < 0.001) was associated with increased fronto-central, tempo-parietal, centro-parietal, and parietal-occipital interhemispheric theta and alpha connectivity in males only from ages 18-31 (beta coefficients ranged from 0.02-0.06, p-values ranged from 10-6-10-12), but not in females. Individuals with higher AD PRS also demonstrated more performance deficits on neuropsychological tasks (Tower of London task, visual span test) as well as increased risk for lifetime DSM-5 alcohol and opioid use disorders. We conclude that measures of neural connectivity, together with neurocognitive performance and substance use behavior, can be used to further understanding of how genetic risk variants from large GWAS of AUD may influence brain function. In addition, these data indicate the importance of examining sex and developmental effects, which otherwise may be masked. Understanding of neural mechanisms linking genetic variants emerging from GWAS to risk for AUD throughout development may help to identify specific points when neurocognitive prevention and intervention efforts may be most effective.Item Association of substance dependence phenotypes in the COGA sample(Wiley, 2015-05) Wetherill, Leah; Agrawal, Arpana; Kapoor, Manav; Bertelsen, Sarah; Bierut, Laura J.; Brooks, Andrew; Dick, Danielle; Hesselbrock, Michie; Hesselbrock, Victor; Koller, Daniel L.; Le, Nhung; Nurnberger Jr., John I.; Salvatore, Jessica E.; Schuckit, Marc; Tischfield, Jay A.; Wang, Jen-Chyong; Xuei, Xiaoling; Edenberg, Howard J.; Porjesz, Bernice; Bucholz, Kathleen; Goate, Alison M.; Foroud, Tatiana; Department of Medical & Molecular Genetics, IU School of MedicineAlcohol and drug use disorders are individually heritable (50%). Twin studies indicate that alcohol and substance use disorders share common genetic influences, and therefore may represent a more heritable form of addiction and thus be more powerful for genetic studies. This study utilized data from 2322 subjects from 118 European-American families in the Collaborative Study on the Genetics of Alcoholism sample to conduct genome-wide association analysis of a binary and a continuous index of general substance dependence liability. The binary phenotype (ANYDEP) was based on meeting lifetime criteria for any DSM-IV dependence on alcohol, cannabis, cocaine or opioids. The quantitative trait (QUANTDEP) was constructed from factor analysis based on endorsement across the seven DSM-IV criteria for each of the four substances. Heritability was estimated to be 54% for ANYDEP and 86% for QUANTDEP. One single-nucleotide polymorphism (SNP), rs2952621 in the uncharacterized gene LOC151121 on chromosome 2, was associated with ANYDEP (P = 1.8 × 10(-8) ), with support from surrounding imputed SNPs and replication in an independent sample [Study of Addiction: Genetics and Environment (SAGE); P = 0.02]. One SNP, rs2567261 in ARHGAP28 (Rho GTPase-activating protein 28), was associated with QUANTDEP (P = 3.8 × 10(-8) ), and supported by imputed SNPs in the region, but did not replicate in an independent sample (SAGE; P = 0.29). The results of this study provide evidence that there are common variants that contribute to the risk for a general liability to substance dependence.Item Associations Between Cannabis Use, Polygenic Liability for Schizophrenia, and Cannabis-related Experiences in a Sample of Cannabis Users(Oxford University Press, 2023) Johnson, Emma C.; Colbert, Sarah M. C.; Jeffries, Paul W.; Tillman, Rebecca; Bigdeli, Tim B.; Karcher, Nicole R.; Chan, Grace; Kuperman, Samuel; Meyers, Jacquelyn L.; Nurnberger, John I.; Plawecki, Martin H.; Degenhardt, Louisa; Martin, Nicholas G.; Kamarajan, Chella; Schuckit, Marc A.; Murray, Robin M.; Dick, Danielle M.; Edenberg, Howard J.; D'Souza, Deepak Cyril; Di Forti, Marta; Porjesz, Bernice; Nelson, Elliot C.; Agrawal, Arpana; Medical and Molecular Genetics, School of MedicineBackground and hypothesis: Risk for cannabis use and schizophrenia is influenced in part by genetic factors, and there is evidence that genetic risk for schizophrenia is associated with subclinical psychotic-like experiences (PLEs). Few studies to date have examined whether genetic risk for schizophrenia is associated with cannabis-related PLEs. Study design: We tested whether measures of cannabis involvement and polygenic risk scores (PRS) for schizophrenia were associated with self-reported cannabis-related experiences in a sample ascertained for alcohol use disorders (AUDs), the Collaborative Study on the Genetics of Alcoholism (COGA). We analyzed 4832 subjects (3128 of European ancestry and 1704 of African ancestry; 42% female; 74% meeting lifetime criteria for an AUD). Study results: Cannabis use disorder (CUD) was prevalent in this analytic sample (70%), with 40% classified as mild, 25% as moderate, and 35% as severe. Polygenic risk for schizophrenia was positively associated with cannabis-related paranoia, feeling depressed or anhedonia, social withdrawal, and cognitive difficulties, even when controlling for duration of daily cannabis use, CUD, and age at first cannabis use. The schizophrenia PRS was most robustly associated with cannabis-related cognitive difficulties (β = 0.22, SE = 0.04, P = 5.2e-7). In an independent replication sample (N = 1446), associations between the schizophrenia PRS and cannabis-related experiences were in the expected direction and not statistically different in magnitude from those in the COGA sample. Conclusions: Among individuals who regularly use cannabis, genetic liability for schizophrenia-even in those without clinical features-may increase the likelihood of reporting unusual experiences related to cannabis use.Item Associations between Suicidal Thoughts and Behaviors and Genetic Liability for Cognitive Performance, Depression, and Risk-Taking in a High-Risk Sample(Karger, 2021) Johnson, Emma C.; Aliev, Fazil; Meyers, Jacquelyn L.; Salvatore, Jessica