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Item Common genetic variants influence human subcortical brain structures(Nature Publishing Group, 2015-04-09) Hibar, Derrek P.; Stein, Jason L.; Renteria, Miguel E.; Arias-Vasquez, Alejandro; Desrivières, Sylvane; Jahanshad, Neda; Toro, Roberto; Wittfeld, Katharina; Abramovic, Lucija; Andersson, Micael; Aribisala, Benjamin S.; Armstrong, Nicola J.; Bernard, Manon; Bohlken, Marc M.; Boks, Marco P.; Bralten, Janita; Brown, Andrew A.; Chakravarty, M. Mallar; Chen, Qiang; Ching, Christopher R. K.; Cuellar-Partida, Gabriel; den Braber, Anouk; Giddaluru, Sudheer; Goldman, Aaron L.; Grimm, Oliver; Guadalupe, Tulio; Hass, Johanna; Woldehawariat, Girma; Holmes, Avram J.; Hoogman, Martine; Janowitz, Deborah; Jia, Tianye; Kim, Sungeun; Klein, Marieke; Kraemer, Bernd; Lee, Phil H.; Olde Loohuis, Loes M.; Luciano, Michelle; Macare, Christine; Mather, Karen A.; Mattheisen, Manuel; Milaneschi, Yuri; Nho, Kwangsik; Papmeyer, Martina; Ramasamy, Adaikalavan; Risacher, Shannon L.; Roiz-Santiañez, Roberto; Rose, Emma J.; Salami, Alireza; Sämann, Philipp G.; Schmaal, Lianne; Schork, Andrew J.; Shin, Jean; Strike, Lachlan T.; Teumer, Alexander; van Donkelaar, Marjolein M. J.; van Eijk, Kristel R.; Walters, Raymond K.; Westlye, Lars T.; Whelan, Christopher D.; Winkler, Anderson M.; Zwiers, Marcel P.; Alhusaini, Saud; Athanasiu, Lavinia; Ehrlich, Stefan; Hakobjan, Marina M. H.; Hartberg, Cecilie B.; Haukvik, Unn K.; Heister, Angelien J. G. A. M.; Hoehn, David; Kasperaviciute, Dalia; Liewald, David C. M.; Lopez, Lorna M.; Makkinje, Remco R. R.; Matarin, Mar; Naber, Marlies A. M.; McKay, D. Reese; Needham, Margaret; Nugent, Allison C.; Pütz, Benno; Royle, Natalie A.; Shen, Li; Sprooten, Emma; Trabzuni, Daniah; van der Marel, Saskia S. L.; van Hulzen, Kimm J. E.; Walton, Esther; Wolf, Christiane; Almasy, Laura; Ames, David; Arepalli, Sampath; Assareh, Amelia A.; Bastin, Mark E.; Brodaty, Henry; Bulayeva, Kazima B.; Carless, Melanie A.; Cichon, Sven; Corvin, Aiden; Curran, Joanne E.; Czisch, Michael; de Zubicaray, Greig I.; Dillman, Allissa; Duggirala, Ravi; Dyer, Thomas D.; Erk, Susanne; Fedko, Iryna O.; Ferrucci, Luigi; Foroud, Tatiana M.; Fox, Peter T.; Fukunaga, Masaki; Gibbs, J. Raphael; Göring, Harald H. H.; Green, Robert C.; Guelfi, Sebastian; Hansell, Narelle K.; Hartman, Catharina A.; Hegenscheid, Katrin; Heinz, Andreas; Hernandez, Dena G.; Heslenfeld, Dirk J.; Hoekstra, Pieter J.; Holsboer, Florian; Homuth, Georg; Hottenga, Jouke-Jan; Ikeda, Masashi; Jack, Clifford R.; Jenkinson, Mark; Johnson, Robert; Kanai, Ryota; Keil, Maria; Kent, Jack W.; Kochunov, Peter; Kwok, John B.; Lawrie, Stephen M.; Liu, Xinmin; Longo, Dan L.; McMahon, Katie L.; Meisenzahl, Eva; Melle, Ingrid; Mohnke, Sebastian; Montgomery, Grant W.; Mostert, Jeanette C.; Mühleisen, Thomas W.; Nalls, Michael A.; Nichols, Thomas E.; Nilsson, Lars G.; Nöthen, Markus M.; Ohi, Kazutaka; Olvera, Rene L.; Perez-Iglesias, Rocio; Pike, G. Bruce; Potkin, Steven G.; Reinvang, Ivar; Reppermund, Simone; Rietschel, Marcella; Romanczuk-Seiferth, Nina; Rosen, Glenn D.; Rujescu, Dan; Schnell, Knut; Schofield, Peter R.; Smith, Colin; Steen, Vidar M.; Sussmann, Jessika E.; Thalamuthu, Anbupalam; Toga, Arthur W.; Traynor, Bryan