Browsing by Subject "Genome-wide association study"

Now showing 1 - 10 of 48
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    A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies
    (Springer Nature, 2022) Li, Zilin; Li, Xihao; Zhou, Hufeng; Gaynor, Sheila M.; Selvaraj, Margaret Sunitha; Arapoglou, Theodore; Quick, Corbin; Liu, Yaowu; Chen, Han; Sun, Ryan; Dey, Rounak; Arnett, Donna K.; Auer, Paul L.; Bielak, Lawrence F.; Bis, Joshua C.; Blackwell, Thomas W.; Blangero, John; Boerwinkle, Eric; Bowden, Donald W.; Brody, Jennifer A.; Cade, Brian E.; Conomos, Matthew P.; Correa, Adolfo; Cupples, L. Adrienne; Curran, Joanne E.; de Vries, Paul S.; Duggirala, Ravindranath; Franceschini, Nora; Freedman, Barry I.; Göring, Harald H. H.; Guo, Xiuqing; Kalyani, Rita R.; Kooperberg, Charles; Kral, Brian G.; Lange, Leslie A.; Lin, Bridget M.; Manichaikul, Ani; Manning, Alisa K.; Martin, Lisa W.; Mathias, Rasika A.; Meigs, James B.; Mitchell, Braxton D.; Montasser, May E.; Morrison, Alanna C.; Naseri, Take; O'Connell, Jeffrey R.; Palmer, Nicholette D.; Peyser, Patricia A.; Psaty, Bruce M.; Raffield, Laura M.; Redline, Susan; Reiner, Alexander P.; Reupena, Muagututi'a Sefuiva; Rice, Kenneth M.; Rich, Stephen S.; Smith, Jennifer A.; Taylor, Kent D.; Taub, Margaret A.; Vasan, Ramachandran S.; Weeks, Daniel E.; Wilson, James G.; Yanek, Lisa R.; Zhao, Wei; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; TOPMed Lipids Working Group; Rotter, Jerome I.; Willer, Cristen J.; Natarajan, Pradeep; Peloso, Gina M.; Lin, Xihong; Biostatistics and Health Data Science, School of Medicine
    Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.
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    A genome-wide search for pleiotropy in more than 100,000 harmonized longitudinal cognitive domain scores
    (BMC, 2023-06-22) Kang, Moonil; Ang, Ting Fang Alvin; Devine, Sherral A.; Sherva, Richard; Mukherjee, Shubhabrata; Trittschuh, Emily H.; Gibbons, Laura E.; Scollard, Phoebe; Lee, Michael; Choi, Seo-Eun; Klinedinst, Brandon; Nakano, Connie; Dumitrescu, Logan C.; Durant, Alaina; Hohman, Timothy J.; Cuccaro, Michael L.; Saykin, Andrew J.; Kukull, Walter A.; Bennett, David A.; Wang, Li-San; Mayeux, Richard P.; Haines, Jonathan L.; Pericak-Vance, Margaret A.; Schellenberg, Gerard D.; Crane, Paul K.; Au, Rhoda; Lunetta, Kathryn L.; Mez, Jesse B.; Farrer, Lindsay A.; Radiology and Imaging Sciences, School of Medicine
    Background: More than 75 common variant loci account for only a portion of the heritability for Alzheimer's disease (AD). A more complete understanding of the genetic basis of AD can be deduced by exploring associations with AD-related endophenotypes. Methods: We conducted genome-wide scans for cognitive domain performance using harmonized and co-calibrated scores derived by confirmatory factor analyses for executive function, language, and memory. We analyzed 103,796 longitudinal observations from 23,066 members of community-based (FHS, ACT, and ROSMAP) and clinic-based (ADRCs and ADNI) cohorts using generalized linear mixed models including terms for SNP, age, SNP × age interaction, sex, education, and five ancestry principal components. Significance was determined based on a joint test of the SNP's main effect and interaction with age. Results across datasets were combined using inverse-variance