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Identification of Novel Genetic Loci Associated with Thyroid Peroxidase Antibodies and Clinical Thyroid Disease

  • Marco Medici ,

    Contributed equally to this work with: Marco Medici, Eleonora Porcu, Giorgio Pistis

    m.medici@erasmusmc.nl

    Affiliation Department of Internal Medicine, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands

  • Eleonora Porcu ,

    Contributed equally to this work with: Marco Medici, Eleonora Porcu, Giorgio Pistis

    Affiliations Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche, c/o Cittadella Universitaria di Monserrato, Monserrato, Cagliari, Italy, Dipartimento di Scienze Biomediche, Universita di Sassari, Sassari, Italy

  • Giorgio Pistis ,

    Contributed equally to this work with: Marco Medici, Eleonora Porcu, Giorgio Pistis

    Affiliation Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy

  • Alexander Teumer,

    Affiliation Interfaculty Institute for Genetics and Functional Genomics, University Medicine and Ernst-Moritz-Arndt-University Greifswald, Greifswald, Germany

  • Suzanne J. Brown,

    Affiliation Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia

  • Richard A. Jensen,

    Affiliation Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology and Health Services, University of Washington, Seattle, Washington, United States of America

  • Rajesh Rawal,

    Affiliation Institute for Genetic Epidemiology, Helmholtz Zentrum Munich, Munich/Neuherberg, Germany

  • Greet L. Roef,

    Affiliation Department of Endocrinology and Internal Medicine, University Hospital Ghent and Faculty of Medicine, Ghent University, Ghent, Belgium

  • Theo S. Plantinga,

    Affiliation Internal Medicine, Division of Endocrinology, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands

  • Sita H. Vermeulen,

    Affiliation Department for Health Evidence, Radboud University Medical Centre, Nijmegen, The Netherlands

  • Jari Lahti,

    Affiliation Institute of Behavioural Sciences, University of Helsinki, Helsinki, Finland

  • Matthew J. Simmonds,

    Affiliation Oxford Centre for Diabetes, Endocrinology and Metabolism and NIHR Oxford Biomedical Research Centre, Oxford, UK Churchill Hospital, Headington, Oxford, United Kingdom

  • Lise Lotte N. Husemoen,

    Affiliation Research Centre for Prevention and Health, Glostrup University Hospital, the Capital Region of Denmark, Glostrup, Denmark

  • Rachel M. Freathy,

    Affiliation Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, United Kingdom

  • Beverley M. Shields,

    Affiliation Peninsula NIHR Clinical Research Facility, University of Exeter Medical School, University of Exeter, Exeter, United Kingdom

  • Diana Pietzner,

    Affiliation Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin-Luther-University Halle-Wittenberg, Halle, Germany

  • Rebecca Nagy,

    Affiliation Comprehensive Cancer Center, Ohio State University, Columbus, Ohio, United States of America

  • Linda Broer,

    Affiliation Department of Epidemiology, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands

  • Layal Chaker,

    Affiliation Department of Internal Medicine, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands

  • Tim I. M. Korevaar,

    Affiliation Department of Internal Medicine, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands

  • Maria Grazia Plia,

    Affiliation Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche, c/o Cittadella Universitaria di Monserrato, Monserrato, Cagliari, Italy

  • Cinzia Sala,

    Affiliation Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy

  • Uwe Völker,

    Affiliation Interfaculty Institute for Genetics and Functional Genomics, University Medicine and Ernst-Moritz-Arndt-University Greifswald, Greifswald, Germany

  • J. Brent Richards,

    Affiliations Departments of Medicine, Human Genetics, Epidemiology and Biostatistics, Lady Davis Institute, McGill University, Montreal, Canada, Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom

  • Fred C. Sweep,

    Affiliation Department for Health Evidence, Radboud University Medical Centre, Nijmegen, The Netherlands

  • Christian Gieger,

    Affiliation Institute for Genetic Epidemiology, Helmholtz Zentrum Munich, Munich/Neuherberg, Germany

  • Tanguy Corre,

    Affiliation Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy

  • Eero Kajantie,

    Affiliations National Institute for Health and Welfare, Helsinki, Finland, Hospital for Children and Adolescents, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland

  • Betina Thuesen,

    Affiliation Research Centre for Prevention and Health, Glostrup University Hospital, the Capital Region of Denmark, Glostrup, Denmark

  • Youri E. Taes,

    Affiliation Department of Endocrinology and Internal Medicine, University Hospital Ghent and Faculty of Medicine, Ghent University, Ghent, Belgium

  • W. Edward Visser,

    Affiliation Department of Internal Medicine, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands

  • Andrew T. Hattersley,

    Affiliation Peninsula NIHR Clinical Research Facility, University of Exeter Medical School, University of Exeter, Exeter, United Kingdom

  • Jürgen Kratzsch,

    Affiliation Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig, Germany

  • Alexander Hamilton,

    Affiliation Oxford Centre for Diabetes, Endocrinology and Metabolism and NIHR Oxford Biomedical Research Centre, Oxford, UK Churchill Hospital, Headington, Oxford, United Kingdom

