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  • 1. Abel, Olubunmi
    et al.
    Powell, John F.
    Andersen, Peter M.
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Al-Chalabi, Ammar
    ALSoD: A user-friendly online bioinformatics tool for amyotrophic lateral sclerosis genetics2012In: Human Mutation, ISSN 1059-7794, E-ISSN 1098-1004, Vol. 33, no 9, p. 1345-1351Article in journal (Refereed)
    Abstract [en]

    Amyotrophic lateral sclerosis (ALS) is the commonest adult onset motor neuron disease, with a peak age of onset in the seventh decade. With advances in genetic technology, there is an enormous increase in the volume of genetic data produced, and a corresponding need for storage, analysis, and interpretation, particularly as our understanding of the relationships between genotype and phenotype mature. Here, we present a system to enable this in the form of the ALS Online Database (ALSoD at http://alsod.iop.kcl.ac.uk), a freely available database that has been transformed from a single gene storage facility recording mutations in the SOD1 gene to a multigene ALS bioinformatics repository and analytical instrument combining genotype, phenotype, and geographical information with associated analysis tools. These include a comparison tool to evaluate genes side by side or jointly with user configurable features, a pathogenicity prediction tool using a combination of computational approaches to distinguish variants with nonfunctional characteristics from disease-associated mutations with more dangerous consequences, and a credibility tool to enable ALS researchers to objectively assess the evidence for gene causation in ALS. Furthermore, integration of external tools, systems for feedback, annotation by users, and two-way links to collaborators hosting complementary databases further enhance the functionality of ALSoD. Hum Mutat 33:1345-1351, 2012. (c) 2012 Wiley Periodicals, Inc.

  • 2.
    Achour, Cyrinne
    et al.
    Umeå University, Faculty of Medicine, Wallenberg Centre for Molecular Medicine at Umeå University (WCMM). Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Aguilo, Francesca
    Umeå University, Faculty of Medicine, Wallenberg Centre for Molecular Medicine at Umeå University (WCMM). Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Long non-coding RNA and Polycomb: an intricate partnership in cancer biology2018In: Frontiers in Bioscience, ISSN 1093-9946, E-ISSN 1093-4715, Vol. 23, p. 2106-2132Article in journal (Refereed)
    Abstract [en]

    High-throughput analyses have revealed that the vast majority of the transcriptome does not code for proteins. These non-translated transcripts, when larger than 200 nucleotides, are termed long non-coding RNAs (lncRNAs), and play fundamental roles in diverse cellular processes. LncRNAs are subject to dynamic chemical modification, adding another layer of complexity to our understanding of the potential roles that lncRNAs play in health and disease. Many lncRNAs regulate transcriptional programs by influencing the epigenetic state through direct interactions with chromatin-modifying proteins. Among these proteins, Polycomb repressive complexes 1 and 2 (PRC1 and PRC2) have been shown to be recruited by lncRNAs to silence target genes. Aberrant expression, deficiency or mutation of both lncRNA and Polycomb have been associated with numerous human diseases, including cancer. In this review, we have highlighted recent findings regarding the concerted mechanism of action of Polycomb group proteins (PcG), acting together with some classically defined lncRNAs including X-inactive specific transcript (XIST), antisense non-coding RNA in the INK4 locus (ANRIL), metastasis associated lung adenocarcinoma transcript 1 (MALAT1), and HOX transcript antisense RNA (HOTAIR).

  • 3.
    Adams, David
    et al.
    CHU Bicêtre, APHP, French Reference Centre For FAP (NNERF), LE KREMLIN-BICETRE, France.
    Suhr, Ole B.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine.
    Conceicao, Isabel
    Centro Hospitalar Lisboa Norte-Hospital de Santa Maria, Department of Neurology, Lisbon, Portugal.
    Waddington-Cruz, Marcia
    Hospital Universitario Clementino Fraga Filho, UFRJ, Rio de Janeiro, Brazil.
    Schmidt, Hartmut
    University Hospital of Münster, Department of Transplantation, Münster, Germany.
    Buades, Juan
    Hospital Son Llatzer, Servicio de Medicina Interna, Palma de Mallorca, Spain.
    Campistol, Josep
    Hospital Clinic Barcelona, Instituto Clinic de Nefrologia y Urologia (ICNU), Barcelona, Spain.
    Coehlo, Teresa
    Hospital de Santo Antonio, Unidade Clinica de Paramiloidose, Porto, Portugal.
    Phase 2 open-label extention (OLE) study of patisiran, an investigational siRNA agent for familial amyloidotic polyneuropathy (FAP)2015In: Orphanet Journal of Rare Diseases, E-ISSN 1750-1172, Vol. 10, article id O20Article in journal (Refereed)
    Abstract [en]

    Background: Familial amyloidotic polyneuropathy (FAP) is a progressive and fatal, autosomal dominant disease caused by deposition of mutant and wild-type transthyretin (TTR). Patisiran is an investigational, systemically administered lipid nanoparticle (LNP) formulation of a small interfering RNA (siRNA) targeting wild-type and mutant TTR. This formulation delivers the siRNA predominantly to the liver, thereby inhibiting synthesis of TTR at the primary site of production. A recently completed multi-center, multi-dose Phase 2 trial of patisiran in FAP patients (N=29) showed >80% sustained mean knockdown of serum TTR when administered at a dose of 0.3 mg/kg every 3 weeks with a generally favorable safety profile (Suhr O, ISA 2014).

    Methods: A Phase 2 open-label extension (OLE) study of patisiran in patients with FAP who participated in the aforementioned trial, was initiated in October 2013. The primary objective of the study is to evaluate the safety and tolerability of 0.3 mg/kg patisiran administered intravenously once every 3 weeks for up to 2 years. Secondary objectives include assessment of patisiran's effect on serum TTR levels, as well as evaluation every 6 months of its impact on clinical measures, including the mNIS+7 composite neurologic impairment score and quality of life (QOL).

    Results: Twenty-seven patients were enrolled; median age 64 years (range: 29-77 years). Chronic dosing with patisiran has been generally well tolerated. Three patients experienced serious adverse events unrelated to study drug. Flushing and infusion-related reactions were observed in 22.2% and 18.5% of the patients, respectively; these were mild in severity, and did not result in any discontinuations. Sustained mean serum TTR lowering of approximately 80% was achieved, with further mean nadir of up to 88% between doses for approximately 16 months. Stabilization of quality of life (QOL) measures was observed. Among the 20 evaluable patients at the time of data cutoff, neuropathy impairment scores were stable through 12 months with a mean change in mNIS+7 and NIS of -2.5 and 0.4 points, respectively; this compares favorably to the 10-18 point increase in neurologic impairment scores estimated at 12 months from prior FAP studies in a patient population with similar baseline NIS.

    Conclusion: Data from this Phase 2 OLE study demonstrate that 12-months of patisiran administration was well-tolerated, resulted in sustained mean serum TTR lowering, and has the potential to halt neuropathy progression. As of March 2015, dosing continues for all patients; 18-month results will be presented.

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  • 4. Adams, Hieab H. H.
    et al.
    Hibar, Derrek P.
    Chouraki, Vincent
    Stein, Jason L.
    Nyquist, Paul A.
    Renteria, Miguel E.
    Trompet, Stella
    Arias-Vasquez, Alejandro
    Seshadri, Sudha
    Desrivieres, Sylvane
    Beecham, Ashley H.
    Jahanshad, Neda
    Wittfeld, Katharine
    Van der Lee, Sven J.
    Abramovic, Lucija
    Alhusaini, Saud
    Amin, Najaf
    Andersson, Micael
    Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB). Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI).
    Arfanakis, Konstantinos
    Aribisala, Benjamin S.
    Armstrong, Nicola J.
    Athanasiu, Lavinia
    Axelsson, Tomas
    Beiser, Alexa
    Bernard, Manon
    Bis, Joshua C.
    Blanken, Laura M. E.
    Blanton, Susan H.
    Bohlken, Marc M.
    Boks, Marco P.
    Bralten, Janita
    Brickman, Adam M.
    Carmichael, Owen
    Chakravarty, M. Mallar
    Chauhan, Ganesh
    Chen, Qiang
    Ching, Christopher R. K.
    Cuellar-Partida, Gabriel
    Den Braber, Anouk
    Doan, Nhat Trung
    Ehrlich, Stefan
    Filippi, Irina
    Ge, Tian
    Giddaluru, Sudheer
    Goldman, Aaron L.
    Gottesman, Rebecca F.
    Greven, Corina U.
    Grimm, Oliver
    Griswold, Michael E.
    Guadalupe, Tulio
    Hass, Johanna
    Haukvik, Unn K.
    Hilal, Saima
    Hofer, Edith
    Hoehn, David
    Holmes, Avram J.
    Hoogman, Martine
    Janowitz, Deborah
    Jia, Tianye
    Kasperaviciute, Dalia
    Kim, Sungeun
    Klein, Marieke
    Kraemer, Bernd
    Lee, Phil H.
    Liao, Jiemin
    Liewald, David C. M.
    Lopez, Lorna M.
    Luciano, Michelle
    Macare, Christine
    Marquand, Andre
    Matarin, Mar
    Mather, Karen A.
    Mattheisen, Manuel
    Mazoyer, Bernard
    Mckay, David R.
    McWhirter, Rebekah
    Milaneschi, Yuri
    Mirza-Schreiber, Nazanin
    Muetzel, Ryan L.
    Maniega, Susana Munoz
    Nho, Kwangsik
    Nugent, Allison C.
    Loohuis, Loes M. Olde
    Oosterlaan, Jaap
    Papmeyer, Martina
    Pappa, Irene
    Pirpamer, Lukas
    Pudas, Sara
    Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB). Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI).
    Puetz, Benno
    Rajan, Kumar B.
    Ramasamy, Adaikalavan
    Richards, Jennifer S.
    Risacher, Shannon L.
    Roiz-Santianez, Roberto
    Rommelse, Nanda
    Rose, Emma J.
    Royle, Natalie A.
    Rundek, Tatjana
    Saemann, Philipp G.
    Satizabal, Claudia L.
    Schmaal, Lianne
    Schork, Andrew J.
    Shen, Li
    Shin, Jean
    Shumskaya, Elena
    Smith, Albert V.
    Sprooten, Emma
    Strike, Lachlan T.
    Teumer, Alexander
    Thomson, Russell
    Tordesillas-Gutierrez, Diana
    Toro, Roberto
    Trabzuni, Daniah
    Vaidya, Dhananjay
    Van der Grond, Jeroen
    Van der Meer, Dennis
    Van Donkelaar, Marjolein M. J.
    Van Eijk, Kristel R.
    Van Erp, Theo G. M.
    Van Rooij, Daan
    Walton, Esther
    Westlye, Lars T.
    Whelan, Christopher D.
    Windham, Beverly G.
    Winkler, Anderson M.
    Woldehawariat, Girma
    Wolf, Christiane
    Wolfers, Thomas
    Xu, Bing
    Yanek, Lisa R.
    Yang, Jingyun
    Zijdenbos, Alex
    Zwiers, Marcel P.
    Agartz, Ingrid
    Aggarwal, Neelum T.
    Almasy, Laura
    Ames, David
    Amouyel, Philippe
    Andreassen, Ole A.
    Arepalli, Sampath
    Assareh, Amelia A.
    Barral, Sandra
    Bastin, Mark E.
    Becker, Diane M.
    Becker, James T.
    Bennett, David A.
    Blangero, John
    van Bokhoven, Hans
    Boomsma, Dorret I.
    Brodaty, Henry
    Brouwer, Rachel M.
    Brunner, Han G.
    Buckner, Randy L.
    Buitelaar, Jan K.
    Bulayeva, Kazima B.
    Cahn, Wiepke
    Calhoun, Vince D.
    Cannon, Dara M.
    Cavalleri, Gianpiero L.
    Chen, Christopher
    Cheng, Ching -Yu
    Cichon, Sven
    Cookson, Mark R.
    Corvin, Aiden
    Crespo-Facorro, Benedicto
    Curran, Joanne E.
    Czisch, Michael
    Dale, Anders M.
    Davies, Gareth E.
    De Geus, Eco J. C.
    De Jager, Philip L.
    de Zubicaray, Greig I.
    Delanty, Norman
    Depondt, Chantal
    DeStefano, Anita L.
    Dillman, Allissa
    Djurovic, Srdjan
    Donohoe, Gary
    Drevets, Wayne C.
    Duggirala, Ravi
    Dyer, Thomas D.
    Erk, Susanne
    Espeseth, Thomas
    Evans, Denis A.
    Fedko, Iryna
    Fernandez, Guillen
    Ferrucci, Luigi
    Fisher, Simon E.
    Fleischman, Debra A.
    Ford, Ian
    Foroud, Tatiana M.
    Fox, Peter T.
    Francks, Clyde
    Fukunaga, Masaki
    Gibbs, J. Raphael
    Glahn, David C.
    Gollub, Randy L.
    Goring, Harald H. H.
    Grabe, Hans J.
    Green, Robert C.
    Gruber, Oliver
    Gudnason, Vilmundur
    Guelfi, Sebastian
    Hansell, Narelle K.
    Hardy, John
    Hartman, Catharina A.
    Hashimoto, Ryota
    Hegenscheid, Katrin
    Heinz, Andreas
    Le Hellard, Stephanie
    Hernandez, Dena G.
    Heslenfeld, Dirk J.
    Ho, Beng-Choon
    Hoekstra, Pieter J.
    Hoffmann, Wolfgang
    Hofman, Albert
    Holsboer, Florian
    Homuth, Georg
    Hosten, Norbert
    Hottenga, Jouke-Jan
    Pol, Hilleke E. Hulshoff
    Ikeda, Masashi
    Ikram, M. Kamran
    Jack, Clifford R., Jr.
    Jenldnson, Mark
    Johnson, Robert
    Jonsson, Erik G.
    Jukema, J. Wouter
    Kahn, Rene S.
    Kanai, Ryota
    Kloszewska, Iwona
    Knopman, David S.
    Kochunov, Peter
    Kwok, John B.
    Lawrie, Stephen M.
    Lemaitre, Herve
    Liu, Xinmin
    Longo, Dan L.
    Longstreth, W. T., Jr.
    Lopez, Oscar L.
    Lovestone, Simon
    Martinez, Oliver
    Martinot, Jean-Luc
    Mattay, Venkata S.
    McDonald, Colm
    McIntosh, Andrew M.
    McMahon, Katie L.
    McMahon, Francis J.
    Mecocci, Patrizia
    Melle, Ingrid
    Meyer-Lindenberg, Andreas
    Mohnke, Sebastian
    Montgomery, Grant W.
    Morris, Derek W.
    Mosley, Thomas H.
    Muhleisen, Thomas W.
    Mueller-Myhsok, Bertram
    Nalls, Michael A.
    Nauck, Matthias
    Nichols, Thomas E.
    Niessen, Wiro J.
    Noethen, Markus M.
    Nyberg, Lars
    Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB). Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI).
    Ohi, Kazutaka
    Olvera, Rene L.
    Ophoff, Roel A.
    Pandolfo, Massimo
    Paus, Tomas
    Pausova, Zdenka
    Penninx, Brenda W. J. H.
    Pike, G. Bruce
    Potkin, Steven G.
    Psaty, Bruce M.
    Reppermund, Simone
    Rietschel, Marcella
    Roffman, Joshua L.
    Romanczuk-Seiferth, Nina
    Rotter, Jerome I.
    Ryten, Mina
    Sacco, Ralph L.
    Sachdev, Perminder S.
    Saykin, Andrew J.
    Schmidt, Reinhold
    Schofield, Peter R.
    Sigurdsson, Sigurdur
    Simmons, Andy
    Singleton, Andrew
    Sisodiya, Sanjay M.
    Smith, Colin
    Smoller, Jordan W.
    Soininen, Hindu.
    Srikanth, Velandai
    Steen, Vidar M.
    Stott, David J.
    Sussmann, Jessika E.
    Thalamuthu, Anbupalam
    Tiemeier, Henning
    Toga, Arthur W.
    Traynor, Bryan J.
    Troncoso, Juan
    Turner, Jessica A.
    Tzourio, Christophe
    Uitterlinden, Andre G.
    Hernandez, Maria C. Valdes
    Van der Brug, Marcel
    Van der Lugt, Aad
    Van der Wee, Nic J. A.
    Van Duijn, Cornelia M.
    Van Haren, Neeltje E. M.
    Van't Ent, Dennis
    Van Tol, Marie Jose
    Vardarajan, Badri N.
    Veltman, Dick J.
    Vernooij, Meike W.
    Voelzke, Henry
    Walter, Henrik
    Wardlaw, Joanna M.
    Wassink, Thomas H.
    Weale, Michael E.
    Weinberger, Daniel R.
    Weiner, Michael W.
    Wen, Wei
    Westman, Eric
    White, Tonya
    Wong, Tien Y.
    Wright, Clinton B.
    Zielke, H. Ronald
    Zonderman, Alan B.
    Deary, Ian J.
    DeCarli, Charles
    Schmidt, Helena
    Martin, Nicholas G.
    De Craen, Anton J. M.
    Wright, Margaret J.
    Launer, Lenore J.
    Schumann, Gunter
    Fornage, Myriam
    Franke, Barbara
    Debette, Stephanie
    Medland, Sarah E.
    Ikram, M. Arfan
    Thompson, Paul M.
    Novel genetic loci underlying human intracranial volume identified through genome-wide association2016In: Nature Neuroscience, ISSN 1097-6256, E-ISSN 1546-1726, Vol. 19, no 12, p. 1569-1582Article in journal (Refereed)
    Abstract [en]

    Intracranial volume reflects the maximally attained brain size during development, and remains stable with loss of tissue in late life. It is highly heritable, but the underlying genes remain largely undetermined. In a genome-wide association study of 32,438 adults, we discovered five previously unknown loci for intracranial volume and confirmed two known signals. Four of the loci were also associated with adult human stature, but these remained associated with intracranial volume after adjusting for height. We found a high genetic correlation with child head circumference (rho(genetic) = 0.748), which indicates a similar genetic background and allowed us to identify four additional loci through meta-analysis (N-combined = 37,345). Variants for intracranial volume were also related to childhood and adult cognitive function, and Parkinson's disease, and were enriched near genes involved in growth pathways, including PI3K-AKT signaling. These findings identify the biological underpinnings of intracranial volume and their link to physiological and pathological traits.