J.; Troncoso, Juan; Turner, Jessica A.; Valdés Hernández, Maria C.; van ’t Ent, Dennis; van der Brug, Marcel; van der Wee, Nic J. A.; van Tol, Marie-Jose; Veltman, Dick J.; Wassink, Thomas H.; Westman, Eric; Zielke, Ronald H.; Zonderman, Alan B.; Ashbrook, David G.; Hager, Reinmar; Lu, Lu; McMahon, Francis J.; Morris, Derek W.; Williams, Robert W.; Brunner, Han G.; Buckner, Randy L.; Buitelaar, Jan K.; Cahn, Wiepke; Calhoun, Vince D.; Cavalleri, Gianpiero L.; Crespo-Facorro, Benedicto; Dale, Anders M.; Davies, Gareth E.; Delanty, Norman; Depondt, Chantal; Djurovic, Srdjan; Drevets, Wayne C.; Espeseth, Thomas; Gollub, Randy L.; Ho, Beng-Choon; Hoffmann, Wolfgang; Hosten, Norbert; Kahn, René S.; Le Hellard, Stephanie; Meyer-Lindenberg, Andreas; Müller-Myhsok, Bertram; Nauck, Matthias; Nyberg, Lars; Pandolfo, Massimo; Penninx, Brenda W. J. H.; Roffman, Joshua L.; Sisodiya, Sanjay M.; Smoller, Jordan W.; van Bokhoven, Hans; van Haren, Neeltje E. M.; Völzke, Henry; Walter, Henrik; Weiner, Michael W.; Wen, Wei; White, Tonya; Agartz, Ingrid; Andreassen, Ole A.; Blangero, John; Boomsma, Dorret I.; Brouwer, Rachel M.; Cannon, Dara M.; Cookson, Mark R.; de Geus, Eco J. C.; Deary, Ian J.; Donohoe, Gary; Fernández, Guillén; Fisher, Simon E.; Francks, Clyde; Glahn, David C.; Grabe, Hans J.; Gruber, Oliver; Hardy, John; Hashimoto, Ryota; Hulshoff Pol, Hilleke E.; Jönsson, Erik G.; Kloszewska, Iwona; Lovestone, Simon; Mattay, Venkata S.; Mecocci, Patrizia; McDonald, Colm; McIntosh, Andrew M.; Ophoff, Roel A.; Paus, Tomas; Pausova, Zdenka; Ryten, Mina; Sachdev, Perminder S.; Saykin, Andrew J.; Simmons, Andy; Singleton, Andrew; Soininen, Hilkka; Wardlaw, Joanna M.; Weale, Michael E.; Weinberger, Daniel R.; Adams, Hieab H. H.; Launer, Lenore J.; Seiler, Stephan; Schmidt, Reinhold; Chauhan, Ganesh; Satizabal, Claudia L.; Becker, James T.; Yanek, Lisa; van der Lee, Sven J.; Ebling, Maritza; Fischl, Bruce; Longstreth, W. T.; Greve, Douglas; Schmidt, Helena; Nyquist, Paul; Vinke, Louis N.; van Duijn, Cornelia M.; Xue, Luting; Mazoyer, Bernard; Bis, Joshua C.; Gudnason, Vilmundur; Seshadri, Sudha; Ikram, M. Arfan; Martin, Nicholas G.; Wright, Margaret J.; Schumann, Gunter; Franke, Barbara; Thompson, Paul M.; Medland, Sarah E.; Department of Radiology and Imaging Sciences, IU School of MedicineThe highly complex structure of the human brain is strongly shaped by genetic influences. Subcortical brain regions form circuits with cortical areas to coordinate movement, learning, memory and motivation, and altered circuits can lead to abnormal behaviour and disease. To investigate how common genetic variants affect the structure of these brain regions, here we conduct genome-wide association studies of the volumes of seven subcortical regions and the intracranial volume derived from magnetic resonance images of 30,717 individuals from 50 cohorts. We identify five novel genetic variants influencing the volumes of the putamen and caudate nucleus. We also find stronger evidence for three loci with previously established influences on hippocampal volume and intracranial volume. These variants show specific volumetric effects on brain structures rather than global effects across structures. The strongest effects were found for the putamen, where