  • Wei Li,

    Affiliation Comprehensive Cancer Center, Ohio State University, Columbus, Ohio, United States of America

  • Georg Homuth,

    Affiliation Interfaculty Institute for Genetics and Functional Genomics, University Medicine and Ernst-Moritz-Arndt-University Greifswald, Greifswald, Germany

  • Monia Lobina,

    Affiliation Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche, c/o Cittadella Universitaria di Monserrato, Monserrato, Cagliari, Italy

  • Stefano Mariotti,

    Affiliation Dipartimento di Scienze Biomediche, Universita di Sassari, Sassari, Italy

  • Nicole Soranzo,

    Affiliation Wellcome Trust Sanger Institute, Hixton, United Kingdom

  • Massimiliano Cocca,

    Affiliation Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy

  • Matthias Nauck,

    Affiliation Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany

  • Christin Spielhagen,

    Affiliation Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany

  • Alec Ross,

    Affiliation Department for Health Evidence, Radboud University Medical Centre, Nijmegen, The Netherlands

  • Alice Arnold,

    Affiliation Department of Biostatistics, University of Washington, Seattle, Washington, United States of America

  • Martijn van de Bunt,

    Affiliation Oxford Centre for Diabetes, Endocrinology and Metabolism and NIHR Oxford Biomedical Research Centre, Oxford, UK Churchill Hospital, Headington, Oxford, United Kingdom

  • Sandya Liyanarachchi,

    Affiliation Comprehensive Cancer Center, Ohio State University, Columbus, Ohio, United States of America

  • Margit Heier,

    Affiliation Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Institute of Epidemiology II, Neuherberg, Germany

  • Hans Jörgen Grabe,

    Affiliation Department of Psychiatry and Psychotherapy, University Medicine Greifswald, HELIOS Hospital Stralsund, Greifswald, Germany

  • Corrado Masciullo,

    Affiliation Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy

  • Tessel E. Galesloot,

    Affiliation Department for Health Evidence, Radboud University Medical Centre, Nijmegen, The Netherlands

  • Ee M. Lim,

    Affiliation Pathwest Laboratory Medicine WA, Nedlands, Western Australia, Australia

  • Eva Reischl,

    Affiliation Research Unit of Molecular Epidemiology Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany

  • Peter J. Leedman,

    Affiliations School of Medicine and Pharmacology, the University of Western Australia, Crawley, Western Australia, Australia, UWA Centre for Medical Research, Western Australian Institute for Medical Research, Perth, Western Australia, Australia

  • Sandra Lai,

    Affiliation Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche, c/o Cittadella Universitaria di Monserrato, Monserrato, Cagliari, Italy

  • Alessandro Delitala,

    Affiliation Dipartimento di Scienze Biomediche, Universita di Sassari, Sassari, Italy

  • Alexandra P. Bremner,

    Affiliation School of Population Health, University of Western Australia, Nedlands, Western Australia, Australia

  • David I. W. Philips,

    Affiliation MRC Lifecourse Epidemiology Unit, Southampton General Hospital, Southampton, United Kingdom

  • John P. Beilby,

    Affiliations Pathwest Laboratory Medicine WA, Nedlands, Western Australia, Australia, School of Pathology and Laboratory Medicine, University of Western Australia, Crawley, Western Australia, Australia

  • Antonella Mulas,

    Affiliation Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche, c/o Cittadella Universitaria di Monserrato, Monserrato, Cagliari, Italy

  • Matteo Vocale,

    Affiliation High Performance Computing and Network, CRS4, Parco Tecnologico della Sardegna, Pula, Italy

  • Goncalo Abecasis,

    Affiliation Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America

  • Tom Forsen,

    Affiliations Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland, Vaasa Health Care Centre, Diabetes Unit, Vaasa, Finland

  • Alan James,

    Affiliations School of Medicine and Pharmacology, the University of Western Australia, Crawley, Western Australia, Australia, Department of Respiratory Medicine, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia

  • Elisabeth Widen,

    Affiliation Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland

  • Jennie Hui,

    Affiliation Pathwest Laboratory Medicine WA, Nedlands, Western Australia, Australia

  • Holger Prokisch,

    Affiliations Institute of Human Genetics, Helmholtz Zentrum Munich, Munich, Germany, Institute of Human Genetics, Technische Universität München, Munich, Germany

  • Ernst E. Rietzschel,

    Affiliation Department of Cardiology and Internal Medicine, University Hospital Ghent and Faculty of Medicine, Ghent University, Ghent, Belgium

  • Aarno Palotie,

    Affiliations Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, United Kingdom, Department of Medical Genetics, University of Helsinki and University Central Hospital, Helsinki, Finland

  • Peter Feddema,

    Affiliation Diagnostica Stago, Doncaster, Victoria, Australia

  • Stephen J. Fletcher,

    Affiliation Pathwest Laboratory Medicine WA, Nedlands, Western Australia, Australia

