Browsing by Subject "Multifactorial inheritance"

Now showing 1 - 7 of 7
  • Thumbnail Image
    Item
    A polygenic risk score for alcohol-associated cirrhosis among heavy drinkers with European ancestry
    (Wolters Kluwer, 2024-05-10) Schwantes-An, Tae-Hwi; Whitfield, John B.; Aithal, Guruprasad P.; Atkinson, Stephen R.; Bataller, Ramon; Botwin, Greg; Chalasani, Naga P.; Cordell, Heather J.; Daly, Ann K.; Darlay, Rebecca; Day, Christopher P.; Eyer, Florian; Foroud, Tatiana; Gawrieh, Samer; Gleeson, Dermot; Goldman, David; Haber, Paul S.; Jacquet, Jean-Marc; Lammert, Craig S.; Liang, Tiebing; Liangpunsakul, Suthat; Masson, Steven; Mathurin, Philippe; Moirand, Romain; McQuillin, Andrew; Moreno, Christophe; Morgan, Marsha Y.; Mueller, Sebastian; Müllhaupt, Beat; Nagy, Laura E.; Nahon, Pierre; Nalpas, Bertrand; Naveau, Sylvie; Perney, Pascal; Pirmohamed, Munir; Seitz, Helmut K.; Soyka, Michael; Stickel, Felix; Thompson, Andrew; Thursz, Mark R.; Trépo, Eric; Morgan, Timothy R.; Seth, Devanshi; GenomALC Consortium; Medical and Molecular Genetics, School of Medicine
    Background: Polygenic Risk Scores (PRS) based on results from genome-wide association studies offer the prospect of risk stratification for many common and complex diseases. We developed a PRS for alcohol-associated cirrhosis by comparing single-nucleotide polymorphisms among patients with alcohol-associated cirrhosis (ALC) versus drinkers who did not have evidence of liver fibrosis/cirrhosis. Methods: Using a data-driven approach, a PRS for ALC was generated using a meta-genome-wide association study of ALC (N=4305) and an independent cohort of heavy drinkers with ALC and without significant liver disease (N=3037). It was validated in 2 additional independent cohorts from the UK Biobank with diagnosed ALC (N=467) and high-risk drinking controls (N=8981) and participants in the Indiana Biobank Liver cohort with alcohol-associated liver disease (N=121) and controls without liver disease (N=3239). Results: A 20-single-nucleotide polymorphisms PRS for ALC (PRSALC) was generated that stratified risk for ALC comparing the top and bottom deciles of PRS in the 2 validation cohorts (ORs: 2.83 [95% CI: 1.82 -4.39] in UK Biobank; 4.40 [1.56 -12.44] in Indiana Biobank Liver cohort). Furthermore, PRSALC improved the prediction of ALC risk when added to the models of clinically known predictors of ALC risk. It also stratified the risk for metabolic dysfunction -associated steatotic liver disease -cirrhosis (3.94 [2.23 -6.95]) in the Indiana Biobank Liver cohort -based exploratory analysis. Conclusions: PRSALC incorporates 20 single-nucleotide polymorphisms, predicts increased risk for ALC, and improves risk stratification for ALC compared with the models that only include clinical risk factors. This new score has the potential for early detection of heavy drinking patients who are at high risk for ALC.