  • 5.
    Aglago, Elom K.
    et al.
    Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, London, United Kingdom.
    Kim, Andre
    Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    Lin, Yi
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States.
    Qu, Conghui
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States.
    Evangelou, Marina
    Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, London, United Kingdom.
    Ren, Yu
    Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, London, United Kingdom.
    Morrison, John
    Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    Albanes, Demetrius
    Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, MD, Bethesda, Liberia.
    Arndt, Volker
    Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
    Barry, Elizabeth L.
    Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire.
    Baurley, James W.
    Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia.
    Berndt, Sonja I.
    Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, MD, Bethesda, Liberia.
    Bien, Stephanie A.
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States.
    Bishop, D Timothy
    Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, United Kingdom.
    Bouras, Emmanouil
    Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.
    Brenner, Hermann
    Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
    Buchanan, Daniel D.
    Colorectal Oncogenomics Group, Department of Clinical Pathology, University of Melbourne, VIC, Parkville, Australia; University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, VIC, Parkville, Australia; Genomic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, VIC, Parkville, Australia.
    Budiarto, Arif
    Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia; Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia.
    Carreras-Torres, Robert
    ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain; Digestive Diseases and Microbiota Group, Girona Biomedical Research Institute (IDIBGI), Salt, Girona, Spain.
    Casey, Graham
    Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, VA, Charlottesville, United States.
    Cenggoro, Tjeng Wawan
    Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia.
    Chan, Andrew T.
    Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, MA, Boston, United States; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, MA, Boston, United States; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, MA, Boston, United States; Broad Institute of Harvard and MIT, MA, Cambridge, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, MA, Boston, United States; Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, MA, Boston, United States.
    Chang-Claude, Jenny
    Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; University Medical Centre Hamburg-Eppendorf, University Cancer Centre Hamburg (UCCH), Hamburg, Germany.
    Chen, Xuechen
    Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany.
    Conti, David V.
    Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    Devall, Matthew
    Department of Family Medicine, University of Virginia, VA, Charlottesville, United States.
    Diez-Obrero, Virginia
    ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain; Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, Barcelona, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain; Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain; Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark.
    Dimou, Niki
    Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France.
    Drew, David
    Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, MA, Boston, United States.
    Figueiredo, Jane C.
    Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States; Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, CA, Los Angeles, United States.
    Gallinger, Steven
    Lunenfeld Tanenbaum Research Institute, University of Toronto, Mount Sinai Hospital, ON, Toronto, Canada.
    Giles, Graham G.
    Cancer Epidemiology Division, Cancer Council Victoria, VIC, Melbourne, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, VIC, Clayton, Australia.
    Gruber, Stephen B.
    Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center.
    Gsur, Andrea
    Center for Cancer Research, Medical University of Vienna, Vienna, Austria.
    Gunter, Marc J.
    Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France.
    Hampel, Heather
    Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center.
    Harlid, Sophia
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Hidaka, Akihisa
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States.
    Harrison, Tabitha A.
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States.
    Hoffmeister, Michael
    Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
    Huyghe, Jeroen R.
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States.
    Jenkins, Mark A.
    Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.
    Jordahl, Kristina
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States.
    Joshi, Amit D.
    Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, MA, Boston, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, MA, Boston, United States.
    Kawaguchi, Eric S.
    Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    Keku, Temitope O.
    Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, NC, United States.
    Kundaje, Anshul
    Department of Genetics, Stanford University, CA, Stanford, United States; Department of Computer Science, Stanford University, CA, Stanford, United States.
    Larsson, Susanna C.
    Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
    Marchand, Loic Le
    University of Hawaii Cancer Center, HI, Honolulu, United States.
    Lewinger, Juan Pablo
    Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    Li, Li
    Department of Family Medicine, University of Virginia, VA, Charlottesville, United States.
    Lynch, Brigid M.
    Cancer Epidemiology Division, Cancer Council Victoria, VIC, Melbourne, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia; Physical Activity Laboratory, Baker Heart and Diabetes Institute, Melbourne, Australia.
    Mahesworo, Bharuno
    Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia.
    Mandic, Marko
    Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany.
    Obón-Santacana, Mireia
    ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain; Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, Barcelona, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain.
    Moreno, Victor
    ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain; Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, Barcelona, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain; Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain.
    Murphy, Neil
    Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France.
    Nan, Hongmei
    Department of Epidemiology, Richard M. Fairbanks School of Public Health, IN, Indianapolis, United States; IU Melvin and Bren Simon Cancer Center, Indiana University, IN, Indianapolis, United States.
    Nassir, Rami
    Department of Pathology, School of Medicine, Umm Al-Qura'a University, Mecca, Saudi Arabia.
    Newcomb, Polly A.
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States; Department of Epidemiology, University of Washington School of Public Health, WA, Seattle, United States.
    Ogino, Shuji
    Broad Institute of Harvard and MIT, MA, Cambridge, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, MA, Boston, United States; Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, MA, Boston, United States; Department of Oncologic Pathology, Dana-Farber Cancer Institute, MA, Boston, United States.
    Ose, Jennifer
    Huntsman Cancer Institute, UT, Salt Lake City, United States; Department of Population Health Sciences, University of Utah, UT, Salt Lake City, United States.
    Pai, Rish K.
    Department of Laboratory Medicine and Pathology, Mayo Clinic Arizona, AZ, Scottsdale, United States.
    Palmer, Julie R.
    Department of Medicine, Boston University School of Medicine, Slone Epidemiology Center, Boston University, MA, Boston, United States.
    Papadimitriou, Nikos
    Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France.
    Pardamean, Bens
    Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia.
    Peoples, Anita R.
    Huntsman Cancer Institute, UT, Salt Lake City, United States; Department of Population Health Sciences, University of Utah, UT, Salt Lake City, United States.
    Platz, Elizabeth A.
    Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, MD, Baltimore, Liberia.
    Potter, John D.
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States; Department of Epidemiology, University of Washington School of Public Health, WA, Seattle, United States; Research Centre for Hauora and Health, Massey University, Wellington, New Zealand.
    Prentice, Ross L.
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States.
    Rennert, Gad
    Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel; Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel; Clalit National Cancer Control Center, Haifa, Israel.
    Ruiz-Narvaez, Edward
    Department of Nutritional Sciences, University of Michigan School of Public Health, MI, Ann Arbor, United States.
    Sakoda, Lori C.
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States; Division of Research, Kaiser Permanente Northern California, CA, Oakland, United States.
    Scacheri, Peter C.
    Department of Genetics and Genome Sciences, Case Western Reserve University, OH, Cleveland, United States.
    Schmit, Stephanie L.
    Genomic Medicine Institute, Cleveland Clinic, OH, Cleveland, United States.
    Schoen, Robert E.
    Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, PA, Pittsburgh, United States.
    Shcherbina, Anna
    Department of Genetics, Stanford University, CA, Stanford, United States; Department of Computer Science, Stanford University, CA, Stanford, United States.
    Slattery, Martha L.
    Department of Internal Medicine, University of Utah, UT, Salt Lake City, United States.
    Stern, Mariana C.
    Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    Su, Yu-Ru
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States.
    Tangen, Catherine M.
    SWOG Statistical Center, Fred Hutchinson Cancer Research Center, WA, Seattle, United States.
    Thibodeau, Stephen N.
    Division of Laboratory Genetics, Department of Laboratory Medicine and Pathology, Mayo Clinic, MN, Rochester, United States.
    Thomas, Duncan C.
    Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    Tian, Yu
    Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; School of Public Health, Capital Medical University, Beijing, China.
    Ulrich, Cornelia M.
    Huntsman Cancer Institute, UT, Salt Lake City, United States; Department of Population Health Sciences, University of Utah, UT, Salt Lake City, United States.
    van Duijnhoven, Franzel Jb
    Division of Human Nutrition and Health, Wageningen University & Research, Wageningen, Netherlands.
    van Guelpen, Bethany
    Umeå University, Faculty of Medicine, Wallenberg Centre for Molecular Medicine at Umeå University (WCMM). Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Visvanathan, Kala
    Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, MD, Baltimore, Liberia.
    Vodicka, Pavel
    Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic; Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic; Faculty of Medicine and Biomedical Center in Pilsen, Charles University, Pilsen, Czech Republic.
    Wang, Jun
    Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    White, Emily
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States; Department of Epidemiology, University of Washington School of Public Health, WA, Seattle, United States.
    Wolk, Alicja
    Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
    Woods, Michael O.
    Memorial University of Newfoundland, Discipline of Genetics, St. John's, Canada.
    Wu, Anna H.
    Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    Zemlianskaia, Natalia
    Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    Hsu, Li
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States; Department of Biostatistics, University of Washington, WA, Seattle, United States.
    Gauderman, W James
    Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    Peters, Ulrike
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States; Department of Epidemiology, University of Washington School of Public Health, WA, Seattle, United States.
    Tsilidis, Konstantinos K.
    Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, London, United Kingdom; Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.
    Campbell, Peter T.
    Department of Epidemiology and Population Health, Albert Einstein College of Medicine, NY, Bronx, United States.
    A Genetic Locus within the FMN1/GREM1 Gene Region Interacts with Body Mass Index in Colorectal Cancer Risk2023In: Cancer Research, ISSN 0008-5472, E-ISSN 1538-7445, Vol. 83, no 15, p. 2572-2583Article in journal (Refereed)
    Abstract [en]

    Colorectal cancer risk can be impacted by genetic, environmental, and lifestyle factors, including diet and obesity. Gene-environment interactions (G × E) can provide biological insights into the effects of obesity on colorectal cancer risk. Here, we assessed potential genome-wide G × E interactions between body mass index (BMI) and common SNPs for colorectal cancer risk using data from 36,415 colorectal cancer cases and 48,451 controls from three international colorectal cancer consortia (CCFR, CORECT, and GECCO). The G × E tests included the conventional logistic regression using multiplicative terms (one degree of freedom, 1DF test), the two-step EDGE method, and the joint 3DF test, each of which is powerful for detecting G × E interactions under specific conditions. BMI was associated with higher colorectal cancer risk. The two-step approach revealed a statistically significant G×BMI interaction located within the Formin 1/Gremlin 1 (FMN1/GREM1) gene region (rs58349661). This SNP was also identified by the 3DF test, with a suggestive statistical significance in the 1DF test. Among participants with the CC genotype of rs58349661, overweight and obesity categories were associated with higher colorectal cancer risk, whereas null associations were observed across BMI categories in those with the TT genotype. Using data from three large international consortia, this study discovered a locus in the FMN1/GREM1 gene region that interacts with BMI on the association with colorectal cancer risk. Further studies should examine the potential mechanisms through which this locus modifies the etiologic link between obesity and colorectal cancer.

    SIGNIFICANCE: This gene-environment interaction analysis revealed a genetic locus in FMN1/GREM1 that interacts with body mass index in colorectal cancer risk, suggesting potential implications for precision prevention strategies.

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  • 6.
    Ahmad, Sajjad
    et al.
    Institute of Basic Medical Science, Khyber Medical University, KP, Peshawar, Pakistan.
    Ahmed, Jawad
    Institute of Pathology and Diagnostic Medicine, Khyber Medical University, Peshawar, Pakistan.
    Khalifa, Eman H.
    Al Baha University Faculty of Applied Medical Sciences, Saudi Arabia.
    Khattak, Farhad Ali
    Research & development Cell, Khyber College of Dentistry (KCD), Peshawar, Pakistan.
    khan, Anwar Sheed
    Provincial TB Reference laboratory, Hayatabad Medical Complex, PK, Peshawar, Pakistan.
    Farooq, Syed Umar
    Department of oral pathology, Khyber College of Dentistry, Peshawar, Pakistan.
    Osman, Sannaa M.A.
    Alzaiem Alazhari University Faculty of Medicine, Sudan.
    Salih, Magdi M.
    Taif University College of Science, Saudi Arabia.
    Ullah, Nadeem
    Umeå University, Faculty of Medicine, Department of Clinical Microbiology.
    Khan, Taj Ali
    Institute of Pathology and Diagnostic Medicine, Khyber Medical University, Peshawar, Pakistan; Division of Infectious Diseases & Global Medicine,Department of Medicine, University of Florida, FL, Gainesville, United States.
    Novel mutations in genes of the IL-12/IFN-γ axis cause susceptibility to tuberculosis2023In: Journal of Infection and Public Health, ISSN 1876-0341, E-ISSN 1876-035X, Vol. 16, no 9, p. 1368-1378Article in journal (Refereed)
    Abstract [en]

    Background: The IL-12/23/ISG15-IFN-γ pathway is the main immunological pathway for controlling intra-macrophagic microorganisms such as Mycobacteria, Salmonella, and Leishmania spp. Consequently, upon mutations in genes of the IL-12/23/ISG15-IFN-γ pathway cause increased susceptibility to intra-macrophagic pathogens, particularly to Mycobacteria. Therefore, the purpose of this study was to characterize the mutations in genes of the IL-12/23/ISG15-IFN-γ pathway in severe tuberculosis (TB) patients.

    Methods: Clinically suspected TB was initially confirmed in four patients (P) (P1, P2, P3, and P4) using the GeneXpert MTB/RIF and culturing techniques. The patients' Peripheral blood mononuclear cells (PBMCs) were then subjected to ELISA to measure Interleukin 12 (IL-12) and interferon gamma (IFN-γ). Flow cytometry was used to detect the surface expressions of IFN-γR1 and IFN-γR2 as well as IL-12Rβ1and IL-12Rβ2 on monocytes and T lymphocytes, respectively.The phosphorylation of signal transducer and activator of transcription 1(STAT1) on monocytes and STAT4 on T lymphocytes were also detected by flow cytometry. Sanger sequencing was used to identify mutations in the IL-12Rβ1, STAT1, NEMO, and CYBB genes.

    Results: P1's PBMCs exhibited reduced IFN-γ production, while P2's and P3's PBMCs exhibited impaired IL-12 induction. Low IL-12Rβ1 surface expression and reduced STAT4 phosphorylation were demonstrated by P1's T lymphocytes, while impaired STAT1 phosphorylation was detected in P2's monocytes. The impaired IκB-α degradation and abolished H2O2 production in monocytes and neutrophils of P3 and P4 were observed, respectively. Sanger sequencing revealed novel nonsense homozygous mutation: c.191 G>A/p.W64 * in exon 3 of the IL-12Rβ1 gene in P1, novel missense homozygous mutation: c.107 A>T/p.Q36L in exon 3 of the STAT1 gene in P2, missense hemizygous mutation:: c.950 A>C/p.Q317P in exon 8 of the NEMO gene in P3, and nonsense hemizygous mutation: c.868 C>T/p.R290X in exon 8 of CYBB gene in P4.

    Conclusion: Our findings broaden the clinical and genetic spectra associated with IL-12/23/ISG15-IFN-γ axis anomalies. Additionally, our data suggest that TB patients in Pakistan should be investigated for potential genetic defects due to high prevalence of parental consanguinity and increased incidence of TB in the country.

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  • 7. Ahmad, Shafqat
    et al.
    Mora, Samia
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA; Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Clinical Research Centre, Lund University, Skåne University Hospital, Malmö, Sweden.
    Orho-Melander, Marju
    Ridker, Paul M.
    Hu, Frank B.
    Chasman, Daniel I.
    Adiposity and Genetic Factors in Relation to Triglycerides and Triglyceride-Rich Lipoproteins in the Women's Genome Health Study2018In: Clinical Chemistry, ISSN 0009-9147, E-ISSN 1530-8561, Vol. 64, no 1, p. 231-241Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Previous results from Scandinavian cohorts have shown that obesity accentuates the effects of common genetic susceptibility variants on increased triglycerides (TG). Whether such interactions are present in the US population and further selective for particular TG-rich lipoprotein subfractions is unknown.

    METHODS: We examined these questions using body mass index (BMI) and waist circumference (WC) among women of European ancestry from the Women's Genome Health Study (WGHS) (n = 21840 for BMI; n = 19313 for WC). A weighted genetic risk score (TG-wGRS) based on 40 published TG-associated single-nucleotide polymorphisms was calculated using published effect estimates.

    RESULTS: Comparing overweight (BMI ≥ 25 kg/m2) and normal weight (BMI < 25 kg/m2) WGHS women, each unit increase of TG-wGRS was associated with TG increases of 1.013% and 1.011%, respectively, and this differential association was significant (Pinteraction = 0.014). Metaanalyses combining results for WGHS BMI with the 4 Scandinavian cohorts (INTER99, HEALTH2006, GLACIER, MDC) (total n = 40026) yielded a more significant interaction (Pinteraction = 0.001). Similarly, we observed differential association of the TG-wGRS with TG (Pinteraction = 0.006) in strata of WC (<80 cm vs ≥80 cm). Metaanalysis with 2 additional cohorts reporting WC (INTER99 and HEALTH2006) (total n = 27834) was significant with consistent effects (Pinteraction = 0.006). We also observed highly significant interactions of the TG-wGRS across the strata of BMI with very large, medium, and small TG-rich lipoprotein subfractions measured by nuclear magnetic resonance spectroscopy (all Pinteractions < 0.0001). The differential effects were strongest for very large TG-rich lipoprotein.

    CONCLUSIONS: Our results support the original findings and suggest that obese individuals may be more susceptible to aggregated genetic risk associated with common TG-raising alleles, with effects accentuated in the large TG-rich lipoprotein subfraction.

  • 8. Ahmad, Shafqat
    et al.
    Rukh, Gull
    Varga, Tibor V
    Ali, Ashfaq
    Kurbasic, Azra
    Shungin, Dmitry
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Umeå University, Faculty of Medicine, Department of Odontology. Lund University.
    Ericson, Ulrika
    Koivula, Robert W
    Chu, Audrey Y
    Rose, Lynda M
    Ganna, Andrea
    Qi, Qibin
    Stancakova, Alena
    Sandholt, Camilla H
    Elks, Cathy E
    Curhan, Gary
    Jensen, Majken K
    Tamimi, Rulla M
    Allin, Kristine H
    Jorgensen, Torben
    Brage, Soren
    Langenberg, Claudia
    Aadahl, Mette
    Grarup, Niels
    Linneberg, Allan
    Pare, Guillaume
    Magnusson, Patrik KE
    Pedersen, Nancy L
    Boehnke, Michael
    Hamsten, Anders
    Mohlke, Karen L
    Pasquale, Louis T
    Pedersen, Oluf
    Scott, Robert A
    Ridker, Paul M
    Ingelsson, Erik
    Laakso, Markku
    Hansen, Torben
    Qi, Lu
    Wareham, Nicholas J
    Chasman, Daniel I
    Hallmans, Göran
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Nutritional Research. Umeå University, Faculty of Medicine, Department of Biobank Research.
    Hu, Frank B
    Renström, Frida
    Orho-Melander, Marju
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Lund University and Harvard University.
    Gene x physical activity interactions in obesity: combined analysis of 111,421 individuals of European ancestry2013In: PLOS Genetics, ISSN 1553-7390, E-ISSN 1553-7404, Vol. 9, no 7, p. e1003607-Article in journal (Refereed)
    Abstract [en]

    Numerous obesity loci have been identified using genome-wide association studies. A UK study indicated that physical activity may attenuate the cumulative effect of 12 of these loci, but replication studies are lacking. Therefore, we tested whether the aggregate effect of these loci is diminished in adults of European ancestry reporting high levels of physical activity. Twelve obesity-susceptibility loci were genotyped or imputed in 111,421 participants. A genetic risk score (GRS) was calculated by summing the BMI-associated alleles of each genetic variant. Physical activity was assessed using self-administered questionnaires. Multiplicative interactions between the GRS and physical activity on BMI were tested in linear and logistic regression models in each cohort, with adjustment for age, age(2), sex, study center (for multicenter studies), and the marginal terms for physical activity and the GRS. These results were combined using meta-analysis weighted by cohort sample size. The meta-analysis yielded a statistically significant GRS x physical activity interaction effect estimate (P-interaction = 0.015). However, a statistically significant interaction effect was only apparent in North American cohorts (n = 39,810, P-interaction = 0.014 vs. n = 71,611, P-interaction = 0.275 for Europeans). In secondary analyses, both the FTO rs1121980 (P-interaction = 0.003) and the SEC16B rs10913469 (P-interaction = 0.025) variants showed evidence of SNP x physical activity interactions. This meta-analysis of 111,421 individuals provides further support for an interaction between physical activity and a GRS in obesity disposition, although these findings hinge on the inclusion of cohorts from North America, indicating that these results are either population-specific or non-causal.

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  • 9. Ahmad, Shafqat
    et al.
    Varga, Tibor V
    Franks, Paul W
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Gene x environment interactions in obesity: the state of the evidence2013In: Human Heredity, ISSN 0001-5652, E-ISSN 1423-0062, Vol. 75, no 2-4, p. 106-115Article in journal (Refereed)
    Abstract [en]

    Background/Aims: Obesity is a pervasive and highly prevalent disease that poses substantial health risks to those it affects. The rapid emergence of obesity as a global epidemic and the patterns and distributions of the condition within and between populations suggest that interactions between inherited biological factors (e.g. genes) and relevant environmental factors (e.g. diet and physical activity) may underlie the current obesity epidemic.

    Methods: We discuss the rationale for the assertion that gene x lifestyle interactions cause obesity, systematically appraise relevant literature, and consider knowledge gaps future studies might seek to bridge. Results: We identified >200 relevant studies, of which most are relatively small scale and few provide replication data.

    Conclusion: Although studies on gene x lifestyle interactions in obesity point toward the presence of such interactions, improved data standardization, appropriate pooling of data and resources, innovative study designs, and the application of powerful statistical methods will be required if translatable examples of gene x lifestyle interactions in obesity are to be identified. Future studies, of which most will be observational, should ideally be accompanied by appropriate replication data and, where possible, by analogous findings from experimental settings where clinically relevant traits (e.g. weight regain and weight cycling) are outcomes.

    (C) 2013 S. Karger AG, Basel

  • 10.
    Akimoto, Chizuru
    et al.
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Volk, Alexander E.
    van Blitterswijk, Marka
    Van den Broeck, Marleen
    Leblond, Claire S.
    Lumbroso, Serge
    Camu, William
    Neitzel, Birgit
    Onodera, Osamu
    van Rheenen, Wouter
    Pinto, Susana
    Weber, Markus
    Smith, Bradley
    Proven, Melanie
    Talbot, Kevin
    Keagle, Pamela
    Chesi, Alessandra
    Ratti, Antonia
    van der Zee, Julie
    Alstermark, Helena
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Birve, Anna
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Calini, Daniela
    Nordin, Angelica
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Tradowsky, Daniela C.
    Just, Walter
    Daoud, Hussein
    Angerbauer, Sabrina
    DeJesus-Hernandez, Mariely
    Konno, Takuya
    Lloyd-Jani, Anjali
    de Carvalho, Mamede
    Mouzat, Kevin
    Landers, John E.
    Veldink, Jan H.
    Silani, Vincenzo
    Gitler, Aaron D.
    Shaw, Christopher E.
    Rouleau, Guy A.
    van den Berg, Leonard H.
    Van Broeckhoven, Christine
    Rademakers, Rosa
    Andersen, Peter M.
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Kubisch, Christian
    A blinded international study on the reliability of genetic testing for GGGGCC-repeat expansions in C9orf72 reveals marked differences in results among 14 laboratories2014In: Journal of Medical Genetics, ISSN 0022-2593, E-ISSN 1468-6244, Vol. 51, no 6, p. 419-424Article in journal (Refereed)
    Abstract [en]

    Background The GGGGCC-repeat expansion in C9orf72 is the most frequent mutation found in patients with amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Most of the studies on C9orf72 have relied on repeat-primed PCR (RP-PCR) methods for detection of the expansions. To investigate the inherent limitations of this technique, we compared methods and results of 14 laboratories. Methods The 14 laboratories genotyped DNA from 78 individuals (diagnosed with ALS or FTD) in a blinded fashion. Eleven laboratories used a combination of amplicon-length analysis and RP-PCR, whereas three laboratories used RP-PCR alone; Southern blotting techniques were used as a reference. Results Using PCR-based techniques, 5 of the 14 laboratories got results in full accordance with the Southern blotting results. Only 50 of the 78 DNA samples got the same genotype result in all 14 laboratories. There was a high degree of false positive and false negative results, and at least one sample could not be genotyped at all in 9 of the 14 laboratories. The mean sensitivity of a combination of amplicon-length analysis and RP-PCR was 95.0% (73.9-100%), and the mean specificity was 98.0% (87.5-100%). Overall, a sensitivity and specificity of more than 95% was observed in only seven laboratories. Conclusions Because of the wide range seen in genotyping results, we recommend using a combination of amplicon-length analysis and RP-PCR as a minimum in a research setting. We propose that Southern blotting techniques should be the gold standard, and be made obligatory in a clinical diagnostic setting.

