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Browsing by Author "Hatoum, Alexander S."
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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 Genes identified in rodent studies of alcohol intake are enriched for heritability of human substance use(Wiley, 2021) Huggett, Spencer B.; Johnson, Emma C.; Hatoum, Alexander S.; Lai, Dongbing; Srijeyanthan, Jenani; Bubier, Jason A.; Chesler, Elissa J.; Agrawal, Arpana; Palmer, Abraham A.; Edenberg, Howard J.; Palmer, Rohan H. C.; Medical and Molecular Genetics, School of MedicineBackground: Rodent paradigms and human genome-wide association studies (GWAS) on drug use have the potential to provide biological insight into the pathophysiology of addiction. Methods: Using GeneWeaver, we created rodent alcohol and nicotine gene-sets derived from 19 gene expression studies on alcohol and nicotine outcomes. We partitioned the SNP heritability of these gene-sets using four large human GWAS: (1) alcoholic drinks per week, (2) problematic alcohol use, (3) cigarettes per day, and (4) smoking cessation. We benchmarked our findings with curated human alcohol and nicotine addiction gene-sets and performed specificity analyses using other rodent gene-sets (e.g., locomotor behavior) and other human GWAS (e.g., height). Results: The rodent alcohol gene-set was enriched for heritability of drinks per week, cigarettes per day, and smoking cessation, but not problematic alcohol use. However, the rodent nicotine gene-set was not significantly associated with any of these traits. Both rodent gene-sets showed enrichment for several non-substance-use GWAS, and the extent of this relationship tended to increase as a function of trait heritability. In general, larger gene-sets demonstrated more significant enrichment. Finally, when evaluating human traits with similar heritabilities, both rodent gene-sets showed greater enrichment for substance use traits. Conclusion: Our results suggest that rodent gene expression studies can help to identify genes that contribute to the heritability of some substance use traits in humans, yet there was less specificity than expected. We outline various limitations, interpretations, and considerations for future research.Item Genome-wide association study in individuals of European and African ancestry and multi-trait analysis of opioid use disorder identifies 19 independent genome-wide significant risk loci(Springer, 2022-10) Deak, Joseph D.; Zhou, Hang; Galimberti, Marco; Levey, Daniel F.; Wendt, Frank R.; Sanchez-Roige, Sandra; Hatoum, Alexander S.; Johnson, Emma C.; Nunez, Yaira Z.; Demontis, Ditte; Børglum, Anders D.; Rajagopal, Veera M.; Jennings, Mariela V.; Kember, Rachel L.; Justice, Amy C.; Edenberg, Howard J.; Agrawal, Arpana; Polimanti, Renato; Kranzler, Henry R.; Gelernter, Joel; Biochemistry and Molecular Biology, School of MedicineDespite the large toll of opioid use disorder (OUD), genome-wide association studies (GWAS) of OUD to date have yielded few susceptibility loci. We performed a large-scale GWAS of OUD in individuals of European (EUR) and African (AFR) ancestry, optimizing genetic informativeness by performing MTAG (Multi-trait analysis of GWAS) with genetically correlated substance use disorders (SUDs). Meta-analysis included seven cohorts: the Million Veteran Program, Psychiatric Genomics Consortium, iPSYCH, FinnGen, Partners Biobank, BioVU, and Yale-Penn 3, resulting in a total N = 639,063 (Ncases = 20,686;Neffective = 77,026) across ancestries. OUD cases were defined as having a lifetime OUD diagnosis, and controls as anyone not known to meet OUD criteria. We estimated SNP-heritability (h2SNP) and genetic correlations (rg). Based on genetic correlation, we performed MTAG on OUD, alcohol use disorder (AUD), and cannabis use disorder (CanUD). A leave-one-out polygenic risk score (PRS) analysis was performed to compare OUD and OUD-MTAG PRS as predictors of OUD case status in Yale-Penn 3. The EUR meta-analysis identified three genome-wide significant (GWS; p ≤ 5 × 10−8) lead SNPs—one at FURIN (rs11372849; p = 9.54 × 10−10) and two OPRM1 variants (rs1799971, p = 4.92 × 10−09; rs79704991, p = 1.11 × 10−08; r2 = 0.02). Rs1799971 (p = 4.91 × 10−08) and another OPRM1 variant (rs9478500; p = 1.95 × 10−08; r2 = 0.03) were identified in the cross-ancestry meta-analysis. Estimated h2SNP was 12.75%, with strong rg with CanUD (rg = 0.82; p = 1.14 × 10−47) and