  • Thumbnail Image
    Item
    Associations Between Cannabis Use, Polygenic Liability for Schizophrenia, and Cannabis-related Experiences in a Sample of Cannabis Users
    (Oxford University Press, 2023) Johnson, Emma C.; Colbert, Sarah M. C.; Jeffries, Paul W.; Tillman, Rebecca; Bigdeli, Tim B.; Karcher, Nicole R.; Chan, Grace; Kuperman, Samuel; Meyers, Jacquelyn L.; Nurnberger, John I.; Plawecki, Martin H.; Degenhardt, Louisa; Martin, Nicholas G.; Kamarajan, Chella; Schuckit, Marc A.; Murray, Robin M.; Dick, Danielle M.; Edenberg, Howard J.; D'Souza, Deepak Cyril; Di Forti, Marta; Porjesz, Bernice; Nelson, Elliot C.; Agrawal, Arpana; Medical and Molecular Genetics, School of Medicine
    Background and hypothesis: Risk for cannabis use and schizophrenia is influenced in part by genetic factors, and there is evidence that genetic risk for schizophrenia is associated with subclinical psychotic-like experiences (PLEs). Few studies to date have examined whether genetic risk for schizophrenia is associated with cannabis-related PLEs. Study design: We tested whether measures of cannabis involvement and polygenic risk scores (PRS) for schizophrenia were associated with self-reported cannabis-related experiences in a sample ascertained for alcohol use disorders (AUDs), the Collaborative Study on the Genetics of Alcoholism (COGA). We analyzed 4832 subjects (3128 of European ancestry and 1704 of African ancestry; 42% female; 74% meeting lifetime criteria for an AUD). Study results: Cannabis use disorder (CUD) was prevalent in this analytic sample (70%), with 40% classified as mild, 25% as moderate, and 35% as severe. Polygenic risk for schizophrenia was positively associated with cannabis-related paranoia, feeling depressed or anhedonia, social withdrawal, and cognitive difficulties, even when controlling for duration of daily cannabis use, CUD, and age at first cannabis use. The schizophrenia PRS was most robustly associated with cannabis-related cognitive difficulties (β = 0.22, SE = 0.04, P = 5.2e-7). In an independent replication sample (N = 1446), associations between the schizophrenia PRS and cannabis-related experiences were in the expected direction and not statistically different in magnitude from those in the COGA sample. Conclusions: Among individuals who regularly use cannabis, genetic liability for schizophrenia-even in those without clinical features-may increase the likelihood of reporting unusual experiences related to cannabis use.
  • Thumbnail Image
    Item
    Family environment and polygenic risk in the bipolar high-risk context
    (Wiley, 2023-03-16) Stapp, Emma K.; Fullerton, Janice M.; Musci, Rashelle J.; Zandi, Peter P.; McInnis, Melvin G.; Mitchell, Philip B.; Hulvershorn, Leslie A.; Ghaziuddin, Neera; Roberts, Gloria; Ferrera, Alessandra G.; Nurnberger, John I.; Wilcox, Holly C.; Psychiatry, School of Medicine
    Background: The interaction of polygenic risk (PRS) and environmental effects on development of bipolar disorder (BD) is understudied, as are high-risk offspring perceptions of their family environment (FE). We tested the association of offspring-perceived FE in interaction with BD-PRS on liability for BD in offspring at high or low familial risk for BD. Methods: Offspring of a parent with BD (oBD; n = 266) or no psychiatric disorders (n = 174), aged 12-21 at recruitment, participated in the US and Australia. Empirically-derived profiles of FE classified offspring by their perceived levels of familial cohesion, flexibility, and conflict. Offspring BD-PRS were derived from Psychiatric Genomics Consortium BD-GWAS. Lifetime DSM-IV bipolar disorders were derived from the Schedule for Affective Disorders and Schizophrenia for School-Aged Children. We used a novel stepwise approach for latent class modeling with predictors and distal outcomes. Results: Fifty-two offspring were diagnosed with BD. For those with well-functioning FE (two-thirds of the sample), higher BD-PRS tracked positively with liability for BD. However, for those with high-conflict FEs, the relationship between BD-PRS and liability to BD was negative, with highest risk for BD observed with lower BD-PRS. In exploratory analyses, European-ancestry offspring with BD had elevated history of suicidal ideation in high-conflict FE compared to well-functioning-FE, and of suicide attempt with low-BD-PRS and high-conflict FE. Conclusions: The data suggest that the relationship of BD-PRS and offspring liability for BD differed between well-functioning versus high-conflict FE, potentially in line with a multifactorial liability threshold model and supporting future study of and interventions improving family dynamics.