  • Katharina Schramm,

    Affiliation Institute of Human Genetics, Helmholtz Zentrum Munich, Munich, Germany

  • Jerome I. Rotter,

    Affiliations Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute, Torrance, California, United States of America, Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, California, United States of America

  • Alexander Kluttig,

    Affiliation Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin-Luther-University Halle-Wittenberg, Halle, Germany

  • Dörte Radke,

    Affiliation Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany

  • Michela Traglia,

    Affiliation Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy

  • Gabriela L. Surdulescu,

    Affiliation Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom

  • Huiling He,

    Affiliation Comprehensive Cancer Center, Ohio State University, Columbus, Ohio, United States of America

  • Jayne A. Franklyn,

    Affiliation School of Clinical and Experimental Medicine, College of Medical and Dental Sciences, Univeristy of Birmingham, Edgbaston, Birmingham, United Kingdom

  • Daniel Tiller,

    Affiliation Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin-Luther-University Halle-Wittenberg, Halle, Germany

  • Bijay Vaidya,

    Affiliation Diabetes, Endocrinology and Vascular Health Centre, Royal Devon and Exeter NHS Foundation Trust, Exeter, United Kingdom

  • Tim de Meyer,

    Affiliation BIOBIX Lab. for Bioinformatics and Computational Genomics, Dept. of Mathematical Modelling, Statistics and Bioinformatics. Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium

  • Torben Jørgensen,

    Affiliations Research Centre for Prevention and Health, Glostrup University Hospital, the Capital Region of Denmark, Glostrup, Denmark, Faculty of Health Science, University of Copenhagen, Copenhagen, Denmark

  • Johan G. Eriksson,

    Affiliations National Institute for Health and Welfare, Helsinki, Finland, Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland, Helsinki University Central Hospital, Unit of General Practice, Helsinki, Finland, Folkhalsan Research Centre, Helsinki, Finland, Vasa Central Hospital, Vasa, Finland

  • Peter C. O'Leary,

    Affiliations School of Pathology and Laboratory Medicine, University of Western Australia, Crawley, Western Australia, Australia, Curtin Health Innovation Research Institute, Curtin University of Technology, Bentley, Western Australia, Australia

  • Eric Wichmann,

    Affiliation Institute of Epidemiology I, Helmholtz Zentrum Munich, Munich, Germany

  • Ad R. Hermus,

    Affiliation Internal Medicine, Division of Endocrinology, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands

  • Bruce M. Psaty,

    Affiliations Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology and Health Services, University of Washington, Seattle, Washington, United States of America, Group Health Research Institute, Group Health Cooperative, Seattle, Washington, United States of America

  • Till Ittermann,

    Affiliation Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany

  • Albert Hofman,

    Affiliation Department of Epidemiology, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands

  • Emanuele Bosi,

    Affiliation Department of Internal Medicine, Diabetes & Endocrinology Unit, San Raffaele Scientific Institute and Vita-Salute San Raffaele University, Milan, Italy

  • David Schlessinger,

    Affiliation Laboratory of Genetics, National Institute on Aging, Baltimore, Maryland, United States of America

  • Henri Wallaschofski,

    Affiliation Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig, Germany

  • Nicola Pirastu,

    Affiliations Institute for Maternal and Child Health - IRCCS “Burlo Garofolo”, Trieste, Italy, University of Trieste, Trieste, Italy

  • Yurii S. Aulchenko,

    Affiliation Department of Epidemiology, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands

  • Albert de la Chapelle,

    Affiliation Comprehensive Cancer Center, Ohio State University, Columbus, Ohio, United States of America

  • Romana T. Netea-Maier,

    Affiliation Internal Medicine, Division of Endocrinology, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands

  • Stephen C. L. Gough,

    Affiliation Oxford Centre for Diabetes, Endocrinology and Metabolism and NIHR Oxford Biomedical Research Centre, Oxford, UK Churchill Hospital, Headington, Oxford, United Kingdom

  • Henriette Meyer zu Schwabedissen,

    Affiliation Biopharmacy, Department of Pharmaceutical Sciences, University Basel, Basel, Switzerland

  • Timothy M. Frayling,

    Affiliation Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, United Kingdom

  • Jean-Marc Kaufman,

    Affiliation Department of Endocrinology and Internal Medicine, University Hospital Ghent and Faculty of Medicine, Ghent University, Ghent, Belgium

  • Allan Linneberg,

    Affiliation Research Centre for Prevention and Health, Glostrup University Hospital, the Capital Region of Denmark, Glostrup, Denmark

  • Katri Räikkönen,

    Affiliation Institute of Behavioural Sciences, University of Helsinki, Helsinki, Finland

  • Johannes W. A. Smit,

    Affiliation Internal Medicine, Division of Endocrinology, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands

  • Lambertus A. Kiemeney,

    Affiliation Department for Health Evidence, Radboud University Medical Centre, Nijmegen, The Netherlands