a novel intergenic locus with replicable influence on volume (rs945270Item Examining Sex-Differentiated Genetic Effects Across Neuropsychiatric and Behavioral Traits(Elsevier, 2021-06-15) Martin, Joanna; Khramtsova, Ekaterina A.; Goleva, Slavina B.; Blokland, Gabriëlla A.M.; Traglia, Michela; Walters, Raymond K.; Hübel, Christopher; Coleman, Jonathan R.I.; Breen, Gerome; Børglum, Anders D.; Demontis, Ditte; Grove, Jakob; Werge, Thomas; Bralten, Janita; Bulik, Cynthia M.; Lee, Phil H.; Mathews, Carol A.; Peterson, Roseann E.; Winham, Stacey J.; Wray, Naomi; Edenberg, Howard J.; Guo, Wei; Yao, Yin; Neale, Benjamin M.; Faraone, Stephen V.; Petryshen, Tracey L.; Weiss, Lauren A.; Duncan, Laramie E.; Goldstein, Jill M.; Smoller, Jordan W.; Stranger, Barbara E.; Davis, Lea K.; Biochemistry and Molecular Biology, School of MedicineBackground: The origin of sex differences in prevalence and presentation of neuropsychiatric and behavioral traits is largely unknown. Given established genetic contributions and correlations, we tested for a sex-differentiated genetic architecture within and between traits. Methods: Using European ancestry genome-wide association summary statistics for 20 neuropsychiatric and behavioral traits, we tested for sex differences in single nucleotide polymorphism (SNP)-based heritability and genetic correlation (rg < 1). For each trait, we computed per-SNP z scores from sex-stratified regression coefficients and identified genes with sex-differentiated effects using a gene-based approach. We calculated correlation coefficients between z scores to test for shared sex-differentiated effects. Finally, we tested for sex differences in across-trait genetic correlations. Results: We observed no consistent sex differences in SNP-based heritability. Between-sex, within-trait genetic correlations were high, although <1 for educational attainment and risk-taking behavior. We identified 4 genes with significant sex-differentiated effects across 3 traits. Several trait pairs shared sex-differentiated effects. The top genes with sex-differentiated effects were enriched for multiple gene sets, including neuron- and synapse-related sets. Most between-trait genetic correlation estimates were not significantly different between sexes, with exceptions (educational attainment and risk-taking behavior). Conclusions: Sex differences in the common autosomal genetic architecture of neuropsychiatric and behavioral phenotypes are small and polygenic and unlikely to fully account for observed sex-differentiated attributes. Larger sample sizes are needed to identify sex-differentiated effects for most traits. For well-powered studies, we identified genes with sex-differentiated effects that were enriched for neuron-related and other biological functions. This work motivates further investigation of genetic and environmental influences on sex differences.Item Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations(Elsevier, 2019-10-10) Peterson, Roseann E.; Kuchenbaecker, Karoline; Walters, Raymond K.; Chen, Chia-Yen; Popejoy, Alice B.; Periyasamy, Sathish; Lam, Max; Iyegbe, Conrad; Strawbridge, Rona J.; Brick, Leslie; Carey, Caitlin E.; Martin, Alicia R.; Meyers, Jacquelyn L.; Su, Jinni; Chen, Junfang; Edwards, Alexis