AUD (rg = 0.77; p = 6.36 × 10−78). The OUD-MTAG resulted in a GWAS Nequivalent = 128,748 and 18 independent GWS loci, some mapping to genes or gene regions that have previously been associated with psychiatric or addiction phenotypes. The OUD-MTAG PRS accounted for 3.81% of OUD variance (beta = 0.61;s.e. = 0.066; p = 2.00 × 10−16) compared to 2.41% (beta = 0.45; s.e. = 0.058; p = 2.90 × 10−13) explained by the OUD PRS. The current study identified OUD variant associations at OPRM1, single variant associations with FURIN, and 18 GWS associations in the OUD-MTAG. The genetic architecture of OUD is likely influenced by both OUD-specific loci and loci shared across SUDs.Item Investigation of convergent and divergent genetic influences underlying schizophrenia and alcohol use disorder(Cambridge University Press, 2023) Johnson, Emma C.; Kapoor, Manav; Hatoum, Alexander S.; Zhou, Hang; Polimanti, Renato; Wendt, Frank R.; Walters, Raymond K.; Lai, Dongbing; Kember, Rachel L.; Hartz, Sarah; Meyers, Jacquelyn L.; Peterson, Roseann E.; Ripke, Stephan; Bigdeli, Tim B.; Fanous, Ayman H.; Pato, Carlos N.; Pato, Michele T.; Goate, Alison M.; Kranzler, Henry R.; O’Donovan, Michael C.; Walters, James T. R.; Gelernter, Joel; Edenberg, Howard J.; Agrawal, Arpana; Medical and Molecular Genetics, School of MedicineBackground: Alcohol use disorder (AUD) and schizophrenia (SCZ) frequently co-occur, and large-scale genome-wide association studies (GWAS) have identified significant genetic correlations between these disorders. Methods: We used the largest published GWAS for AUD (total cases = 77 822) and SCZ (total cases = 46 827) to identify genetic variants that influence both disorders (with either the same or opposite direction of effect) and those that are disorder specific. Results: We identified 55 independent genome-wide significant single nucleotide polymorphisms with the same direction of effect on AUD and SCZ, 8 with robust effects in opposite directions, and 98 with disorder-specific effects. We also found evidence for 12 genes whose pleiotropic associations with AUD and SCZ are consistent with mediation via gene expression in the prefrontal cortex. The genetic covariance between AUD and SCZ was concentrated in genomic regions functional in brain tissues (p = 0.001). Conclusions: Our findings provide further evidence that SCZ shares meaningful genetic overlap with AUD.Item A large-scale genome-wide association study meta-analysis of cannabis use disorder(Elsevier, 2020-12) Johnson, Emma C.; Demontis, Ditte; Thorgeirsson, Thorgeir E.; Walters, Raymond K.; Polimanti, Renato; Hatoum, Alexander S.; Sanchez-Roige, Sandra; Paul, Sarah E.; Wendt, Frank R.; Clarke, Toni-Kim; Lai, Dongbing; Reginsson, Gunnar W.; Zhou, Hang; He, June; Baranger, David A.A.; Gudbjartsson, Daniel F.; Wedow, Robbee; Adkins, Daniel E.; Adkins, Amy E.; Alexander, Jeffry; Bacanu, Silviu-Alin; Bigdeli, Tim B.; Boden, Joseph; Brown, Sandra A.; Bucholz, Kathleen K.; Bybjerg-Grauholm, Jonas; Corley, Robin P.; Degenhardt, Louisa; Dick, Danielle M.; Domingue, Benjamin W.; Fox, Louis; Goate, Alison M.; Gordon, Scott D.; Hack, Laura M.; Hancock, Dana B.; Hartz, Sarah M.; Hickie, Ian B.; Hougaard, David M.; Krauter, Kenneth; Lind, Penelope A.; McClintick, Jeanette N.; McQueen, Matthew B.; Meyers, Jacquelyn L.; Montgomery, Grant W.; Mors, Ole; Mortensen, Preben B.; Nordentoft, Merete; Pearson, John F.; Peterson, Roseann E.; Reynolds, Maureen D.; Rice, John P.; Runarsdottir, Valgerdur; Saccone, Nancy L.; Sherva, Richard; Silberg, Judy L.; Tarter, Ralph E.; Tyrfingsson, Thorarinn; Wall, Tamara L.; Webb, Bradley T.; Werge, Thomas; Wetherill, Leah; Wright, Margaret J.; Zellers, Stephanie; Adams, Mark J.; Bierut, Laura J.; Boardman, Jason D.; Copeland, William E.; Farrer, Lindsay A.; Foroud, Tatiana M.; Gillespie, Nathan A.; Grucza, Richard A.; Mullan Harris, Kathleen; Heath, Andrew C.; Hesselbrock, Victor; Hewitt, John K.; Hopfer, Christian J.; Horwood, John; Iacono, William G.; Johnson, Eric O.; Kendler, Kenneth S.; Kennedy, Martin A.; Kranzler, Henry R.; Madden, Pamela A.F.; Maes, Hermine H.; Maher, Brion S.; Martin, Nicholas G.; McGue, Matthew; McIntosh, Andrew M.; Medland, Sarah E.; Nelson, Elliot C.; Porjesz, Bernice; Riley, Brien P.; Stallings, Michael C.; Vanyukov, Michael M.; Vrieze, Scott; Davis, Lea K.; Bogdan, Ryan; Gelernter, Joel; Edenberg, Howard J.; Stefansson, Kari; Børglum, Anders D.; Agrawal, Arpana; Medical and Molecular