  • Thumbnail Image
    Item
    Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology
    (Springer Nature, 2021-06) Mullins, Niamh; Forstner, Andreas J.; O'Connell, Kevin S.; Coombes, Brandon; Coleman, Jonathan R.I.; Qiao, Zhen; Als, Thomas D.; Bigdeli, Tim B.; Børte, Sigrid; Bryois, Julien; Charney, Alexander W.; Drange, Ole Kristian; Gandal, Michael J.; Hagenaars, Saskia P.; Ikeda, Masashi; Kamitaki, Nolan; Kim, Minsoo; Krebs, Kristi; Panagiotaropoulou, Georgia; Schilder, Brian M.; Sloofman, Laura G.; Steinberg, Stacy; Trubetskoy, Vassily; Winsvold, Bendik S.; Won, Hong-Hee; Abramova, Liliya; Adorjan, Kristina; Agerbo, Esben; Al Eissa, Mariam; Albani, Diego; Alliey-Rodriguez, Ney; Anjorin, Adebayo; Antilla, Verneri; Antoniou, Anastasia; Awasthi, Swapnil; Baek, Ji Hyun; Bækvad-Hansen, Marie; Bass, Nicholas; Bauer, Michael; Beins, Eva C.; Bergen, Sarah E.; Birner, Armin; Pedersen, Carsten Bøcker; Bøen, Erlend; Boks, Marco P.; Bosch, Rosa; Brum, Murielle; Brumpton, Ben M.; Brunkhorst-Kanaan, Nathalie; Budde, Monika; Bybjerg-Grauholm, Jonas; Byerley, William; Cairns, Murray; Casas, Miquel; Cervantes, Pablo; Clarke, Toni-Kim; Cruceanu, Cristiana; Cuellar-Barboza, Alfredo; Cunningham, Julie; Curtis, David; Czerski, Piotr M.; Dale, Anders M.; Dalkner, Nina; David, Friederike S.; Degenhardt, Franziska; Djurovic, Srdjan; Dobbyn, Amanda L.; Douzenis, Athanassios; Elvsåshagen, Torbjørn; Escott-Price, Valentina; Ferrier, I. Nicol; Fiorentino, Alessia; Foroud, Tatiana M.; Forty, Liz; Frank, Josef; Frei, Oleksandr; Freimer, Nelson B.; Frisén, Louise; Gade, Katrin; Garnham, Julie; Gelernter, Joel; Pedersen, Marianne Giørtz; Gizer, Ian R.; Gordon, Scott D.; Gordon-Smith, Katherine; Greenwood, Tiffany A.; Grove, Jakob; Guzman-Parra, José; Ha, Kyooseob; Haraldsson, Magnus; Hautzinger, Martin; Heilbronner, Urs; Hellgren, Dennis; Herms, Stefan; Hoffmann, Per; Holmans, Peter A.; Huckins, Laura; Jamain, Stéphane; Johnson, Jessica S.; Kalman, Janos L.; Kamatani, Yoichiro; Kennedy, James L.; Kittel-Schneider, Sarah; Knowles, James A.; Kogevinas, Manolis; Koromina, Maria; Kranz, Thorsten M.; Kranzler, Henry R.; Kubo, Michiaki; Kupka, Ralph; Kushner, Steven A.; Lavebratt, Catharina; Lawrence, Jacob; Leber, Markus; Lee, Heon-Jeong; Lee, Phil H.; Levy, Shawn E.; Lewis, Catrin; Liao, Calwing; Lucae, Susanne; Lundberg, Martin; MacIntyre, Donald J.; Magnusson, Sigurdur H.; Maier, Wolfgang; Maihofer, Adam; Malaspina, Dolores; Maratou, Eirini; Martinsson, Lina; Mattheisen, Manuel; McCarroll, Steven A.; McGregor, Nathaniel W.; McGuffin, Peter; McKay, James D.; Medeiros, Helena; Medland, Sarah E.; Millischer, Vincent; Montgomery, Grant W.; Moran, Jennifer L.; Morris, Derek W.; Mühleisen, Thomas W.; O'Brien, Niamh; O'Donovan, Claire; Loohuis, Loes M. Olde; Oruc, Lilijana; Papiol, Sergi; Pardiñas, Antonio F.; Perry, Amy; Pfennig, Andrea; Porichi, Evgenia; Potash, James B.; Quested, Digby; Raj, Towfique; Rapaport, Mark H.; DePaulo, J. Raymond; Regeer, Eline J.; Rice, John P.; Rivas, Fabio; Rivera, Margarita; Roth, Julian; Roussos, Panos; Ruderfer, Douglas