  • Fernando Rivadeneira,

    Affiliations Department of Internal Medicine, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands, Department of Epidemiology, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands, Netherlands Consortium for Healthy Aging, Netherlands Genomics Initiative, Leiden, The Netherlands

  • André G. Uitterlinden,

    Affiliations Department of Internal Medicine, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands, Department of Epidemiology, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands, Netherlands Consortium for Healthy Aging, Netherlands Genomics Initiative, Leiden, The Netherlands

  • John P. Walsh,

    Affiliations Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia, School of Medicine and Pharmacology, the University of Western Australia, Crawley, Western Australia, Australia

  • Christa Meisinger,

    Affiliation Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Institute of Epidemiology II, Neuherberg, Germany

  • Martin den Heijer,

    Affiliation Department of Internal Medicine, VU Medical Center, Amsterdam, The Netherlands

  • Theo J. Visser,

    Affiliation Department of Internal Medicine, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands

  • Timothy D. Spector,

    Affiliation Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom

  • Scott G. Wilson,

    Affiliations Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia, School of Medicine and Pharmacology, the University of Western Australia, Crawley, Western Australia, Australia, Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom

  • Henry Völzke,

    Affiliation Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany

  • Anne Cappola,

    Affiliation Division of Endocrinology, Diabetes, and Metabolism, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America

  • Daniela Toniolo,

    Affiliations Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy, Institute of Molecular Genetics-CNR, Pavia, Italy

  • Serena Sanna ,

    SS, SN and RPP also contributed equally to this work.

    Affiliation Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche, c/o Cittadella Universitaria di Monserrato, Monserrato, Cagliari, Italy

  • Silvia Naitza ,

    SS, SN and RPP also contributed equally to this work.

    Affiliation Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche, c/o Cittadella Universitaria di Monserrato, Monserrato, Cagliari, Italy

  •  [ ... ],
  • Robin P. Peeters

    SS, SN and RPP also contributed equally to this work.

    Affiliation Department of Internal Medicine, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands

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Identification of Novel Genetic Loci Associated with Thyroid Peroxidase Antibodies and Clinical Thyroid Disease

  • Marco Medici, 
  • Eleonora Porcu, 
  • Giorgio Pistis, 
  • Alexander Teumer, 
  • Suzanne J. Brown, 
  • Richard A. Jensen, 
  • Rajesh Rawal, 
  • Greet L. Roef, 
  • Theo S. Plantinga, 
  • Sita H. Vermeulen
PLOS
x

Abstract

Autoimmune thyroid diseases (AITD) are common, affecting 2-5% of the general population. Individuals with positive thyroid peroxidase antibodies (TPOAbs) have an increased risk of autoimmune hypothyroidism (Hashimoto's thyroiditis), as well as autoimmune hyperthyroidism (Graves' disease). As the possible causative genes of TPOAbs and AITD remain largely unknown, we performed GWAS meta-analyses in 18,297 individuals for TPOAb-positivity (1769 TPOAb-positives and 16,528 TPOAb-negatives) and in 12,353 individuals for TPOAb serum levels, with replication in 8,990 individuals. Significant associations (P<5×10−8) were detected at TPO-rs11675434, ATXN2-rs653178, and BACH2-rs10944479 for TPOAb-positivity, and at TPO-rs11675434, MAGI3-rs1230666, and KALRN-rs2010099 for TPOAb levels. Individual and combined effects (genetic risk scores) of these variants on (subclinical) hypo- and hyperthyroidism, goiter and thyroid cancer were studied. Individuals with a high genetic risk score had, besides an increased risk of TPOAb-positivity (OR: 2.18, 95% CI 1.68–2.81, P = 8.1×10−8), a higher risk of increased thyroid-stimulating hormone levels (OR: 1.51, 95% CI 1.26–1.82, P = 2.9×10−6), as well as a decreased risk of goiter (OR: 0.77, 95% CI 0.66–0.89, P = 6.5×10−4). The MAGI3 and BACH2 variants were associated with an increased risk of hyperthyroidism, which was replicated in an independent cohort of patients with Graves' disease (OR: 1.37, 95% CI 1.22–1.54, P = 1.2×10−7 and OR: 1.25, 95% CI 1.12–1.39, P = 6.2×10−5). The MAGI3 variant was also associated with an increased risk of hypothyroidism (OR: 1.57, 95% CI 1.18–2.10, P = 1.9×10−3). This first GWAS meta-analysis for TPOAbs identified five newly associated loci, three of which were also associated with clinical thyroid disease. With these markers we identified a large subgroup in the general population with a substantially increased risk of TPOAbs. The results provide insight into why individuals with thyroid autoimmunity do or do not eventually develop thyroid disease, and these markers may therefore predict which TPOAb-positives are particularly at risk of developing clinical thyroid dysfunction.