C.; Kalungi, Allan; Koen, Nastassja; Majara, Lerato; Schwarz, Emanuel; Smoller, Jordan W.; Stahl, Eli A.; Sullivan, Patrick F.; Vassos, Evangelos; Mowry, Bryan; Prieto, Miguel L.; Cuellar-Barboza, Alfredo; Bigdeli, Tim B.; Edenberg, Howard J.; Huang, Hailiang; Duncan, Laramie E.; Biochemistry and Molecular Biology, School of MedicineGenome-wide association studies (GWAS) have focused primarily on populations of European descent, but it is essential that diverse populations become better represented. Increasing diversity among study participants will advance our understanding of genetic architecture in all populations and ensure that genetic research is broadly applicable. To facilitate and promote research in multi-ancestry and admixed cohorts, we outline key methodological considerations and highlight opportunities, challenges, solutions, and areas in need of development. Despite the perception that analyzing genetic data from diverse populations is difficult, it is scientifically and ethically imperative, and there is an expanding analytical toolbox to do it well.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 Leveraging genome-wide data to investigate differences between opioid use vs. opioid dependence in 41,176 individuals from the Psychiatric Genomics Consortium(Springer Nature, 2020-08) Polimanti, Renato; Walters, Raymond K.; Johnson, Emma C.; McClintick, Jeanette N.; Adkins, Amy E.; Adkins, Daniel E.; Bacanu, Silviu-Alin; Bierut, Laura J.; Bigdeli, Tim B.; Brown, Sandra; Bucholz, Kathleen K.; Copeland, William E.; Costello, E. Jane; Degenhardt, Louisa; Farrer, Lindsay A.; Foroud, Tatiana M.; Fox, Louis; Goate, Alison M.; Grucza, Richard; Hack, Laura M.; Hancock, Dana B.; Hartz, Sarah M.; Heath, Andrew C.; Hewitt, John K.; Hopfer, Christian J.; Johnson, Eric O.; Kendler, Kenneth S.; Kranzler, Henry R.; Krauter, Kenneth; Lai, Dongbing; Madden, Pamela A.F.; Martin, Nicholas G.; Maes, Hermine H.; Nelson, Elliot C.; Peterson, Roseann E.; Porjesz, Bernice; Riley, Brien P.; Saccone, Nancy; Stallings, Michael; Wall, Tamara L.; Webb, Bradley T.; Wetherill, Leah; Biochemistry and Molecular Biology, School of MedicineTo provide insights into the biology of opioid dependence (OD) and opioid use (i.e., exposure, OE), we completed a genome-wide analysis comparing 4503 OD cases, 4173 opioid-exposed controls, and 32,500 opioid-unexposed controls, including participants of European and African descent (EUR and AFR, respectively). Among the variants identified, rs9291211 was associated with OE (exposed vs. unexposed controls; EUR z = -5.39, p = 7.2 × 10-8). This variant regulates the transcriptomic profiles of SLC30A9 and BEND4 in multiple brain tissues and was previously associated with depression, alcohol consumption, and neuroticism. A phenome-wide scan of rs9291211 in the UK Biobank (N > 360,000) found association of this variant with propensity to use dietary supplements (p = 1.68 × 10-8). With respect to the same OE phenotype in the gene-based analysis, we identified SDCCAG8 (EUR + AFR z = 4.69, p = 10-6), which was previously associated with educational attainment, risk-taking behaviors, and schizophrenia. In addition, rs201123820 showed a genome-wide significant difference between OD cases and