Browsing by Subject "Multifactorial inheritance"
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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 MedicineBackground: 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.Item Alcohol Use Disorder Polygenic Score Compared With Family History and ADH1B(American Medical Association, 2024-12-02) Lai, Dongbing; Zhang, Michael; Abreu, Marco; Schwantes-An, Tae-Hwi; Chan, Grace; Dick, Danielle M.; Kamarajan, Chella; Kuang, Weipeng; Nurnberger, John I.; Plawecki, Martin H.; Rice, John; Schuckit, Marc; Porjesz, Bernice; Liu, Yunlong; Foroud, Tatiana; Medical and Molecular Genetics, School of MedicineImportance: Identification of individuals at high risk of alcohol use disorder (AUD) and subsequent application of prevention and intervention programs has been reported to decrease the incidence of AUD. The polygenic score (PGS), which measures an individual's genetic liability to a disease, can potentially be used to evaluate AUD risk. Objective: To assess the estimability and generalizability of the PGS, compared with family history and ADH1B, in evaluating the risk of AUD among populations of European ancestry. Design, setting, and participants: This genetic association study was conducted between October 1, 2023, and May 21, 2024. A 2-stage design was used. First, the pruning and thresholding method was used to calculate PGSs in the screening stage. Second, the estimability and generalizability of the best PGS was determined using 2 independent samples in the testing stage. Three cohorts ascertained to study AUD were used in the screening stage: the Collaborative Study on the Genetics of Alcoholism (COGA), the Study of Addiction: Genetics and Environment (SAGE), and the Australian Twin-Family Study of Alcohol Use Disorder (OZALC). The All of Us Research Program (AOU), which comprises participants with diverse backgrounds and conditions, and the Indiana Biobank (IB), consisting of Indiana University Health system patients, were used to test the best PGS. For the COGA, SAGE, and OZALC cohorts, cases with AUD were determined using Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) or Fifth Edition (DSM-5) criteria; controls did not meet any criteria or did not have any other substance use disorders. For the AOU and IB cohorts, cases with AUD were identified using International Classification of Diseases, Ninth Revision (ICD-9) or International Classification of Diseases, Tenth Revision (ICD-10) codes; controls were aged 21 years or older and did not have AUD. Exposure: The PGS was calculated using single-nucleotide variants with concordant effects in 3 large-scale genome-wide association studies of AUD-related phenotypes. Main outcomes and measures: The main outcome was AUD determined with DSM-IV or DSM-5 criteria and ICD-9 or ICD-10 codes. Generalized linear mixed models and logistic regression models were used to analyze related and unrelated samples, respectively. Results: The COGA, SAGE, and OZALC cohorts included a total of 8799 samples (6323 cases and 2476 controls; 50.6% were men). The AOU cohort had a total of 116 064 samples (5660 cases and 110 404 controls; 60.4% were women). The IB cohort had 6373 samples (936 cases and 5437 controls; 54.9% were women). The 5% of samples with the highest PGS in the AOU and IB cohorts were approximately 2 times more likely to develop AUD (odds ratio [OR], 1.96 [95% CI, 1.78-2.16]; P = 4.10 × 10-43; and OR, 2.07 [95% CI, 1.59-2.71]; P = 9.15 × 10-8, respectively) compared with the remaining 95% of samples; these ORs were comparable to family history of AUD. For the 5% of samples with the lowest PGS in the AOU and IB cohorts, the risk of AUD development was approximately half (OR, 0.53 [95% CI, 0.45-0.62]; P = 6.98 × 10-15; and OR, 0.57 [95% CI, 0.39-0.84]; P = 4.88 × 10-3) compared with the remaining 95% of samples; these ORs were comparable to the protective effect of ADH1B. PGS had similar estimabilities in male and female individuals. Conclusions and relevance: In this study of AUD risk among populations of European ancestry, PGSs were calculated using concordant single-nucleotide variants and the best PGS was tested in targeted datasets. The findings suggest that the PGS may potentially be used to evaluate AUD risk. More datasets with similar AUD prevalence as in general populations are needed to further test the generalizability of PGS.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 MedicineBackground 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.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 MedicineBackground: 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.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 MedicineBipolar 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.