  • 11.
    Alaerts, Maaike
    et al.
    Applied Molecular Genomics Group, Department of Molecular Genetics, Flanders Interuniversity Institute for Biotechnology (VIB), University of Antwerp (UA), Belgium.
    Venken, Tine
    Applied Molecular Genomics Group, Department of Molecular Genetics, Flanders Interuniversity Institute for Biotechnology (VIB), University of Antwerp (UA), Belgium.
    Lenaerts, An-Sofie
    Applied Molecular Genomics Group, Department of Molecular Genetics, Flanders Interuniversity Institute for Biotechnology (VIB), University of Antwerp (UA), Belgium.
    De Zutter, Sonia
    Applied Molecular Genomics Group, Department of Molecular Genetics, Flanders Interuniversity Institute for Biotechnology (VIB), University of Antwerp (UA), Belgium.
    Norrback, Karl-Fredrik
    Umeå University, Faculty of Medicine, Department of Clinical Sciences, Psychiatry.
    Adolfsson, Rolf
    Umeå University, Faculty of Medicine, Department of Clinical Sciences, Psychiatry.
    Del-Favero, Jurgen
    Applied Molecular Genomics Group, Department of Molecular Genetics, Flanders Interuniversity Institute for Biotechnology (VIB), University of Antwerp (UA), Belgium.
    Lack of association of an insertion/deletion polymorphism in the G protein-coupled receptor 50 with bipolar disorder in a Northern Swedish population2006In: Psychiatric Genetics, ISSN 0955-8829, E-ISSN 1473-5873, Vol. 16, no 6, p. 235-236Article in journal (Refereed)
    Abstract [en]

    GPR50 is a G protein-coupled receptor, located on Xq28 and related to the melatonin receptor family. It is suggested as a functional and positional candidate gene for bipolar disorder (BP). Recently an insertion/deletion polymorphism in GPR50, Delta502-505, was found to be associated with BP in a Scottish association sample (P=0.007). When the analysis was restricted to female subjects, the association increased in significance (P=0.00023). We attempted to replicate this finding in a Northern Swedish association sample, but no significant association was detected (P=0.7, women only: P=0.65).

  • 12. Alarcon, Flora
    et al.
    Plante-Bordeneuve, Violaine
    Olsson, Malin
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Norrlands university hospital, NUS M31, Umeå, Sweden.
    Nuel, Gregory
    Non-parametric estimation of survival in age-dependent genetic disease and application to the transthyretin-related hereditary amyloidosis2018In: PLOS ONE, E-ISSN 1932-6203, Vol. 13, no 9, article id e0203860Article in journal (Refereed)
    Abstract [en]

    In genetic diseases with variable age of onset, survival function estimation for the mutation carriers as well as estimation of the modifying factors effects are essential to provide individual risk assessment, both for mutation carriers management and prevention strategies. In practice, this survival function is classically estimated from pedigrees data where most genotypes are unobserved. In this article, we present a unifying Expectation-Maximization (EM) framework combining probabilistic computations in Bayesian networks with standard statistical survival procedures in order to provide mutation carrier survival estimates. The proposed approach allows to obtain previously published parametric estimates (e.g. Weibull survival) as particular cases as well as more general Kaplan-Meier non-parametric estimates, which is the main contribution. Note that covariates can also be taken into account using a proportional hazard model. The whole methodology is both validated on simulated data and applied to family samples with transthyretin-related hereditary amyloidosis (a rare autosomal dominant disease with highly variable age of onset), showing very promising results.

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  • 13. Alaridah, Nader
    et al.
    Hallbäck, Erika Tång
    Tångrot, Jeanette
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine). National Bioinformatics Infrastructure Sweden (NBIS), SciLifeLab, Computational Life Science Cluster, Umeå University, Umeå, Sweden.
    Winqvistz, Niclas
    Sturegard, Erik
    Floren-Johanssons, Kerstin
    Jonsson, Bodil
    Tenland, Erik
    Welinder-Olssons, Christina
    Medstrand, Patrik
    Kaijser, Bertil
    Godaly, Gabriela
    Transmission dynamics study of tuberculosis isolates with whole genome sequencing in southern Sweden2019In: Scientific Reports, E-ISSN 2045-2322, Vol. 9, article id 4931Article in journal (Refereed)
    Abstract [en]

    Epidemiological contact tracing complemented with genotyping of clinical Mycobacterium tuberculosis isolates is important for understanding disease transmission. In Sweden, tuberculosis (TB) is mostly reported in migrant and homeless where epidemiologic contact tracing could pose a problem. This study compared epidemiologic linking with genotyping in a low burden country. Mycobacterium tuberculosis isolates (n = 93) collected at Scania University Hospital in Southern Sweden were analysed with the standard genotyping method mycobacterial interspersed repetitive units-variable number tandem repeats (MIRU-VNTR) and the results were compared with whole genome sequencing (WGS). Using a maximum of twelve single nucleotide polymorphisms (SNPs) as the upper threshold of genomic relatedness noted among hosts, we identified 18 clusters with WGS comprising 52 patients with overall pairwise genetic maximum distances ranging from zero to nine SNPs. MIRU-VNTR and WGS clustered the same isolates, although the distribution differed depending on MIRU-VNTR limitations. Both genotyping techniques identified clusters where epidemiologic linking was insufficient, although WGS had higher correlation with epidemiologic data. To summarize, WGS provided better resolution of transmission than MIRU-VNTR in a setting with low TB incidence. WGS predicted epidemiologic links better which could consolidate and correct the epidemiologically linked cases, avoiding thus false clustering.

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  • 14.
    Albagha, O M E
    et al.
    University of Aberdeen.
    Pettersson, Ulrika
    University of Aberdeen .
    Stewart, A
    University of Aberdeen.
    McGuigan, F E A
    University of Aberdeen.
    MacDonald, H M
    University of Aberdeen.
    Reid, D M
    University of Aberdeen.
    Ralston, S H
    University of Aberdeen.
    Association of oestrogen receptor alpha gene polymorphisms with postmenopausal bone loss, bone mass, and quantitative ultrasound properties of bone.2005In: Journal of Medical Genetics, ISSN 0022-2593, E-ISSN 1468-6244, Vol. 42, no 3, p. 240-6Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: The gene encoding oestrogen receptor alpha (ESR1) appears to regulate bone mineral density (BMD) and other determinants of osteoporotic fracture risk.

    OBJECTIVE: To investigate the relation between common polymorphisms and haplotypes of the ESR1 gene and osteoporosis related phenotypes in a population based cohort of 3054 Scottish women.

    RESULTS: There was a significant association between a common haplotype "px", defined by the PvuII and XbaI restriction fragment length polymorphisms within intron 1 of the ESR1 gene, and femoral neck bone loss in postmenopausal women who had not received hormone replacement therapy (n = 945; p = 0.009). Annual rates of femoral neck bone loss were approximately 14% higher in subjects who carried one copy of px and 22% higher in those who carried two copies, compared with those who did not carry the px haplotype. The px haplotype was associated with lower femoral neck BMD in the postmenopausal women (p = 0.02), and with reduced calcaneal broadband ultrasound attenuation (BUA) values in the whole study population (p = 0.005). There was no association between a TA repeat polymorphism in the ESR1 promoter and any phenotype studied, though on long range haplotype analysis subjects with a smaller number of TA repeats who also carried the px haplotype had reduced BUA values.

    CONCLUSIONS: The ESR1px haplotype is associated with reduced hip BMD values and increased rates of femoral neck bone loss in postmenopausal women. An association with BUA may explain the fact that ESR1 intron 1 alleles predict osteoporotic fractures by a mechanism partly independent of differences in BMD.

  • 15.
    Alhaidan, Yazeid
    et al.
    Department of Clinical Genetics, Odense University Hospital, Odense, Denmark; Department of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark; Department of Medical Genomics Research, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.
    Christesen, Henrik Thybo
    Department of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark; Hans Christian Andersen Children’s Hospital, Odense University Hospital, Odense, Denmark; Odense Pancreas Center, Odense, Denmark.
    Lundberg, Elena
    Umeå University, Faculty of Medicine, Department of Clinical Sciences, Paediatrics.
    Balwi, Mohammed A. Al
    Department of Medical Genomics Research, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia; Department of Pathology and Laboratory Medicine, King Abdulaziz Medical City, NGHA, Riyadh, Saudi Arabia.
    Brusgaard, Klaus
    Department of Clinical Genetics, Odense University Hospital, Odense, Denmark; Department of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark; Near East University, Nicosia, Cyprus.
    CRISPR/Cas9 ADCY7 Knockout Stimulates the Insulin Secretion Pathway Leading to Excessive Insulin Secretion2021In: Frontiers in Endocrinology, E-ISSN 1664-2392, Vol. 12, article id 657873Article in journal (Refereed)
    Abstract [en]

    Aim: Despite the enormous efforts to understand Congenital hyperinsulinism (CHI), up to 50% of the patients are genetically unexplained. We aimed to functionally characterize a novel candidate gene in CHI.

    Patient: A 4-month-old boy presented severe hyperinsulinemic hypoglycemia. A routine CHI genetic panel was negative.

    Methods: A trio-based whole-exome sequencing (WES) was performed. Gene knockout in the RIN-m cell line was established by CRISPR/Cas9. Gene expression was performed using real-time PCR.

    Results: Hyperinsulinemic hypoglycemia with diffuse beta-cell involvement was demonstrated in the patient, who was diazoxide-responsive. By WES, compound heterozygous variants were identified in the adenylyl cyclase 7, ADCY7 gene p.(Asp439Glu) and p.(Gly1045Arg). ADCY7 is calcium-sensitive, expressed in beta-cells and converts ATP to cAMP. The variants located in the cytoplasmic domains C1 and C2 in a highly conserved and functional amino acid region. RIN-m(-/-Adcy7) cells showed a significant increase in insulin secretion reaching 54% at low, and 49% at high glucose concentrations, compared to wild-type. In genetic expression analysis Adcy7 loss of function led to a 34.1-fold to 362.8-fold increase in mRNA levels of the insulin regulator genes Ins1 and Ins2 (p ≤ 0.0002), as well as increased glucose uptake and sensing indicated by higher mRNA levels of Scl2a2 and Gck via upregulation of Pdx1, and Foxa2 leading to the activation of the glucose stimulated-insulin secretion (GSIS) pathway.

    Conclusion: This study identified a novel candidate gene, ADCY7, to cause CHI via activation of the GSIS pathway.

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  • 16. Ali, Ashfaq
    et al.
    Varga, Tibor V.
    Stojkovic, Ivana A.
    Schulz, Christina-Alexandra
    Hallmans, Göran
    Umeå University, Faculty of Medicine, Department of Biobank Research.
    Barroso, Ines
    Poveda, Alaitz
    Renström, Frida
    Umeå University, Faculty of Medicine, Department of Biobank Research.
    Orho-Melander, Marju
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.
    Do Genetic Factors Modify the Relationship Between Obesity and Hypertriglyceridemia?: Findings From the GLACIER and the MDC Studies2016In: Circulation: Cardiovascular Genetics, ISSN 1942-325X, E-ISSN 1942-3268, Vol. 9, no 2, p. 162-171Article in journal (Refereed)
    Abstract [en]

    Background Obesity is a major risk factor for dyslipidemia, but this relationship is highly variable. Recently published data from 2 Danish cohorts suggest that genetic factors may underlie some of this variability.

    Methods and Results We tested whether established triglyceride-associated loci modify the relationship of body mass index (BMI) and triglyceride concentrations in 2 Swedish cohorts (the Gene-Lifestyle Interactions and Complex Traits Involved in Elevated Disease Risk [GLACIER Study; N=4312] and the Malmo Diet and Cancer Study [N=5352]). The genetic loci were amalgamated into a weighted genetic risk score (WGRS(TG)) by summing the triglyceride-elevating alleles (weighted by their established marginal effects) for all loci. Both BMI and the WGRS(TG) were strongly associated with triglyceride concentrations in GLACIER, with each additional BMI unit (kg/m(2)) associated with 2.8% (P=8.4x10(-84)) higher triglyceride concentration and each additional WGRS(TG) unit with 2% (P=7.6x10(-48)) higher triglyceride concentration. Each unit of the WGRS(TG) was associated with 1.5% higher triglyceride concentrations in normal weight and 2.4% higher concentrations in overweight/obese participants (P-interaction=0.056). Meta-analyses of results from the Swedish cohorts yielded a statistically significant WGRS(TG)xBMI interaction effect (P-interaction=6.0x10(-4)), which was strengthened by including data from the Danish cohorts (P-interaction=6.5x10(-7)). In the meta-analysis of the Swedish cohorts, nominal evidence of a 3-way interaction (WGRS(TG)xBMIxsex) was observed (P-interaction=0.03), where the WGRS(TG)xBMI interaction was only statistically significant in females. Using protein-protein interaction network analyses, we identified molecular interactions and pathways elucidating the metabolic relationships between BMI and triglyceride-associated loci.

    Conclusions Our findings provide evidence that body fatness accentuates the effects of genetic susceptibility variants in hypertriglyceridemia, effects that are most evident in females.

  • 17. Almlöf, Jonas Carlsson
    et al.
    Alexsson, Andrei
    Imgenberg-Kreuz, Juliana
    Sylwan, Lina
    Backlin, Christofer
    Leonard, Dag
    Nordmark, Gunnel
    Tandre, Karolina
    Eloranta, Maija-Leena
    Padyukov, Leonid
    Bengtsson, Christine
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Rheumatology.
    Jonsen, Andreas
    Dahlqvist, Solbritt Rantapaa
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Rheumatology.
    Sjowall, Christopher
    Bengtsson, Anders A.
    Gunnarsson, Iva
    Svenungsson, Elisabet
    Ronnblom, Lars
    Sandling, Johanna K.
    Syvanen, Ann-Christine
    Novel risk genes for systemic lupus erythematosus predicted by random forest classification2017In: Scientific Reports, E-ISSN 2045-2322, Vol. 7, article id 6236Article in journal (Refereed)
    Abstract [en]

    Genome-wide association studies have identified risk loci for SLE, but a large proportion of the genetic contribution to SLE still remains unexplained. To detect novel risk genes, and to predict an individual's SLE risk we designed a random forest classifier using SNP genotype data generated on the "Immunochip" from 1,160 patients with SLE and 2,711 controls. Using gene importance scores defined by the random forest classifier, we identified 15 potential novel risk genes for SLE. Of them 12 are associated with other autoimmune diseases than SLE, whereas three genes (ZNF804A, CDK1, and MANF) have not previously been associated with autoimmunity. Random forest classification also allowed prediction of patients at risk for lupus nephritis with an area under the curve of 0.94. By allele-specific gene expression analysis we detected cis-regulatory SNPs that affect the expression levels of six of the top 40 genes designed by the random forest analysis, indicating a regulatory role for the identified risk variants. The 40 top genes from the prediction were overrepresented for differential expression in B and T cells according to RNA-sequencing of samples from five healthy donors, with more frequent over-expression in B cells compared to T cells.

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  • 18. Aminoff, Anna
    et al.
    Gunnar, Erika
    Barbaro, Michela
    Mannila, Maria Nastase
    Duponchel, Christiane
    Tosi, Mario
    Robinson, Kristina Lagerstedt
    Hernell, Olle
    Umeå University, Faculty of Medicine, Department of Clinical Sciences, Paediatrics.
    Ehrenborg, Ewa
    Novel mutations in microsomal triglyceride transfer protein including maternal uniparental disomy in two patients with abetalipoproteinemia2012In: Clinical Genetics, ISSN 0009-9163, E-ISSN 1399-0004, Vol. 82, no 2, p. 197-200Article in journal (Refereed)
  • 19.
    Andersen, Peter M.
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Is all ALS genetic?2017In: Neurology, ISSN 0028-3878, E-ISSN 1526-632X, Vol. 89, no 3, p. 220-221Article in journal (Other academic)
  • 20. Ashar, Foram N.
    et al.
    Mitchell, Rebecca N.
    Albert, Christine M.
    Newton-Cheh, Christopher
    Brody, Jennifer A.
    Mueller-Nurasyid, Martina
    Moes, Anna
    Meitinger, Thomas
    Mak, Angel
    Huikuri, Heikki
    Junttila, M. Juhani
    Goyette, Philippe
    Pulit, Sara L.
    Pazoki, Raha
    Tanck, MichaelW.
    Blom, Marieke T.
    Zhao, XiaoQing
    Havulinna, Aki S.
    Jabbari, Reza
    Glinge, Charlotte
    Tragante, Vinicius
    Escher, Stefan A.
    Chakravarti, Aravinda
    Ehret, Georg
    Coresh, Josef
    Li, Man
    Prineas, Ronald J.
    Franco, Oscar H.
    Kwok, Pui-Yan
    Lumley, Thomas
    Dumas, Florence
    McKnight, Barbara
    Rotter, Jerome I.
    Lemaitre, Rozenn N.
    Heckbert, Susan R.
    O'Donnell, Christopher J.
    Hwang, Shih-Jen
    Tardif, Jean-Claude
    VanDenburgh, Martin
    Uitterlinden, Andre G.
    Hofman, Albert
    Stricker, Bruno H. C.
    de Bakker, Paul I. W.
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Section of Medicine.
    Jansson, Jan-Håkan
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Asselbergs, Folkert W.
    Halushka, Marc K.
    Maleszewski, Joseph J.
    Tfelt-Hansen, Jacob
    Engstrom, Thomas
    Salomaa, Veikko
    Virmani, Renu
    Kolodgie, Frank
    Wilde, Arthur A. M.
    Tan, Hanno L.
    Bezzina, Connie R.
    Eijgelsheim, Mark
    Rioux, John D.
    Jouven, Xavier
    Kääb, Stefan
    Psaty, Bruce M.
    Siscovick, David S.
    Arking, Dan E.
    Sotoodehnia, Nona
    A comprehensive evaluation of the genetic architecture of sudden cardiac arrest2018In: European Heart Journal, ISSN 0195-668X, E-ISSN 1522-9645, Vol. 39, no 44, p. 3961-+Article in journal (Refereed)
    Abstract [en]

    Aims: Sudden cardiac arrest (SCA) accounts for 10% of adult mortality in Western populations. We aim to identify potential loci associated with SCA and to identify risk factors causally associated with SCA.

    Methods and results: We carried out a large genome-wide association study (GWAS) for SCA (n = 3939 cases, 25 989 non-cases) to examine common variation genome-wide and in candidate arrhythmia genes. We also exploited Mendelian randomization (MR) methods using cross-trait multi-variant genetic risk score associations (GRSA) to assess causal relationships of 18 risk factors with SCA. No variants were associated with SCA at genome-wide significance, nor were common variants in candidate arrhythmia genes associated with SCA at nominal significance. Using cross-trait GRSA, we established genetic correlation between SCA and (i) coronary artery disease (CAD) and traditional CAD risk factors (blood pressure, lipids, and diabetes), (ii) height and BMI, and (iii) electrical instability traits (QT and atrial fibrillation), suggesting aetiologic roles for these traits in SCA risk.

    Conclusions: Our findings show that a comprehensive approach to the genetic architecture of SCA can shed light on the determinants of a complex life-threatening condition with multiple influencing factors in the general population. The results of this genetic analysis, both positive and negative findings, have implications for evaluating the genetic architecture of patients with a family history of SCA, and for efforts to prevent SCA in high-risk populations and the general community.