Author Summary

Individuals with thyroid peroxidase antibodies (TPOAbs) have an increased risk of autoimmune thyroid diseases (AITD), which are common in the general population and associated with increased cardiovascular, metabolic and psychiatric morbidity and mortality. As the causative genes of TPOAbs and AITD remain largely unknown, we performed a genome-wide scan for TPOAbs in 18,297 individuals, with replication in 8,990 individuals. Significant associations were detected with variants at TPO, ATXN2, BACH2, MAGI3, and KALRN. Individuals carrying multiple risk variants also had a higher risk of increased thyroid-stimulating hormone levels (including subclinical and overt hypothyroidism), and a decreased risk of goiter. The MAGI3 and BACH2 variants were associated with an increased risk of hyperthyroidism, and the MAGI3 variant was also associated with an increased risk of hypothyroidism. This first genome-wide scan for TPOAbs identified five newly associated loci, three of which were also associated with clinical thyroid disease. With these markers we identified a large subgroup in the general population with a substantially increased risk of TPOAbs. These results provide insight into why individuals with thyroid autoimmunity do or do not eventually develop thyroid disease, and these markers may therefore predict which individuals are particularly at risk of developing clinical thyroid dysfunction.

Introduction

Autoimmune thyroid disease (AITD), including Hashimoto's thyroiditis and Graves' disease, is one of the most common autoimmune diseases, affecting 2–5% of the general population [1], [2], [3]. Thyroid dysfunction has been associated with osteoporosis, depression, atrial fibrillation, heart failure, metabolic syndrome, and mortality [4], [5], [6], [7], [8], [9], [10], [11]. High serum antibodies against the enzyme thyroid peroxidase (TPO), which is located in the thyroid and plays a key role in thyroid hormone synthesis, are present in 90% of patients with Hashimoto's thyroiditis [12], [13], the most frequent cause of hypothyroidism and goiter. Although TPO antibodies (TPOAbs) are a useful clinical marker for the detection of early AITD, it remains controversial if these antibodies play a causative role in the pathogenesis of Hashimoto's thyroiditis [14], [15], [16].

Interestingly, TPOAb-positive persons also have an increased risk of developing autoimmune hyperthyroidism (Graves' disease) [17], [18], which is caused by stimulating antibodies against the thyroid stimulating hormone (TSH) receptor [19]. Numerous studies have shown that Graves' hyperthyroidism and Hashimoto's thyroiditis show co-inheritance [17], [20], [21]. Finally, thyroid autoimmunity is the most common autoimmune disorder in women of childbearing age, and TPOAb-positive women have an increased risk of developing pregnancy complications such as miscarriage and pre-term delivery [17], [18], [22], [23], [24], [25], [26].

The prevalence of TPOAb-positivity in the general population ranges from 5–24%, but it is currently unknown why these people develop TPOAbs, nor is it known why not all individuals with thyroid autoimmunity develop clinical thyroid disease [27], [28]. It is estimated that around 70% of the susceptibility to develop thyroid autoantibodies is due to genetic factors [29]. In this context it is remarkable to note that little is known about the genetic factors that determine TPOAb-positivity and the risk of AITD.

We therefore performed a genome wide association study (GWAS) meta-analysis for TPOAbs in the general population in 18,297 individuals from 11 populations. Newly identified genetic variants were studied in relation to subclinical and overt hypo- and hyperthyroidism, goiter, thyroid autoimmunity during pregnancy and thyroid cancer risk.

Results

Characteristics of the studied populations are shown in Table 1 and the Supplementary Material S1. Heritability estimates in the family-based cohorts SardiNIA, TwinsUK and Val Borbera were, respectively, 0.65, 0.66, and 0.54 for TPOAb-positivity, and 0.43, 0.66, and 0.30 for TPOAb levels.

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Table 1. Population characteristics and serum TPOAb, TSH, and FT4 level measurements specifications.

https://doi.org/10.1371/journal.pgen.1004123.t001

Loci associated with TPOAb-positivity and TPOAb levels

See Table 1 and Supplementary Figure S1 for TPOAb measurements and Supplementary Table S1 for genotyping procedures. In most autoimmune diseases, both the presence and the level of autoantibodies are relevant for the disease onset [18], [30], [31]. Furthermore, different pathophysiological processes may be involved in the initiation and severity of the autoimmune response. We therefore performed a GWAS on TPOAb-positivity (including 1769 TPOAb-positives and 16,528 TPOAb–negatives), as well as a GWAS on continuous TPOAb levels (including 12,353 individuals) in stage 1. See Supplementary Figures S2 and S3 for QQ (quantile-quantile) and Manhattan plots.