meta-analysis. Genome-wide tests of pleiotropy for each domain pair as the outcome were performed using PLACO software. Results: Individual domain and pleiotropy analyses revealed genome-wide significant (GWS) associations with five established loci for AD and AD-related disorders (BIN1, CR1, GRN, MS4A6A, and APOE) and eight novel loci. ULK2 was associated with executive function in the community-based cohorts (rs157405, P = 2.19 × 10-9). GWS associations for language were identified with CDK14 in the clinic-based cohorts (rs705353, P = 1.73 × 10-8) and LINC02712 in the total sample (rs145012974, P = 3.66 × 10-8). GRN (rs5848, P = 4.21 × 10-8) and PURG (rs117523305, P = 1.73 × 10-8) were associated with memory in the total and community-based cohorts, respectively. GWS pleiotropy was observed for language and memory with LOC107984373 (rs73005629, P = 3.12 × 10-8) in the clinic-based cohorts, and with NCALD (rs56162098, P = 1.23 × 10-9) and PTPRD (rs145989094, P = 8.34 × 10-9) in the community-based cohorts. GWS pleiotropy was also found for executive function and memory with OSGIN1 (rs12447050, P = 4.09 × 10-8) and PTPRD (rs145989094, P = 3.85 × 10-8) in the community-based cohorts. Functional studies have previously linked AD to ULK2, NCALD, and PTPRD. Conclusion: Our results provide some insight into biological pathways underlying processes leading to domain-specific cognitive impairment and AD, as well as a conduit toward a syndrome-specific precision medicine approach to AD. Increasing the number of participants with harmonized cognitive domain scores will enhance the discovery of additional genetic factors of cognitive decline leading to AD and related dementias.
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    A global view of the genetic basis of Alzheimer disease
    (Springer Nature, 2023) Reitz, Christiane; Pericak-Vance, Margaret A.; Foroud, Tatiana; Mayeux, Richard; Medical and Molecular Genetics, School of Medicine
    The risk of Alzheimer disease (AD) increases with age, family history and informative genetic variants. Sadly, there is still no cure or means of prevention. As in other complex diseases, uncovering genetic causes of AD could identify underlying pathological mechanisms and lead to potential treatments. Rare, autosomal dominant forms of AD occur in middle age as a result of highly penetrant genetic mutations, but the most common form of AD occurs later in life. Large-scale, genome-wide analyses indicate that 70 or more genes or loci contribute to AD. One of the major factors limiting progress is that most genetic data have been obtained from non-Hispanic white individuals in Europe and North America, preventing the development of personalized approaches to AD in individuals of other ethnicities. Fortunately, emerging genetic data from other regions - including Africa, Asia, India and South America - are now providing information on the disease from a broader range of ethnicities. Here, we summarize the current knowledge on AD genetics in populations across the world. We predominantly focus on replicated genetic discoveries but also include studies in ethnic groups where replication might not be feasible. We attempt to identify gaps that need to be addressed to achieve a complete picture of the genetic and molecular factors that drive AD in individuals across the globe.