  • 21.
    Asim, Muhammad Nabeel
    et al.
    Department of Computer Science, Technical University of Kaiserslautern, Rhineland-Palatinate, Kaiserslautern, Germany; German Research Center for Artificial Intelligence GmbH, Rhineland-Palatinate, Kaiserslautern, Germany.
    Ibrahim, Muhammad Ali
    Department of Computer Science, Technical University of Kaiserslautern, Rhineland-Palatinate, Kaiserslautern, Germany; German Research Center for Artificial Intelligence GmbH, Rhineland-Palatinate, Kaiserslautern, Germany.
    Zehe, Christoph
    Sartorius Stedim Cellca GmbH, Baden-Wurttemberg, Laupheim, Germany.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry. Sartorius Stedim Cellca GmbH, Baden-Wurttemberg, Laupheim, Germany.
    Dengel, Andreas
    Department of Computer Science, Technical University of Kaiserslautern, Rhineland-Palatinate, Kaiserslautern, Germany; German Research Center for Artificial Intelligence GmbH, Rhineland-Palatinate, Kaiserslautern, Germany.
    Ahmed, Sheraz
    Umeå University. German Research Center for Artificial Intelligence GmbH, Rhineland-Palatinate, Kaiserslautern, Germany.
    BoT-Net: a lightweight bag of tricks-based neural network for efficient LncRNA–miRNA interaction prediction2022In: Interdisciplinary Sciences: Computational Life Sciences, ISSN 1913-2751, Vol. 14, no 4, p. 841-862Article in journal (Refereed)
    Abstract [en]

    Background and objective: Interactions of long non-coding ribonucleic acids (lncRNAs) with micro-ribonucleic acids (miRNAs) play an essential role in gene regulation, cellular metabolic, and pathological processes. Existing purely sequence based computational approaches lack robustness and efficiency mainly due to the high length variability of lncRNA sequences. Hence, the prime focus of the current study is to find optimal length trade-offs between highly flexible length lncRNA sequences.

    Method: The paper at hand performs in-depth exploration of diverse copy padding, sequence truncation approaches, and presents a novel idea of utilizing only subregions of lncRNA sequences to generate fixed-length lncRNA sequences. Furthermore, it presents a novel bag of tricks-based deep learning approach “Bot-Net” which leverages a single layer long-short-term memory network regularized through DropConnect to capture higher order residue dependencies, pooling to retain most salient features, normalization to prevent exploding and vanishing gradient issues, learning rate decay, and dropout to regularize precise neural network for lncRNA–miRNA interaction prediction.

    Results: BoT-Net outperforms the state-of-the-art lncRNA–miRNA interaction prediction approach by 2%, 8%, and 4% in terms of accuracy, specificity, and matthews correlation coefficient. Furthermore, a case study analysis indicates that BoT-Net also outperforms state-of-the-art lncRNA–protein interaction predictor on a benchmark dataset by accuracy of 10%, sensitivity of 19%, specificity of 6%, precision of 14%, and matthews correlation coefficient of 26%.

    Conclusion: In the benchmark lncRNA–miRNA interaction prediction dataset, the length of the lncRNA sequence varies from 213 residues to 22,743 residues and in the benchmark lncRNA–protein interaction prediction dataset, lncRNA sequences vary from 15 residues to 1504 residues. For such highly flexible length sequences, fixed length generation using copy padding introduces a significant level of bias which makes a large number of lncRNA sequences very much identical to each other and eventually derail classifier generalizeability. Empirical evaluation reveals that within 50 residues of only the starting region of long lncRNA sequences, a highly informative distribution for lncRNA–miRNA interaction prediction is contained, a crucial finding exploited by the proposed BoT-Net approach to optimize the lncRNA fixed length generation process.

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  • 22. Atkins, Isabelle
    et al.
    Kinnersley, Ben
    Ostrom, Quinn T.
    Labreche, Karim
    Il'yasova, Dora
    Armstrong, Georgina N.
    Eckel-Passow, Jeanette E.
    Schoemaker, Minouk J.
    Nothen, Markus M.
    Barnholtz-Sloan, Jill S.
    Swerdlow, Anthony J.
    Simon, Matthias
    Rajaraman, Preetha
    Chanock, Stephen J.
    Shildkraut, Joellen
    Bernstein, Jonine L.
    Hoffman, Per
    Jockel, Karl-Heinz
    Lai, Rose K.
    Claus, Elizabeth B.
    Olson, Sara H.
    Johansen, Christoffer
    Wrensch, Margaret R.
    Melin, Beatrice S.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Jenkins, Robert B.
    Sanson, Marc
    Bondy, Melissa L.
    Houlston, Richard S.
    Transcriptome-Wide Association Study Identifies New Candidate Susceptibility Genes for Glioma2019In: Cancer Research, ISSN 0008-5472, E-ISSN 1538-7445, Vol. 79, no 8, p. 2065-2071Article in journal (Refereed)
    Abstract [en]

    Genome-wide association studies (GWAS) have so far identified 25 loci associated with glioma risk, with most showing specificity for either glioblastoma (GBM) or non-GBM tumors. The majority of these GWAS susceptibility variants reside in noncoding regions and the causal genes underlying the associations are largely unknown. Here we performed a transcriptome-wide association study to search for novel risk loci and candidate causal genes at known GWAS loci using Genotype-Tissue Expression Project (GTEx) data to predict cis-predicted gene expression in relation to GBM and non-GBM risk in conjunction with GWAS summary statistics on 12,488 glioma cases (6,183 GBM and 5,820 non-GBM) and 18,169 controls. Imposing a Bonferroni-corrected significance level of P < 5.69 x 10(-6), candidate novel risk locus for GBM (mean Z = 4.43; P = 5.68 x 10(-6)). GALNT6 resides at least 55 Mb away from any previously identified glioma risk variant, while all other 30 significantly associated genes were located within 1 Mb of known GWAS-identified loci and were not significant after conditioning on the known GWAS-identified variants. These data identify a novel locus (GALNT6 at 12q13.33) and 30 genes at 12 known glioma risk loci associated with glioma risk, providing further insights into glioma tumorigenesis.

    Significance: This study identifies new genes associated with glioma risk, increasing understanding of how these tumors develop.

  • 23. Auer-Grumbach, Michaela
    et al.
    Toegel, Stefan
    Schabhuettl, Maria
    Weinmann, Daniela
    Chiari, Catharina
    Bennett, David L. H.
    Beetz, Christian
    Klein, Dennis
    Andersen, Peter M.
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Boehme, Ilka
    Fink-Puches, Regina
    Gonzalez, Michael
    Harms, Matthew B.
    Motley, William
    Reilly, Mary M.
    Renner, Wilfried
    Rudnik-Schoeneborn, Sabine
    Schlotter-Weigel, Beate
    Themistocleous, Andreas C.
    Weishaupt, Jochen H.
    Ludolph, Albert C.
    Wieland, Thomas
    Tao, Feifei
    Abreu, Lisa
    Windhager, Reinhard
    Zitzelsberger, Manuela
    Strom, Tim M.
    Walther, Thomas
    Scherer, Steven S.
    Zuchner, Stephan
    Martini, Rudolf
    Senderek, Jan
    Rare Variants in MME, Encoding Metalloprotease Neprilysin, Are Linked to Late-Onset Autosomal-Dominant Axonal Polyneuropathies2016In: American Journal of Human Genetics, ISSN 0002-9297, E-ISSN 1537-6605, Vol. 99, no 3, p. 607-623Article in journal (Refereed)
    Abstract [en]

    Axonal polyneuropathies are a frequent cause of progressive disability in the elderly. Common etiologies comprise diabetes mellitus, paraproteinaemia, and inflammatory disorders, but often the underlying causes remain elusive. Late-onset axonal Charcot-Marie-Tooth neuropathy (CMT2) is an autosomal-dominantly inherited condition that manifests in the second half of life and is genetically largely unexplained. We assumed age-dependent penetrance of mutations in a so far unknown gene causing late-onset CMT2. We screened 51 index case subjects with late-onset CMT2 for mutations by whole-exome (WES) and Sanger sequencing and subsequently queried WES repositories for further case subjects carrying mutations in the identified candidate gene. We studied nerve pathology and tissue levels and function of the abnormal protein in order to explore consequences of the mutations. Altogether, we observed heterozygous rare loss-of-function and missense mutations in MME encoding the metalloprotease neprilysin in 19 index case subjects diagnosed with axonal polyneuropathies or neurodegenerative conditions involving the peripheral nervous system. MME mutations segregated in an autosomal-dominant fashion with age-related incomplete penetrance and some affected individuals were isolated case subjects. We also found that MME mutations resulted in strongly decreased tissue availability of neprilysin and impaired enzymatic activity. Although neprilysin is known to degrade beta-amyloid, we observed no increased amyloid deposition or increased incidence of dementia in individuals with MME mutations. Detection of MME mutations is expected to increase the diagnostic yield in late-onset polyneuropathies, and it will be tempting to explore whether substances that can elevate neprilysin activity could be a rational option for treatment.

  • 24. Battram, Thomas
    et al.
    Richmond, Rebecca C.
    Baglietto, Laura
    Haycock, Philip C.
    Perduca, Vittorio
    Bojesen, Stig E.
    Gaunt, Tom R.
    Hemani, Gibran
    Guida, Florence
    Carreras-Torres, Robert
    Hung, Rayjean
    Amos, Christopher, I
    Freeman, Joshua R.
    Sandanger, Torkjel M.
    Nøst, Therese H.
    Nordestgaard, Børge G.
    Teschendorff, Andrew E.
    Polidoro, Silvia
    Vineis, Paolo
    Severi, Gianluca
    Hodge, Allison M.
    Giles, Graham G.
    Grankvist, Kjell
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Clinical chemistry.
    Johansson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Johansson, Mattias
    Smith, George Davey
    Relton, Caroline L.
    Appraising the causal relevance of DNA methylation for risk of lung cancer2019In: International Journal of Epidemiology, ISSN 0300-5771, E-ISSN 1464-3685, Vol. 48, no 5, p. 1493-1504Article in journal (Refereed)
    Abstract [en]

    Background: DNA methylation changes in peripheral blood have recently been identified in relation to lung cancer risk. Some of these changes have been suggested to mediate part of the effect of smoking on lung cancer. However, limitations with conventional mediation analyses mean that the causal nature of these methylation changes has yet to be fully elucidated.

    Methods: We first performed a meta-analysis of four epigenome-wide association studies (EWAS) of lung cancer (918 cases, 918 controls). Next, we conducted a two-sample Mendelian randomization analysis, using genetic instruments for methylation at CpG sites identified in the EWAS meta-analysis, and 29 863 cases and 55 586 controls from the TRICL-ILCCO lung cancer consortium, to appraise the possible causal role of methylation at these sites on lung cancer.

    Results: Sixteen CpG sites were identified from the EWAS meta-analysis [false discovery rate (FDR) < 0.05], for 14 of which we could identify genetic instruments. Mendelian randomization provided little evidence that DNA methylation in peripheral blood at the 14 CpG sites plays a causal role in lung cancer development (FDR > 0.05), including for cg05575921-AHRR where methylation is strongly associated with both smoke exposure and lung cancer risk.

    Conclusions: The results contrast with previous observational and mediation analysis, which have made strong claims regarding the causal role of DNA methylation. Thus, previous suggestions of a mediating role of methylation at sites identified in peripheral blood, such as cg05575921-AHRR, could be unfounded. However, this study does not preclude the possibility that differential DNA methylation at other sites is causally involved in lung cancer development, especially within lung tissue.

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  • 25. Berge-Seidl, Victoria
    et al.
    Pihlstrøm, Lasse
    Maple-Grødem, Jodi
    Forsgren, Lars
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Linder, Jan
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Larsen, Jan Petter
    Tysnes, Ole-Bjørn
    Toft, Mathias
    The GBA variant E326K is associated with Parkinson's disease and explains a genome-wide association signal2017In: Neuroscience Letters, ISSN 0304-3940, E-ISSN 1872-7972, Vol. 658, p. 48-52Article in journal (Refereed)
    Abstract [en]

    Objective: Coding variants in the GBA gene have been identified as the numerically most important genetic risk factors for Parkinson's disease (PD). In addition, genome-wide association studies (GWAS) have identified associations with PD in the SYT11-GBA region on chromosome 1q22, but the relationship to GBA coding variants have remained unclear. The aim of this study was to sequence the complete GBA gene in a clinical cohort and to investigate whether coding variants within the GBA gene may be driving reported association signals. Methods: We analyzed high-throughput sequencing data of all coding exons of GBA in 366 patients with PD. The identified low-frequency coding variants were genotyped in three Scandinavian case-controls series (786 patients and 713 controls). Previously reported risk variants from two independent association signals within the SYT11-GBA locus on chromosome 1 were also genotyped in the same samples. We performed association analyses and evaluated linkage disequilibrium (LD) between the variants. Results: We identified six rare mutations (1.6%) and two low-frequency coding variants in GBA. E326K (rs2230288) was significantly more frequent in PD patients compared to controls (OR 1.65, p = 0.03). There was no clear association of T369M (rs75548401) with disease (OR 1.43, p = 0.24). Genotyping the two GWAS hits rs35749011 and rs114138760 in the same sample set, we replicated the association between rs35749011 and disease status (OR 1.67, p = 0.03), while rs114138760 was found to have similar allele frequencies in patients and controls. Analyses revealed that E326K and rs35749011 are in very high LD (r(2) 0.95). Conclusions: Our results confirm that the GBA variant E326K is a susceptibility allele for PD. The results suggest that E326K may fully account for the primary association signal observed at chromosome 1q22 in previous GWAS of PD.

  • 26. Berndt, Sonja I.
    et al.
    Gustafsson, Stefan
    Maegi, Reedik
    Ganna, Andrea
    Wheeler, Eleanor
    Feitosa, Mary F.
    Justice, Anne E.
    Monda, Keri L.
    Croteau-Chonka, Damien C.
    Day, Felix R.
    Esko, Tonu
    Fall, Tove
    Ferreira, Teresa
    Gentilini, Davide
    Jackson, Anne U.
    Luan, Jian'an
    Randall, Joshua C.
    Vedantam, Sailaja
    Willer, Cristen J.
    Winkler, Thomas W.
    Wood, Andrew R.
    Workalemahu, Tsegaselassie
    Hu, Yi-Juan
    Lee, Sang Hong
    Liang, Liming
    Lin, Dan-Yu
    Min, Josine L.
    Neale, Benjamin M.
    Thorleifsson, Gudmar
    Yang, Jian
    Albrecht, Eva
    Amin, Najaf
    Bragg-Gresham, Jennifer L.
    Cadby, Gemma
    den Heijer, Martin
    Eklund, Niina
    Fischer, Krista
    Goel, Anuj
    Hottenga, Jouke-Jan
    Huffman, Jennifer E.
    Jarick, Ivonne
    Johansson, Asa
    Johnson, Toby
    Kanoni, Stavroula
    Kleber, Marcus E.
    Koenig, Inke R.
    Kristiansson, Kati
    Kutalik, Zoltn
    Lamina, Claudia
    Lecoeur, Cecile
    Li, Guo
    Mangino, Massimo
    McArdle, Wendy L.
    Medina-Gomez, Carolina
    Mueller-Nurasyid, Martina
    Ngwa, Julius S.
    Nolte, Ilja M.
    Paternoster, Lavinia
    Pechlivanis, Sonali
    Perola, Markus
    Peters, Marjolein J.
    Preuss, Michael
    Rose, Lynda M.
    Shi, Jianxin
    Shungin, Dmitry
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Smith, Albert Vernon
    Strawbridge, Rona J.
    Surakka, Ida
    Teumer, Alexander
    Trip, Mieke D.
    Tyrer, Jonathan
    Van Vliet-Ostaptchouk, Jana V.
    Vandenput, Liesbeth
    Waite, Lindsay L.
    Zhao, Jing Hua
    Absher, Devin
    Asselbergs, Folkert W.
    Atalay, Mustafa
    Attwood, Antony P.
    Balmforth, Anthony J.
    Basart, Hanneke
    Beilby, John
    Bonnycastle, Lori L.
    Brambilla, Paolo
    Bruinenberg, Marcel
    Campbell, Harry
    Chasman, Daniel I.
    Chines, Peter S.
    Collins, Francis S.
    Connell, John M.
    Cookson, William O.
    de Faire, Ulf
    de Vegt, Femmie
    Dei, Mariano
    Dimitriou, Maria
    Edkins, Sarah
    Estrada, Karol
    Evans, David M.
    Farrall, Martin
    Ferrario, Marco M.
    Ferrieres, Jean
    Franke, Lude
    Frau, Francesca
    Gejman, Pablo V.
    Grallert, Harald
    Groenberg, Henrik
    Gudnason, Vilmundur
    Hall, Alistair S.
    Hall, Per
    Hartikainen, Anna-Liisa
    Hayward, Caroline
    Heard-Costa, Nancy L.
    Heath, Andrew C.
    Hebebrand, Johannes
    Homuth, Georg
    Hu, Frank B.
    Hunt, Sarah E.
    Hyppoenen, Elina
    Iribarren, Carlos
    Jacobs, Kevin B.
    Jansson, John-Olov
    Jula, Antti
    Kahonen, Mika
    Kathiresan, Sekar
    Kee, Frank
    Khaw, Kay-Tee
    Kivimaki, Mika
    Koenig, Wolfgang
    Kraja, Aldi T.
    Kumari, Meena
    Kuulasmaa, Kari
    Kuusisto, Johanna
    Laitinen, Jaana H.
    Lakka, Timo A.
    Langenberg, Claudia
    Launer, Lenore J.
    Lind, Lars
    Lindstrom, Jaana
    Liu, Jianjun
    Liuzzi, Antonio
    Lokki, Marja-Liisa
    Lorentzon, Mattias
    Madden, Pamela A.
    Magnusson, Patrik K.
    Manunta, Paolo
    Marek, Diana
    Maerz, Winfried
    Leach, Irene Mateo
    McKnight, Barbara
    Medland, Sarah E.
    Mihailov, Evelin
    Milani, Lili
    Montgomery, Grant W.
    Mooser, Vincent
    Muehleisen, Thomas W.
    Munroe, Patricia B.
    Musk, Arthur W.
    Narisu, Narisu
    Navis, Gerjan
    Nicholson, George
    Nohr, Ellen A.
    Ong, Ken K.
    Oostra, Ben A.
    Palmer, Colin N. A.
    Palotie, Aarno
    Peden, John F.
    Pedersen, Nancy
    Peters, Annette
    Polasek, Ozren
    Pouta, Anneli
    Pramstaller, Peter P.
    Prokopenko, Inga
    Puetter, Carolin
    Radhakrishnan, Aparna
    Raitakari, Olli
    Rendon, Augusto
    Rivadeneira, Fernando
    Rudan, Igor
    Saaristo, Timo E.
    Sambrook, Jennifer G.
    Sanders, Alan R.
    Sanna, Serena
    Saramies, Jouko
    Schipf, Sabine
    Schreiber, Stefan
    Schunkert, Heribert
    Shin, So-Youn
    Signorini, Stefano
    Sinisalo, Juha
    Skrobek, Boris
    Soranzo, Nicole
    Stancakova, Alena
    Stark, Klaus
    Stephens, Jonathan C.
    Stirrups, Kathleen
    Stolk, Ronald P.
    Stumvoll, Michael
    Swift, Amy J.
    Theodoraki, Eirini V.
    Thorand, Barbara
    Tregouet, David-Alexandre
    Tremoli, Elena
    Van der Klauw, Melanie M.
    van Meurs, Joyce B. J.
    Vermeulen, Sita H.
    Viikari, Jorma
    Virtamo, Jarmo
    Vitart, Veronique
    Waeber, Gerard
    Wang, Zhaoming
    Widen, Elisabeth
    Wild, Sarah H.
    Willemsen, Gonneke
    Winkelmann, Bernhard R.
    Witteman, Jacqueline C. M.
    Wolffenbuttel, Bruce H. R.
    Wong, Andrew
    Wright, Alan F.
    Zillikens, M. Carola
    Amouyel, Philippe
    Boehm, Bernhard O.
    Boerwinkle, Eric
    Boomsma, Dorret I.
    Caulfield, Mark J.
    Chanock, Stephen J.
    Cupples, L. Adrienne
    Cusi, Daniele
    Dedoussis, George V.
    Erdmann, Jeanette
    Eriksson, Johan G.
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Froguel, Philippe
    Gieger, Christian
    Gyllensten, Ulf
    Hamsten, Anders
    Harris, Tamara B.
    Hengstenberg, Christian
    Hicks, Andrew A.
    Hingorani, Aroon
    Hinney, Anke
    Hofman, Albert
    Hovingh, Kees G.
    Hveem, Kristian
    Illig, Thomas
    Jarvelin, Marjo-Riitta
    Joeckel, Karl-Heinz
    Keinanen-Kiukaanniemi, Sirkka M.
    Kiemeney, Lambertus A.
    Kuh, Diana
    Laakso, Markku
    Lehtimaki, Terho
    Levinson, Douglas F.
    Martin, Nicholas G.
    Metspalu, Andres
    Morris, Andrew D.
    Nieminen, Markku S.
    Njolstad, Inger
    Ohlsson, Claes
    Oldehinkel, Albertine J.
    Ouwehand, Willem H.
    Palmer, Lyle J.
    Penninx, Brenda
    Power, Chris
    Province, Michael A.
    Psaty, Bruce M.
    Qi, Lu
    Rauramaa, Rainer
    Ridker, Paul M.
    Ripatti, Samuli
    Salomaa, Veikko
    Samani, Nilesh J.
    Snieder, Harold
    Sorensen, Thorkild I. A.
    Spector, Timothy D.
    Stefansson, Kari
    Tonjes, Anke
    Tuomilehto, Jaakko
    Uitterlinden, Andre G.
    Uusitupa, Matti
    van der Harst, Pim
    Vollenweider, Peter
    Wallaschofski, Henri
    Wareham, Nicholas J.
    Watkins, Hugh
    Wichmann, H-Erich
    Wilson, James F.
    Abecasis, Goncalo R.
    Assimes, Themistocles L.
    Barroso, Ines
    Boehnke, Michael
    Borecki, Ingrid B.
    Deloukas, Panos
    Fox, Caroline S.
    Frayling, Timothy
    Groop, Leif C.
    Haritunian, Talin
    Heid, Iris M.
    Hunter, David
    Kaplan, Robert C.
    Karpe, Fredrik
    Moffatt, Miriam F.
    Mohlke, Karen L.
    O'Connell, Jeffrey R.
    Pawitan, Yudi
    Schadt, Eric E.
    Schlessinger, David
    Steinthorsdottir, Valgerdur
    Strachan, David P.
    Thorsteinsdottir, Unnur
    van Duijn, Cornelia M.
    Visscher, Peter M.
    Di Blasio, Anna Maria
    Hirschhorn, Joel N.
    Lindgren, Cecilia M.
    Morris, Andrew P.
    Meyre, David
    Scherag, Andr
    McCarthy, Mark I.
    Speliotes, Elizabeth K.
    North, Kari E.
    Loos, Ruth J. F.
    Ingelsson, Erik
    Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture2013In: Nature Genetics, ISSN 1061-4036, E-ISSN 1546-1718, Vol. 45, no 5, p. 501-U69Article in journal (Refereed)
    Abstract [en]

    Approaches exploiting trait distribution extremes may be used to identify loci associated with common traits, but it is unknown whether these loci are generalizable to the broader population. In a genome-wide search for loci associated with the upper versus the lower 5th percentiles of body mass index, height and waist-to-hip ratio, as well as clinical classes of obesity, including up to 263,407 individuals of European ancestry, we identified 4 new loci (IGFBP4, H6PD, RSRC1 and PPP2R2A) influencing height detected in the distribution tails and 7 new loci (HNF4G, RPTOR, GNAT2, MRPS33P4, ADCY9, HS6ST3 and ZZZ3) for clinical classes of obesity. Further, we find a large overlap in genetic structure and the distribution of variants between traits based on extremes and the general population and little etiological heterogeneity between obesity subgroups.