In stage 2, we followed-up 20 stage 1 SNPs (P<5×10−6; 13 TPOAb-positivity and 10 TPOAb level SNPs, with 3 SNPs overlapping) in 5 populations, including up to 8,990 individuals for TPOAb-positivity (922 TPOAb-positives and 8068 TPOAb–negatives) and 8,159 individuals for TPOAb level analyses (see Supplementary Material S1). Results of the combined stage 1 and 2 meta-analyses, including heterogeneity analyses, are shown in Supplementary Tables S2 and S3. Regional association plots are shown in Supplementary Figures S4 and S5. In the combined stage 1 and 2 meta-analyses GWAS significant associations (P<5×10−8) were observed near TPO (Chr 2p25; rs11675434), at ATXN2 (Chr 12q24.1; rs653178), and BACH2 (Chr 6q15; rs10944479) for TPOAb-positivity, and near TPO (rs11675434), at MAGI3 (Chr 6q15; rs1230666), and KALRN (Chr 3q21; rs2010099) for TPOAb levels (Table 2 and Figure 1). The TPOAb level meta-analysis P-values for the 3 GWAS significant TPOAb-positivity loci were: TPO-rs11675434: P = 7.4×10−13, ATXN2-rs653178: P = 1.3×10−7, and BACH2-rs10944479: P = 2.0×10−4.

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Figure 1. Genome wide association studies meta-analyses: Loci associated with TPOAb-positivity (a–c) and TPOAb levels (d–f) on a genome-wide level of significance.

Regional association plots of the genome-wide significant loci associated with TPOAb positivity (a–c) and TPOAb levels (d–f). The y-axis on the left indicates the – log10 P value for the association with TPOAb –positivity (a–c) or TPOAb levels (d–f). SNPs are plotted on the x-axis according to their chromosomal position against the association with the phenotype on the y-axis. The most significant stage 1 SNP is indicated in purple. The combined stage 1 and 2 result of this SNP is indicated in yellow. The SNPs surrounding the most significant SNP are color-coded to reflect their LD with this SNP. Symbols reflect functional genomic annotation, as indicated in the legend. The blue y-axes on the right of each plot indicate the estimated recombination rates (based on HapMap Phase II); the bottom of each panel shows the respective annotated genes at the locus and their transcriptional direction. Mb, megabases.

https://doi.org/10.1371/journal.pgen.1004123.g001

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Table 2. Newly identified loci associated with TPOAb-positivity and/or serum TPOAb levels reaching genome wide significance.

https://doi.org/10.1371/journal.pgen.1004123.t002

As the 3 GWAS significant loci for TPOAb levels also showed associations with TPOAb-positivity (TPO-rs11675434: OR, 1.21 [95% CI, 1.15–1.28)], P = 1.5×10−16; MAGI3-rs1230666: OR, 1.23 [95% CI, 1.14–1.33], P = 1.5×10−6; KALRN-rs2010099: OR, 1.24 [95% CI, 1.12–1.37], P = 7.4×10−5), we subsequently studied the (combined) effects of these 5 SNPs on clinical thyroid disease. Genetic risk scores were calculated as described in the Supplementary Material. The variance explained by these 5 SNPs was 3.1% for TPOAb-positivity and 3.2% for TPOAb levels. Subjects with a high genetic risk score had a 2.2 times increased risk of TPOAb-positivity compared to subjects with a low genetic risk score (P = 8.1×10−8) (Table 3).

Table S4 shows the stage 1 TPOAb-positivity and TPOAb level meta-analyses results for GWAS significant SNPs reported in previous GWAS on thyroid related phenotypes.

Associations with hypo- and hyperthyroidism

The associations between the 5 GWAS significant SNPs and the risk of abnormal thyroid function tests are shown in Table 4. MAGI3- rs1230666 was associated with an increased risk of overt hypothyroidism and increased TSH levels below the Bonferroni threshold (i.e., P = 0.05/5 = 0.01). Borderline significant signals were observed at BACH2- rs10944479 with a higher risk of increased TSH levels as well as overt hyperthyroidism (P = 0.011 and P = 0.012), and at the KALRN-rs2010099 SNP with a lower risk of decreased TSH levels (P = 0.010).

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Table 4. Newly identified TPOAb associated loci and the risk of thyroid disease in stage 1 and 2 populations.

https://doi.org/10.1371/journal.pgen.1004123.t004

Furthermore, a higher genetic risk score was associated with a higher risk of increased TSH levels (Supplementary Table S5). No effects of the genetic risk score on the risk of overt hypothyroidism, hyperthyroidism or decreased TSH levels were observed.

Associations with goiter

Individuals with a high genetic risk score had a 30.4% risk of sonographically-proven goiter, compared to 35.2% in subjects with a low score (P = 6.5×10−4) (Table 5). None of the individual SNPs was significantly associated with goiter risk.

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Table 5. Newly identified TPOAb associated loci, genetic risk scores and the risk of goiter.

https://doi.org/10.1371/journal.pgen.1004123.t005

Thyroid autoimmunity during pregnancy

As autoimmunity significantly changes during pregnancy [25], we additionally studied these effects in an independent pregnant population. Pregnant women with a high genetic risk score had a 2.4 times increased risk of TPOAb-positivity compared to women with a low score (10.3% vs 4.8%, P = 0.03). These women did not have a higher risk of increased TSH levels. However, a borderline significant signal with a lower risk of increased TSH levels was observed at ATXN2- rs653178 (OR, 0.54 [95% CI, 0.34–0.87], P = 0.012).