unexposed controls (AFR z = 5.55, p = 2.9 × 10-8) and a significant association with musculoskeletal disorders in the UK Biobank (p = 4.88 × 10-7). A polygenic risk score (PRS) based on a GWAS of risk-tolerance (n = 466,571) was positively associated with OD (OD vs. unexposed controls, p = 8.1 × 10-5; OD cases vs. exposed controls, p = 0.054) and OE (exposed vs. unexposed controls, p = 3.6 × 10-5). A PRS based on a GWAS of neuroticism (n = 390,278) was positively associated with OD (OD vs. unexposed controls, p = 3.2 × 10-5; OD vs. exposed controls, p = 0.002) but not with OE (p = 0.67). Our analyses highlight the difference between dependence and exposure and the importance of considering the definition of controls in studies of addiction.Item Patterns of item nonresponse behaviour to survey questionnaires are systematic and associated with genetic loci(Springer Nature, 2023) Mignogna, Gianmarco; Carey, Caitlin E.; Wedow, Robbee; Baya, Nikolas; Cordioli, Mattia; Pirastu, Nicola; Bellocco, Rino; Fiuza Malerbi, Kathryn; Nivard, Michel G.; Neale, Benjamin M.; Walters, Raymond K.; Ganna, Andrea; Medical and Molecular Genetics, School of MedicineResponse to survey questionnaires is vital for social and behavioural research, and most analyses assume full and accurate response by participants. However, nonresponse is common and impedes proper interpretation and generalizability of results. We examined item nonresponse behaviour across 109 questionnaire items in the UK Biobank (N = 360,628). Phenotypic factor scores for two participant-selected nonresponse answers, 'Prefer not to answer' (PNA) and 'I don't know' (IDK), each predicted participant nonresponse in follow-up surveys (incremental pseudo-R2 = 0.056), even when controlling for education and self-reported health (incremental pseudo-R2 = 0.046). After performing genome-wide association studies of our factors, PNA and IDK were highly genetically correlated with one another (rg = 0.73 (s.e. = 0.03)) and with education (rg,PNA = -0.51 (s.e. = 0.03); rg,IDK = -0.38 (s.e. = 0.02)), health (rg,PNA = 0.51 (s.e. = 0.03); rg,IDK = 0.49 (s.e. = 0.02)) and income (rg,PNA = -0.57 (s.e. = 0.04); rg,IDK = -0.46 (s.e. = 0.02)), with additional unique genetic associations observed for both PNA and IDK (P < 5 × 10-8). We discuss how these associations may bias studies of traits correlated with item nonresponse and demonstrate how this bias may substantially affect genome-wide association studies. While the UK Biobank data are deidentified, we further protected participant privacy by avoiding exploring non-response behaviour to single questions, assuring that no information can be used to associate results with any particular respondents.Item Principled distillation of UK Biobank phenotype data reveals underlying structure in human variation(Springer Nature, 2024) Carey, Caitlin E.; Shafee, Rebecca; Wedow, Robbee; Elliott, Amanda; Palmer, Duncan S.; Compitello, John; Kanai, Masahiro; Abbott, Liam; Schultz, Patrick; Karczewski, Konrad J.; Bryant, Samuel C.; Cusick, Caroline M.; Churchhouse, Claire; Howrigan, Daniel P.; King, Daniel; Smith, George Davey; Neale, Benjamin M.; Walters, Raymond K.; Robinson, Elise B.; Medical and Molecular Genetics, School of MedicineData within