M.; Sánchez-Mora, Cristina; Schulte, Eva C.; Senner, Fanny; Sharp, Sally; Shilling, Paul D.; Sigurdsson, Engilbert; Sirignano, Lea; Slaney, Claire; Smeland, Olav B.; Smith, Daniel J.; Sobell, Janet L.; Søholm Hansen, Christine; Artigas, Maria Soler; Spijker, Anne T.; Stein, Dan J.; Strauss, John S.; Świątkowska, Beata; Terao, Chikashi; Thorgeirsson, Thorgeir E.; Toma, Claudio; Tooney, Paul; Tsermpini, Evangelia-Eirini; Vawter, Marquis P.; Vedder, Helmut; Walters, James T.R.; Witt, Stephanie H.; Xi, Simon; Xu, Wei; Yang, Jessica Mei Kay; Young, Allan H.; Young, Hannah; Zandi, Peter P.; Zhou, Hang; Zillich, Lea; Adolfsson, Rolf; Agartz, Ingrid; Alda, Martin; Alfredsson, Lars; Babadjanova, Gulja; Backlund, Lena; Baune, Bernhard T.; Bellivier, Frank; Bengesser, Susanne; Berrettini, Wade H.; Blackwood, Douglas H.R.; Boehnke, Michael; Børglum, Anders D.; Breen, Gerome; Carr, Vaughan J.; Catts, Stanley; Corvin, Aiden; Craddock, Nicholas; Dannlowski, Udo; Dikeos, Dimitris; Esko, Tõnu; Etain, Bruno; Ferentinos, Panagiotis; Frye, Mark; Fullerton, Janice M.; Gawlik, Micha; Gershon, Elliot S.; Goes, Fernando S.; Green, Melissa J.; Grigoroiu-Serbanescu, Maria; Hauser, Joanna; Henskens, Frans; Hillert, Jan; Hong, Kyung Sue; Hougaard, David M.; Hultman, Christina M.; Hveem, Kristian; Iwata, Nakao; Jablensky, Assen V.; Jones, Ian; Jones, Lisa A.; Kahn, René S.; Kelsoe, John R.; Kirov, George; Landén, Mikael; Leboyer, Marion; Lewis, Cathryn M.; Li, Qingqin S.; Lissowska, Jolanta; Lochner, Christine; Loughland, Carmel; Martin, Nicholas G.; Mathews, Carol A.; Mayoral, Fermin; McElroy, Susan L.; McIntosh, Andrew M.; McMahon, Francis J.; Melle, Ingrid; Michie, Patricia; Milani, Lili; Mitchell, Philip B.; Morken, Gunnar; Mors, Ole; Mortensen, Preben Bo; Mowry, Bryan; Müller-Myhsok, Bertram; Myers, Richard M.; Neale, Benjamin M.; Nievergelt, Caroline M.; Nordentoft, Merete; Nöthen, Markus M.; O'Donovan, Michael C.; Oedegaard, Ketil J.; Olsson, Tomas; Owen, Michael J.; Paciga, Sara A.; Pantelis, Chris; Pato, Carlos; Pato, Michele T.; Patrinos, George P.; Perlis, Roy H.; Posthuma, Danielle; Ramos-Quiroga, Josep Antoni; Reif, Andreas; Reininghaus, Eva Z.; Ribasés, Marta; Rietschel, Marcella; Ripke, Stephan; Rouleau, Guy A.; Saito, Takeo; Schall, Ulrich; Schalling, Martin; Schofield, Peter R.; Schulze, Thomas G.; Scott, Laura J.; Scott, Rodney J.; Serretti, Alessandro; Weickert, Cynthia Shannon; Smoller, Jordan W.; Stefansson, Hreinn; Stefansson, Kari; Stordal, Eystein; Streit, Fabian; Sullivan, Patrick F.; Turecki, Gustavo; Vaaler, Arne E.; Vieta, Eduard; Vincent, John B.; Waldman, Irwin D.; Weickert, Thomas W.; Werge, Thomas; Wray, Naomi R.; Zwart, John-Anker; Biernacka, Joanna M.; Nurnberger, John I.; Cichon, Sven; Edenberg, Howard J.; Stahl, Eli A.; McQuillin, Andrew; Florio, Arianna Di; Ophoff, Roel A.; Andreassen, Ole A.; Medical and Molecular Genetics, School of Medicine