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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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    Beyond GWAS of Colorectal Cancer: Evidence of Interaction with Alcohol Consumption and Putative Causal Variant for the 10q24.2 Region
    (American Association for Cancer Research, 2022) Jordahl, Kristina M.; Shcherbina, Anna; Kim, Andre E.; Su, Yu-Ru; Lin, Yi; Wang, Jun; Qu, Conghui; Albanes, Demetrius; Arndt, Volker; Baurley, James W.; Berndt, Sonja I.; Bien, Stephanie A.; Bishop, D. Timothy; Bouras, Emmanouil; Brenner, Hermann; Buchanan, Daniel D.; Budiarto, Arif; Campbell, Peter T.; Carreras-Torres, Robert; Casey, Graham; Cenggoro, Tjeng Wawan; Chan, Andrew T.; Conti, David V.; Dampier, Christopher H.; Devall, Matthew A.; Díez-Obrero, Virginia; Dimou, Niki; Drew, David A.; Figueiredo, Jane C.; Gallinger, Steven; Giles, Graham G.; Gruber, Stephen B.; Gsur, Andrea; Gunter, Marc J.; Hampel, Heather; Harlid, Sophia; Harrison, Tabitha A.; Hidaka, Akihisa; Hoffmeister, Michael; Huyghe, Jeroen R.; Jenkins, Mark A.; Joshi, Amit D.; Keku, Temitope O.; Larsson, Susanna C.; Le Marchand, Loic; Lewinger, Juan Pablo; Li, Li; Mahesworo, Bharuno; Moreno, Victor; Morrison, John L.; Murphy, Neil; Nan, Hongmei; Nassir, Rami; Newcomb, Polly A.; Obón-Santacana, Mireia; Ogino, Shuji; Ose, Jennifer; Pai, Rish K.; Palmer, Julie R.; Papadimitriou, Nikos; Pardamean, Bens; Peoples, Anita R.; Pharoah, Paul D. P.; Platz, Elizabeth A.; Potter, John D.; Prentice, Ross L.; Rennert, Gad; Ruiz-Narvaez, Edward; Sakoda, Lori C.; Scacheri, Peter C.; Schmit, Stephanie L.; Schoen, Robert E.; Slattery, Martha L.; Stern, Mariana C.; Tangen, Catherine M.; Thibodeau, Stephen N.; Thomas, Duncan C.; Tian, Yu; Tsilidis, Konstantinos K.; Ulrich, Cornelia M.; van Duijnhoven, Franzel J. B.; Van Guelpen, Bethany; Visvanathan, Kala; Vodicka, Pavel; White, Emily; Wolk, Alicja; Woods, Michael O.; Wu, Anna H.; Zemlianskaia, Natalia; Chang-Claude, Jenny; Gauderman, W. James; Hsu, Li; Kundaje, Anshul; Peters, Ulrike; Epidemiology, Richard M. Fairbanks School of Public Health
    Background: Currently known associations between common genetic variants and colorectal cancer explain less than half of its heritability of 25%. As alcohol consumption has a J-shape association with colorectal cancer risk, nondrinking and heavy drinking are both risk factors for colorectal cancer. Methods: Individual-level data was pooled from the Colon Cancer Family Registry, Colorectal Transdisciplinary Study, and Genetics and Epidemiology of Colorectal Cancer Consortium to compare nondrinkers (≤1 g/day) and heavy drinkers (>28 g/day) with light-to-moderate drinkers (1-28 g/day) in GxE analyses. To improve power, we implemented joint 2df and 3df tests and a novel two-step method that modifies the weighted hypothesis testing framework. We prioritized putative causal variants by predicting allelic effects using support vector machine models. Results: For nondrinking as compared with light-to-moderate drinking, the hybrid two-step approach identified 13 significant SNPs with pairwise r2 > 0.9 in the 10q24.2/COX15 region. When stratified by alcohol intake, the A allele of lead SNP rs2300985 has a dose-response increase in risk of colorectal cancer as compared with the G allele in light-to-moderate drinkers [OR for GA genotype = 1.11; 95% confidence interval (CI), 1.06-1.17; OR for AA genotype = 1.22; 95% CI, 1.14-1.31], but not in nondrinkers or heavy drinkers. Among the correlated candidate SNPs in the 10q24.2/COX15 region, rs1318920 was predicted to disrupt an HNF4 transcription factor binding motif. Conclusions: Our study suggests that the association with colorectal cancer in 10q24.2/COX15 observed in genome-wide association study is strongest in nondrinkers. We also identified rs1318920 as the putative causal regulatory variant for the region. Impact: The study identifies multifaceted evidence of a possible functional effect for rs1318920.