  • 27. Berthelot, Claire C
    et al.
    Kamita, Shizuo George
    Sacchi, Romina
    Yang, Jun
    Nording, Malin L
    Umeå University, Faculty of Science and Technology, Department of Chemistry. Department of Entomology, University of California Davis, Davis, CA, United States Of America.
    Georgi, Katrin
    Karbowski, Christine Hegedus
    German, J Bruce
    Weiss, Robert H
    Hogg, Ronald J
    Hammock, Bruce D
    Zivkovic, Angela M
    Changes in PTGS1 and ALOX12 Gene Expression in Peripheral Blood Mononuclear Cells Are Associated with Changes in Arachidonic Acid, Oxylipins, and Oxylipin/Fatty Acid Ratios in Response to Omega-3 Fatty Acid Supplementation2015In: PLOS ONE, E-ISSN 1932-6203, Vol. 10, no 12, article id e0144996Article in journal (Refereed)
    Abstract [en]

    Introduction: There is a high degree of inter-individual variability among people in response to intervention with omega-3 fatty acids (FA), which may partly explain conflicting results on the effectiveness of omega-3 FA for the treatment and prevention of chronic inflammatory diseases. In this study we sought to evaluate whether part of this inter-individual variability in response is related to the regulation of key oxylipin metabolic genes in circulating peripheral blood mononuclear cells (PBMCs). Methods: Plasma FA and oxylipin profiles from 12 healthy individuals were compared to PBMC gene expression profiles following six weeks of supplementation with fish oil, which delivered 1.9 g/d eicosapentaenoic acid (EPA) and 1.5 g/d docosahexaenoic acid (DHA). Fold changes in gene expression were measured by a quantitative polymerase chain reaction (qPCR). Results: Healthy individuals supplemented with omega-3 FA had differential responses in prostaglandin-endoperoxide synthase 1 (PTGS1), prostaglandin-endoperoxide synthase 2 (PTGS2), arachidonate 12-lipoxygenase (ALOX12), and interleukin 8 (IL-8) gene expression in isolated PBMCs. In those individuals for whom plasma arachidonic acid (ARA) in the phosphatidylethanolamine (PE) lipid class decreased in response to omega-3 intervention, there was a corresponding decrease in gene expression for PTGS1 and ALOX12. Several oxylipin product/FA precursor ratios (e.g. prostaglandin E-2 (PGE(2))/ARA for PTGS1 and 12-hydroxyeicosatetraenoic acid (12-HETE)/ARA for ALOX12) were also associated with fold change in gene expression, suggesting an association between enzyme activity and gene expression. The fold-change in PTGS1 gene expression was highly positively correlated with ALOX12 gene expression but not with PTGS2, whereas IL-8 and PTGS2 were positively correlated. Conclusions: The regulation of important oxylipin metabolic genes in PBMCs varied with the extent of change in ARA concentrations in the case of PTGS1 and ALOX12 regulation. PBMC gene expression changes in response to omega-3 supplementation varied among healthy individuals, and were associated with changes in plasma FA and oxylipin composition to different degrees in different individuals.

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  • 28. Beyder, Arthur
    et al.
    Mazzone, Amelia
    Strege, Peter R.
    Tester, David J.
    Saito, Yuri A.
    Bernard, Cheryl E.
    Enders, Felicity T.
    Ek, Weronica E.
    Schmidt, Peter T.
    Dlugosz, Aldona
    Lindberg, Greger
    Karling, Pontus
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Ohlsson, Bodil
    Gazouli, Maria
    Nardone, Gerardo
    Cuomo, Rosario
    Usai-Satta, Paolo
    Galeazzi, Francesca
    Neri, Matteo
    Portincasa, Piero
    Bellini, Massimo
    Barbara, Giovanni
    Camilleri, Michael
    Locke, G. Richard, III
    Talley, Nicholas J.
    D'Amato, Mauro
    Ackerman, Michael J.
    Farrugia, Gianrico
    Loss-of-Function of the Voltage-Gated Sodium Channel Na(V)1.5 (Channelopathies) in Patients With Irritable Bowel Syndrome2014In: Gastroenterology, ISSN 0016-5085, E-ISSN 1528-0012, Vol. 146, no 7, p. 1659-1668Article in journal (Refereed)
    Abstract [en]

    BACKGROUND & AIMS: SCN5A encodes the a-subunit of the voltage-gated sodium channel Na(V)1.5. Many patients with cardiac arrhythmias caused by mutations in SCN5A also have symptoms of irritable bowel syndrome (IBS). We investigated whether patients with IBS have SCN5A variants that affect the function of Na(V)1.5. METHODS: We performed genotype analysis of SCN5A in 584 persons with IBS and 1380 without IBS (controls). Mutant forms of SCN5A were expressed in human embryonic kidney-293 cells, and functions were assessed by voltage clamp analysis. A genome-wide association study was analyzed for an association signal for the SCN5A gene, and replicated in 1745 patients in 4 independent cohorts of IBS patients and controls. RESULTS: Missense mutations were found in SCN5A in 13 of 584 patients (2.2%, probands). Diarrhea-predominant IBS was the most prevalent form of IBS in the overall study population (25%). However, a greater percentage of individuals with SCN5A mutations had constipation-predominant IBS (31%) than diarrhea-predominant IBS (10%; P < .05). Electrophysiologic analysis showed that 10 of 13 detected mutations disrupted Na(V)1.5 function (9 loss-of-function and 1 gain-of-function function). The p. A997T-Na(V)1.5 had the greatest effect in reducing Na(V)1.5 function. Incubation of cells that expressed this variant with mexiletine restored their sodium current and administration of mexiletine to 1 carrier of this mutation (who had constipation-predominant IBS) normalized their bowel habits. In the genome-wide association study and 4 replicated studies, the SCN5A locus was strongly associated with IBS. CONCLUSIONS: About 2% of patients with IBS carry mutations in SCN5A. Most of these are loss-of-function mutations that disrupt Na(V)1.5 channel function. These findings provide a new pathogenic mechanism for IBS and possible treatment options.

  • 29.
    Bhattacharjee, Samsiddhi
    et al.
    Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, 6120 Executive Boulevard, Rockville, Maryland 20852, USA.
    Rajaraman, Preetha
    Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, 6120 Executive Boulevard, Rockville, Maryland 20852, USA.
    Jacobs, Kevin B
    Core Genotyping Facility, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, 8717 Grovemont Circle, Gaithersburg, Maryland 20877, USA.
    Wheeler, William A
    Information Management Services, Rockville, MD 20852, USA.
    Melin, Beatrice S
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Hartge, Patricia
    Epidemiology and Biostatistics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20892, USA.
    GliomaScan Consortium,
    GliomaScan Consortium investigators and affiliations are available in the Supplemental Data.
    Yeager, Meredith
    Core Genotyping Facility, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, 8717 Grovemont Circle, Gaithersburg, Maryland 20877, USA.
    Chung, Charles C
    Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20892, USA.
    Chanock, Stephen J
    Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20892, USA.
    Chatterjee, Nilanjan
    Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, 6120 Executive Boulevard, Rockville, Maryland 20852, USA.
    A subset-based approach improves power and interpretation for the combined analysis of genetic association studies of heterogeneous traits2012In: American Journal of Human Genetics, ISSN 0002-9297, E-ISSN 1537-6605, Vol. 90, no 5, p. 821-835Article in journal (Refereed)
    Abstract [en]

    Pooling genome-wide association studies (GWASs) increases power but also poses methodological challenges because studies are often heterogeneous. For example, combining GWASs of related but distinct traits can provide promising directions for the discovery of loci with small but common pleiotropic effects. Classical approaches for meta-analysis or pooled analysis, however, might not be suitable for such analysis because individual variants are likely to be associated with only a subset of the traits or might demonstrate effects in different directions. We propose a method that exhaustively explores subsets of studies for the presence of true association signals that are in either the same direction or possibly opposite directions. An efficient approximation is used for rapid evaluation of p values. We present two illustrative applications, one for a meta-analysis of separate case-control studies of six distinct cancers and another for pooled analysis of a case-control study of glioma, a class of brain tumors that contains heterogeneous subtypes. Both the applications and additional simulation studies demonstrate that the proposed methods offer improved power and more interpretable results when compared to traditional methods for the analysis of heterogeneous traits. The proposed framework has applications beyond genetic association studies.

  • 30. Bien, Stephanie A.
    et al.
    Su, Yu-Ru
    Conti, David V.
    Harrison, Tabitha A.
    Qu, Conghui
    Guo, Xingyi
    Lu, Yingchang
    Albanes, Demetrius
    Auer, Paul L.
    Banbury, Barbara L.
    Berndt, Sonja I.
    Bezieau, Stephane
    Brenner, Hermann
    Buchanan, Daniel D.
    Caan, Bette J.
    Campbell, Peter T.
    Carlson, Christopher S.
    Chan, Andrew T.
    Chang-Claude, Jenny
    Chen, Sai
    Connolly, Charles M.
    Easton, Douglas F.
    Feskens, Edith J. M.
    Gallinger, Steven
    Giles, Graham G.
    Gunter, Marc J.
    Hampe, Jochen
    Huyghe, Jeroen R.
    Hoffmeister, Michael
    Hudson, Thomas J.
    Jacobs, Eric J.
    Jenkins, Mark A.
    Kampman, Ellen
    Kang, Hyun Min
    Kuehn, Tilman
    Kury, Sebastien
    Lejbkowicz, Flavio
    Le Marchand, Loic
    Milne, Roger L.
    Li, Li
    Li, Christopher I.
    Lindblom, Annika
    Lindor, Noralane M.
    Martin, Vicente
    McNeil, Caroline E.
    Melas, Marilena
    Moreno, Victor
    Newcomb, Polly A.
    Offit, Kenneth
    Pharaoh, Paul D. P.
    Potter, John D.
    Qu, Chenxu
    Riboli, Elio
    Rennert, Gad
    Sala, Nuria
    Schafmayer, Clemens
    Scacheri, Peter C.
    Schmit, Stephanie L.
    Severi, Gianluca
    Slattery, Martha L.
    Smith, Joshua D.
    Trichopoulou, Antonia
    Tumino, Rosario
    Ulrich, Cornelia M.
    van Duijnhoven, Franzel J. B.
    van Guelpen, Bethany
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology. Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, USA.
    Weinstein, Stephanie J.
    White, Emily
    Wolk, Alicja
    Woods, Michael O.
    Wu, Anna H.
    Abecasis, Goncalo R.
    Casey, Graham
    Nickerson, Deborah A.
    Gruber, Stephen B.
    Hsu, Li
    Zheng, Wei
    Peters, Ulrike
    Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer2019In: Human Genetics, ISSN 0340-6717, E-ISSN 1432-1203, Vol. 138, no 4, p. 307-326Article in journal (Refereed)
    Abstract [en]

    Genome-wide association studies have reported 56 independently associated colorectal cancer (CRC) risk variants, most of which are non-coding and believed to exert their effects by modulating gene expression. The computational method PrediXcan uses cis-regulatory variant predictors to impute expression and perform gene-level association tests in GWAS without directly measured transcriptomes. In this study, we used reference datasets from colon (n=169) and whole blood (n=922) transcriptomes to test CRC association with genetically determined expression levels in a genome-wide analysis of 12,186 cases and 14,718 controls. Three novel associations were discovered from colon transverse models at FDR0.2 and further evaluated in an independent replication including 32,825 cases and 39,933 controls. After adjusting for multiple comparisons, we found statistically significant associations using colon transcriptome models with TRIM4 (discovery P=2.2x10(-4), replication P=0.01), and PYGL (discovery P=2.3x10(-4), replication P=6.7x10(-4)). Interestingly, both genes encode proteins that influence redox homeostasis and are related to cellular metabolic reprogramming in tumors, implicating a novel CRC pathway linked to cell growth and proliferation. Defining CRC risk regions as one megabase up- and downstream of one of the 56 independent risk variants, we defined 44 non-overlapping CRC-risk regions. Among these risk regions, we identified genes associated with CRC (P<0.05) in 34/44 CRC-risk regions. Importantly, CRC association was found for two genes in the previously reported 2q25 locus, CXCR1 and CXCR2, which are potential cancer therapeutic targets. These findings provide strong candidate genes to prioritize for subsequent laboratory follow-up of GWAS loci. This study is the first to implement PrediXcan in a large colorectal cancer study and findings highlight the utility of integrating transcriptome data in GWAS for discovery of, and biological insight into, risk loci.

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  • 31.
    Birve, Anna
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
    Suppressor of zeste 12, a Polycomb group gene in Drosophila melanogaster; one piece in the epigenetic puzzle2003Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    In multicellular organisms all cells in one individual have an identical genotype, and yet their bodies consist of many and very different tissues and thus many different cell types. Somehow there must be a difference in how genes are interpreted. So, there must be signals that tell the genes when and where to be active and inactive, respectively. In some instances a specific an expression pattern (active or inactive) is epigenetic; it is established and maintained throughout multiple rounds of cell divisions. In the developing Drosophila embryo, the proper expression pattern of e.g. the homeotic genes Abd-B and Ubx is to be kept active in the posterior part and silenced in the anterior. Properly silenced homeotic genes are crucial for the correct segmentation pattern of the fly and the Polycomb group (Pc-G) proteins are vital for maintaining this type of stable repression.

    As part of this thesis, Suppressor of zeste 12 (Su(z)12) is characterized as a Drosophila Pc-G gene. Mutations in the gene cause widespread misexpression of several homeotic genes in embryos and larvae. Results show that the silencing of the homeotic genes Abd-B and Ubx, probably is mediated via physical binding of SU(Z)12 to Polycomb Response Elements in the BX-C. Su(z)12 mutations are strong suppressors of position-effect-variegation and the SU(Z)12 protein binds weakly to the heterochromatic centromeric region. These results indicate that SU(Z)12 has a function in heterochromatin-mediated repression, which is an unusual feature for a Pc-G protein. The structure of the Su(z)12 gene was determined and the deduced protein contains a C2-H2 zinc finger domain, several nuclear localization signals, and a region, the VEFS box, with high homology to mammalian and plant homologues. Su(z)12 was originally isolated in a screen for modifiers of the zeste-white interaction and I present results that suggests that this effect is mediated through an interaction between Su(z)12 and zeste. I also show that Su(z)12 interact genetically with other Pc-G mutants and that the SU(Z)12 protein binds more than 100 euchromatic bands on polytene chromosomes. I also present results showing that SU(Z)12 is a subunit of two different E(Z)/ESC embryonic silencing complexes, one 1MDa and one 600 kDa complex, where the larger complex also contains PCL and RPD3.

    In conclusion, results presented in this thesis show that the recently identified Pc-G gene, Su(z)12, is of vital importance for correct maintenance of silencing of the developmentally important homeotic genes.

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  • 32. Blasco, Helene
    et al.
    Bernard-Marissal, Nathalie
    Vourc'h, Patrick
    Guettard, Yves Olivier
    Sunyach, Claire
    Augereau, Olivier
    Khederchah, Joelle
    Mouzat, Kevin
    Antar, Catherine
    Gordon, Paul H.
    Veyrat-Durebex, Charlotte
    Besson, Gerard
    Andersen, Peter M.
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Salachas, Francois
    Meininger, Vincent
    Camu, William
    Pettmann, Brigitte
    Andres, Christian R.
    Corcia, Philippe
    A Rare Motor Neuron Deleterious Missense Mutation in the DPYSL3 (CRMP4) Gene is Associated with ALS2013In: Human Mutation, ISSN 1059-7794, E-ISSN 1098-1004, Vol. 34, no 7, p. 953-960Article in journal (Refereed)
    Abstract [en]

    The dihydropyrimidinase-like 3 (DPYSL3) or Collapsin Response Mediator Protein 4a (CRMP4a) expression is modified in neurodegeneration and is involved in several ALS-associated pathways including axonal transport, glutamate excitotoxicity, and oxidative stress. The objective of the study was to analyze CRMP4 as a risk factor for ALS. We analyzed the DPYSL3/CRMP4 gene in French ALS patients (n=468) and matched-controls (n=394). We subsequently examined a variant in a Swedish population (184 SALS, 186 controls), and evaluated its functional effects on axonal growth and survival in motor neuron cell culture. The rs147541241:A>G missense mutation occurred in higher frequency among French ALS patients (odds ratio=2.99) but the association was not confirmed in the Swedish population. In vitro expression of mutated DPYSL3 in motor neurons reduced axonal growth and accelerated cell death compared with wild type protein. Thus, the association between the rs147541241 variant and ALS was limited to the French population, highlighting the geographic particularities of genetic influences (risks, contributors). The identified variant appears to shorten motor neuron survival through a detrimental effect on axonal growth and CRMP4 could act as a key unifier in transduction pathways leading to neurodegeneration through effects on early axon development.

  • 33. Blaydon, Diana C
    et al.
    Lind, Lisbet K
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Medical and Clinical Genetics.
    Plagnol, Vincent
    Linton, Kenneth J
    Smith, Francis JD
    Wilson, Neil J
    McLean, WH Irwin
    Munro, Colin S
    South, Andrew P
    Leigh, Irene M
    O'Toole, Edel A
    Lundström, Anita
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Dermatology and Venerology.
    Kelsell, David P
    Mutations in AQP5, encoding a water-channel protein, cause autosomal-dominant diffuse nonepidermolytic palmoplantar keratoderma2013In: American Journal of Human Genetics, ISSN 0002-9297, E-ISSN 1537-6605, Vol. 93, no 2, p. 330-335Article in journal (Refereed)
  • 34. Blink, Marjolein
    et al.
    Zimmermann, Martin
    von Neuhoff, Christine
    Reinhardt, Dirk
    de Haas, Valerie
    Hasle, Henrik
    O'Brien, Maureen M
    Stark, Batia
    Tandonnet, Julie
    Pession, Andrea
    Tousovska, Katerina
    Cheuk, Daniel K L
    Kudo, Kazuko
    Taga, Takashi
    Rubnitz, Jeffrey E
    Haltrich, Iren
    Balwierz, Walentyna
    Pieters, Rob
    Forestier, Erik
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Medical and Clinical Genetics.
    Johansson, Bertil
    van den Heuvel-Eibrink, Marry M
    Zwaan, C Michel
    Normal karyotype is a poor prognostic factor in myeloid leukemia of Down syndrome: a retrospective, international study2014In: Haematologica, ISSN 0390-6078, E-ISSN 1592-8721, Vol. 99, no 2, p. 299-307Article in journal (Refereed)
    Abstract [en]

    Myeloid leukemia of Down syndrome has a better prognosis than sporadic pediatric acute myeloid leukemia. Most cases of myeloid leukemia of Down syndrome are characterized by additional cytogenetic changes besides the constitutional trisomy 21, but their potential prognostic impact is not known. We, therefore, conducted an international retrospective study of clinical characteristics, cytogenetics, treatment, and outcome of 451 children with myeloid leukemia of Down syndrome. All karyotypes were centrally reviewed before assigning patients to subgroups. The overall 7-year event-free survival for the entire cohort was 78% (± 2%), with the overall survival rate being 79% (± 2%), the cumulative incidence of relapse 12% (± 2%), and the cumulative incidence of toxic death 7% (± 1%). Outcome estimates showed large differences across the different cytogenetic subgroups. Based on the cumulative incidence of relapse, we could risk-stratify patients into two groups: cases with a normal karyotype (n=103) with a higher cumulative incidence of relapse (21%± 4%) than cases with an aberrant karyotype (n=255) with a cumulative incidence of relapse of 9% (± 2%) (P=0.004). Multivariate analyses revealed that white blood cell count ≥ 20 × 10(9)/L and age >3 years were independent predictors for poor event-free survival, while normal karyotype independently predicted inferior overall survival, event-free survival, and relapse-free survival. In conclusion, this study showed large differences in outcome within patients with myeloid leukemia of Down syndrome and identified novel prognostic groups that predicted clinical outcome and hence may be used for stratification in future treatment protocols.

