Browsing by Author "Gelernter, Joel"
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Item A phenome-wide association and Mendelian randomisation study of alcohol use variants in a diverse cohort comprising over 3 million individuals(Elsevier, 2024) Jennings, Mariela V.; Martínez-Magaña, José Jaime; Courchesne-Krak, Natasia S.; Cupertino, Renata B.; Vilar-Ribó, Laura; Bianchi, Sevim B.; Hatoum, Alexander S.; Atkinson, Elizabeth G.; Giusti-Rodriguez, Paola; Montalvo-Ortiz, Janitza L.; Gelernter, Joel; Soler Artigas, María; 23andMe, Inc. Research Team; Elson, Sarah L.; Edenberg, Howard J.; Fontanillas, Pierre; Palmer, Abraham A.; Sanchez-Roige, Sandra; Biochemistry and Molecular Biology, School of MedicineBackground: Alcohol consumption is associated with numerous negative social and health outcomes. These associations may be direct consequences of drinking, or they may reflect common genetic factors that influence both alcohol consumption and other outcomes. Methods: We performed exploratory phenome-wide association studies (PheWAS) of three of the best studied protective single nucleotide polymorphisms (SNPs) in genes encoding ethanol metabolising enzymes (ADH1B: rs1229984-T, rs2066702-A; ADH1C: rs698-T) using up to 1109 health outcomes across 28 phenotypic categories (e.g., substance-use, mental health, sleep, immune, cardiovascular, metabolic) from a diverse 23andMe cohort, including European (N ≤ 2,619,939), Latin American (N ≤ 446,646) and African American (N ≤ 146,776) populations to uncover new and perhaps unexpected associations. These SNPs have been consistently implicated by both candidate gene studies and genome-wide association studies of alcohol-related behaviours but have not been investigated in detail for other relevant phenotypes in a hypothesis-free approach in such a large cohort of multiple ancestries. To provide insight into potential causal effects of alcohol consumption on the outcomes significant in the PheWAS, we performed univariable two-sample and one-sample Mendelian randomisation (MR) analyses. Findings: The minor allele rs1229984-T, which is protective against alcohol behaviours, showed the highest number of PheWAS associations across the three cohorts (N = 232, European; N = 29, Latin American; N = 7, African American). rs1229984-T influenced multiple domains of health. We replicated associations with alcohol-related behaviours, mental and sleep conditions, and cardio-metabolic health. We also found associations with understudied traits related to neurological (migraines, epilepsy), immune (allergies), musculoskeletal (fibromyalgia), and reproductive health (preeclampsia). MR analyses identified evidence of causal effects of alcohol consumption on liability for 35 of these outcomes in the European cohort. Interpretation: Our work demonstrates that polymorphisms in genes encoding alcohol metabolising enzymes affect multiple domains of health beyond alcohol-related behaviours. Understanding the underlying mechanisms of these effects could have implications for treatments and preventative medicine.Item Alcohol metabolizing genes and alcohol phenotypes in an Israeli household sample(Wiley, 2013-11) Meyers, Jacquelyn L.; Shmulewitz, Dvora; Aharonovich, Efrat; Waxman, Rachel; Frisch, Amos; Weizman, Abraham; Spivak, Baruch; Edenberg, Howard J.; Gelernter, Joel; Hasin, Deborah; Biochemistry & Molecular Biology, School of MedicineBACKGROUND: Alcohol dehydrogenase 1B and 1C (ADH1B and ADH1C) variants have been robustly associated with alcohol phenotypes in East Asian populations, but less so in non-Asian populations where prevalence of the most protective ADH1B allele is low (generally <5%). Further, the joint effects of ADH1B and ADH1C on alcohol phenotypes have been unclear. Therefore, we tested the independent and joint effects of ADH1B and ADH1C on alcohol phenotypes in an Israeli sample, with higher prevalence of the most protective ADH1B allele than other non-Asian populations. METHODS: A structured interview assessed lifetime drinking and alcohol use disorders (AUDs) in adult Israeli household residents. Four single nucleotide polymorphisms (SNPs) were genotyped: ADH1B (rs1229984, rs1229982, and rs1159918) and ADH1C (rs698). Regression analysis examined the association between alcohol phenotypes and each SNP (absence vs. presence of the protective allele) as well as rs698/rs1229984 