biobanks capture broad yet detailed indices of human variation, but biobank-wide insights can be difficult to extract due to complexity and scale. Here, using large-scale factor analysis, we distill hundreds of variables (diagnoses, assessments and survey items) into 35 latent constructs, using data from unrelated individuals with predominantly estimated European genetic ancestry in UK Biobank. These factors recapitulate known disease classifications, disentangle elements of socioeconomic status, highlight the relevance of psychiatric constructs to health and improve measurement of pro-health behaviours. We go on to demonstrate the power of this approach to clarify genetic signal, enhance discovery and identify associations between underlying phenotypic structure and health outcomes. In building a deeper understanding of ways in which constructs such as socioeconomic status, trauma, or physical activity are structured in the dataset, we emphasize the importance of considering the interwoven nature of the human phenome when evaluating public health patterns.Item Shared Genetic Risk between Eating Disorder and Substance Use-Related Phenotypes: Evidence from Genome-Wide Association Studies(Wiley, 2021) Munn-Chernoff, Melissa A.; Johnson, Emma C.; Chou, Yi-Ling; Coleman, Jonathan R.I.; Thornton, Laura M.; Walters, Raymond K.; Yilmaz, Zeynep; Baker, Jessica H.; Hübel, Christopher; Gordon, Scott; Medland, Sarah E.; Watson, Hunna J.; Gaspar, Héléna A.; Bryois, Julien; Hinney, Anke; Leppä, Virpi M.; Mattheisen, Manuel; Ripke, Stephan; Yao, Shuyang; Giusti-Rodríguez, Paola; Hanscombe, Ken B.; Adan, Roger A.H.; Alfredsson, Lars; Ando, Tetsuya; Andreassen, Ole A.; Berrettini, Wade H.; Boehm, Ilka; Boni, Claudette; Perica, Vesna Boraska; Buehren, Katharina; Burghardt, Roland; Cassina, Matgteo; Cichon, Sven; Clementi, Maurizio; Cone, Roger D.; Courtet, Philippe; Crow, Scott; Crowley, James J.; Danner, Unna N.; Davis, Oliver S.P.; de Zwaan, Martina; Dedoussis, George; Degortes, Daniela; DeSocio, Janiece E.; Dick, Danielle M.; Dikeos, Dimitris; Dina, Christian; Dmitrzak-Weglarz, Monika; Docampo, Elisa; Duncan, Laramie E.; Egberts, Karin; Ehrlich, Stefan; Escaramís, Geòrgia; Esko, Tõnu; Estivill, Xavier; Farmer, Anne; Favaro, Angela; Fernández-Aranda, Fernando; Fichter, Manfred M.; Fischer, Krista; Föcker, Manuel; Foretova, Lenka; Forstner, Andreas J.; Forzan, Monica; Franklin, Christopher S.; Gallinger, Steven; Giegling, Ina; Giuranna, Johanna; Gonidakis, Fragiskos; Gorwood, Philip; Gratacos Mayora, Monica; Guillaume, Sébastien; Guo, Yiran; Hakonarson, Hakon; Hatzikotoulas, Konstantinos; Hauser, Joanna; Hebebrand, Johannes; Helder, Sietske G.; Herms, Stefan; Herpertz-Dahlmann, Beate; Herzog, Wolfgang; Huckins, Laura M.; Hudson, James I.; Imgart, Hartmut; Inoko, Hidetoshi; Janout, Vladimir; Jiménez-Murcia, Susana; Julià, Antonio; Kalsi, Gursharan; Kaminská, Deborah; Karhunen, Leila; Karwautz, Andreas; Kas, Martien J.H.; Kennedy, James L.; Keski-Rahkonen, Anna; Kiezebrink, Kirsty; Kim, Youl-Ri; Klump, Kelly L.; Knudsen, Gun Peggy S.; La Via, Maria C.; Le Hellard, Stephanie; Levitan, Robert D.; Li, Dong; Lilenfeld, Lisa; Lin, Bochao Danae; Lissowska, Jolanta; Luykx, Jurjen; Magistretti, Pierre J.; Maj, Mario; Mannik, Katrin; Marsal, Sara; Marshall, Christian