  • 35.
    Bolin, Karin
    et al.
    Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
    Imgenberg-Kreuz, Juliana
    Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
    Leonard, Dag
    Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
    Sandling, Johanna K.
    Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
    Alexsson, Andrei
    Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
    Pucholt, Pascal
    Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
    Haarhaus, Malena Loberg
    Department of Medicine Solna, Karolinska Institutet, Karolinska University Hospital Stockholm, Stockholm, Sweden.
    Almlöf, Jonas Carlsson
    Molecular Medicine, Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
    Nititham, Joanne
    Russell/Engleman Rheumatology Research Center, Department of Medicine, University of California San Francisco, CA, San Francisco, United States.
    Jönsen, Andreas
    Department of Rheumatology, Lund University, Lund, Sweden.
    Sjöwall, Christopher
    Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden.
    Bengtsson, Anders A.
    Department of Rheumatology, Lund University, Lund, Sweden.
    Rantapää-Dahlqvist, Solbritt
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Section of Medicine.
    Svenungsson, Elisabet
    Department of Medicine Solna, Karolinska Institutet, Karolinska University Hospital Stockholm, Stockholm, Sweden.
    Gunnarsson, Iva
    Department of Medicine Solna, Karolinska Institutet, Karolinska University Hospital Stockholm, Stockholm, Sweden.
    Syvänen, Ann-Christine
    Molecular Medicine, Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
    Lerang, Karoline
    Department of Rheumatology, University of Oslo, Oslo, Norway.
    Troldborg, Anne
    Department of Rheumatology, Aarhus University Hospital and Department of Biomedicine, Aarhus University, Aarhus, Denmark.
    Voss, Anne
    Department of Rheumatology, Odense University Hospital, Odense, Denmark.
    Molberg, Øyvind
    Department of Rheumatology, University of Oslo, Oslo, Norway.
    Jacobsen, Søren
    Department of Clinical Medicine, Copenhagen University Hospital, Copenhagen, Denmark.
    Criswell, Lindsey
    Russell/Engleman Rheumatology Research Center, Department of Medicine, University of California San Francisco, CA, San Francisco, United States.
    Rönnblom, Lars
    Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
    Nordmark, Gunnel
    Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
    Variants in BANK1 are associated with lupus nephritis of European ancestry2021In: Genes and Immunity, ISSN 1466-4879, E-ISSN 1476-5470, Vol. 22, no 3, p. 194-202Article in journal (Refereed)
    Abstract [en]

    The genetic background of lupus nephritis (LN) has not been completely elucidated. We performed a case-only study of 2886 SLE patients, including 947 (33%) with LN. Renal biopsies were available from 396 patients. The discovery cohort (Sweden, n = 1091) and replication cohort 1 (US, n = 962) were genotyped on the Immunochip and replication cohort 2 (Denmark/Norway, n = 833) on a custom array. Patients with LN, proliferative nephritis, or LN with end-stage renal disease were compared with SLE without nephritis. Six loci were associated with LN (p < 1 × 10−4, NFKBIA, CACNA1S, ITGA1, BANK1, OR2Y, and ACER3) in the discovery cohort. Variants in BANK1 showed the strongest association with LN in replication cohort 1 (p = 9.5 × 10−4) and proliferative nephritis in a meta-analysis of discovery and replication cohort 1. There was a weak association between BANK1 and LN in replication cohort 2 (p = 0.052), and in the meta-analysis of all three cohorts the association was strengthened (p = 2.2 × 10−7). DNA methylation data in 180 LN patients demonstrated methylation quantitative trait loci (meQTL) effects between a CpG site and BANK1 variants. To conclude, we describe genetic variations in BANK1 associated with LN and evidence for genetic regulation of DNA methylation within the BANK1 locus. This indicates a role for BANK1 in LN pathogenesis.

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  • 36. Bonaïti, Bernard
    et al.
    Olsson, Malin
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Hellman, Urban
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Suhr, Ole B
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Bonaïti-Pellié, Catherine
    Planté-Bordeneuve, Violaine
    TTR familial amyloid polyneuropathy: does a mitochondrial polymorphism entirely explain the parent-of-origin difference in penetrance?2010In: European Journal of Human Genetics, ISSN 1018-4813, E-ISSN 1476-5438Article in journal (Refereed)
    Abstract [en]

    The Val30Met transthyretin familial amyloid polyneuropathy (TTR-V30M-FAP) is the most frequent familial amyloidosis, with autosomal dominant transmission. This severe disease shows important differences in age of onset and penetrance. Recently, a difference in penetrance according to the gender of the transmitting parent was elicited in different geographic areas with a higher penetrance in case of maternal transmission of the trait. In addition, differences in mitochondrial haplogroup distribution in early and late onset Swedish and French cases of TTR-V30M-FAP suggested that a polymorphism of mitochondrial DNA could be one underlying mechanism of the phenotypic variation. We further investigated this hypothesis by modeling the penetrance function with a parent-of-origin and/or a mitochondrial polymorphism effect in samples of Portuguese (n=33) and Swedish families (n=86) with TTR-V30M-FAP in which several individuals had been tested for mitochondrial haplogroups. Our analysis showed that a mitochondrial polymorphism effect was sufficient to explain the observed difference in penetrance according to gender of the transmitting parent in the Portuguese sample, whereas, in the Swedish sample, a clear residual parent-of-origin effect remained. This study further supported the role of a mitochondrial polymorphism effect that might induce a higher penetrance in case of maternal inheritance of the disease. In clinical practice, these results might help to better delineate the individual disease risk and have a significant impact on the management of both patients and carriers.

  • 37. Bosse, Yohan
    et al.
    Li, Zhonglin
    Xia, Jun
    Manem, Venkata
    Carreras-Torres, Robert
    Gabriel, Aurelie
    Gaudreault, Nathalie
    Albanes, Demetrius
    Aldrich, Melinda C.
    Andrew, Angeline
    Arnold, Susanne
    Bickeboeller, Heike
    Bojesen, Stig E.
    Brennan, Paul
    Brunnstrom, Hans
    Caporaso, Neil
    Chen, Chu
    Christiani, David C.
    Field, John K.
    Goodman, Gary
    Grankvist, Kjell
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Clinical chemistry.
    Houlston, Richard
    Johansson, Mattias
    Johansson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Kiemeney, Lambertus A.
    Lam, Stephen
    Landi, Maria T.
    Lazarus, Philip
    Le Marchand, Loic
    Liu, Geoffrey
    Melander, Olle
    Rennert, Gadi
    Risch, Angela
    Rosenberg, Susan M.
    Schabath, Matthew B.
    Shete, Sanjay
    Song, Zhuoyi
    Stevens, Victoria L.
    Tardon, Adonina
    Wichmann, H-Erich
    Woll, Penella
    Zienolddiny, Shan
    Obeidat, Ma'en
    Timens, Wim
    Hung, Rayjean J.
    Joubert, Philippe
    Amos, Christopher I.
    McKay, James D.
    Transcriptome-wide association study reveals candidate causal genes for lung cancer2020In: International Journal of Cancer, ISSN 0020-7136, E-ISSN 1097-0215, Vol. 146, no 7, p. 1862-1878Article in journal (Refereed)
    Abstract [en]

    We have recently completed the largest GWAS on lung cancer including 29,266 cases and 56,450 controls of European descent. The goal of our study has been to integrate the complete GWAS results with a large‐scale expression quantitative trait loci (eQTL) mapping study in human lung tissues (n = 1,038) to identify candidate causal genes for lung cancer. We performed transcriptome‐wide association study (TWAS) for lung cancer overall, by histology (adenocarcinoma, squamous cell carcinoma and small cell lung cancer) and smoking subgroups (never‐ and ever‐smokers). We performed replication analysis using lung data from the Genotype‐Tissue Expression (GTEx) project. DNA damage assays were performed in human lung fibroblasts for selected TWAS genes. As expected, the main TWAS signal for all histological subtypes and ever‐smokers was on chromosome 15q25. The gene most strongly associated with lung cancer at this locus using the TWAS approach was IREB2 (pTWAS = 1.09E−99), where lower predicted expression increased lung cancer risk. A new lung adenocarcinoma susceptibility locus was revealed on 9p13.3 and associated with higher predicted expression of AQP3 (pTWAS = 3.72E−6). Among the 45 previously described lung cancer GWAS loci, we mapped candidate target gene for 17 of them. The association AQP3‐adenocarcinoma on 9p13.3 was replicated using GTEx (pTWAS = 6.55E−5). Consistent with the effect of risk alleles on gene expression levels, IREB2 knockdown and AQP3 overproduction promote endogenous DNA damage. These findings indicate genes whose expression in lung tissue directly influences lung cancer risk.

  • 38. Bostrom, Adrian E.
    et al.
    Chatzittofis, Andreas
    Umeå University, Faculty of Medicine, Department of Clinical Sciences, Psychiatry.
    Ciuculete, Diana-Maria
    Flanagan, John N.
    Krattinger, Regina
    Bandstein, Marcus
    Mwinyi, Jessica
    Kullak-Ublick, Gerd A.
    Oberg, Katarina Gorts
    Arver, Stefan
    Schioth, Helgi B.
    Jokinen, Jussi
    Umeå University, Faculty of Medicine, Department of Clinical Sciences, Psychiatry. Department of Clinical Neuroscience/Psychiatry, Karolinska Institutet, Stockholm, Sweden.
    Hypermethylation-associated downregulation of microRNA-4456 in hypersexual disorder with putative influence on oxytocin signalling: A DNA methylation analysis of miRNA genes2020In: Epigenetics, ISSN 1559-2294, E-ISSN 1559-2308, Vol. 15, no 1-2, p. 145-160Article in journal (Refereed)
    Abstract [en]

    Hypersexual disorder (HD) was proposed as a diagnosis in the DSM-5 and the classification ‘Compulsive Sexual Behavior Disorder’ is now presented as an impulse-control disorder in ICD-11. HD incorporates several pathophysiological mechanisms; including impulsivity, compulsivity, sexual desire dysregulation and sexual addiction. No previous study investigated HD in a methylation analysis limited to microRNA (miRNA) associated CpG-sites. The genome wide methylation pattern was measured in whole blood from 60 subjects with HD and 33 healthy volunteers using the Illumina EPIC BeadChip. 8,852 miRNA associated CpG-sites were investigated in multiple linear regression analyses of methylation M-values to a binary independent variable of disease state (HD or healthy volunteer), adjusting for optimally determined covariates. Expression levels of candidate miRNAs were investigated in the same individuals for differential expression analysis. Candidate methylation loci were further studied for an association with alcohol dependence in an independent cohort of 107 subjects. Two CpG-sites were borderline significant in HD – cg18222192 (MIR708)(p < 10E-05,pFDR = 5.81E-02) and cg01299774 (MIR4456)(p < 10E-06, pFDR = 5.81E-02). MIR4456 was significantly lower expressed in HD in both univariate (p < 0.0001) and multivariate (p < 0.05) analyses. Cg01299774 methylation levels were inversely correlated with expression levels of MIR4456 (p < 0.01) and were also differentially methylated in alcohol dependence (p = 0.026). Gene target prediction and pathway analysis revealed that MIR4456 putatively targets genes preferentially expressed in brain and that are involved in major neuronal molecular mechanisms thought to be relevant for HD, e.g., the oxytocin signalling pathway. In summary, our study implicates a potential contribution of MIR4456 in the pathophysiology of HD by putatively influencing oxytocin signalling.

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  • 39.
    Bouras, Emmanouil
    et al.
    Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.
    Kim, Andre E.
    Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    Lin, Yi
    Public Health Sciences Division, Fred Hutchinson Cancer Center, WA, Seattle, United States.
    Morrison, John
    Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    Du, Mengmeng
    Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, NY, New York, United States.
    Albanes, Demetrius
    Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, MD, Bethesda, United States.
    Barry, Elizabeth L.
    Department of Epidemiology, Geisel School of Medicine at Dartmouth, NH, Hanover, United States.
    Baurley, James W.
    Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia; BioRealm LLC, CA, Walnut, United States.
    Berndt, Sonja I.
    Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, MD, Bethesda, United States.
    Bien, Stephanie A.
    Public Health Sciences Division, Fred Hutchinson Cancer Center, WA, Seattle, United States.
    Bishop, Timothy D.
    Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, United Kingdom.
    Brenner, Hermann
    Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
    Budiarto, Arif
    Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia; Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia.
    Burnett-Hartman, Andrea
    Institute for Health Research, Kaiser Permanente Colorado, CO, Denver, United States.
    Campbell, Peter T.
    Department of Epidemiology and Population Health, Albert Einstein College of Medicine, NY, Bronx, United States.
    Carreras-Torres, Robert
    Unit of Biomarkers and Suceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, Barcelona, Spain; ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain; Digestive Diseases and Microbiota Group, Girona Biomedical Research Institute (IDIBGI), Salt, Girona, Spain.
    Casey, Graham
    Center for Public Health Genomics, University of Virginia, VA, Charlottesville, United States.
    Cenggoro, Tjeng Wawan
    Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia; Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia.
    Chan, Andrew T.
    Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, MA, Boston, United States; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, MA, Boston, United States; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, MA, Boston, United States; Broad Institute of Harvard and MIT, MA, Cambridge, United States; Department of Epidemiology, Harvard TH Chan School of Public Health, Harvard University, MA, Boston, United States; Department of Immunology and Infectious Diseases, Harvard TH Chan School of Public Health, Harvard University, MA, Boston, United States.
    Chang-Claude, Jenny
    Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; University Medical Centre Hamburg-Eppendorf, University Cancer Centre Hamburg (UCCH), Hamburg, Germany.
    Conti, David V.
    Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    Cotterchio, Michelle
    Ontario Health (Cancer Care Ontario), ON, Toronto, Canada.
    Devall, Matthew
    Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, VA, Charlottesville, United States; Department of Public Health Sciences, Center for Public Health Genomics, VA, Charlottesville, United States.
    Diez-Obrero, Virginia
    Unit of Biomarkers and Suceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, Barcelona, Spain; ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain.
    Dimou, Niki
    Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France.
    Drew, David A.
    Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, MA, Boston, United States.
    Figueiredo, Jane C.
    Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, CA, Los Angeles, United States.
    Giles, Graham G.
    Cancer Epidemiology Division, Cancer Council Victoria, VIC, Melbourne, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, VIC, Melbourne, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, VIC, Clayton, Australia.
    Gruber, Stephen B.
    Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, CA, Duarte, United States.
    Gunter, Marc J.
    Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France.
    Harrison, Tabitha A.
    Public Health Sciences Division, Fred Hutchinson Cancer Center, WA, Seattle, United States.
    Hidaka, Akihisa
    Public Health Sciences Division, Fred Hutchinson Cancer Center, WA, Seattle, United States.
    Hoffmeister, Michael
    Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
    Huyghe, Jeroen R.
    Public Health Sciences Division, Fred Hutchinson Cancer Center, WA, Seattle, United States.
    Joshi, Amit D.
    Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, MA, Boston, United States; Department of Epidemiology, Harvard TH Chan School of Public Health, Harvard University, MA, Boston, United States.
    Kawaguchi, Eric S.
    Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States; Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA, Los Angeles, United States.
    Keku, Temitope O.
    Center for Gastrointestinal Biology and Disease, University of North Carolina, NC, Chapel Hill, United States.
    Kundaje, Anshul
    Department of Genetics, Stanford University, CA, Stanford, United States; Department of Computer Science, Stanford University, CA, Stanford, United States.
    Le Marchand, Loic
    University of Hawaii Cancer Center, HI, Honolulu, United States.
    Lewinger, Juan Pablo
    Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    Li, Li
    Department of Family Medicine, University of Virginia, VA, Charlottesville, United States.
    Lynch, Brigid M.
    Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, VIC, Melbourne, Australia; Cancer Epidemiology Division, Cancer Council Victoria, VIC, Melbourne, Australia.
    Mahesworo, Bharuno
    Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia.
    Männistö, Satu
    Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland.
    Moreno, Victor
    Unit of Biomarkers and Suceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, Barcelona, Spain; ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain; Department of Clinical Sciences, Faculty of Medicine and health Sciences and Universitat de Barcelona Institute of Complex Systems (UBICS), University of Barcelona (UB), L'Hospitalet de Llobregat, Barcelona, Spain.
    Murphy, Neil
    Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France.
    Newcomb, Polly A.
    Public Health Sciences Division, Fred Hutchinson Cancer Center, WA, Seattle, United States; School of Public Health, University of Washington, WA, Seattle, United States.
    Obón-Santacana, Mireia
    Unit of Biomarkers and Suceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, Barcelona, Spain; ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain.
    Ose, Jennifer
    Huntsman Cancer Institute, University of Utah, Utah, Salt Lake City, United States; Department of Population Health Sciences, University of Utah, UT, Salt Lake City, United States.
    Palmer, Julie R.
    Slone Epidemiology Center at Boston University, MA, Boston, United States.
    Papadimitriou, Nikos
    Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France.
    Pardamean, Bens
    Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia.
    Pellatt, Andrew J.
    Department of Cancer Medicine, MD Anderson Cancer Center, TX, Houston, United States.
    Peoples, Anita R.
    Huntsman Cancer Institute, University of Utah, Utah, Salt Lake City, United States; Department of Population Health Sciences, University of Utah, UT, Salt Lake City, United States.
    Platz, Elizabeth A.
    Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, MD, Baltimore, United States.
    Potter, John D.
    Public Health Sciences Division, Fred Hutchinson Cancer Center, WA, Seattle, United States; Research Centre for Hauora and Health, Massey University, Wellington, New Zealand.
    Qi, Lihong
    Department of Public Health Sciences, University of California Davis, CA, Davis, United States.
    Qu, Conghui
    Public Health Sciences Division, Fred Hutchinson Cancer Center, WA, Seattle, United States.
    Rennert, Gad
    Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel; Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel; Clalit National Cancer Control Center, Haifa, Israel.
    Ruiz-Narvaez, Edward
    Department of Nutritional Sciences, University of Michigan School of Public Health, MI, Ann Arbor, United States.
    Sakoda, Lori C.
    Public Health Sciences Division, Fred Hutchinson Cancer Center, WA, Seattle, United States; Division of Research, Kaiser Permanente Northern California, CA, Oakland, United States.
    Schmit, Stephanie L.
    Genomic Medicine Institute, Cleveland Clinic, OH, Cleveland, United States; Population and Cancer Prevention Program, Case Comprehensive Cancer Center, OH, Cleveland, United States.
    Shcherbina, Anna
    Department of Genetics, Stanford University, CA, Stanford, United States; Department of Computer Science, Stanford University, CA, Stanford, United States.
    Stern, Mariana C.
    Department of Population and Public Health Sciences and Norris Comprehensive Cancer Center, Preventive Medicine, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    Su, Yu-Ru
    Public Health Sciences Division, Fred Hutchinson Cancer Center, WA, Seattle, United States.
    Tangen, Catherine M.
    SWOG Statistical Center, Fred Hutchinson Cancer Research Center, WA, Seattle, United States.
    Thomas, Duncan C.
    Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    Tian, Yu
    Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; School of Public Health, Capital Medical University, Beijing, China.
    Um, Caroline Y.
    Department of Population Science, American Cancer Society, GA, Atlanta, United States.
    van Duijnhoven, Franzel JB.
    Division of Human Nutrition and Health, Wageningen University & Research, Wageningen, Netherlands.
    van Guelpen, Bethany
    Umeå University, Faculty of Medicine, Wallenberg Centre for Molecular Medicine at Umeå University (WCMM). Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Visvanathan, Kala
    Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, MD, Baltimore, United States.
    Wang, Jun
    Department of Population and Public Health Sciences and Norris Comprehensive Cancer Center, Preventive Medicine, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    White, Emily
    Public Health Sciences Division, Fred Hutchinson Cancer Center, WA, Seattle, United States; Department of Epidemiology, University of Washington School of Public Health, WA, Seattle, United States.
    Wolk, Alicja
    Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
    Woods, Michael O.
    Memorial University of Newfoundland, Discipline of Genetics, St John's, Canada.
    Ulrich, Cornelia M.
    Huntsman Cancer Institute, University of Utah, Utah, Salt Lake City, United States; Department of Population Health Sciences, University of Utah, UT, Salt Lake City, United States.
    Hsu, Li
    Public Health Sciences Division, Fred Hutchinson Cancer Center, WA, Seattle, United States; Department of Biostatistics, University of Washington, WA, Seattle, United States.
    Gauderman, W James
    Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    Peters, Ulrike
    Public Health Sciences Division, Fred Hutchinson Cancer Center, WA, Seattle, United States; Department of Epidemiology, University of Washington, WA, Seattle, United States.
    Tsilidis, Konstantinos K.
    Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece; Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, London, United Kingdom.
    Genome-wide interaction analysis of folate for colorectal cancer risk2023In: American Journal of Clinical Nutrition, ISSN 0002-9165, E-ISSN 1938-3207, Vol. 118, no 5, p. 881-891Article in journal (Refereed)
    Abstract [en]

    Background: Epidemiological and experimental evidence suggests that higher folate intake is associated with decreased colorectal cancer (CRC) risk; however, the mechanisms underlying this relationship are not fully understood. Genetic variation that may have a direct or indirect impact on folate metabolism can provide insights into folate's role in CRC.