diplotypes (also indicating absence or presence of protective alleles) in lifetime drinkers (n = 1,129). RESULTS: Lack of the ADH1B rs1229984 protective allele was significantly associated with consumption- and AUD-related phenotypes (OR = 1.77 for AUD; OR = 1.83 for risk drinking), while lack of the ADH1C rs698 protective allele was significantly associated with AUD-related phenotypes (OR = 2.32 for AUD). Diplotype analysis indicated that jointly ADH1B and ADH1C significantly influenced AUD-related phenotypes. For example, among those without protective alleles for ADH1B or ADH1C, OR for AUD was 1.87 as compared to those without the protective allele for ADH1B only and was 3.16 as compared to those with protective alleles for both ADH1B and ADH1C. CONCLUSIONS: This study adds support for the relationship of ADH1B and ADH1C and alcohol phenotypes in non-Asians. Further, these findings help clarify the mixed results from previous studies by showing that ADH1B and ADH1C jointly effect AUDs, but not consumption. Studies of the association between alcohol phenotypes and either ADH1B or ADH1C alone may employ an oversimplified model, masking relevant information.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 the OPRM1 Variant rs1799971 (A118G) with Non-Specific Liability to Substance Dependence in a Collaborative de novo Meta-Analysis of European-Ancestry Cohorts(Springer, 2016-03) Schwantes-An, Tae-Hwi; Zhang, Juan; Chen, Li-Shiun; Hartz, Sarah M.; Culverhouse, Robert C.; Chen, Xiangning; Coon, Hilary; Frank, Josef; Kamens, Helen M.; Konte, Bettina; Kovanen, Leena; Latvala, Antti; Legrand, Lisa N.; Maher, Brion S.; Melroy, Whitney E.; Nelson, Elliot C.; Reid, Mark W.; Robinson, Jason D.; Shen, Pei-Hong; Yang, Bao-Zhu; Andrews, Judy A.; Aveyard, Paul; Beltcheva, Olga; Brown, Sandra A.; Cannon, Dale S.; Cichon, Sven; Corley, Robin P.; Dahmen, Norbert; Degenhardt, Louisa; Foroud, Tatiana; Gaebel, Wolfgang; Giegling, Ina; Glatt, Stephen J.; Grucza, Richard A.; Hardin, Jill; Hartmann, Annette M.; Heath, Andrew C.; Herms, Stefan; Hodgkinson, Colin A.; Hoffmann, Per; Hops, Hyman; Huizinga, David; Ising, Marcus; Johnson, Eric O.; Johnstone, Elaine; Kaneva, Radka P.; Kendler, Kenneth S.; Kiefer, Falk; Kranzler, Henry R.; Krauter, Ken S.; Levran, Orna; Lucae, Susanne; Lynskey, Michael T.; Maier, Wolfgang; Mann, Karl; Martin, Nicholas G.; Mattheisen, Manuel; Montgomery, Grant W.; Müller-Myhsok, Bertram; Murphy, Michael F.; Neale, Michael C.; Nikolov, Momchil A.; Nishita, Denise; Nöthen, Markus M.; Nurnberger, John; Partonen, Timo; Pergadia, Michele L.; Reynolds, Maureen; Ridinger, Monika; Rose, Richard J.; Rouvinen-Lagerström, Noora; Scherbaum, Norbert; Schmäl, Christine; Soyka, Michael; Stallings, Michael C.; Steffens, Michael; Treutlein, Jens; Tsuang, Ming; Wallace, Tamara L.; Wodarz, Norbert; Yuferov, Vadim; Zill, Peter; Bergen, Andrew W.; Chen, Jingchun; Cinciripini, Paul M.; Edenberg, Howard J.; Ehringer, Marissa A.; Ferrell, Robert E.; Gelernter, Joel; Goldman, David; Hewitt, John K.; Hopfer, Christian J.; Iacono, William G.; Kaprio, Jaakko; Kreek, Mary Jeanne; Kremensky, Ivo M.; Madden, Pamela A.F.; McGue, Matt; Munafò, Marcus R.; Philibert, Robert A.; Rietschel, Marcella; Roy, Alec; Rujescu, Dan; Saarikoski, Sirkku T.; Swan, Gary E.; Todorov, Alexandre A.; Vanyukov, Michael M.; Weiss, Robert B.; Bierut, Laura J.; Saccone, Nancy L.; Department of Medical & Molecular Genetics, IU School of MedicineThe 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.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.Item CHRNA5/A3/B4 Variant rs3743078 and Nicotine-Related Phenotypes: Indirect Effects Through Nicotine Craving(Rutgers Center of Alcohol Studies, 2016-03) Shmulewitz, Dvora; Meyers, Jacquelyn L.; Wall, Melanie M.; Aharonovich, Efrat; Frisch, Amos; Spivak, Baruch; Weizman, Abraham; Edenberg, Howard J.; Gelernter, Joel; Hasin, Deborah S.; Department of Biochemistry & Molecular Biology, IU School of MedicineOBJECTIVE: Nicotine craving is considered an important element in the persistence of cigarette smoking, but little is known about the role of craving in the widely recognized association between variants mapped to the neuronal nicotinic acetylcholine receptor (CHRN) genes on chromosome 15 and nicotine phenotypes. METHOD: The associations between CHRNA5-CHRNA3-CHRNB4 variants and cigarettes per day (CPD), the