R.; Mattingsdal, Morten; McDevitt, Sara; McGuffin, Peter; Metspalu, Andres; Meulenbelt, Ingrid; Micali, Nadia; Mitchell, Karen; Monteleone, Alessio Maria; Monteleone, Palmiero; Nacmias, Benedetta; Navratilova, Marie; Ntalla, Ioanna; O’Toole, Julie K.; Ophoff, Roel A.; Padyukov, Leonid; Palotie, Aarno; Pantel, Jacques; Papezova, Hana; Pinto, Dalila; Rabionet, Raquel; Raevuori, Anu; Ramoz, Nicolas; Reichborn-Kjennerud, Ted; Ricca, Valdo; Ripatti, Samuli; Ritschel, Franziska; Roberts, Marion; Rotondo, Alessandro; Rujescu, Dan; Rybakowski, Filip; Santonastaso, Paolo; Scherag, André; Scherer, Stephen W.; Schmidt, Ulrike; Schork, Nicholas J.; Schosser, Alexandra; Seitz, Jochen; Slachtova, Lenka; Slagboom, P. Eline; Slof-Op’t Landt, Margarita C.T.; Slopien, Agnieszka; Sorbi, Sandro; Świątkowska, Beata; Szatkiewicz, Jin P.; Tachmazidou, Ioanna; Tenconi, Elena; Tortorella, Alfonso; Tozzi, Federica; Treasure, Janet; Tsitsika, Artemis; Tyszkiewicz-Nwafor, Marta; Tziouvas, Konstantinos; van Elburg, Annemarie A.; van Furth, Eric F.; Wagner, Gudrun; Walton, Esther; Widen, Elisabeth; Zeggini, Eleftheria; Zerwas, Stephanie; Zipfel, Stephan; Bergen, Andrew W.; Boden, Joseph M.; Brandt, Harry; Crawford, Steven; Halmi, Katherine A.; Horwood, L. John; Johnson, Craig; Kaplan, Allan S.; Kaye, Walter H.; Mitchell, James; Olsen, Catherine M.; Pearson, John F.; Pedersen, Nancy L.; Strober, Michael; Werge, Thomas; Whiteman, David C.; Woodside, D. Blake; Stuber, Garret D.; Grove, Jakob; Henders, Anjali K.; Larsen, Janne T.; Parker, Richard; Petersen, Liselotte V.; Jordan, Jennifer; Kennedy, Martin A.; Birgegård, Andreas; Lichtenstein, Paul; Norring, Claes; Landén, Mikael; Mortensen, Preben Bo; Polimanti, Renato; McClintick, Jeanette N.; Adams, Mark J.; Adkins, Amy E.; Aliev, Fazil; Bacanu, Silviu-Alin; Batzler, Anthony; Bertelsen, Sarah; Biernacka, Joanna M.; Bigdeli, Tim B.; Chen, Li-Shiun; Clarke, Toni-Kim; Degenhardt, Franziska; Docherty, Anna R.; Edwards, Alexis C.; Foo, Jerome C.; Fox, Louis; Frank, Josef; Hack, Laura M.; Hartmann, Annette M.; Hartz, Sarah M.; Heilmann-Heimbach, Stefanie; Hodgkinson, Colin; Hoffmann, Per; Hottenga, Jouke-Jan; Konte, Bettina; Lahti, Jari; Lahti-Pulkkinen, Marius; Lai, Dongbing; Ligthart, Lannie; Loukola, Anu; Maher, Marion S.; Mbarek, Hamdi; McIntosh, Andrew M.; McQueen, Matthew B.; Meyers, Jacquelyn L.; Milaneschi, Yuri; Palviainen, Teemu; Peterson, Roseann E.; Ryu, Euijung; Saccone, Nancy L.; Salvatore, Jessica E.; Sanchez-Roige, Sandra; Schwandt, Melanie; Sherva, Richard; Streit, Fabian; Strohmaier, Jana; Thomas, Nathaniel; Wang, Jen-Chyong; Webb, Bradley T.; Wedow, Robbee; Wetherill, Leah; Wills, Amanda G.; Zhou, Hang; Boardman, Jason D.; Chen, Danfeng; Choi, Doo-Sup; Copeland, William E.; Culverhouse, Robert C.; Dahmen, Norbert; Degenhardt, Louisa; Domingue, Benjamin W.; Frye, Mark A.; Gäbel, Wolfgang; Hayward, Caroline; Ising, Marcus; Keyes, Margaret; Kiefer, Falk; Koller, Gabrielle; Kramer, John; Kuperman, Samuel; Lucae, Susanne; Lynskey, Michael T.; Maier, Wolfgang; Mann, Karl; Männistö, Satu; Müller-Myhsok, Bertram; Murray, Alison D.; Nurnberger, John I.; Preuss, Ulrich; Räikkönen, Katri; Reynolds, Maureen D.; Ridinger, Monika; Scherbaum, Norbert; Schuckit, Marc