    Objectives: Our aim was to perform a genome-wide interaction analysis to identify genetic variants that may modify the association of folate on CRC risk.

    Methods: We applied traditional case-control logistic regression, joint 3-degree of freedom, and a 2-step weighted hypothesis approach to test the interactions of common variants (allele frequency >1%) across the genome and dietary folate, folic acid supplement use, and total folate in relation to risk of CRC in 30,550 cases and 42,336 controls from 51 studies from 3 genetic consortia (CCFR, CORECT, GECCO).

    Results: Inverse associations of dietary, total folate, and folic acid supplement with CRC were found (odds ratio [OR]: 0.93; 95% confidence interval [CI]: 0.90, 0.96; and 0.91; 95% CI: 0.89, 0.94 per quartile higher intake, and 0.82 (95% CI: 0.78, 0.88) for users compared with nonusers, respectively). Interactions (P-interaction < 5×10-8) of folic acid supplement and variants in the 3p25.2 locus (in the region of Synapsin II [SYN2]/tissue inhibitor of metalloproteinase 4 [TIMP4]) were found using traditional interaction analysis, with variant rs150924902 (located upstream to SYN2) showing the strongest interaction. In stratified analyses by rs150924902 genotypes, folate supplementation was associated with decreased CRC risk among those carrying the TT genotype (OR: 0.82; 95% CI: 0.79, 0.86) but increased CRC risk among those carrying the TA genotype (OR: 1.63; 95% CI: 1.29, 2.05), suggesting a qualitative interaction (P-interaction = 1.4×10-8). No interactions were observed for dietary and total folate.

    Conclusions: Variation in 3p25.2 locus may modify the association of folate supplement with CRC risk. Experimental studies and studies incorporating other relevant omics data are warranted to validate this finding.

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  • 40. Brazel, David M.
    et al.
    Jiang, Yu
    Hughey, Jordan M.
    Turcot, Valérie
    Zhan, Xiaowei
    Gong, Jian
    Batini, Chiara
    Weissenkampen, J. Dylan
    Liu, MengZhen
    Barnes, Daniel R.
    Bertelsen, Sarah
    Chou, Yi-Ling
    Erzurumluoglu, A. Mesut
    Faul, Jessica D.
    Haessler, Jeff
    Hammerschlag, Anke R.
    Hsu, Chris
    Kapoor, Manav
    Lai, Dongbing
    Le, Nhung
    de Leeuw, Christiaan A.
    Loukola, Anu
    Mangino, Massimo
    Melbourne, Carl A.
    Pistis, Giorgio
    Qaiser, Beenish
    Rohde, Rebecca
    Shao, Yaming
    Stringham, Heather
    Wetherill, Leah
    Zhao, Wei
    Agrawal, Arpana
    Bierut, Laura
    Chen, Chu
    Eaton, Charles B.
    Goate, Alison
    Haiman, Christopher
    Heath, Andrew
    Iacono, William G.
    Martin, Nicholas G.
    Polderman, Tinca J.
    Reiner, Alex
    Rice, John
    Schlessinger, David
    Scholte, H. Steven
    Smith, Jennifer A.
    Tardif, Jean-Claude
    Tindle, Hilary A.
    van der Leij, Andries R.
    Boehnke, Michael
    Chang-Claude, Jenny
    Cucca, Francesco
    David, Sean P.
    Foroud, Tatiana
    Howson, Joanna M. M.
    Kardia, Sharon L. R.
    Kooperberg, Charles
    Laakso, Markku
    Lettre, Guillaume
    Madden, Pamela
    McGue, Matt
    North, Kari
    Posthuma, Danielle
    Spector, Timothy
    Stram, Daniel
    Tobin, Martin D.
    Weir, David R.
    Kaprio, Jaakko
    Abecasis, Gonçalo R.
    Liu, Dajiang J.
    Vrieze, Scott
    Franks, Paul W. (Contributor)
    Exome Chip Meta-analysis Fine Maps Causal Variants and Elucidates the Genetic Architecture of Rare Coding Variants in Smoking and Alcohol Use2019In: Biological Psychiatry, ISSN 0006-3223, E-ISSN 1873-2402, Vol. 85, no 11, p. 946-955Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Smoking and alcohol use have been associated with common genetic variants in multiple loci. Rare variants within these loci hold promise in the identification of biological mechanisms in substance use. Exome arrays and genotype imputation can now efficiently genotype rare nonsynonymous and loss of function variants. Such variants are expected to have deleterious functional consequences and to contribute to disease risk.

    METHODS: We analyzed ∼250,000 rare variants from 16 independent studies genotyped with exome arrays and augmented this dataset with imputed data from the UK Biobank. Associations were tested for five phenotypes: cigarettes per day, pack-years, smoking initiation, age of smoking initiation, and alcoholic drinks per week. We conducted stratified heritability analyses, single-variant tests, and gene-based burden tests of nonsynonymous/loss-of-function coding variants. We performed a novel fine-mapping analysis to winnow the number of putative causal variants within associated loci.

    RESULTS: Meta-analytic sample sizes ranged from 152,348 to 433,216, depending on the phenotype. Rare coding variation explained 1.1% to 2.2% of phenotypic variance, reflecting 11% to 18% of the total single nucleotide polymorphism heritability of these phenotypes. We identified 171 genome-wide associated loci across all phenotypes. Fine mapping identified putative causal variants with double base-pair resolution at 24 of these loci, and between three and 10 variants for 65 loci. Twenty loci contained rare coding variants in the 95% credible intervals.

    CONCLUSIONS: Rare coding variation significantly contributes to the heritability of smoking and alcohol use. Fine-mapping genome-wide association study loci identifies specific variants contributing to the biological etiology of substance use behavior.

  • 41.
    Brännström, Thomas
    et al.
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Sirkka, S.
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    Induction of ubiquilin differs between different SOD1 transgenic strains2014In: Neuropathology and Applied Neurobiology, ISSN 0305-1846, E-ISSN 1365-2990, Vol. 40, no S1, p. 22-22Article in journal (Other academic)
  • 42. Burgess, Stephen
    et al.
    Thompson, Simon G.
    Hallmans, Göran (Contributor)
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Nutritional Research.
    Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables2010In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 29, no 12, p. 1298-1311Article in journal (Refereed)
    Abstract [en]

    Genetic markers can be used as instrumental variables, in an analogous way to randomization in a clinical trial, to estimate the causal relationship between a phenotype and an outcome variable. Our purpose is to extend the existing methods for such Mendelian randomization studies to the context of multiple genetic markers measured in multiple studies, based on the analysis of individual participant data. First, for a single genetic marker in one study, we show that the usual ratio of coefficients approach can be reformulated as a regression with heterogeneous error in the explanatory variable. This can be implemented using a Bayesian approach, which is next extended to include multiple genetic markers. We then propose a hierarchical model for undertaking a meta-analysis of multiple studies, in which it is not necessary that the same genetic markers are measured in each study. This provides an overall estimate of the causal relationship between the phenotype and the outcome, and an assessment of its heterogeneity across studies. As an example, we estimate the causal relationship of blood concentrations of C-reactive protein on fibrinogen levels using data from 11 studies. These methods provide a flexible framework for efficient estimation of causal relationships derived from multiple studies. Issues discussed include weak instrument bias, analysis of binary outcome data such as disease risk, missing genetic data, and the use of haplotypes.

  • 43.
    Byun, Jinyoung
    et al.
    Institute for Clinical and Translational Research, Baylor College of Medicine, TX, Houston, United States; Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, TX, Houston, United States.
    Han, Younghun
    Institute for Clinical and Translational Research, Baylor College of Medicine, TX, Houston, United States; Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, TX, Houston, United States.
    Li, Yafang
    Institute for Clinical and Translational Research, Baylor College of Medicine, TX, Houston, United States; Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, TX, Houston, United States; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, TX, Houston, United States.
    Xia, Jun
    Institute for Clinical and Translational Research, Baylor College of Medicine, TX, Houston, United States; Department of Molecular and Human Genetics, Baylor College of Medicine, TX, Houston, United States.
    Long, Erping
    Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, MD, Bethesda, United States.
    Choi, Jiyeon
    Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, MD, Bethesda, United States.
    Xiao, Xiangjun
    Institute for Clinical and Translational Research, Baylor College of Medicine, TX, Houston, United States.
    Zhu, Meng
    Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
    Zhou, Wen
    Institute for Clinical and Translational Research, Baylor College of Medicine, TX, Houston, United States.
    Sun, Ryan
    Department of Biostatistics, University of Texas, M.D. Anderson Cancer Center, TX, Houston, United States.
    Bossé, Yohan
    Institut universitaire de cardiologie et de pneumologie de Québec – Université Laval, Department of Molecular Medicine, Laval University, QC, Quebec City, Canada.
    Song, Zhuoyi
    Institute for Clinical and Translational Research, Baylor College of Medicine, TX, Houston, United States; Department of Molecular and Human Genetics, Baylor College of Medicine, TX, Houston, United States.
    Schwartz, Ann
    Department of Oncology, Wayne State University School of Medicine, MI, Detroit, United States; Karmanos Cancer Institute, MI, Detroit, United States.
    Lusk, Christine
    Department of Oncology, Wayne State University School of Medicine, MI, Detroit, United States; Karmanos Cancer Institute, MI, Detroit, United States.
    Rafnar, Thorunn
    deCODE genetics/Amgen Sturlugata 8, Reykjavik, Iceland.
    Stefansson, Kari
    deCODE genetics/Amgen Sturlugata 8, Reykjavik, Iceland.
    Zhang, Tongwu
    Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, MD, Bethesda, United States.
    Zhao, Wei
    Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, MD, Bethesda, United States.
    Pettit, Rowland W.
    Institute for Clinical and Translational Research, Baylor College of Medicine, TX, Houston, United States.
    Liu, Yanhong
    Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, TX, Houston, United States; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, TX, Houston, United States.
    Li, Xihao
    Department of Biostatistics, Harvard TH Chan School of Public Health, MA, Boston, United States.
    Zhou, Hufeng
    Department of Biostatistics, Harvard TH Chan School of Public Health, MA, Boston, United States.
    Walsh, Kyle M.
    Duke Cancer Institute, Duke University Medical Center, NC, Durham, United States.
    Gorlov, Ivan
    Institute for Clinical and Translational Research, Baylor College of Medicine, TX, Houston, United States; Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, TX, Houston, United States; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, TX, Houston, United States.
    Gorlova, Olga
    Institute for Clinical and Translational Research, Baylor College of Medicine, TX, Houston, United States; Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, TX, Houston, United States; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, TX, Houston, United States.
    Zhu, Dakai
    Institute for Clinical and Translational Research, Baylor College of Medicine, TX, Houston, United States; Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, TX, Houston, United States.
    Rosenberg, Susan M.
    Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, TX, Houston, United States; Department of Molecular and Human Genetics, Baylor College of Medicine, TX, Houston, United States.
    Pinney, Susan
    University of Cincinnati College of Medicine, OH, Cincinnati, United States.
    Bailey-Wilson, Joan E.
    National Human Genome Research Institute, NIH, MD, Baltimore, United States.
    Mandal, Diptasri
    Louisiana State University Health Sciences Center, LA, New Orleans, United States.
    de Andrade, Mariza
    Mayo Clinic, College of Medicine, MN, Rochester, United States.
    Gaba, Colette
    The University of Toledo College of Medicine and Life Sciences, University of Toledo, OH, Toledo, United States.
    Willey, James C.
    The University of Toledo College of Medicine and Life Sciences, University of Toledo, OH, Toledo, United States.
    You, Ming
    Center for Cancer Prevention, Houston Methodist Research Institute, TX, Houston, United States.
    Anderson, Marshall
    University of Cincinnati College of Medicine, OH, Cincinnati, United States.
    Wiencke, John K.
    Department of Neurological Surgery, University of California, San Francisco, CA, San Francisco, United States.
    Albanes, Demetrius
    Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, MD, Bethesda, United States.
    Lam, Stephan
    Department of Integrative Oncology, BC Cancer, BC, Vancouver, Canada.
    Tardon, Adonina
    Public Health Department, University of Oviedo, ISPA and CIBERESP, Asturias, Spain.
    Chen, Chu
    Program in Epidemiology, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States.
    Goodman, Gary
    Swedish Cancer Institute, WA, Seattle, United States.
    Bojeson, Stig
    Department of Clinical Biochemistry, Herlev Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
    Brenner, Hermann
    Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
    Landi, Maria Teresa
    Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, MD, Bethesda, United States.
    Chanock, Stephen J.
    Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, MD, Bethesda, United States.
    Johansson, Mattias
    Section of Genetics, International Agency for Research on Cancer, World Health Organization, Lyon, France.
    Muley, Thomas
    Division of Cancer Epigenomics, DKFZ – German Cancer Research Center, Heidelberg, Germany; Translational Lung Research Center Heidelberg (TLRC-H), German Center for Lung Research (DZL), Heidelberg, Germany.
    Risch, Angela
    Division of Cancer Epigenomics, DKFZ – German Cancer Research Center, Heidelberg, Germany; Translational Lung Research Center Heidelberg (TLRC-H), German Center for Lung Research (DZL), Heidelberg, Germany; Department of Biosciences and Medical Biology, Allergy-Cancer-BioNano Research Centre, University of Salzburg, Salzburg, Austria; Cancer Cluster Salzburg, Salzburg, Austria.
    Wichmann, H.-Erich
    Institute of Epidemiology, Helmholtz Center, Munich, Germany.
    Bickeböller, Heike
    Department of Genetic Epidemiology, University Medical Center, Georg-August-University Göttingen, Göttingen, Germany.
    Christiani, David C.
    Department of Epidemiology, Harvard T.H.Chan School of Public Health, MA, Boston, United States.
    Rennert, Gad
    Clalit National Cancer Control Center at Carmel Medical Center and Technion Faculty of Medicine, Haifa, Israel.
    Arnold, Susanne
    University of Kentucky, Markey Cancer Center, KY, Lexington, United States.
    Field, John K.
    Roy Castle Lung Cancer Research Programme, Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, United Kingdom.
    Shete, Sanjay
    Department of Biostatistics, University of Texas, M.D. Anderson Cancer Center, TX, Houston, United States; Department of Epidemiology, The University of Texas MD Anderson Cancer Center, TX, Houston, United States.
    Le Marchand, Loic
    Epidemiology Program, University of Hawaii Cancer Center, HI, Honolulu, United States.
    Melander, Olle
    Faculty of Medicine, Lund University, Lund, Sweden.
    Brunnstrom, Hans
    Faculty of Medicine, Lund University, Lund, Sweden.
    Liu, Geoffrey
    University Health Network- The Princess Margaret Cancer Centre, Ontario, Toronto, Canada.
    Andrew, Angeline S.
    Departments of Epidemiology and Community and Family Medicine, Dartmouth College, NH, Hanover, United States.
    Kiemeney, Lambertus A.
    Radboud University Medical Center, Nijmegen, Netherlands.
    Shen, Hongbing
    Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China.
    Zienolddiny, Shanbeh
    National Institute of Occupational Health, Oslo, Norway.
    Grankvist, Kjell
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Clinical chemistry.
    Johansson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Caporaso, Neil
    Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, MD, Bethesda, United States.
    Cox, Angela
    Department of Oncology and Metabolism, University of Sheffield, Sheffield, United Kingdom.
    Hong, Yun-Chul
    Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, South Korea.
    Yuan, Jian-Min
    UPMC Hillman Cancer Center and Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, PA, Pittsburgh, United States.
    Lazarus, Philip
    Department of Pharmaceutical Sciences, College of Pharmacy, Washington State University, WA, Spokane, United States.
    Schabath, Matthew B.
    Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, FL, Tampa, United States.
    Aldrich, Melinda C.
    Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, TN, Nashville, United States.
    Patel, Alpa
    American Cancer Society, GA, Atlanta, United States.
    Lan, Qing
    Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, MD, Bethesda, United States.
    Rothman, Nathaniel
    Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, MD, Bethesda, United States.
    Taylor, Fiona
    Department of Oncology and Metabolism, University of Sheffield, Sheffield, United Kingdom.
    Kachuri, Linda
    Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, San Francisco, United States.
    Witte, John S.
    Department of Epidemiology and Population Health, Stanford University, CA, Stanford, United States.
    Sakoda, Lori C.
    Division of Research, Kaiser Permanente Northern California, CA, Oakland, United States.
    Spitz, Margaret
    Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, TX, Houston, United States.
    Brennan, Paul
    Section of Genetics, International Agency for Research on Cancer, World Health Organization, Lyon, France.
    Lin, Xihong
    Department of Biostatistics, Harvard TH Chan School of Public Health, MA, Boston, United States.
    McKay, James
    Section of Genetics, International Agency for Research on Cancer, World Health Organization, Lyon, France.
    Hung, Rayjean J.
    Lunenfeld-Tanenbaum Research Institute, Sinai Health System, ON, Toronto, Canada; Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, ON, Toronto, Canada.
    Amos, Christopher I.
    Institute for Clinical and Translational Research, Baylor College of Medicine, TX, Houston, United States; Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, TX, Houston, United States; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, TX, Houston, United States.
    Cross-ancestry genome-wide meta-analysis of 61,047 cases and 947,237 controls identifies new susceptibility loci contributing to lung cancer2022In: Nature Genetics, ISSN 1061-4036, E-ISSN 1546-1718, Vol. 54, no 8, p. 1167-1177Article in journal (Refereed)
    Abstract [en]

    To identify new susceptibility loci to lung cancer among diverse populations, we performed cross-ancestry genome-wide association studies in European, East Asian and African populations and discovered five loci that have not been previously reported. We replicated 26 signals and identified 10 new lead associations from previously reported loci. Rare-variant associations tended to be specific to populations, but even common-variant associations influencing smoking behavior, such as those with CHRNA5 and CYP2A6, showed population specificity. Fine-mapping and expression quantitative trait locus colocalization nominated several candidate variants and susceptibility genes such as IRF4 and FUBP1. DNA damage assays of prioritized genes in lung fibroblasts indicated that a subset of these genes, including the pleiotropic gene IRF4, potentially exert effects by promoting endogenous DNA damage.

  • 44.
    Bäckström, Stefan
    Umeå University, Faculty of Science and Technology, Umeå Centre for Molecular Pathogenesis (UCMP).
    The hematopoietic transcription factor RUNX1: a structural view2004Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The malfunction of the transcriptional regulator RUNX1 is the major cause of several variants of acute human leukemias and its normal function is to regulate the development of the blood system in concert with other transcriptional co-regulators. RUNX1 belongs to a conserved family of heterodimeric transcription factors that share a conserved DNA binding domain, the Runt domain (RD), named after the first member of this group – Runt - found in Drosophila melanogaster. The binding partner CBFβ serves as a regulator of RUNX by enhancing its DNA binding affinity through an allosteric mechanism.

    The main focus ofo my thesis work has been the crystallization and structural analysis of the RUNX1 RD and involved also more technical methodological aspects that can be applied to X-ray crystallography in general.

    The high resolution crystal structure of the free RD shows that this immunoglobulin-like molecule undergoes significant structural changes upon binding to both CBFβ and DNA. This involves a large flip of the L11 loop from a closed conformation in the free protein to an open conformation when CBFβ and/or DNA are bound. We refer to this transition as the “S-switch”. Smaller but significant conformational changes in other parts of the RD accompany the “S-switch”. We suggest that CBFβ triggers and stabilizes the “S-switch” which leads to the conversion of the RD into a conformation enhanced for DNA binding.

    During the structural analysis of the RD we identified two chloride ions that are coordinated by residues otherwise involved in DNA binding. In electrophoretic mobility-shift analyses (EMSA) we demonstrated a chloride ion concentration dependent stimulation of the DNA binding affinity of RUNX1. We further showed by NMR line width broadening experiments that the chloride binding occurred within the physiological range. A comparable DNA binding stimulation of RUNX1 was seen in the presence of negative amino acids. This suggests a regulation of the DNA binding activity of RUNX1 proteins through acidic amino acid residues possibly provided by activation domains of transcriptional co-regulators that interact with RUNX1.