Associations with thyroid disease in independent populations

a) Graves' disease.

As MAGI3- rs1230666 and BACH2- rs10944479 showed promising associations (i.e., P≤0.05) with hyperthyroidism in our meta-analyses, we tested these SNPs in an independent population of 2478 patients with Graves' disease and 2682 controls (see Supplementary Material for further details). Both were associated with an increased risk of Graves' disease (MAGI3- rs1230666: OR, 1.37 [95% CI, 1.22–1.54]; P = 1.2×10−7; BACH2- rs10944479: OR, 1.25 [1.12–1.39]; P = 6.2×10−5).

b) Thyroid cancer.

Supplementary Table S6 shows the associations of the 5 GWAS significant SNPs with thyroid cancer. No statistically significant associations were detected, but a borderline significant signal with an increased risk of thyroid cancer was observed at ATXN2- rs653178 (OR, 1.32 [95% CI, 1.02–1.70], P = 0.03).

Pathway analyses

Ingenuity Pathway Analyses (IPA; Ingenuity Systems, Ca, USA) and GRAIL analyses [32] were performed to identify potential pathways involved in AITD, the results of which are shown in Supplementary Tables S7 and S8, and Figure S6. The identified top pathways involved cell death, survival, movement, and OX40 signalling.

Discussion

This is the first GWAS meta-analysis investigating the genetics of TPOAbs in the normal population in up to 18,297 individuals from 11 populations with replication in up to 8,990 individuals from 5 populations. We identified 5 GWAS significant loci associated with TPOAb-positivity and/or levels.

The most significant hit for both TPOAb-positivity and TPOAb levels was located near the TPO gene itself. TPO is a membrane-bound protein located on the apical membranes of the thyroid follicular cell, catalyzing key reactions in thyroid hormone synthesis [33]. Mutations in TPO have been found in patients with congenital hypothyroidism [34], [35]. Although TPOAbs are valid clinical biomarkers of AITD, they are generally considered to be secondary to the thyroid damage inflicted by T-cells.

The FOXE1 gene has been previously associated with hypothyroidism [36], [37] and is known to regulate transcription of TPO [38]. In this context it is interesting to note that we did not find any associations of the variant near TPO with hypothyroidism. Most genes that have been associated with AITD (predominantly Graves' disease) by candidate gene and GWAS studies so far are located in the HLA class I and II regions, or in genes involved in T-cell (i.e., CTLA-4, PTPN22) or other autoimmune responses [28], [39]. Until now, the TPO gene itself had not been associated with AITD, except in one recent candidate gene analysis in a small cohort (n = 188) without replication [40]. A variant near TPO (rs11694732), which is in LD with rs11675434 (r2 = 0.97 in HapMap2), has previously been associated with TSH levels by Gudmundsson et al [41]. However, various other GWAS on serum TSH and FT4 levels have not found any significant associations in or near this locus, including a recent similar sized GWAS by Porcu et al [42].

Three of the other four loci identified here are located in or are in linkage disequilibrium (LD) with genes previously associated with other autoimmune diseases. Rs1230666 is located in intron 9 of MAGI3, encoding a protein that modulates activity of AKT/PKB. AKT/PKB is expressed in the thyroid and regulates apoptosis [43], which seems to play an important role in the development of AITD [44], [45]. In addition, rs1230666 is in LD with rs2476601 (r2 = 0.70 in HapMap2), a variant causing a R620W substitution in PTPN22. PTPN22 is a lymphoid-specific intracellular phosphatase involved in the T-cell receptor signaling pathway. Variations in PTPN22, and specifically R620W, are associated with various autoimmune disorders including type 1 diabetes, rheumatoid arthritis, systemic lupus erythematosus and Graves' disease [46], [47], [48], [49]. The associations of the MAGI3 locus with TPOAb-positivity and Graves' disease may therefore also be explained by linkage with disease-associated variants in PTPN22 [50]. Of note, the association signal at rs2476601 is one order weaker than that of the top variant rs1230666.

The BACH2 locus has been implicated in the susceptibility to several autoimmune diseases, including celiac disease, type 1 diabetes, vitiligo, Crohn's disease, and multiple sclerosis [46], [51], [52], [53], [54]. A recent candidate gene analysis associated the BACH2 locus with an increased risk of AITD, including Hashimoto's thyroiditis and Graves' disease [55]. However, the associations were not significant when Hashimoto's thyroiditis and Graves' disease were studied separately. BACH2 is specifically expressed in early stages of B-cell differentiation and represses different immunoglobulin genes [56]. Interestingly, BACH2 can bind to the co-repressor SMRT (silencing mediator of retinoid and thyroid receptor), which may suggest a more direct effect on thyroid hormone secretion and action as well.

