Browsing by Author "Galimberti, Marco"

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    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 Medicine
    Background: 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.
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    Genome-wide association study identifies 30 obsessive-compulsive disorder associated loci
    (medRxiv, 2024-03-13) Strom, Nora I.; Gerring, Zachary F.; Galimberti, Marco; Yu, Dongmei; Halvorsen, Matthew W.; Abdellaoui, Abdel; Rodriguez-Fontenla, Cristina; Sealock, Julia M.; Bigdeli, Tim; Coleman, Jonathan R.; Mahjani, Behrang; Thorp, Jackson G.; Bey, Katharina; Burton, Christie L.; Luykx, Jurjen J.; Zai, Gwyneth; Alemany, Silvia; Andre, Christine; Askland, Kathleen D.; Banaj, Nerisa; Barlassina, Cristina; Becker Nissen, Judith; Bienvenu, O. Joseph; Black, Donald; Bloch, Michael H.; Boberg, Julia; Børte, Sigrid; Bosch, Rosa; Breen, Michael; Brennan, Brian P.; Brentani, Helena; Buxbaum, Joseph D.; Bybjerg-Grauholm, Jonas; Byrne, Enda M.; Cabana-Dominguez, Judith; Camarena, Beatriz; Camarena, Adrian; Cappi, Carolina; Carracedo, Angel; Casas, Miguel; Cavallini, Maria Cristina; Ciullo, Valentina; Cook, Edwin H.; Crosby, Jesse; Cullen, Bernadette A.; De Schipper, Elles J.; Delorme, Richard; Djurovic, Srdjan; Elias, Jason A.; Estivill, Xavier; Falkenstein, Martha J.; Fundin, Bengt T.; Garner, Lauryn; German, Chris; Gironda, Christina; Goes, Fernando S.; Grados, Marco A.; Grove, Jakob; Guo, Wei; Haavik, Jan; Hagen, Kristen; Harrington, Kelly; Havdahl, Alexandra; Höffler, Kira D.; Hounie, Ana G.; Hucks, Donald; Hultman, Christina; Janecka, Magdalena; Jenike, Eric; Karlsson, Elinor K.; Kelley, Kara; Klawohn, Julia; Krasnow, Janice E.; Krebs, Kristi; Lange, Christoph; Lanzagorta, Nuria; Levey, Daniel; Lindblad-Toh, Kerstin; Macciardi, Fabio; Maher, Brion; Mathes, Brittany; McArthur, Evonne; McGregor, Nathaniel; McLaughlin, Nicole C.; Meier, Sandra; Miguel, Euripedes C.; Mulhern, Maureen; Nestadt, Paul S.; Nurmi, Erika L.; O'Connell, Kevin S.; Osiecki, Lisa; Ousdal, Olga Therese; Palviainen, Teemu; Pedersen, Nancy L.; Piras, Fabrizio; Piras, Federica; Potluri, Sriramya; Rabionet, Raquel; Ramirez, Alfredo; Rauch, Scott; Reichenberg, Abraham; Riddle, Mark A.; Ripke, Stephan; Rosário, Maria C.; Sampaio, Aline S.; Schiele, Miriam A.; Skogholt, Anne Heidi; Sloofman, Laura G.; Smit, Jan; Soler, Artigas María; Thomas, Laurent F.; Tifft, Eric; Vallada, Homero; van Kirk, Nathanial; Veenstra-VanderWeele, Jeremy; Vulink, Nienke N.; Walker, Christopher P.; Wang, Ying; Wendland, Jens R.; Winsvold, Bendik S.; Yao, Yin; Zhou, Hang; 23andMe Research Team; VA Million Veteran Program; Estonian Biobank; CoGa research team; iPSYCH; HUNT research team; NORDiC research team; Agrawal, Arpana; Alonso, Pino; Berberich, Götz; Bucholz, Kathleen K.; Bulik, Cynthia M.; Cath, Danielle; Denys, Damiaan; Eapen, Valsamma; Edenberg, Howard; Falkai, Peter; Fernandez, Thomas V.; Fyer, Abby J.; Gaziano, J. M.; Geller, Dan A.; Grabe, Hans J.; Greenberg, Benjamin D.; Hanna, Gregory L.; Hickie, Ian B.; Hougaard, David M.; Kathmann, Norbert; Kennedy, James; Lai, Dongbing; Landén, Mikael; Le Hellard, Stéphanie; Leboyer, Marion; Lochner, Christine; McCracken, James T.; Medland, Sarah E.; Mortensen, Preben B.; Neale, Benjamin M.; Nicolini, Humberto; Nordentoft, Merete; Pato, Michele; Pato, Carlos; Pauls, David L.; Piacentini, John; Pittenger, Christopher; Posthuma, Danielle; Ramos-Quiroga, Josep Antoni; Rasmussen, Steven A.; Richter, Margaret A.; Rosenberg, David R.; Ruhrmann, Stephan; Samuels, Jack F.; Sandin, Sven; Sandor, Paul; Spalletta, Gianfranco; Stein, Dan J.; Stewart, S. Evelyn; Storch, Eric A.; Stranger, Barbara E.; Turiel, Maurizio; Werge, Thomas; Andreassen, Ole A.; Børglum, Anders D.; Walitza, Susanne; Hveem, Kristian; Hansen, Bjarne K.; Rück, Christian P.; Martin, Nicholas G.; Milani, Lili; Mors, Ole; Reichborn-Kjennerud, Ted; Ribasés, Marta; Kvale, Gerd; Mataix-Cols, David; Domschke, Katharina; Grünblatt, Edna; Wagner, Michael; Zwart, John-Anker; Breen, Gerome; Nestadt, Gerald; Kaprio, Jaakko; Arnold, Paul D.; Grice, Dorothy E.; Knowles, James A.; Ask, Helga; Verweij, Karin J.; Davis, Lea K.; Smit, Dirk J.; Crowley, James J.; Scharf, Jeremiah M.; Stein, Murray B.; Gelernter, Joel; Mathews, Carol A.; Derks, Eske M.; Mattheisen, Manuel; Biochemistry and Molecular Biology, School of Medicine