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    Bivariate genome-wide association meta-analysis of pediatric musculoskeletal traits reveals pleiotropic effects at the SREBF1/TOM1L2 locus
    (Nature Publishing Group, 2017-07-25) Medina-Gomez, Carolina; Kemp, John P.; Dimou, Niki L.; Kreiner, Eskil; Chesi, Alessandra; Zemel, Babette S.; Bønnelykke, Klaus; Boer, Cindy G.; Ahluwalia, Tarunveer S.; Bisgaard, Hans; Evangelou, Evangelos; Heppe, Denise H.M.; Bonewald, Lynda F.; Gorski, Jeffrey P.; Ghanbari, Mohsen; Demissie, Serkalem; Duque, Gustavo; Maurano, Matthew T.; Kiel, Douglas P.; Hsu, Yi-Hsiang; Eerden, Bram C.J. van der; Ackert-Bicknell, Cheryl; Reppe, Sjur; Gautvik, Kaare M.; Raastad, Truls; Karasik, David; Peppel, Jeroen van de; Jaddoe, Vincent W.V.; Uitterlinden, André G.; Tobias, Jonathan H.; Grant, Struan F.A.; Bagos, Pantelis G.; Evans, David M.; Rivadeneira, Fernando; Anatomy and Cell Biology, School of Medicine
    Bone mineral density is known to be a heritable, polygenic trait whereas genetic variants contributing to lean mass variation remain largely unknown. We estimated the shared SNP heritability and performed a bivariate GWAS meta-analysis of total-body lean mass (TB-LM) and total-body less head bone mineral density (TBLH-BMD) regions in 10,414 children. The estimated SNP heritability is 43% (95% CI: 34-52%) for TBLH-BMD, and 39% (95% CI: 30-48%) for TB-LM, with a shared genetic component of 43% (95% CI: 29-56%). We identify variants with pleiotropic effects in eight loci, including seven established bone mineral density loci: WNT4, GALNT3, MEPE, CPED1/WNT16, TNFSF11, RIN3, and PPP6R3/LRP5. Variants in the TOM1L2/SREBF1 locus exert opposing effects TB-LM and TBLH-BMD, and have a stronger association with the former trait. We show that SREBF1 is expressed in murine and human osteoblasts, as well as in human muscle tissue. This is the first bivariate GWAS meta-analysis to demonstrate genetic factors with pleiotropic effects on bone mineral density and lean mass.Bone mineral density and lean skeletal mass are heritable traits. Here, Medina-Gomez and colleagues perform bivariate GWAS analyses of total body lean mass and bone mass density in children, and show genetic loci with pleiotropic effects on both traits.
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    Decoding the Human Face: Progress and Challenges in Understanding the Genetics of Craniofacial Morp
    (Annual Reviews, 2022) Naqvi, Sahin; Hoskens, Hanne; Wilke, Franziska; Weinberg, Seth M.; Shaffer, John R.; Walsh, Susan; Shriver, Mark D.; Wysocka, Joanna; Claes, Peter; Biology, School of Science
    Variations in the form of the human face, which plays a role in our individual identities and societal interactions, have fascinated scientists and artists alike. Here, we review our current understanding of the genetics underlying variation in craniofacial morphology and disease-associated dysmorphology, synthesizing decades of progress on Mendelian syndromes in addition to more recent results from genome-wide association studies of human facial shape and disease risk. We also discuss the various approaches used to phenotype and quantify facial shape, which are of particular importance due to the complex, multipartite nature of the craniofacial form. We close by discussing how experimental studies have contributed and will further contribute to our understanding of human genetic variation and then proposing future directions and applications for the field.
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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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    Dysregulated expression levels of APH1B in peripheral blood are associated with brain atrophy and amyloid-β deposition in Alzheimer's disease
    (BMC, 2021-11-03) Park, Young Ho; Pyun, Jung‑Min; Hodges, Angela; Jang, Jae‑Won; Bice, Paula J.; Kim, SangYun; Saykin, Andrew J.; Nho, Kwangsik; Radiology and Imaging Sciences, School of Medicine
    Background: The interaction between the brain and periphery might play a crucial role in the development of Alzheimer's disease (AD). Methods: Using blood transcriptomic profile data from two independent AD cohorts, we performed expression quantitative trait locus (cis-eQTL) analysis of 29 significant genetic loci from a recent large-scale genome-wide association study to investigate the effects of the AD genetic variants on gene expression levels and identify their potential target genes. We then performed differential gene expression analysis of identified AD target genes and linear regression analysis to evaluate the association of differentially expressed genes with neuroimaging biomarkers. Results: A cis-eQTL analysis identified and replicated significant associations in seven genes (APH1B, BIN1, FCER1G, GATS, MS4A6A, RABEP1, TRIM4). APH1B expression levels in the blood increased in AD and were associated with entorhinal cortical thickness and global cortical amyloid-β deposition. Conclusion: An integrative analysis of genetics, blood-based transcriptomic profiles, and imaging biomarkers suggests that APH1B expression levels in the blood might play a role in the pathogenesis of AD.
