Browsing by Author "VA Million Veteran Program"

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    A multiancestry genome-wide association study of unexplained chronic ALT elevation as a proxy for nonalcoholic fatty liver disease with histological and radiological validation
    (Springer Nature, 2022) Vujkovic, Marijana; Ramdas, Shweta; Lorenz, Kim M.; Guo, Xiuqing; Darlay, Rebecca; Cordell, Heather J.; He, Jing; Gindin, Yevgeniy; Chung, Chuhan; Myers, Robert P.; Schneider, Carolin V.; Park, Joseph; Lee, Kyung Min; Serper, Marina; Carr, Rotonya M.; Kaplan, David E.; Haas, Mary E.; MacLean, Matthew T.; Witschey, Walter R.; Zhu, Xiang; Tcheandjieu, Catherine; Kember, Rachel L.; Kranzler, Henry R.; Verma, Anurag; Giri, Ayush; Klarin, Derek M.; Sun, Yan V.; Huang, Jie; Huffman, Jennifer E.; Townsend Creasy, Kate; Hand, Nicholas J.; Liu, Ching-Ti; Long, Michelle T.; Yao, Jie; Budoff, Matthew; Tan, Jingyi; Li, Xiaohui; Lin, Henry J.; Chen, Yii-Der Ida; Taylor, Kent D.; Chang, Ruey-Kang; Krauss, Ronald M.; Vilarinho, Silvia; Brancale, Joseph; Nielsen, Jonas B.; Locke, Adam E.; Jones, Marcus B.; Verweij, Niek; Baras, Aris; Reddy, K. Rajender; Neuschwander-Tetri, Brent A.; Schwimmer, Jeffrey B.; Sanyal, Arun J.; Chalasani, Naga; Ryan, Kathleen A.; Mitchell, Braxton D.; Gill, Dipender; Wells, Andrew D.; Manduchi, Elisabetta; Saiman, Yedidya; Mahmud, Nadim; Miller, Donald R.; Reaven, Peter D.; Phillips, Lawrence S.; Muralidhar, Sumitra; DuVall, Scott L.; Lee, Jennifer S.; Assimes, Themistocles L.; Pyarajan, Saiju; Cho, Kelly; Edwards, Todd L.; Damrauer, Scott M.; Wilson, Peter W.; Gaziano, J. Michael; O'Donnell, Christopher J.; Khera, Amit V.; Grant, Struan F. A.; Brown, Christopher D.; Tsao, Philip S.; Saleheen, Danish; Lotta, Luca A.; Bastarache, Lisa; Anstee, Quentin M.; Daly, Ann K.; Meigs, James B.; Rotter, Jerome I.; Lynch, Julie A.; Regeneron Genetics Center; Geisinger-Regeneron DiscovEHR Collaboration; EPoS Consortium; VA Million Veteran Program; Rader, Daniel J.; Voight, Benjamin F.; Chang, Kyong-Mi; Medicine, School of Medicine
    Nonalcoholic fatty liver disease (NAFLD) is a growing cause of chronic liver disease. Using a proxy NAFLD definition of chronic elevation of alanine aminotransferase (cALT) levels without other liver diseases, we performed a multiancestry genome-wide association study (GWAS) in the Million Veteran Program (MVP) including 90,408 cALT cases and 128,187 controls. Seventy-seven loci exceeded genome-wide significance, including 25 without prior NAFLD or alanine aminotransferase associations, with one additional locus identified in European American-only and two in African American-only analyses (P < 5 × 10-8). External replication in histology-defined NAFLD cohorts (7,397 cases and 56,785 controls) or radiologic imaging cohorts (n = 44,289) replicated 17 single-nucleotide polymorphisms (SNPs) (P < 6.5 × 10-4), of which 9 were new (TRIB1, PPARG, MTTP, SERPINA1, FTO, IL1RN, COBLL1, APOH and IFI30). Pleiotropy analysis showed that 61 of 77 multiancestry and all 17 replicated SNPs were jointly associated with metabolic and/or inflammatory traits, revealing a complex model of genetic architecture. Our approach integrating cALT, histology and imaging reveals new insights into genetic liability to NAFLD.