Fagerström Test for Nicotine Dependence (FTND), and craving were analyzed in data from 662 lifetime smokers from an Israeli adult Jewish household sample. Indirect effects of genotype on nicotine phenotypes through craving were formally tested using regression and bootstrapping procedures. RESULTS: At CHRNA3, allele G of rs3743078 was associated with increased craving, CPD, and FTND scores: Participants with one or two copies of the G allele had, on average, higher scores on the craving scale (p = .0025), more cigarettes smoked (p = .0057), and higher scores on the FTND (p =.0024). With craving in the model, variant rs3743078 showed a significant indirect effect through craving on CPD (p = .0026) and on FTND score (p = .0024). A sizeable proportion of the total rs3743078 effect on CPD (56.4%) and FTND (65.2%) was indirect through craving. CONCLUSIONS: These results suggest that nicotine craving may play a central role in nicotine use disorders and may have utility as a therapeutic target.Item Cross-ancestry genetic investigation of schizophrenia, cannabis use disorder, and tobacco smoking(Springer Nature, 2024) Johnson, Emma C.; Austin-Zimmerman, Isabelle; Thorpe, Hayley H. A.; Levey, Daniel F.; Baranger, David A. A.; Colbert, Sarah M. C.; Demontis, Ditte; Khokhar, Jibran Y.; Davis, Lea K.; Edenberg, Howard J.; Di Forti, Marta; Sanchez-Roige, Sandra; Gelernter, Joel; Agrawal, Arpana; Medical and Molecular Genetics, School of MedicineIndividuals with schizophrenia frequently experience co-occurring substance use, including tobacco smoking and heavy cannabis use, and substance use disorders. There is interest in understanding the extent to which these relationships are causal, and to what extent shared genetic factors play a role. We explored the relationships between schizophrenia (Scz; European ancestry N = 161,405; African ancestry N = 15,846), cannabis use disorder (CanUD; European ancestry N = 886,025; African ancestry N = 120,208), and ever-regular tobacco smoking (Smk; European ancestry N = 805,431; African ancestry N = 24,278) using the largest available genome-wide studies of these phenotypes in individuals of African and European ancestries. All three phenotypes were positively genetically correlated (rgs = 0.17-0.62). Genetic instrumental variable analyses suggested the presence of shared heritable factors, but evidence for bidirectional causal relationships was also found between all three phenotypes even after correcting for these shared genetic factors. We identified 327 pleiotropic loci with 439 lead SNPs in the European ancestry data, 150 of which were novel (i.e., not genome-wide significant in the original studies). Of these pleiotropic loci, 202 had lead variants which showed convergent effects (i.e., same direction of effect) on Scz, CanUD, and Smk. Genetic variants convergent across all three phenotypes showed strong genetic correlations with risk-taking, executive function, and several mental health conditions. Our results suggest that both shared genetic factors and causal mechanisms may play a role in the relationship between CanUD, Smk, and Scz, but longitudinal, prospective studies are needed to confirm a causal relationship.Item Cross-ancestry genetic investigation of schizophrenia, cannabis use disorder, and tobacco smoking(medRxiv, 2024-01-18) Johnson, Emma C.; Austin-Zimmerman, Isabelle; Thorpe, Hayley H. A.; Levey, Daniel F.; Baranger, David A. A.; Colbert, Sarah M. C.; Demontis, Ditte; Khokhar, Jibran Y.; Davis, Lea K.; Edenberg, Howard J.; Di Forti, Marta; Sanchez-Roige, Sandra; Gelernter, Joel; Agrawal, Arpana; Biochemistry and Molecular Biology, School of MedicineIndividuals with schizophrenia frequently experience co-occurring substance use, including tobacco smoking and heavy cannabis use, and substance use disorders. There is interest in understanding the extent to which these relationships are causal, and to what extent shared genetic factors play a role. We explored the relationships between schizophrenia (Scz), cannabis use disorder (CanUD), and ever-regular tobacco smoking (Smk) using the largest available genome-wide studies of these phenotypes in individuals of African and European ancestries. All three phenotypes were positively genetically correlated (rgs = 0.17 - 0.62). Causal inference analyses suggested the presence of horizontal pleiotropy, but evidence for