    Bipolar disorder is a heritable mental illness with complex etiology. We performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. Bipolar disorder risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating expression quantitative trait locus data implicated 15 genes robustly linked to bipolar disorder via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of bipolar disorder subtypes indicated high but imperfect genetic correlation between bipolar disorder type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of bipolar disorder, identify novel therapeutic leads and prioritize genes for functional follow-up studies.
  • Thumbnail Image
    Item
    Genome-wide meta-analyses reveal novel loci for verbal short-term memory and learning
    (Springer Nature, 2022) Lahti, Jari; Tuominen, Samuli; Yang, Qiong; Pergola, Giulio; Ahmad, Shahzad; Amin, Najaf; Armstrong, Nicola J.; Beiser, Alexa; Bey, Katharina; Bis, Joshua C.; Boerwinkle, Eric; Bressler, Jan; Campbell, Archie; Campbell, Harry; Chen, Qiang; Corley, Janie; Cox, Simon R.; Davies, Gail; De Jager, Philip L.; Derks, Eske M.; Faul, Jessica D.; Fitzpatrick, Annette L.; Fohner, Alison E.; Ford, Ian; Fornage, Myriam; Gerring, Zachary; Grabe, Hans J.; Grodstein, Francine; Gudnason, Vilmundur; Simonsick, Eleanor; Holliday, Elizabeth G.; Joshi, Peter K.; Kajantie, Eero; Kaprio, Jaakko; Karell, Pauliina; Kleineidam, Luca; Knol, Maria J.; Kochan, Nicole A.; Kwok, John B.; Leber, Markus; Lam, Max; Lee, Teresa; Li, Shuo; Loukola, Anu; Luck, Tobias; Marioni, Riccardo E.; Mather, Karen A.; Medland, Sarah; Mirza, Saira S.; Nalls, Mike A.; Nho, Kwangsik; O'Donnell, Adrienne; Oldmeadow, Christopher; Painter, Jodie; Pattie, Alison; Reppermund, Simone; Risacher, Shannon L.; Rose, Richard J.; Sadashivaiah, Vijay; Scholz, Markus; Satizabal, Claudia L.; Schofield, Peter W.; Schraut, Katharina E.; Scott, Rodney J.; Simino, Jeannette; Smith, Albert V.; Smith, Jennifer A.; Stott, David J.; Surakka, Ida; Teumer, Alexander; Thalamuthu, Anbupalam; Trompet, Stella; Turner, Stephen T.; van der Lee, Sven J.; Villringer, Arno; Völker, Uwe; Wilson, Robert S.; Wittfeld, Katharina; Vuoksimaa, Eero; Xia, Rui; Yaffe, Kristine; Yu, Lei; Zare, Habil; Zhao, Wei; Ames, David; Attia, John; Bennett, David A.; Brodaty, Henry; Chasman, Daniel I.; Goldman, Aaron L.; Hayward, Caroline; Ikram, M. Arfan; Jukema, J. Wouter; Kardia, Sharon L.R.; Lencz, Todd; Loeffler, Markus; Mattay, Venkata S.; Palotie, Aarno; Psaty, Bruce M.; Ramirez, Alfredo; Ridker, Paul M.; Riedel-Heller, Steffi G.; Sachdev, Perminder S.; Saykin, Andrew J.; Scherer, Martin; Schofield, Peter R.; Sidney, Stephen; Starr, John M.; Trollor, Julian; Ulrich, William; Wagner, Michael; Weir, David R.; Wilson, James F.; Wright, Margaret J.; Weinberger, Daniel R.; Debette, Stephanie; Eriksson, Johan G.; Mosley, Thomas H., Jr.; Launer, Lenore J.; van Duijn, Cornelia M.; Deary, Ian J.; Seshadri, Sudha; Räikkönen, Katri; Radiology and Imaging Sciences, School of Medicine