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    Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors
    (Elsevier, 2022-02-01) Mullins, Niamh; Kang, JooEun; Campos, Adrian I.; Coleman, Jonathan R. I.; Edwards, Alexis C.; Galfalvy, Hanga; Levey, Daniel F.; Lori, Adriana; Shabalin, Andrey; Starnawska, Anna; Su, Mei-Hsin; Watson, Hunna J.; Adams, Mark; Awasthi, Swapnil; Gandal, Michael; Hafferty, Jonathan D.; Hishimoto, Akitoyo; Kim, Minsoo; Okazaki, Satoshi; Otsuka, Ikuo; Ripke, Stephan; Ware, Erin B.; Bergen, Andrew W.; Berrettini, Wade H.; Bohus, Martin; Brandt, Harry; Chang, Xiao; Chen, Wei J.; Chen, Hsi-Chung; Crawford, Steven; Crow, Scott; DiBlasi, Emily; Duriez, Philibert; Fernández-Aranda, Fernando; Fichter, Manfred M.; Gallinger, Steven; Glatt, Stephen J.; Gorwood, Philip; Guo, Yiran; Hakonarson, Hakon; Halmi, Katherine A.; Hwu, Hai-Gwo; Jain, Sonia; Jamain, Stéphane; Jiménez-Murcia, Susana; Johnson, Craig; Kaplan, Allan S.; Kaye, Walter H.; Keel, Pamela K.; Kennedy, James L.; Klump, Kelly L.; Li, Dong; Liao, Shih-Cheng; Lieb, Klaus; Lilenfeld, Lisa; Liu, Chih-Min; Magistretti, Pierre J.; Marshall, Christian R.; Mitchell, James E.; Monson, Eric T.; Myers, Richard M.; Pinto, Dalila; Powers, Abigail; Ramoz, Nicolas; Roepke, Stefan; Rozanov, Vsevolod; Scherer, Stephen W.; Schmahl, Christian; Sokolowski, Marcus; Strober, Michael; Thornton, Laura M.; Treasure, Janet; Tsuang, Ming T.; Witt, Stephanie H.; Woodside, D. Blake; Yilmaz, Zeynep; Zillich, Lea; Adolfsson, Rolf; Agartz, Ingrid; Air, Tracy M.; Alda, Martin; Alfredsson, Lars; Andreassen, Ole A.; Anjorin, Adebayo; Appadurai, Vivek; Artigas, María Soler; Van der Auwera, Sandra; Azevedo, M. Helena; Bass, Nicholas; Bau, Claiton H. D.; Baune, Bernhard T.; Bellivier, Frank; Berger, Klaus; Biernacka, Joanna M.; Bigdeli, Tim B.; Binder, Elisabeth B.; Boehnke, Michael; Boks, Marco P.; Bosch, Rosa; Braff, David L.; Bryant, Richard; Budde, Monika; Byrne, Enda M.; Cahn, Wiepke; Casas, Miguel; Castelao, Enrique; Cervilla, Jorge A.; Chaumette, Boris; Cichon, Sven; Corvin, Aiden; Craddock, Nicholas; Craig, David; Degenhardt, Franziska; Djurovic, Srdjan; Edenberg, Howard J.; Fanous, Ayman H.; Foo, Jerome C.; Forstner, Andreas J.; Frye, Mark; Fullerton, Janice M.; Gatt, Justine M.; Gejman, Pablo V.; Giegling, Ina; Grabe, Hans J.; Green, Melissa J.; Grevet, Eugenio H.; Grigoroiu-Serbanescu, Maria; Gutierrez, Blanca; Guzman-Parra, Jose; Hamilton, Steven P.; Hamshere, Marian L.; Hartmann, Annette; Hauser, Joanna; Heilmann-Heimbach, Stefanie; Hoffmann, Per; Ising, Marcus; Jones, Ian; Jones, Lisa A.; Jonsson, Lina; Kahn, René S.; Kelsoe, John R.; Kendler, Kenneth S.; Kloiber, Stefan; Koenen, Karestan C.; Kogevinas, Manolis; Konte, Bettina; Krebs, Marie-Odile; Landén, Mikael; Lawrence, Jacob; Leboyer, Marion; Lee, Phil H.; Levinson, Douglas F.; Liao, Calwing; Lissowska, Jolanta; Lucae, Susanne; Mayoral, Fermin; McElroy, Susan L.; McGrath, Patrick; McGuffin, Peter; McQuillin, Andrew; Medland, Sarah E.; Mehta, Divya; Melle, Ingrid; Milaneschi, Yuri; Mitchell, Philip B.; Molina, Esther; Morken, Gunnar; Mortensen, Preben Bo; Müller-Myhsok, Bertram; Nievergelt, Caroline; Nimgaonkar, Vishwajit; Nöthen, Markus M.; O’Donovan, Michael C.; Ophoff, Roel A.; Owen, Michael J.; Pato, Carlos; Pato, Michele T.; Penninx, Brenda W. J. H.; Pimm, Jonathan; Pistis, Giorgio; Potash, James B.; Power, Robert A.; Preisig, Martin; Quested, Digby; Ramos-Quiroga, Josep Antoni; Reif, Andreas; Ribasés , Marta; Richarte, Vanesa; Rietschel, Marcella; Rivera, Margarita; Roberts, Andrea; Roberts, Gloria; Rouleau, Guy