Browsing by Author "Wendt, Frank R."

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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 analyses identify 95 risk loci and provide insights into the neurobiology of post-traumatic stress disorder
    (Springer Nature, 2024) Nievergelt, Caroline M.; Maihofer, Adam X.; Atkinson, Elizabeth G.; Chen, Chia-Yen; Choi, Karmel W.; Coleman, Jonathan R. I.; Daskalakis, Nikolaos P.; Duncan, Laramie E.; Polimanti, Renato; Aaronson, Cindy; Amstadter, Ananda B.; Andersen, Soren B.; Andreassen, Ole A.; Arbisi, Paul A.; Ashley-Koch, Allison E.; Austin, S. Bryn; Avdibegoviç, Esmina; Babić, Dragan; Bacanu, Silviu-Alin; Baker, Dewleen G.; Batzler, Anthony; Beckham, Jean C.; Belangero, Sintia; Benjet, Corina; Bergner, Carisa; Bierer, Linda M.; Biernacka, Joanna M.; Bierut, Laura J.; Bisson, Jonathan I.; Boks, Marco P.; Bolger, Elizabeth A.; Brandolino, Amber; Breen, Gerome; Bressan, Rodrigo Affonseca; Bryant, Richard A.; Bustamante, Angela C.; Bybjerg-Grauholm, Jonas; Bækvad-Hansen, Marie; Børglum, Anders D.; Børte, Sigrid; Cahn, Leah; Calabrese, Joseph R.; Caldas-de-Almeida, Jose Miguel; Chatzinakos, Chris; Cheema, Sheraz; Clouston, Sean A. P.; Colodro-Conde, Lucía; Coombes, Brandon J.; Cruz-Fuentes, Carlos S.; Dale, Anders M.; Dalvie, Shareefa; Davis, Lea K.; Deckert, Jürgen; Delahanty, Douglas L.; Dennis, Michelle F.; Desarnaud, Frank; DiPietro, Christopher P.; Disner, Seth G.; Docherty, Anna R.; Domschke, Katharina; Dyb, Grete; Džubur Kulenović, Alma; Edenberg, Howard J.; Evans, Alexandra; Fabbri, Chiara; Fani, Negar; Farrer, Lindsay A.; Feder, Adriana; Feeny, Norah C.; Flory, Janine D.; Forbes, David; Franz, Carol E.; Galea, Sandro; Garrett, Melanie E.; Gelaye, Bizu; Gelernter, Joel; Geuze, Elbert; Gillespie, Charles F.; Goleva, Slavina B.; Gordon, Scott D.; Goçi, Aferdita; Grasser, Lana Ruvolo; Guindalini, Camila; Haas, Magali; Hagenaars, Saskia; Hauser, Michael A.; Heath, Andrew C.; Hemmings, Sian M. J.; Hesselbrock, Victor; Hickie, Ian B.; Hogan, Kelleigh; Hougaard, David Michael; Huang, Hailiang; Huckins, Laura M.; Hveem, Kristian; Jakovljević, Miro; Javanbakht, Arash; Jenkins, Gregory D.; Johnson, Jessica; Jones, Ian; Jovanovic, Tanja; Karstoft, Karen-Inge; Kaufman, Milissa L.; Kennedy, James L.; Kessler, Ronald C.; Khan, Alaptagin; Kimbrel, Nathan A.; King, Anthony P.; Koen, Nastassja; Kotov, Roman; Kranzler, Henry R.; Krebs, Kristi; Kremen, William S.; Kuan, Pei-Fen; Lawford, Bruce R.; Lebois, Lauren A. M.; Lehto, Kelli; Levey, Daniel F.; Lewis, Catrin; Liberzon, Israel; Linnstaedt, Sarah D.; Logue, Mark W.; Lori, Adriana; Lu, Yi; Luft, Benjamin J.; Lupto, Michelle K.; Luykx, Jurjen J.; Makotkine, Iouri; Maples-Keller, Jessica L.; Marchese, Shelby; Marmar, Charles; Martin, Nicholas G.; Martínez-Levy, Gabriela A.; McAloney, Kerrie; McFarlane, Alexander; McLaughlin, Katie A.; McLean, Samuel A.; Medland, Sarah E.; Mehta, Divya; Meyers, Jacquelyn; Michopoulos, Vasiliki; Mikita, Elizabeth A.; Milani, Lili; Milberg, William; Miller, Mark W.; Morey, Rajendra A.; Morris, Charles Phillip; Mors, Ole; Mortensen, Preben Bo; Mufford, Mary S.; Nelson, Elliot C.; Nordentoft, Merete; Norman, Sonya B.; Nugent, Nicole R.; O'Donnell, Meaghan; Orcutt, Holly K.; Pan, Pedro M.; Panizzon, Matthew S.; Pathak, Gita A.; Peters, Edward S.; Peterson, Alan