E.; Tillman, Rebecca; Chang, Yoonhoo; Docherty, Anna R.; Bogdan, Ryan; Acion, Laura; Chan, Grace; Chorlian, David B.; Kamarajan, Chella; Kuperman, Samuel; Pandey, Ashwini; Plawecki, Martin H.; Schuckit, Marc; Tischfield, Jay; Edenberg, Howard J.; Bucholz, Kathleen K.; Nurnberger, John I.; Porjesz, Bernice; Hesselbrock, Victor; Dick, Danielle M.; Kramer, John R.; Agrawal, Arpana; Psychiatry, School of MedicineBackground: Suicidal thoughts and behaviors (STBs) and nonsuicidal self-injury (NSSI) behaviors are moderately heritable and may reflect an underlying predisposition to depression, impulsivity, and cognitive vulnerabilities to varying degrees. Objectives: We aimed to estimate the degrees of association between genetic liability to depression, impulsivity, and cognitive performance and STBs and NSSI in a high-risk sample. Methods: We used data on 7,482 individuals of European ancestry and 3,359 individuals of African ancestry from the Collaborative Study on the Genetics of Alcoholism to examine the links between polygenic scores (PGSs) for depression, impulsivity/risk-taking, and cognitive performance with 3 self-reported indices of STBs (suicidal ideation, persistent suicidal ideation defined as ideation occurring on at least 7 consecutive days, and suicide attempt) and with NSSI. Results: The PGS for depression was significantly associated with all 4 primary self-harm measures, explaining 0.6-2.5% of the variance. The PGS for risk-taking behaviors was also associated with all 4 self-harm behaviors in baseline models, but was no longer associated after controlling for a lifetime measure of DSM-IV alcohol dependence and abuse symptom counts. Polygenic predisposition for cognitive performance was negatively associated with suicide attempts (q = 3.8e-4) but was not significantly associated with suicidal ideation nor NSSI. We did not find any significant associations in the African ancestry subset, likely due to smaller sample sizes. Conclusions: Our results encourage the study of STB as transdiagnostic outcomes that show genetic overlap with a range of risk factors.Item A Brief Critique of the TATES Procedure(Springer, 2018-03) Aliev, Fazil; Salvatore, Jessica E.; Agrawal, Arpana; Almasy, Laura; Chan, Grace; Edenberg, Howard J.; Hesselbrock, Victor; Kuperman, Samuel; Meyers, Jacquelyn; Dick, Danielle M.; Biochemistry and Molecular Biology, School of MedicineThe Trait-based test that uses the Extended Simes procedure (TATES) was developed as a method for conducting multivariate GWAS for correlated phenotypes whose underlying genetic architecture is complex. In this paper, we provide a brief methodological critique of the TATES method using simulated examples and a mathematical proof. Our simulated examples using correlated phenotypes show that the Type I error rate is higher than expected, and that more TATES p values fall outside of the confidence interval relative to expectation. Thus the method may result in systematic inflation when used with correlated phenotypes. In a mathematical proof we further demonstrate that the distribution of TATES p values deviates from expectation in a manner indicative of inflation. Our findings indicate the need for caution when using TATES for multivariate GWAS of correlated phenotypes.Item Candidate Genes from an FDA-Approved Algorithm Fail to Predict Opioid Use Disorder Risk in Over 450,000 Veterans(medRxiv, 2024-05-16) Davis, Christal N.; Jinwala, Zeal; Hatoum, Alexander S.; Toikumo, Sylvanus; Agrawal, Arpana; Rentsch, Christopher T.; Edenberg, Howard J.; Baurley, James W.; Hartwell, Emily E.; Crist, Richard C.; Gray, Joshua C.; Justice, Amy C.; Gelernter, Joel; Kember, Rachel L.; Kranzler, Henry R.; Biochemistry and Molecular Biology, School of MedicineImportance: Recently, the Food and Drug Administration gave pre-marketing approval to algorithm based on its purported ability to identify genetic risk for opioid use disorder. However, the clinical utility of the candidate genes comprising the algorithm has not been independently demonstrated. Objective: To assess the utility of 15 variants in candidate genes from an algorithm intended to predict opioid use disorder risk. Design: This case-control study examined the association of 15 candidate genetic variants with risk of opioid use disorder using available electronic health record data from December 20, 1992 to September 30, 2022. Setting: Electronic health record data, including pharmacy records, from Million Veteran Program participants across the United States. Participants: Participants were opioid-exposed individuals enrolled in the Million Veteran Program (n = 452,664). Opioid use disorder cases were identified using International Classification of Disease diagnostic codes, and controls were individuals with no opioid use disorder diagnosis. Exposures: Number of risk alleles present across 15 candidate genetic variants. Main outcome and measures: Predictive performance of 15 genetic variants for opioid use disorder risk assessed via logistic regression and machine learning models. Results: Opioid exposed individuals (n=33,669 cases) were on average 61.15 (SD = 13.37) years old, 90.46% male, and had varied genetic similarity to global reference panels. Collectively, the 15 candidate genetic variants accounted for 0.4% of variation in opioid use disorder risk. The accuracy of the ensemble machine learning model using the 15 genes as predictors was 52.8% (95% CI = 52.1 - 53.6%) in an independent testing sample. Conclusions and relevance: Candidate genes that comprise the approved algorithm do not meet reasonable standards of efficacy in predicting opioid use disorder risk. Given the algorithm's limited predictive accuracy, its use in clinical care would lead to high rates of false positive and negative findings. More clinically useful models are needed to identify individuals at risk of developing opioid use disorder.