    The use of the anomalous signal from halide ions has become a powerful technique for obtaining phase information. By replacing the sodium chloride with potassium bromide in the crystallisation conditions of the RD, we could demonstrate in a single wavelength anomalous diffraction (SAD) experiment that the anomalous signal from 2 bromide ions were sufficient to phase a 16 kDa protein. Due to lack of completeness in the low-resolution shells caused by overloaded intensities, density modification schemes failed and the resulting electron density maps were not interpretable. By combining the highresolution

    synchrotron data with low-resolution data from a native data set collected on a home X-ray source, the density modified bromide phases gave easily traceable maps.

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  • 45.
    Cameron, Sarina R.
    et al.
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
    Nandi, Soumyadeep
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
    Kahn, Tatyana G.
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
    Barrasa, Juan I.
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
    Stenberg, Per
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine). Division of Chemical, Biological, Radioactive and Nuclear (CBRN) Security and Defence, FOI–Swedish Defence Research Agency, 906 21 Umeå Sweden.
    Schwartz, Yuri B.
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
    PTE, a novel module to target Polycomb Repressive Complex 1 to the human cyclin D2 (CCND2) oncogene2018In: Journal of Biological Chemistry, ISSN 0021-9258, E-ISSN 1083-351X, Vol. 293, no 37, p. 14342-14358Article in journal (Refereed)
    Abstract [en]

    Polycomb group proteins are essential epigenetic repressors. They form multiple protein complexes of which two kinds, PRC1 and PRC2, are indispensable for repression. Although much is known about their biochemical properties, how mammalian PRC1 and PRC2 are targeted to specific genes is poorly understood. Here, we establish the cyclin D2 (CCND2) oncogene as a simple model to address this question. We provide the evidence that the targeting of PRC1 to CCND2 involves a dedicated PRC1-targeting element (PTE). The PTE appears to act in concert with an adjacent cytosine-phosphate-guanine (CpG) island to arrange for the robust binding of PRC1 and PRC2 to repressed CCND2. Our findings pave the way to identify sequence-specific DNA-binding proteins implicated in the targeting of mammalian PRC1 complexes and provide novel link between polycomb repression and cancer.

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  • 46. Cammaerts, Sophia
    et al.
    Strazisar, Mojca
    Smets, Bart
    Weckhuysen, Sarah
    Nordin, Annelie
    Umeå University, Faculty of Medicine, Department of Clinical Sciences, Psychiatry.
    De Jonghe, Peter
    Adolfsson, Rolf
    Umeå University, Faculty of Medicine, Department of Clinical Sciences, Psychiatry.
    De Rijk, Peter
    Del Favero, Jurgen
    Schizophrenia-Associated MIR204 Regulates Noncoding RNAs and Affects Neurotransmitter and Ion Channel Gene Sets2015In: PLOS ONE, E-ISSN 1932-6203, Vol. 10, no 12, article id e0144428Article in journal (Refereed)
    Abstract [en]

    As regulators of gene expression, microRNAs (miRNAs) are likely to play an important role in the development of disease. In this study we present a large-scale strategy to identify miRNAs with a role in the regulation of neuronal processes. Thereby we found variant rs7861254 located near the MIR204 gene to be significantly associated with schizophrenia. This variant resulted in reduced expression of miR-204 in neuronal-like SH-SY5Y cells. Analysis of the consequences of the altered miR-204 expression on the transcriptome of these cells uncovered a new mode of action for miR-204, being the regulation of noncoding RNAs (ncRNAs), including several miRNAs, such as MIR296. Furthermore, pathway analysis showed downstream effects of miR-204 on neurotransmitter and ion channel related gene sets, potentially mediated by miRNAs regulated through miR-204.

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  • 47. Campa, Daniele
    et al.
    Barrdahl, Myrto
    Gaudet, Mia M.
    Black, Amanda
    Chanock, Stephen J.
    Diver, W. Ryan
    Gapstur, Susan M.
    Haiman, Christopher
    Hankinson, Susan
    Hazra, Aditi
    Henderson, Brian
    Hoover, Robert N.
    Hunter, David J.
    Joshi, Amit D.
    Kraft, Peter
    Le Marchand, Loic
    Lindstrom, Sara
    Willett, Walter
    Travis, Ruth C.
    Amiano, Pilar
    Siddiq, Afshan
    Trichopoulos, Dimitrios
    Sund, Malin
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Surgery.
    Tjonneland, Anne
    Weiderpass, Elisabete
    Peeters, Petra H.
    Panico, Salvatore
    Dossus, Laure
    Ziegler, Regina G.
    Canzian, Federico
    Kaaks, Rudolf
    Genetic risk variants associated with in situ breast cancer2015In: Breast Cancer Research, ISSN 1465-5411, E-ISSN 1465-542X, Vol. 17, article id 82Article in journal (Refereed)
    Abstract [en]

    Introduction: Breast cancer in situ (BCIS) diagnoses, a precursor lesion for invasive breast cancer, comprise about 20 % of all breast cancers (BC) in countries with screening programs. Family history of BC is considered one of the strongest risk factors for BCIS.

    Methods: To evaluate the association of BC susceptibility loci with BCIS risk, we genotyped 39 single nucleotide polymorphisms (SNPs), associated with risk of invasive BC, in 1317 BCIS cases, 10,645 invasive BC cases, and 14,006 healthy controls in the National Cancer Institute's Breast and Prostate Cancer Cohort Consortium (BPC3). Using unconditional logistic regression models adjusted for age and study, we estimated the association of SNPs with BCIS using two different comparison groups: healthy controls and invasive BC subjects to investigate whether BCIS and BC share a common genetic profile.

    Results: We found that five SNPs (CDKN2BAS-rs1011970, FGFR2-rs3750817, FGFR2-rs2981582, TNRC9-rs3803662, 5p12-rs10941679) were significantly associated with BCIS risk (P value adjusted for multiple comparisons <0.0016). Comparing invasive BC and BCIS, the largest difference was for CDKN2BAS-rs1011970, which showed a positive association with BCIS (OR = 1.24, 95 % CI: 1.11-1.38, P = 1.27 x 10(-4)) and no association with invasive BC (OR = 1.03, 95 % CI: 0.99-1.07, P = 0.06), with a P value for case-case comparison of 0.006. Subgroup analyses investigating associations with ductal carcinoma in situ (DCIS) found similar associations, albeit less significant (OR = 1.25, 95 % CI: 1.09-1.42, P = 1.07 x 10(-3)). Additional risk analyses showed significant associations with invasive disease at the 0.05 level for 28 of the alleles and the OR estimates were consistent with those reported by other studies.

    Conclusions: Our study adds to the knowledge that several of the known BC susceptibility loci are risk factors for both BCIS and invasive BC, with the possible exception of rs1011970, a putatively functional SNP situated in the CDKN2BAS gene that may be a specific BCIS susceptibility locus.

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  • 48.
    Campino, Susana
    Umeå University, Faculty of Medicine, Medical Biosciences.
    Genetic analysis of murine malaria2003Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Malaria, an infectious disease caused by Plasmodium parasites, is one of the major world-scale health problems. Despite the efforts aimed at finding an effective way to control the disease, the success has been thwarted by the emergence of parasite drug resistance and mosquito resistance to insecticides. This thesis focuses on the genetic analysis of resistance to murine malaria induced by the lethal Plasmodium berghei ANKA using a wild-derived-inbred strain (WDIS). The aim of this thesis was to exploit the genetic diversity represented among WDIS for identifying loci contributing to resistance/susceptibility to murine malaria.

    The work included a genome-wide polymorphism survey using microsatellite markers performed on 10 WDIS. Comparisons of these strains to laboratory inbred strains confirmed a higher rate of polymorphism among the WDIS. We conclude that these WDIS represent repositories of unique naturally occurring genetic variability that may prove to be invaluable for the study of complex phenotypes. Next, we used the WDIS to search for novel phenotypes related to malaria pathogenesis. Whereas most laboratory strains were susceptible to experimental cerebral malaria (ECM) after infection with P. berghei ANKA, several WDIS were found to be resistant. To study the genetic inheritance of resistant/susceptibility to P. berghei ANKA infection we analysed backcross and F2 cohorts derived from crossing the WLA wild-derived strain with a laboratory mouse strain (C57BL/6). A novel phenotype represented by the cure of infection, clearance of parasitaemia and establishment of immunological memory was observed in the F2 progeny. The backcross progeny was used to genetically map one locus on chromosome 1 (Berr1) and one locus on chromosome 11 (Berr2) that mediate control of resistance to ECM induced by P. berghei ANKA. Genetic mapping using the F2 progeny showed that a locus on chromosome 1 (Berr1) and a locus on chromosome 9 (Berr3) were contributing to control survival time after infection with lethal Plasmodium. Finally, we identified, a locus on chromosome 4 (Berr4) that appears to control time of death due to hyperparasitaemia.

    This thesis underlines the value of using WDIS to reveal genetic factors involved in the aetiology of disease phenotypes. The characterisation of the genetic factors represented by the malaria resistance loci identified here are expected to provide a better understanding of the malaria pathology.

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  • 49.
    Carreras-Torres, Robert
    et al.
    Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain; Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Digestive Diseases and Microbiota Group, Girona Biomedical Research Institute (IDIBGI), Salt, Girona, Spain.
    Kim, Andre E.
    Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    Lin, Yi
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States.
    Díez-Obrero, Virginia
    Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain; Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain.
    Bien, Stephanie A.
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States.
    Qu, Conghui
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States.
    Wang, Jun
    Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    Dimou, Niki
    Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France.
    Aglago, Elom K.
    Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France.
    Albanes, Demetrius
    Division of Cancer Epidemiology and Genetics, NCI, NIH, MD, Bethesda, Liberia.
    Arndt, Volker
    Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
    Baurley, James W.
    Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia.
    Berndt, Sonja I.
    Division of Cancer Epidemiology and Genetics, NCI, NIH, MD, Bethesda, Liberia.
    Bézieau, Stéphane
    Service de Génétique Médicale, Centre Hospitalier Universitaire (CHU) Nantes, Nantes, France.
    Bishop, D Timothy
    Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, United Kingdom.
    Bouras, Emmanouil
    Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.
    Brenner, Hermann
    Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
    Budiarto, Arif
    Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia.
    Campbell, Peter T.
    Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, Georgia.
    Casey, Graham
    Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, VA, Charlottesville, United States.
    Chan, Andrew T.
    Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, MA, Boston, United States.
    Chang-Claude, Jenny
    Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
    Chen, Xuechen
    Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
    Conti, David V.
    Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    Dampier, Christopher H.
    Department of General Surgery, University of Virginia School of Medicine, VA, Charlottesville, United States.
    Devall, Matthew A M
    Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, VA, Charlottesville, United States.
    Drew, David A.
    Clinical & Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, MA, Boston, United States.
    Figueiredo, Jane C.
    Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, CA, Los Angeles, United States.
    Gallinger, Steven
    Lunenfeld Tanenbaum Research Institute, University of Toronto, Mount Sinai Hospital, ON, Toronto, Canada.
    Giles, Graham G.
    Cancer Epidemiology Division, Cancer Council Victoria, VIC, Melbourne, Australia.
    Gruber, Stephen B.
    Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte, California.
    Gsur, Andrea
    Institute of Cancer Research, Department of Medicine I, Medical University Vienna, Vienna, Austria.
    Gunter, Marc J.
    Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France.
    Harrison, Tabitha A.
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States.
    Hidaka, Akihisa
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States.
    Hoffmeister, Michael
    Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
    Huyghe, Jeroen R.
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States.
    Jenkins, Mark A.
    Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, VIC, Melbourne, Australia.
    Jordahl, Kristina M.
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States.
    Kawaguchi, Eric
    Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    Keku, Temitope O.
    Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, NC, United States.
    Kundaje, Anshul
    Department of Genetics, Department of Computer Science, Stanford University, CA, Stanford, United States.
    Le Marchand, Loic
    University of Hawaii Cancer Center, HI, Honolulu, United States.
    Lewinger, Juan Pablo
    Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    Li, Li
    Department of Family Medicine, University of Virginia, VA, Charlottesville, United States.
    Mahesworo, Bharuno
    Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia.
    Morrison, John L.
    Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    Murphy, Neil
    Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France.
    Nan, Hongmei
    Department of Epidemiology, Richard M. Fairbanks School of Public Health, IN, Indianapolis, United States.
    Nassir, Rami
    Department of Pathology, School of Medicine, Umm Al-Qura'a University, Saudi Arabia.
    Newcomb, Polly A.
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States.
    Obón-Santacana, Mireia
    Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain; Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain.
    Ogino, Shuji
    Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, MA, Boston, United States; Department of Oncologic Pathology, Dana-Farber Cancer Institute, MA, Boston, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, MA, Boston, United States; Broad Institute of MIT and Harvard, MA, Cambridge, United States.
    Ose, Jennifer
    Huntsman Cancer Institute, UT, Salt Lake City, United States; Department of Population Health Sciences, University of Utah, UT, Salt Lake City, United States.
    Pai, Rish K.
    Department of Laboratory Medicine and Pathology, Mayo Clinic Arizona, AZ, Scottsdale, United States.
    Palmer, Julie R.
    Slone Epidemiology Center at Boston University, MA, Boston, United States.
    Papadimitriou, Nikos
    Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France.
    Pardamean, Bens
    Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia.
    Peoples, Anita R.
    Huntsman Cancer Institute, UT, Salt Lake City, United States.
    Pharoah, Paul D P
    Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.
    Platz, Elizabeth A.
    Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, MD, Baltimore, Liberia.
    Rennert, Gad
    Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel.
    Ruiz-Narvaez, Edward
    Department of Nutritional Sciences, University of Michigan School of Public Health, MI, Ann Arbor, United States.
    Sakoda, Lori C.
    Division of Research, Kaiser Permanente Northern California, CA, Oakland, United States.
    Scacheri, Peter C.
    Department of Genetics and Genome Sciences, Case Western Reserve University, OH, Cleveland, United States.
    Schmit, Stephanie L.
    Genomic Medicine Institute, Cleveland Clinic, OH, Cleveland, United States; Population and Cancer Prevention Program, Case Comprehensive Cancer Center, OH, Cleveland, United States.
    Schoen, Robert E.
    Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, PA, Pittsburgh, United States.
    Shcherbina, Anna
    Biomedical Informatics Program, Dept. of Biomedical Data Sciences, Stanford University, CA, Stanford, United States.
    Slattery, Martha L.
    Department of Internal Medicine, University of Utah, UT, Salt Lake City, United States.
    Stern, Mariana C.
    Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    Su, Yu-Ru
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States.
    Tangen, Catherine M.
    SWOG Statistical Center, Fred Hutchinson Cancer Research Center, WA, Seattle, United States.
    Thomas, Duncan C.
    Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    Tian, Yu
    Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; School of Public Health, Capital Medical University, Beijing, China.
    Tsilidis, Konstantinos K.
    Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece; Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.
    Ulrich, Cornelia M.
    Huntsman Cancer Institute, UT, Salt Lake City, United States; Department of Population Health Sciences, University of Utah, UT, Salt Lake City, United States.
    van Duijnhoven, Fränzel J B
    Division of Human Nutrition and Health, Wageningen University & Research, Wageningen, Netherlands.
    van Guelpen, Bethany
    Umeå University, Faculty of Medicine, Wallenberg Centre for Molecular Medicine at Umeå University (WCMM). Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Visvanathan, Kala
    Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, MD, Baltimore, Liberia.
    Vodicka, Pavel
    Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, and Biomedical Center, Medical Faculty, Pilsen, Czech Republic.
    Cenggoro, Tjeng Wawan
    Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia.
    Weinstein, Stephanie J.
    Division of Cancer Epidemiology and Genetics, NCI, NIH, MD, Bethesda, Liberia.
    White, Emily
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States.
    Wolk, Alicja
    Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
    Woods, Michael O.
    Memorial University of Newfoundland, Discipline of Genetics, St. John's, Canada.
    Hsu, Li
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States.
    Peters, Ulrike
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, WA, Seattle, United States; School of Public Health, University of Washington, WA, Seattle, United States.
    Moreno, Victor
    Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain; Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain; Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain.
    Gauderman, W. James
    Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, CA, Los Angeles, United States.
    Genome-wide interaction study with smoking for colorectal cancer risk identifies novel genetic loci related to tumor suppression, inflammation, and immune response2023In: Cancer Epidemiology, Biomarkers and Prevention, ISSN 1055-9965, E-ISSN 1538-7755, Vol. 32, no 3, p. 315-328Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Tobacco smoking is an established risk factor for colorectal cancer. However, genetically defined population subgroups may have increased susceptibility to smoking-related effects on colorectal cancer.

    METHODS: A genome-wide interaction scan was performed including 33,756 colorectal cancer cases and 44,346 controls from three genetic consortia.

    RESULTS: Evidence of an interaction was observed between smoking status (ever vs. never smokers) and a locus on 3p12.1 (rs9880919, P = 4.58 × 10-8), with higher associated risk in subjects carrying the GG genotype [OR, 1.25; 95% confidence interval (CI), 1.20-1.30] compared with the other genotypes (OR <1.17 for GA and AA). Among ever smokers, we observed interactions between smoking intensity (increase in 10 cigarettes smoked per day) and two loci on 6p21.33 (rs4151657, P = 1.72 × 10-8) and 8q24.23 (rs7005722, P = 2.88 × 10-8). Subjects carrying the rs4151657 TT genotype showed higher risk (OR, 1.12; 95% CI, 1.09-1.16) compared with the other genotypes (OR <1.06 for TC and CC). Similarly, higher risk was observed among subjects carrying the rs7005722 AA genotype (OR, 1.17; 95% CI, 1.07-1.28) compared with the other genotypes (OR <1.13 for AC and CC). Functional annotation revealed that SNPs in 3p12.1 and 6p21.33 loci were located in regulatory regions, and were associated with expression levels of nearby genes. Genetic models predicting gene expression revealed that smoking parameters were associated with lower colorectal cancer risk with higher expression levels of CADM2 (3p12.1) and ATF6B (6p21.33).

    CONCLUSIONS: Our study identified novel genetic loci that may modulate the risk for colorectal cancer of smoking status and intensity, linked to tumor suppression and immune response.

    IMPACT: These findings can guide potential prevention treatments.

  • 50.
    Cederquist, Kristina
    Umeå University, Faculty of Medicine, Medical Biosciences.
    Genetic and epidemiological studies of hereditary colorectal cancer2005Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Lynch syndrome (Hereditary Nonpolyposis Colorectal Cancer, HNPCC) is the most common hereditary syndrome predisposing to colorectal cancer, accounting for 1-3% of all colorectal cancer. This multi-organ cancer predisposition syndrome is caused by mutations in the mismatch repair (MMR) genes, especially MLH1 and MSH2, and to lesser extents MSH6 and PMS2, which lead to widespread genetic instability and thus microsatellite instability (MSI). Hereditary cancer often manifests in two or more tumours in a single individual; 35-40% of Lynch syndrome patients have synchronous or metachronous tumours of the two major Lynch syndrome-related cancers: colorectal and endometrial.

    The main purposes of the work underlying this thesis were to identify persons at risk of Lynch syndrome or other types of hereditary colorectal cancer, to estimate the cancer risks associated with these predispositions and to identify the underlying genetic causes.

    A population-based cohort of 78 persons with double primary colorectal or colorectal and endometrial cancer was identified. Cancer risks in their 649 first-degree relatives were estimated in relation to tumour MSI status (positive or negative) and age at diagnosis (before or after 50 years of age) in the probands. The overall standardised incidence ratio was 1.69 (95% CI; 1.39-2.03). The highest risks for Lynch syndrome-associated cancers: (colorectal, endometrial, ovarian and gastric) were found in families with young MSI-positive probands, likely representing Lynch syndrome families. Importantly, no overall risk was found in families with old probands, irrespective of MSI status.

    Blood samples were available from 24 MSI-positive patients for mutation screening of MLH1, MSH2 and MSH6. Sequence variants or rearrangements predicted to affect protein function were found in 16 patients. Six novel variants were found: two large rearrangements, two truncating and two missense mutations. The missense mutations were found to segregate in the families. Studies of allele frequencies, MSI and loss of immunostaning in tumours from family members further supports the hypothesis that these missense changes play a role in Lynch syndrome, as do the non-conservative nature and evolutionary conservation of the amino acid exchanges. Five families had mutations in MLH1, five in MSH2, and six in MSH6. The unexpectedly large impact of MSH6 was in genealogical studies shown to be due to a founder effect. Cumulative risk studies showed that the MSH6 families, despite their late age of onset, have a high lifetime risk for all Lynch syndrome-related cancers, significantly higher in women (89% by age 80 years) than in men (69%). The gender differences are in part due to high endometrial (70%) and ovarian cancer risk (33%) in addition to the high colorectal cancer risk (60%). These findings are of great importance for counselling and surveillance of families with MSH6 mutations.

    Finally, in a large family with MSI-negative hereditary colorectal cancer for which the MMR genes and APC had been excluded as possible causes, a genome-wide linkage analysis was performed, resulting in a suggested linkage to chromosome 7.

    Conclusions: Relatives of probands with MSI-positive, double primary colorectal and endometrial cancer diagnosed before the age of 50 years have significantly increased risks of Lynch syndrome-related cancers. MSH6 mutations, which have unusually high impact in this study population due to a founder effect, confer high cumulative risks of cancer despite the generally late age of onset.

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