A.; Rovaris, Diego L.; Rujescu, Dan; Sánchez-Mora, Cristina; Sanders, Alan R.; Schofield, Peter R.; Schulze, Thomas G.; Scott, Laura J.; Serretti, Alessandro; Shi, Jianxin; Shyn, Stanley I.; Sirignano, Lea; Sklar, Pamela; Smeland, Olav B.; Smoller, Jordan W.; Sonuga-Barke, Edmund J. S.; Spalletta, Gianfranco; Strauss, John S.; Świątkowska, Beata; Trzaskowski, Maciej; Turecki, Gustavo; Vilar-Ribó, Laura; Vincent, John B.; Völzke, Henry; Walters, James T. R.; Weickert, Cynthia Shannon; Weickert, Thomas W.; Weissman, Myrna M.; Williams, Leanne M.; Wray, Naomi R.; Zai, Clement C.; Ashley-Koch, Allison E.; Beckham, Jean C.; Hauser, Elizabeth R.; Hauser, Michael A.; Kimbrel, Nathan A.; Lindquist, Jennifer H.; McMahon, Benjamin; Oslin, David W.; Qin, Xuejun; Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium; Bipolar Disorder Working Group of the Psychiatric Genomics Consortium; Eating Disorders Working Group of the Psychiatric Genomics Consortium; German Borderline Genomics Consortium; MVP Suicide Exemplar Workgroup; VA Million Veteran Program; Medical and Molecular Genetics, School of Medicine
    BACKGROUND: Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. METHODS: We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. RESULTS: Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. CONCLUSIONS: Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.
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    Genome-wide association study identifies 30 obsessive-compulsive disorder associated loci
    (medRxiv, 2024-03-13) Strom, Nora I.; Gerring, Zachary F.; Galimberti, Marco; Yu, Dongmei; Halvorsen, Matthew W.; Abdellaoui, Abdel; Rodriguez-Fontenla, Cristina; Sealock, Julia M.; Bigdeli, Tim; Coleman, Jonathan R.; Mahjani, Behrang; Thorp, Jackson G.; Bey, Katharina; Burton, Christie L.; Luykx, Jurjen J.; Zai, Gwyneth; Alemany, Silvia; Andre, Christine; Askland, Kathleen D.; Banaj, Nerisa; Barlassina, Cristina; Becker Nissen, Judith; Bienvenu, O. Joseph; Black, Donald; Bloch, Michael H.; Boberg, Julia; Børte, Sigrid; Bosch, Rosa; Breen, Michael; Brennan, Brian P.; Brentani, Helena; Buxbaum, Joseph D.; Bybjerg-Grauholm, Jonas; Byrne, Enda M.; Cabana-Dominguez, Judith; Camarena, Beatriz; Camarena, Adrian; Cappi, Carolina; Carracedo, Angel; Casas, Miguel; Cavallini, Maria Cristina; Ciullo, Valentina; Cook, Edwin H.; Crosby, Jesse; Cullen, Bernadette A.; De Schipper, Elles J.; Delorme, Richard; Djurovic, Srdjan; Elias, Jason A.; Estivill, Xavier; Falkenstein, Martha J.; Fundin, Bengt T.; Garner, Lauryn; German, Chris; Gironda, Christina; Goes, Fernando S.; Grados, Marco A.; Grove, Jakob; Guo, Wei; Haavik, Jan; Hagen, Kristen; Harrington, Kelly; Havdahl, Alexandra; Höffler, Kira D.; Hounie, Ana G.; Hucks, Donald; Hultman, Christina; Janecka, Magdalena; Jenike, Eric; Karlsson, Elinor K.; Kelley, Kara; Klawohn, Julia; Krasnow, Janice E.; Krebs, Kristi; Lange, Christoph; Lanzagorta, Nuria; Levey, Daniel; Lindblad-Toh, Kerstin; Macciardi, Fabio; Maher, Brion; Mathes, Brittany; McArthur, Evonne; McGregor, Nathaniel; McLaughlin, Nicole C.; Meier, Sandra; Miguel, Euripedes C.; Mulhern, Maureen; Nestadt, Paul S.; Nurmi, Erika L.; O'Connell, Kevin S.; Osiecki, Lisa; Ousdal, Olga Therese; Palviainen, Teemu; Pedersen, Nancy L.; Piras, Fabrizio; Piras, Federica; Potluri, Sriramya; Rabionet, Raquel; Ramirez, Alfredo; Rauch, Scott; Reichenberg, Abraham; Riddle, Mark A.; Ripke, Stephan; Rosário, Maria C.; Sampaio, Aline S.; Schiele, Miriam A.; Skogholt, Anne Heidi; Sloofman, Laura G.; Smit, Jan; Soler, Artigas María; Thomas, Laurent