    Obsessive-compulsive disorder (OCD) affects ~1% of the population and exhibits a high SNP-heritability, yet previous genome-wide association studies (GWAS) have provided limited information on the genetic etiology and underlying biological mechanisms of the disorder. We conducted a GWAS meta-analysis combining 53,660 OCD cases and 2,044,417 controls from 28 European-ancestry cohorts revealing 30 independent genome-wide significant SNPs and a SNP-based heritability of 6.7%. Separate GWAS for clinical, biobank, comorbid, and self-report sub-groups found no evidence of sample ascertainment impacting our results. Functional and positional QTL gene-based approaches identified 249 significant candidate risk genes for OCD, of which 25 were identified as putatively causal, highlighting WDR6, DALRD3, CTNND1 and genes in the MHC region. Tissue and single-cell enrichment analyses highlighted hippocampal and cortical excitatory neurons, along with D1- and D2-type dopamine receptor-containing medium spiny neurons, as playing a role in OCD risk. OCD displayed significant genetic correlations with 65 out of 112 examined phenotypes. Notably, it showed positive genetic correlations with all included psychiatric phenotypes, in particular anxiety, depression, anorexia nervosa, and Tourette syndrome, and negative correlations with a subset of the included autoimmune disorders, educational attainment, and body mass index. This study marks a significant step toward unraveling its genetic landscape and advances understanding of OCD genetics, providing a foundation for future interventions to address this debilitating disorder.
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    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 Medicine
    Despite 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.
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    Multi-ancestry genome-wide association study of cannabis use disorder yields insight into disease biology and public health implications
    (Springer Nature, 2023) Levey, Daniel F.; Galimberti, Marco; Deak, Joseph D.; Wendt, Frank R.; Bhattacharya, Arjun; Koller, Dora; Harrington, Kelly M.; Quaden, Rachel; Johnson, Emma C.; Gupta, Priya; Biradar, Mahantesh; Lam, Max; Cooke, Megan; Rajagopal, Veera M.; Empke, Stefany L. L.; Zhou, Hang; Nunez, Yaira Z.; Kranzler, Henry R.; Edenberg, Howard J.; Agrawal, Arpana; Smoller, Jordan W.; Lencz, Todd; Hougaard, David M.; Børglum, Anders D.; Demontis, Ditte; Veterans Affairs Million Veteran Program; Gaziano, J. Michael; Gandal, Michael J.; Polimanti, Renato; Stein, Murray B.; Gelernter, Joel; Biochemistry and Molecular Biology, School of Medicine
    As recreational use of cannabis is being decriminalized in many places and medical use widely sanctioned, there are growing concerns about increases in cannabis use disorder (CanUD), which is associated with numerous medical comorbidities. Here we performed a genome-wide association study of CanUD in the Million Veteran Program (MVP), followed by meta-analysis in 1,054,365 individuals (ncases = 64,314) from four broad ancestries designated by the reference panel used for assignment (European n = 886,025, African n = 123,208, admixed American n = 38,289 and East Asian n = 6,843). Population-specific methods were applied to calculate single nucleotide polymorphism-based heritability within each ancestry. Statistically significant single nucleotide polymorphism-based heritability for CanUD was observed in all but the smallest population (East Asian). We discovered genome-wide significant loci unique to each ancestry: 22 in European, 2 each in African and East Asian, and 1 in admixed American ancestries. A genetically informed causal relationship analysis indicated a possible effect of genetic liability for CanUD on lung cancer risk, suggesting potential unanticipated future medical and psychiatric public health consequences that require further study to disentangle from other known risk factors such as cigarette smoking.
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    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 Medicine
    Problematic 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.