Genetics, School of MedicineBackground: Variation in liability to cannabis use disorder has a strong genetic component (estimated twin and family heritability about 50-70%) and is associated with negative outcomes, including increased risk of psychopathology. The aim of the study was to conduct a large genome-wide association study (GWAS) to identify novel genetic variants associated with cannabis use disorder. Methods: To conduct this GWAS meta-analysis of cannabis use disorder and identify associations with genetic loci, we used samples from the Psychiatric Genomics Consortium Substance Use Disorders working group, iPSYCH, and deCODE (20 916 case samples, 363 116 control samples in total), contrasting cannabis use disorder cases with controls. To examine the genetic overlap between cannabis use disorder and 22 traits of interest (chosen because of previously published phenotypic correlations [eg, psychiatric disorders] or hypothesised associations [eg, chronotype] with cannabis use disorder), we used linkage disequilibrium score regression to calculate genetic correlations. Findings: We identified two genome-wide significant loci: a novel chromosome 7 locus (FOXP2, lead single-nucleotide polymorphism [SNP] rs7783012; odds ratio [OR] 1·11, 95% CI 1·07-1·15, p=1·84 × 10-9) and the previously identified chromosome 8 locus (near CHRNA2 and EPHX2, lead SNP rs4732724; OR 0·89, 95% CI 0·86-0·93, p=6·46 × 10-9). Cannabis use disorder and cannabis use were genetically correlated (rg 0·50, p=1·50 × 10-21), but they showed significantly different genetic correlations with 12 of the 22 traits we tested, suggesting at least partially different genetic underpinnings of cannabis use and cannabis use disorder. Cannabis use disorder was positively genetically correlated with other psychopathology, including ADHD, major depression, and schizophrenia. Interpretation: These findings support the theory that cannabis use disorder has shared genetic liability with other psychopathology, and there is a distinction between genetic liability to cannabis use and cannabis use disorder.Item Multi-ancestry study of the genetics of problematic alcohol use in over 1 million individuals(Springer Nature, 2023) Zhou, Hang; Kember, Rachel L.; Deak, Joseph D.; Xu, Heng; Toikumo, Sylvanus; Yuan, Kai; Lind, Penelope A.; Farajzadeh, Leila; Wang, Lu; Hatoum, Alexander S.; Johnson, Jessica; Lee, Hyunjoon; Mallard, Travis T.; Xu, Jiayi; Johnston, Keira J. A.; Johnson, Emma C.; Galimberti, Marco; Dao, Cecilia; Levey, Daniel F.; Overstreet, Cassie; Byrne, Enda M.; Gillespie, Nathan A.; Gordon, Scott; Hickie, Ian B.; Whitfield, John B.; Xu, Ke; Zhao, Hongyu; Huckins, Laura M.; Davis, Lea K.; Sanchez-Roige, Sandra; Madden, Pamela A. F.; Heath, Andrew C.; Medland, Sarah E.; Martin, Nicholas G.; Ge, Tian; Smoller, Jordan W.; Hougaard, David M.; Børglum, Anders D.; Demontis, Ditte; Krystal, John H.; Gaziano, J. Michael; Edenberg, Howard J.; Agrawal, Arpana; Million Veteran Program; Justice, Amy C.; Stein, Murray B.; Kranzler, Henry R.; Gelernter, Joel; Biochemistry and Molecular Biology, School of MedicineProblematic alcohol use (PAU), a trait that combines alcohol use disorder and alcohol-related problems assessed with a questionnaire, is a leading cause of death and morbidity worldwide. Here we conducted a large cross-ancestry meta-analysis of PAU in 1,079,947 individuals (European, N = 903,147; African, N = 122,571; Latin American, N = 38,962; East Asian, N = 13,551; and South Asian, N = 1,716 ancestries). We observed a high degree of cross-ancestral similarity in the genetic architecture of PAU and identified 110 independent risk variants in within- and cross-ancestry analyses. Cross-ancestry fine mapping improved the identification of likely causal variants. Prioritizing genes through gene expression and chromatin interaction in brain tissues identified multiple genes associated with PAU. We identified existing medications for potential pharmacological studies by a computational drug repurposing analysis. Cross-ancestry polygenic risk scores showed better performance of association in independent samples than single-ancestry polygenic risk scores. Genetic correlations between PAU and other traits were observed in multiple ancestries, with other substance use traits having the highest correlations. This study advances our knowledge of the genetic etiology of PAU, and these findings may bring possible clinical applicability of