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 MedicineUnderstanding 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.Item The Associations Between Polygenic Risk, Sensation Seeking, Social Support and Alcohol Use in Adulthood(American Psychological Association, 2021) Su, Jinni; Kuo, Sally I-Chun; Aliev, Fazil; Chan, Grace; Edenberg, Howard J.; Kamarajan, Chella; McCutcheon, Vivia V.; Meyers, Jacquelyn L.; Schuckit, Marc; Tischfield, Jay; Dick, Danielle M.; Biochemistry and Molecular Biology, School of MedicineGenetic predispositions play an important role in alcohol use. Understanding the psychosocial mechanisms through which genetic risk unfolds to influence alcohol use outcomes is critical for identifying modifiable targets and developing prevention and intervention efforts. In this study, we examined the role of sensation seeking and social support from family and friends in linking genetic risk to alcohol use. We also examined the role of social support in moderating the associations between genetic risk and sensation seeking and alcohol use. Data were drawn from a sample of 2,836 European American adults from the Collaborative Study on the Genetics of Alcoholism (46% male, mean age = 35.65, standard deviation [SD] = 10.78). Results from path analysis indicated that genome-wide polygenic scores for alcohol consumption (alc-GPS) were associated with higher sensation seeking, which in turn was associated with higher levels of alcohol use. alc-GPS was also associated with higher alcohol use indirectly via lower levels of family support. In addition, high friend support attenuated the association between alc-GPS and sensation seeking and alcohol use. The pattern of associations was similar for males and females, with some differences in the associations between social support and alcohol use observed across age. Our findings highlight the important role of intermediate phenotypes and gene-environment interplay in the pathways of risk from genetic predispositions to complex alcohol use outcomes.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 MedicineThe 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.Item Uncovering the genetic architecture of broad antisocial behavior through a genome-wide association study meta-analysis(Springer Nature, 2022) Tielbeek, Jorim J.; Uffelmann, Emil; Williams, Benjamin S.; Colodro-Conde, Lucía; Gagnon, Éloi; Mallard, Travis T.; Levitt, Brandt E.; Jansen, Philip R.; Johansson, Ada; Sallis, Hannah M.; Pistis, Giorgio; Saunders, Gretchen R. B.; Allegrini, Andrea G.; Rimfeld, Kaili; Konte, Bettina; Klein, Marieke; Hartmann, Annette M.; Salvatore, Jessica E.; Nolte, Ilja M.; Demontis, Ditte; Malmberg, Anni L. K.; Burt, S. Alexandra; Savage, Jeanne E.; Sugden, Karen; Poulton, Richie; Mullan Harris, Kathleen; Vrieze, Scott; McGue, Matt; Iacono, William G.; Roth Mota, Nina; Mill, Jonathan; Viana, Joana F.; Mitchell, Brittany L.; Morosoli, Jose J.; Andlauer, Till F. M.; Ouellet-Morin, Isabelle; Tremblay, Richard E.; Côté, Sylvana M.; Gouin, Jean-Philippe; Brendgen, Mara R.; Dionne, Ginette; Vitaro, Frank; Lupton, Michelle K.; Martin, Nicholas G.; COGA Consortium; Spit for Science Working Group; Castelao, Enrique; Räikkönen, Katri; Eriksson, Johan G.; Lahti, Jari; Hartman, Catharina A.; Oldehinkel, Albertine J.; Snieder, Harold; Liu, Hexuan; Preisig, Martin; Whipp, Alyce; Vuoksimaa, Eero; Lu, Yi; Jern, Patrick; Rujescu, Dan; Giegling, Ina; Palviainen, Teemu; Kaprio, Jaakko; Harden, Kathryn Paige; Munafò, Marcus R.; Morneau-Vaillancourt, Geneviève; Plomin, Robert; Viding, Essi; Boutwell, Brian B.; Aliev, Fazil; Dick, Danielle M.; Popma, Arne; Faraone, Stephen V.; Børglum, Anders D.; Medland, Sarah E.; Franke, Barbara; Boivin, Michel; Pingault, Jean-Baptiste; Glennon, Jeffrey C.; Barnes, J. C.; Fisher, Simon E.; Moffitt, Terrie E.; Caspi, Avshalom; Polderman, Tinca J. C.; Posthuma, Danielle; Medical and Molecular Genetics, School of MedicineDespite the substantial heritability of antisocial behavior (ASB), specific genetic variants robustly associated with the trait have not been identified. The present study by the Broad Antisocial Behavior Consortium (BroadABC) meta-analyzed data from 28 discovery samples (N = 85,359) and five independent replication samples (N = 8058) with genotypic data and broad measures of ASB. We identified the first significant genetic associations with broad ASB, involving common intronic variants in the forkhead box protein P2 (FOXP2) gene (lead SNP rs12536335, p = 6.32 × 10-10). Furthermore, we observed intronic variation in Foxp2 and one of its targets (Cntnap2) distinguishing a mouse model of pathological aggression (BALB/cJ strain) from controls (BALB/cByJ strain). Polygenic risk score (PRS) analyses in independent samples revealed that the genetic risk for ASB was associated with several antisocial outcomes across the lifespan, including diagnosis of conduct disorder, official criminal convictions, and trajectories of antisocial development. We found substantial genetic correlations of ASB with mental health (depression rg = 0.63, insomnia rg = 0.47), physical health (overweight rg = 0.19, waist-to-hip ratio rg = 0.32), smoking (rg = 0.54), cognitive ability (intelligence rg = -0.40), educational attainment (years of schooling rg = -0.46) and reproductive traits (age at first birth rg = -0.58, father's age at death rg = -0.54). Our findings provide a starting point toward identifying critical biosocial risk mechanisms for the development of ASB.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 MedicineAlthough 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.