F.; Tifft, Eric; Vallada, Homero; van Kirk, Nathanial; Veenstra-VanderWeele, Jeremy; Vulink, Nienke N.; Walker, Christopher P.; Wang, Ying; Wendland, Jens R.; Winsvold, Bendik S.; Yao, Yin; Zhou, Hang; 23andMe Research Team; VA Million Veteran Program; Estonian Biobank; CoGa research team; iPSYCH; HUNT research team; NORDiC research team; Agrawal, Arpana; Alonso, Pino; Berberich, Götz; Bucholz, Kathleen K.; Bulik, Cynthia M.; Cath, Danielle; Denys, Damiaan; Eapen, Valsamma; Edenberg, Howard; Falkai, Peter; Fernandez, Thomas V.; Fyer, Abby J.; Gaziano, J. M.; Geller, Dan A.; Grabe, Hans J.; Greenberg, Benjamin D.; Hanna, Gregory L.; Hickie, Ian B.; Hougaard, David M.; Kathmann, Norbert; Kennedy, James; Lai, Dongbing; Landén, Mikael; Le Hellard, Stéphanie; Leboyer, Marion; Lochner, Christine; McCracken, James T.; Medland, Sarah E.; Mortensen, Preben B.; Neale, Benjamin M.; Nicolini, Humberto; Nordentoft, Merete; Pato, Michele; Pato, Carlos; Pauls, David L.; Piacentini, John; Pittenger, Christopher; Posthuma, Danielle; Ramos-Quiroga, Josep Antoni; Rasmussen, Steven A.; Richter, Margaret A.; Rosenberg, David R.; Ruhrmann, Stephan; Samuels, Jack F.; Sandin, Sven; Sandor, Paul; Spalletta, Gianfranco; Stein, Dan J.; Stewart, S. Evelyn; Storch, Eric A.; Stranger, Barbara E.; Turiel, Maurizio; Werge, Thomas; Andreassen, Ole A.; Børglum, Anders D.; Walitza, Susanne; Hveem, Kristian; Hansen, Bjarne K.; Rück, Christian P.; Martin, Nicholas G.; Milani, Lili; Mors, Ole; Reichborn-Kjennerud, Ted; Ribasés, Marta; Kvale, Gerd; Mataix-Cols, David; Domschke, Katharina; Grünblatt, Edna; Wagner, Michael; Zwart, John-Anker; Breen, Gerome; Nestadt, Gerald; Kaprio, Jaakko; Arnold, Paul D.; Grice, Dorothy E.; Knowles, James A.; Ask, Helga; Verweij, Karin J.; Davis, Lea K.; Smit, Dirk J.; Crowley, James J.; Scharf, Jeremiah M.; Stein, Murray B.; Gelernter, Joel; Mathews, Carol A.; Derks, Eske M.; Mattheisen, Manuel; Biochemistry and Molecular Biology, School of Medicine
    Obsessive-compulsive disorder (OCD) affects ~1% of the population and exhibits a high SNP-heritability, yet previous genome-wide association studies (GWAS) have provided limited information on the genetic etiology and underlying biological mechanisms of the disorder. We conducted a GWAS meta-analysis combining 53,660 OCD cases and 2,044,417 controls from 28 European-ancestry cohorts revealing 30 independent genome-wide significant SNPs and a SNP-based heritability of 6.7%. Separate GWAS for clinical, biobank, comorbid, and self-report sub-groups found no evidence of sample ascertainment impacting our results. Functional and positional QTL gene-based approaches identified 249 significant candidate risk genes for OCD, of which 25 were identified as putatively causal, highlighting WDR6, DALRD3, CTNND1 and genes in the MHC region. Tissue and single-cell enrichment analyses highlighted hippocampal and cortical excitatory neurons, along with D1- and D2-type dopamine receptor-containing medium spiny neurons, as playing a role in OCD risk. OCD displayed significant genetic correlations with 65 out of 112 examined phenotypes. Notably, it showed positive genetic correlations with all included psychiatric phenotypes, in particular anxiety, depression, anorexia nervosa, and Tourette syndrome, and negative correlations with a subset of the included autoimmune disorders, educational attainment, and body mass index. This study marks a significant step toward unraveling its genetic landscape and advances understanding of OCD genetics, providing a foundation for future interventions to address this debilitating disorder.