genetics insights-together with neuroscience, biology and data science-closer.Item Multi-omics cannot replace sample size in genome-wide association studies(Wiley, 2023) Baranger, David A. A.; Hatoum, Alexander S.; Polimanti, Renato; Gelernter, Joel; Edenberg, Howard J.; Bogdan, Ryan; Agrawal, Arpana; Biochemistry and Molecular Biology, School of MedicineThe integration of multi-omics information (e.g., epigenetics and transcriptomics) can be useful for interpreting findings from genome-wide association studies (GWAS). It has been suggested that multi-omics could circumvent or greatly reduce the need to increase GWAS sample sizes for novel variant discovery. We tested whether incorporating multi-omics information in earlier and smaller-sized GWAS boosts true-positive discovery of genes that were later revealed by larger GWAS of the same/similar traits. We applied 10 different analytic approaches to integrating multi-omics data from 12 sources (e.g., Genotype-Tissue Expression project) to test whether earlier and smaller GWAS of 4 brain-related traits (alcohol use disorder/problematic alcohol use, major depression/depression, schizophrenia, and intracranial volume/brain volume) could detect genes that were revealed by a later and larger GWAS. Multi-omics data did not reliably identify novel genes in earlier less-powered GWAS (PPV <0.2; 80% false-positive associations). Machine learning predictions marginally increased the number of identified novel genes, correctly identifying 1-8 additional genes, but only for well-powered early GWAS of highly heritable traits (i.e., intracranial volume and schizophrenia). Although multi-omics, particularly positional mapping (i.e., fastBAT, MAGMA, and H-MAGMA), can help to prioritize genes within genome-wide significant loci (PPVs = 0.5-1.0) and translate them into information about disease biology, it does not reliably increase novel gene discovery in brain-related GWAS. To increase power for discovery of novel genes and loci, increasing sample size is required.Item Multivariate genome-wide association meta-analysis of over 1 million subjects identifies loci underlying multiple substance use disorders(Springer Nature, 2023) Hatoum, Alexander S.; Colbert, Sarah M. C.; Johnson, Emma C.; Huggett, Spencer B.; Deak, Joseph D.; Pathak, Gita; Jennings, Mariela V.; Paul, Sarah E.; Karcher, Nicole R.; Hansen, Isabella; Baranger, David A. A.; Edwards, Alexis; Grotzinger, Andrew; Substance Use Disorder Working Group of the Psychiatric Genomics Consortium; Tucker-Drob, Elliot M.; Kranzler, Henry R.; Davis, Lea K.; Sanchez-Roige, Sandra; Polimanti, Renato; Gelernter, Joel; Edenberg, Howard J.; Bogdan, Ryan; Agrawal, Arpana; Medical and Molecular Genetics, School of MedicineGenetic liability to substance use disorders can be parsed into loci that confer general or substance-specific addiction risk. We report a multivariate genome-wide association meta-analysis that disaggregates general and substance-specific loci for published summary statistics of problematic alcohol use, problematic tobacco use, cannabis use disorder, and opioid use disorder in a sample of 1,025,550 individuals of European descent and 92,630 individuals of African descent. Nineteen independent SNPs were genome-wide significant (P < 5e-8) for the general addiction risk factor (addiction-rf), which showed high polygenicity. Across ancestries, PDE4B was significant (among other genes), suggesting dopamine regulation as a cross-substance vulnerability. An addiction-rf polygenic risk score was associated with substance use disorders, psychopathologies, somatic conditions, and environments associated with the onset of addictions. Substance-specific loci (9 for alcohol, 32 for tobacco, 5 for cannabis, 1 for opioids) included metabolic and receptor genes. These findings provide insight into genetic risk loci for substance use disorders that could be leveraged as treatment targetsItem The addiction risk factor: A unitary genetic vulnerability characterizes substance use disorders and their associations with common correlates(Springer Nature, 2022) Hatoum, Alexander S.; Johnson, Emma C.; Colbert, Sarah M. C.; Polimanti, Renato; Zhou, Hang; Walters, Raymond K.; Gelernter, Joel; Edenberg, Howard J.; Bogdan, Ryan; Agrawal, Arpana; Medical and Molecular Genetics, School of MedicineSubstance use disorders commonly co-occur with one another and with other psychiatric disorders. They share common features