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    ENIGMA and the individual: Predicting factors that affect the brain in 35 countries worldwide
    (Elsevier, 2017-01-15) Thompson, Paul M.; Andreassen, Ole A.; Arias-Vasquez, Alejandro; Bearden, Carrie E.; Boedhoe, Premika S.; Brouwer, Rachel M.; Buckner, Randy L.; Buitelaar, Jan K.; Bulayeva, Kazima B.; Cannon, Dara M.; Cohen, Ronald A.; Conrod, Patricia J.; Dale, Anders M.; Deary, Ian J.; Dennis, Emily L.; de Reus, Marcel A.; Desrivieres, Sylvane; Dima, Danai; Donohoe, Gary; Fisher, Simon E.; Fouche, Jean-Paul; Francks, Clyde; Frangou, Sophia; Franke, Barbara; Ganjgahi, Habib; Garavan, Hugh; Glahn, David C.; Grabe, Hans J.; Guadalupe, Tulio; Gutman, Boris A.; Hashimoto, Ryota; Hibar, Derrek P.; Holland, Dominic; Hoogman, Martine; Pol, Hilleke E. Hulshoff; Hosten, Norbert; Jahanshad, Neda; Kelly, Sinead; Kochunov, Peter; Kremen, William S.; Lee, Phil H.; Mackey, Scott; Martin, Nicholas G.; Mazoyer, Bernard; McDonald, Colm; Medland, Sarah E.; Morey, Rajendra A.; Nichols, Thomas E.; Paus, Tomas; Pausova, Zdenka; Schmaal, Lianne; Schumann, Gunter; Shen, Li; Sisodiya, Sanjay M.; Smit, Dirk J.A.; Smoller, Jordan W.; Stein, Dan J.; Stein, Jason L.; Toro, Roberto; Turner, Jessica A.; Heuvel, Martijn P. van den; Heuvel, Odile L. van den; Erp, Theo G.M. van; Rooij, Daan van; Veltman, Dick J.; Walter, Henrik; Wang, Yalin; Wardlaw, Joanna M.; Whelan, Christopher D.; Wright, Margaret J.; Ye, Jieping; ENIGMA Consortium; Radiology and Imaging Sciences, School of Medicine
    In this review, we discuss recent work by the ENIGMA Consortium (http://enigma.ini.usc.edu) – a global alliance of over 500 scientists spread across 200 institutions in 35 countries collectively analyzing brain imaging, clinical, and genetic data. Initially formed to detect genetic influences on brain measures, ENIGMA has grown to over 30 working groups studying 12 major brain diseases by pooling and comparing brain data. In some of the largest neuroimaging studies to date – of schizophrenia and major depression – ENIGMA has found replicable disease effects on the brain that are consistent worldwide, as well as factors that modulate disease effects. In partnership with other consortia including ADNI, CHARGE, IMAGEN and others1, ENIGMA's genomic screens – now numbering over 30,000 MRI scans – have revealed at least 8 genetic loci that affect brain volumes. Downstream of gene findings, ENIGMA has revealed how these individual variants – and genetic variants in general – may affect both the brain and risk for a range of diseases. The ENIGMA consortium is discovering factors that consistently affect brain structure and function that will serve as future predictors linking individual brain scans and genomic data. It is generating vast pools of normative data on brain measures – from tens of thousands of people – that may help detect deviations from normal development or aging in specific groups of subjects. We discuss challenges and opportunities in applying these predictors to individual subjects and new cohorts, as well as lessons we have learned in ENIGMA's efforts so far.