L.; Peverill, Matthew; Pietrzak, Robert H.; Polusny, Melissa A.; Porjesz, Bernice; Powers, Abigail; Qin, Xue-Jun; Ratanatharathorn, Andrew; Risbrough, Victoria B.; Roberts, Andrea L.; Rothbaum, Alex O.; Rothbaum, Barbara O.; Roy-Byrne, Peter; Ruggiero, Kenneth J.; Rung, Ariane; Runz, Heiko; Rutten, Bart P. F.; Saenz de Viteri, Stacey; Salum, Giovanni Abrahão; Sampson, Laura; Sanchez, Sixto E.; Santoro, Marcos; Seah, Carina; Seedat, Soraya; Seng, Julia S.; Shabalin, Andrey; Sheerin, Christina M.; Silove, Derrick; Smith, Alicia K.; Smoller, Jordan W.; Sponheim, Scott R.; Stein, Dan J.; Stensland, Synne; Stevens, Jennifer S.; Sumner, Jennifer A.; Teicher, Martin H.; Thompson, Wesley K.; Tiwari, Arun K.; Trapido, Edward; Uddin, Monica; Ursano, Robert J.; Valdimarsdóttir, Unnur; Van Hooff, Miranda; Vermetten, Eric; Vinkers, Christiaan H.; Voisey, Joanne; Wang, Yunpeng; Wang, Zhewu; Waszczuk, Monika; Weber, Heike; Wendt, Frank R.; Werge, Thomas; Williams, Michelle A.; Williamson, Douglas E.; Winsvold, Bendik S.; Winternitz, Sherry; Wolf, Christiane; Wolf, Erika J.; Xia, Yan; Xiong, Ying; Yehuda, Rachel; Young, Keith A.; Young, Ross McD.; Zai, Clement C.; Zai, Gwyneth C.; Zervas, Mark; Zhao, Hongyu; Zoellner, Lori A.; Zwart, John-Anker; deRoon-Cassini, Terri; van Rooij, Sanne J. H.; van den Heuvel, Leigh L.; AURORA Study; Estonian Biobank Research Team; FinnGen Investigators; HUNT All-In Psychiatry; Stein, Murray B.; Ressler, Kerry J.; Koenen, Karestan C.; Biochemistry and Molecular Biology, School of Medicine
    Post-traumatic stress disorder (PTSD) genetics are characterized by lower discoverability than most other psychiatric disorders. The contribution to biological understanding from previous genetic studies has thus been limited. We performed a multi-ancestry meta-analysis of genome-wide association studies across 1,222,882 individuals of European ancestry (137,136 cases) and 58,051 admixed individuals with African and Native American ancestry (13,624 cases). We identified 95 genome-wide significant loci (80 new). Convergent multi-omic approaches identified 43 potential causal genes, broadly classified as neurotransmitter and ion channel synaptic modulators (for example, GRIA1, GRM8 and CACNA1E), developmental, axon guidance and transcription factors (for example, FOXP2, EFNA5 and DCC), synaptic structure and function genes (for example, PCLO, NCAM1 and PDE4B) and endocrine or immune regulators (for example, ESR1, TRAF3 and TANK). Additional top genes influence stress, immune, fear and threat-related processes, previously hypothesized to underlie PTSD neurobiology. These findings strengthen our understanding of neurobiological systems relevant to PTSD pathophysiology, while also opening new areas for investigation.
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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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    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 Medicine
    Background: 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.
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    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 Medicine
    Background: 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.
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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.