A.; Soyka, Michael; Treutlein, Jens; Witt, Stephanie H.; Wodarz, Norbert; Zill, Peter; Adkins, Daniel E.; Boomsma, Dorret I.; Bierut, Laura J.; Brown, Sandra A.; Bucholz, Kathleen K.; Costello, E. Jane; de Wit, Harriet; Diazgranados, Nancy; Eriksson, Johan G.; Farrer, Lindsay A.; Foroud, Tatiana M.; Gillespie, Nathan A.; Goate, Alison M.; Goldman, David; Grucza, Richard A.; Hancock, Dana B.; Mullan Harris, Kathleen; Hesselbrock, Victor; Hewitt, John K.; Hopfer, Christian; Iacono, William G.; Johnson, Eric O.; Karpyak, Victor M.; Kendler, Kenneth S.; Kranzler, Henry R.; Krauter, Kenneth; Lind, Penelope A.; McGue, Matt; MacKillop, James; Madden, Pamela A.F.; Maes, Hermine H.; Magnusson, Patrik K.E.; Nelson, Elliot C.; Nöthen, Markus M.; Palmer, Abraham A.; Penninx, Brenda W.J.H.; Porjesz, Bernice; Rice, John P.; Rietschel, Marcella; Riley, Brien P.; Rose, Richard J.; Shen, Pei-Hong; Silberg, Judy; Stallings, Michael C.; Tarter, Ralph E.; Vanyukov, Michael M.; Vrieze, Scott; Wall, Tamara L.; Whitfield, John B.; Zhao, Hongyu; Neale, Benjamin M.; Wade, Tracey D.; Heath, Andrew C.; Montgomery, Grant W.; Martin, Nicholas G.; Sullivan, Patrick F.; Kaprio, Jaakko; Breen, Gerome; Gelernter, Joel; Edenberg, Howard J.; Bulik, Cynthia M.; Agrawal, Arpana; Biochemistry and Molecular Biology, School of MedicineEating disorders and substance use disorders frequently co-occur. Twin studies reveal shared genetic risk between eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa (BN) and problem alcohol use, mainly abuse and dependence (twin-based genetic correlation [rg]=0.23–0.53). Analytic advances facilitate the computation of genetic correlations using summary statistics from existing genome-wide association studies (GWAS). We investigated shared genetic risk between eating disorder and substance use and disorder phenotypes using GWAS data. Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge-eating, AN without binge-eating, and a BN factor score), and eight substance use-related phenotypes (drinks per week, alcohol use disorder [AUD], smoking initiation, current smoking, cigarettes per day, nicotine dependence, cannabis initiation, and cannabis use disorder) from eight studies were included. Total sample sizes per phenotype ranged from ~2,400 to ~537,000 individuals. We used linkage disequilibrium score regression to calculate single nucleotide polymorphism-based genetic correlations between eating disorder and substance use-related phenotypes. Significant positive genetic associations emerged between AUD and AN (rg=0.18; false discovery rate q=0.0006), cannabis initiation and AN (rg=0.23; q<0.0001), and cannabis initiation and AN with binge-eating (rg=0.27; q=0.0016). Conversely, significant negative genetic correlations were observed between three non-diagnostic smoking phenotypes (smoking initiation, smoking cessation, and cigarettes per day) and AN without binge-eating (rgs=−0.19 to −0.23; qs<0.04). The observed patterns of association between different eating disorder and substance use-related phenotypes highlights the potentially complex and substance-specific relationships between these behaviors associated with significant public health burden.Item 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.