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    Identifying intragenic functional modules of genomic variations associated with cancer phenotypes by learning representation of association networks
    (Springer Nature, 2022-07-06) Kim, Minsu; Huffman, Jennifer E.; Justice, Amy; Goethert, Ian; Agasthya, Greeshma; VA Million Veteran Program; Danciu, Ioana; Medicine, School of Medicine
    Background: Genome-wide Association Studies (GWAS) aims to uncover the link between genomic variation and phenotype. They have been actively applied in cancer biology to investigate associations between variations and cancer phenotypes, such as susceptibility to certain types of cancer and predisposed responsiveness to specific treatments. Since GWAS primarily focuses on finding associations between individual genomic variations and cancer phenotypes, there are limitations in understanding the mechanisms by which cancer phenotypes are cooperatively affected by more than one genomic variation. Results: This paper proposes a network representation learning approach to learn associations among genomic variations using a prostate cancer cohort. The learned associations are encoded into representations that can be used to identify functional modules of genomic variations within genes associated with early- and late-onset prostate cancer. The proposed method was applied to a prostate cancer cohort provided by the Veterans Administration's Million Veteran Program to identify candidates for functional modules associated with early-onset prostate cancer. The cohort included 33,159 prostate cancer patients, 3181 early-onset patients, and 29,978 late-onset patients. The reproducibility of the proposed approach clearly showed that the proposed approach can improve the model performance in terms of robustness. Conclusions: To our knowledge, this is the first attempt to use a network representation learning approach to learn associations among genomic variations within genes. Associations learned in this way can lead to an understanding of the underlying mechanisms of how genomic variations cooperatively affect each cancer phenotype. This method can reveal unknown knowledge in the field of cancer biology and can be utilized to design more advanced cancer-targeted therapies.
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    Utility of Candidate Genes From an Algorithm Designed to Predict Genetic Risk for Opioid Use Disorder
    (American Medical Association, 2025-01-02) Davis, Christal N.; Jinwala, Zeal; Hatoum, Alexander S.; Toikumo, Sylvanus; Agrawal, Arpana; Rentsch, Christopher T.; Edenberg, Howard J.; Baurley, James W.; Hartwell, Emily E.; Crist, Richard C.; Gray, Joshua C.; Justice, Amy C.; Gelernter, Joel; Kember, Rachel L.; Kranzler, Henry R.; VA Million Veteran Program; Biochemistry and Molecular Biology, School of Medicine
    Importance: Recently, the US Food and Drug Administration gave premarketing approval to an algorithm based on its purported ability to identify individuals at genetic risk for opioid use disorder (OUD). However, the clinical utility of the candidate genetic variants included in the algorithm has not been independently demonstrated. Objective: To assess the utility of 15 genetic variants from an algorithm intended to predict OUD risk. Design, setting, and participants: This case-control study examined the association of 15 candidate genetic variants with risk of OUD using electronic health record data from December 20, 1992, to September 30, 2022. Electronic health record data, including pharmacy records, were accrued from participants in the Million Veteran Program across the US with opioid exposure (n = 452 664). Cases with OUD were identified using International Classification of Diseases, Ninth Revision, or International Classification of Diseases, Tenth Revision, diagnostic codes, and controls were individuals with no OUD diagnosis. Exposures: Number of risk alleles present across 15 candidate genetic variants. Main outcome and measures: Performance of 15 genetic variants for identifying OUD risk assessed via logistic regression and machine learning models. Results: A total of 452 664 individuals with opioid exposure (including 33 669 with OUD) had a mean (SD) age of 61.15 (13.37) years, and 90.46% were male; the sample was ancestrally diverse (with individuals of genetically inferred European, African, and admixed American ancestries). Using Nagelkerke R2, collectively, the 15 candidate genes accounted for 0.40% of variation in OUD risk. In comparison, age and sex alone accounted for 3.27% of the variation. The ensemble machine learning. The ensemble machine learning model using the 15 variants as predictive factors correctly classified 52.83% (95% CI, 52.07%-53.59%) of individuals in an independent testing sample. Conclusions and relevance: Results of this study suggest that the candidate genetic variants included in the approved algorithm do not meet reasonable standards of efficacy in identifying OUD risk. Given the algorithm's limited predictive accuracy, its use in clinical care would lead to high rates of both false-positive and false-negative findings. More clinically useful models are needed to identify individuals at risk of developing OUD.