    Understanding the genomic basis of memory processes may help in combating neurodegenerative disorders. Hence, we examined the associations of common genetic variants with verbal short-term memory and verbal learning in adults without dementia or stroke (N = 53,637). We identified novel loci in the intronic region of CDH18, and at 13q21 and 3p21.1, as well as an expected signal in the APOE/APOC1/TOMM40 region. These results replicated in an independent sample. Functional and bioinformatic analyses supported many of these loci and further implicated POC1. We showed that polygenic score for verbal learning associated with brain activation in right parieto-occipital region during working memory task. Finally, we showed genetic correlations of these memory traits with several neurocognitive and health outcomes. Our findings suggest a role of several genomic loci in verbal memory processes.
  • Thumbnail Image
    Item
    The Polygenic Risk Score Knowledge Base offers a centralized online repository for calculating and contextualizing polygenic risk scores
    (Springer Nature, 2022-09-02) Page, Madeline L.; Vance, Elizabeth L.; Cloward, Matthew E.; Ringger, Ed; Dayton, Louisa; Ebbert, Mark T. W.; Alzheimer’s Disease Neuroimaging Initiative; Miller, Justin B.; Kauwe, John S. K.; Radiology and Imaging Sciences, School of Medicine
    The process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu ) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer's Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research.
  • Thumbnail Image
    Item
    Unsupervised representation learning on high-dimensional clinical data improves genomic discovery and prediction
    (Springer Nature, 2024) Yun, Taedong; Cosentino, Justin; Behsaz, Babak; McCaw, Zachary R.; Hill, Davin; Luben, Robert; Lai, Dongbing; Bates, John; Yang, Howard; Schwantes-An, Tae-Hwi; Zhou, Yuchen; Khawaja, Anthony P.; Carroll, Andrew; Hobbs, Brian D.; Cho, Michael H.; McLean, Cory Y.; Hormozdiari, Farhad; Medical and Molecular Genetics, School of Medicine
    Although high-dimensional clinical data (HDCD) are increasingly available in biobank-scale datasets, their use for genetic discovery remains challenging. Here we introduce an unsupervised deep learning model, Representation Learning for Genetic Discovery on Low-Dimensional Embeddings (REGLE), for discovering associations between genetic variants and HDCD. REGLE leverages variational autoencoders to compute nonlinear disentangled embeddings of HDCD, which become the inputs to genome-wide association studies (GWAS). REGLE can uncover features not captured by existing expert-defined features and enables the creation of accurate disease-specific polygenic risk scores (PRSs) in datasets with very few labeled data. We apply REGLE to perform GWAS on respiratory and circulatory HDCD-spirograms measuring lung function and photoplethysmograms measuring blood volume changes. REGLE replicates known loci while identifying others not previously detected. REGLE are predictive of overall survival, and PRSs constructed from REGLE loci improve disease prediction across multiple biobanks. Overall, REGLE contain clinically relevant information beyond that captured by existing expert-defined features, leading to improved genetic discovery and disease prediction.