bidirectional causal relationships was also found between all three phenotypes even after correcting for horizontal pleiotropy. We identified 439 pleiotropic loci in the European ancestry data, 150 of which were novel (i.e., not genome-wide significant in the original studies). Of these pleiotropic loci, 202 had lead variants which showed convergent effects (i.e., same direction of effect) on Scz, CanUD, and Smk. Genetic variants convergent across all three phenotypes showed strong genetic correlations with risk-taking, executive function, and several mental health conditions. Our results suggest that both horizontal pleiotropy and causal mechanisms may play a role in the relationship between CanUD, Smk, and Scz, but longitudinal, prospective studies are needed to confirm a causal relationship.Item Evidence of causal effect of major depression on alcohol dependence: findings from the psychiatric genomics consortium(Cambridge University Press, 2019-05) Polimanti, Renato; Peterson, Roseann E.; Ong, Jue-Sheng; MacGregor, Stuart; Edwards, Alexis C.; Clarke, Toni-Kim; Frank, Josef; Gerring, Zachary; Gillespie, Nathan A.; Lind, Penelope A.; Maes, Hermine H.; Martin, Nicholas G.; Mbarek, Hamdi; Medland, Sarah E.; Streit, Fabian; Agrawal, Arpana; Edenberg, Howard J.; Kendler, Kenneth S.; Lewis, Cathryn M.; Sullivan, Patrick F.; Wray, Naomi R.; Gelernter, Joel; Derks, Eske M.; Biochemistry and Molecular Biology, School of MedicineBACKGROUND: Despite established clinical associations among major depression (MD), alcohol dependence (AD), and alcohol consumption (AC), the nature of the causal relationship between them is not completely understood. We leveraged genome-wide data from the Psychiatric Genomics Consortium (PGC) and UK Biobank to test for the presence of shared genetic mechanisms and causal relationships among MD, AD, and AC. METHODS: Linkage disequilibrium score regression and Mendelian randomization (MR) were performed using genome-wide data from the PGC (MD: 135 458 cases and 344 901 controls; AD: 10 206 cases and 28 480 controls) and UK Biobank (AC-frequency: 438 308 individuals; AC-quantity: 307 098 individuals). RESULTS: Positive genetic correlation was observed between MD and AD (rgMD-AD = + 0.47, P = 6.6 × 10-10). AC-quantity showed positive genetic correlation with both AD (rgAD-AC quantity = + 0.75, P = 1.8 × 10-14) and MD (rgMD-AC quantity = + 0.14, P = 2.9 × 10-7), while there was negative correlation of AC-frequency with MD (rgMD-AC frequency = -0.17, P = 1.5 × 10-10) and a non-significant result with AD. MR analyses confirmed the presence of pleiotropy among these four traits. However, the MD-AD results reflect a mediated-pleiotropy mechanism (i.e. causal relationship) with an effect of MD on AD (beta = 0.28, P = 1.29 × 10-6). There was no evidence for reverse causation. CONCLUSION: This study supports a causal role for genetic liability of MD on AD based on genetic datasets including thousands of individuals. Understanding mechanisms underlying MD-AD comorbidity addresses important public health concerns and has the potential to facilitate prevention and intervention efforts.Item Genetics of Alcoholism(Springer, 2019-04) Edenberg, Howard J.; Gelernter, Joel; Agrawal, Arpana; Biochemistry and Molecular Biology, School of MedicinePurpose of Review We review the search for genetic variants that affect the risk for alcohol dependence and alcohol consumption. Recent Findings Variations in genes affecting alcohol metabolism (ADH1B, ALDH2) are protective against both alcohol dependence and excessive consumption, but different variants are found in different populations. There are different patterns of risk variants for alcohol dependence vs. consumption. Variants for alcohol dependence, but not consumption, are associated with risk for other psychiatric illnesses. Summary ADH1B and ALDH2 strongly affect both consumption and dependence. Variations in many other genes affect both consumption and dependence—or one or the other of these traits—but individual effect sizes are small. Evidence for other specific genes that affect dependence is not yet strong. Most current knowledge derives from studies of European-ancestry populations, and large studies of carefully phenotyped subjects from different populations are needed to understand the genetic contributions to alcohol consumption and alcohol use disorders.