including high impulsivity, negative affect, and lower executive function. We tested whether a common genetic factor undergirds liability to problematic alcohol use (PAU), problematic tobacco use (PTU), cannabis use disorder (CUD), and opioid use disorder (OUD) by applying genomic structural equation modeling to genome-wide association study summary statistics for individuals of European ancestry (Total N = 1,019,521; substance-specific Ns range: 82,707–435,563) while adjusting for the genetics of substance use (Ns = 184,765−632,802). We also tested whether shared liability across SUDs is associated with behavioral constructs (risk-taking, executive function, neuroticism; Ns = 328,339−427,037) and non-substance use psychopathology (psychotic, compulsive, and early neurodevelopmental disorders). Shared genetic liability to PAU, PTU, CUD, and OUD was characterized by a unidimensional addiction risk factor (termed The Addiction-Risk-Factor, independent of substance use. OUD and CUD demonstrated the largest loadings, while problematic tobacco use showed the lowest loading. The Addiction-Risk-Factor was associated with risk-taking, neuroticism, executive function, and non-substance psychopathology, but retained specific variance before and after accounting for the genetics of substance use. Thus, a common genetic factor partly explains susceptibility for alcohol, tobacco, cannabis, and opioid use disorder. The Addiction-Risk-Factor has a unique genetic architecture that is not shared with normative substance use or non-substance psychopathology, suggesting that addiction is not the linear combination of substance use and psychopathology.Item The relationship between cannabis and schizophrenia: a genetically informed perspective(Wiley, 2021) Johnson, Emma C.; Hatoum, Alexander S.; Deak, Joseph D.; Polimanti, Renato; Murray, Robin M.; Edenberg, Howard J.; Gelernter, Joel; Di Forti, Marta; Agrawal, Arpana; Medical and Molecular Genetics, School of MedicineBackground and aims: While epidemiological studies support a role for heavy, high-potency cannabis use on first-episode psychosis, genetic models of causation suggest reverse causal effects of schizophrenia on cannabis use liability. We estimated the genetic relationship between cannabis use disorder (CUD) and schizophrenia (SCZ) and tested whether liability for CUD is causally associated with increased liability to SCZ while adjusting for tobacco smoking. Design: This study used summary statistics from published genome-wide association studies (GWAS). We used genomic structural equation modeling, latent causal variable analysis, and multivariable Mendelian randomization to examine genetic relationships between CUD, cannabis ever-use, ever-smoked tobacco regularly, nicotine dependence and SCZ, and to test for a causal relationship between liability to CUD and liability to SCZ. Setting: Genome-wide association studies were published previously as part of international consortia. Participants: Sample sizes of the GWAS summary statistics used in this study ranged from 161 405 to 357 806 individuals of European ancestry. Measurements: Genome-wide summary statistics for CUD and SCZ were the primary measurements, while summary statistics for cannabis ever-use, ever-smoked tobacco regularly and nicotine dependence were included as additional variables in the genomic structural equation models and the multivariable Mendelian randomization analyses. Findings: Genetic liability to CUD was significantly associated with SCZ [β = 0.29, 95% confidence interval (CI) = 0.11, 0.46, P = 0.001], even when accounting for cannabis ever-use, ever-smoked tobacco regularly and nicotine dependence as simultaneous predictors. We found mixed evidence of a causal relationship, with the latent causal variable analysis finding no evidence of causality (genetic causality proportion = -0.08, 95% CI = -0.40, 0.23, P = 0.87) but the multivariable Mendelian randomization analyses suggesting a significant, risk-increasing effect of CUD on liability to SCZ (β = 0.10, 95% CI = 0.02, 0.18, P = 0.02), accounting for the additional risk factors (cannabis ever-use, ever-smoked tobacco regularly and nicotine dependence). Conclusions: Genetic liability for cannabis use disorder appears to be robustly associated with schizophrenia, above and beyond tobacco smoking and cannabis ever-use, with mixed evidence to support a causal relationship between cannabis use disorder and schizophrenia.