Polymorphisms in ATXN2 have been associated with multiple neurodegenerative diseases, including spinocerebellar ataxia and Parkinson's disease [57], [58], [59]. Different epidemiological studies have associated thyroid dysfunction with cerebellar ataxia [60], [61]. Furthermore, the identified SNP in ATXN2 has been previously associated with renal function, serum urate levels and blood pressure [62], [63], [64]. However, this SNP is in high LD with rs3184504 (r2 = 0.873), a variant causing a Trp262Arg substitution of SH2B adaptor protein 3 (SH2B3). SH2B3 encodes the adaptor protein LNK, a key negative regulator of cytokine signaling playing a critical role in hematopoiesis. This variant is associated with susceptibility to several autoimmune diseases, including celiac disease, type 1 diabetes, vitiligo, and rheumatoid arthritis [46], [51], [53], [65], suggesting more relevance for TPOAb levels than ATXN2. This is supported by a recent study which showed that variants in LD with SH2B3, BACH2, and PTPN22 are associated with TPOAb levels in patients with type 1 diabetes [66].

Whereas the above four loci are located in genes involved in the immune response or the autoantigen, the KALRN (Kalirin) gene encodes a multi-domain guanine nucleotide exchange factor for GTP-binding proteins of the Rho family. The relation of KALRN with levels of TPOAbs is unclear. This gene has recently been found to be associated with megakaryopoiesis and platelet formation [67], which may suggest a function in the immune system [68]. We furthermore performed pathway analyses on the stage 1 TPOAb-positivity and TPOAb level lead SNPs, and identified the cell death, survival and movement pathway as an important pathway for TPOAbs. This finding is supported by previous studies, which show an important role for apoptosis in the development of AITD [44], [45]. Another top pathway involved was the OX40 signalling pathway, and it is of interest to note that OX40 is a T-cell activator promoting the survival of CD4+ T-cells at sites of inflammation [69].

Our results have potential clinical relevance for several reasons. Genetic risk scores based on these novel common (risk allele frequencies: 9–40%) TPOAb-associated SNPs enabled us to identify a large subgroup in the general population with a two-fold increased risk of TPOAb-positivity (10.4% vs 5.4%). These individuals also have a higher risk of increased TSH levels and a lower risk of goiter, suggesting an advanced stage of destruction of the thyroid due to autoimmune processes. Furthermore, pregnant women with high genetic risk scores had a 2.4 times increased risk of TPOAb-positivity during pregnancy. In this context it is interesting to note that TPOAb-positive pregnant women have an increased risk of miscarriages and preterm births independent of thyroid function [70].

Associations with thyroid disease were also found on an individual SNP level. The MAGI3 SNP was associated with a substantially increased risk of hypothyroidism, and the BACH2 SNP showed a borderline significant association (P = 0.011) with a higher risk of increased TSH levels, which includes subjects with subclinical and overt hypothyroidism. Furthermore, both loci were significantly associated with an increased risk of Graves' hyperthyroidism in an independent population. To predict which patients with first or second degree relatives with documented Hashimoto's or Graves' disease will develop clinical thyroid disease, a clinical algorithm has been developed (i.e., the THEA score) [18]. Future studies should analyze if these genetic markers increase the sensitivity of the THEA score. Graves' hyperthyroidism and Hashimoto's thyroiditis co-segregate in families and subjects with TPOAbs have an increased risk of both diseases [17], [18], [20], [21], [22], [26]. The current study provides insight into this phenomenon by showing that specific loci associated with TPOAbs and (subclinical) hypothyroidism, i.e. MAGI3 and BACH2, are also associated with Graves' hyperthyroidism in an independent case-control study.

The prevalence of TPOAb-positivity in the general population is high (5–24%), but it is currently unknown why part of the individuals with thyroid autoimmunity develop clinical thyroid disease whereas others do not [27], [28]. In this context it is interesting to note that the TPOAb-associated SNPs located in TPO and ATXN2 were not associated with clinical thyroid disease. This suggests that the TPOAbs in these individuals may be of less clinical relevance, providing insight into why TPOAb-positive individuals do or do not eventually develop clinical thyroid disease.

Our study has some limitations. The validity of the results is restricted to individuals from populations of European ancestry. Future GWASs in populations from non-European descent will be required to determine to which extent our results can be generalized to other ethnic groups. Secondly, we did not perform conditional analyses to further identify secondary association signals within the identified loci, nor did we perform functional studies for the identified variants. Further research is therefore needed to unravel the exact biological mechanism behind the observed associations. The fact that various TPOAb assays were used across the participating cohorts could lead to bias. We therefore used TPOAb-positivity cut-off values as provided by the respective assay manufacturer, instead of using one fixed cut-off value. This is also of clinical importance as in clinical practice most institutions rely on the TPOAb-positivity cut-off as provided by the assay manufacturer. Furthermore, we did not detect heterogeneity in our results, supporting the fact that results obtained with different assays can be combined across cohorts using the z-score based meta-analysis. Finally, as AITD coincides with other autoimmune diseases, our results could be driven by indirect associations with other autoimmune diseases. However, AITD is the most common autoimmune disease in the general population. We furthermore show that carriage of multiple risk alleles is associated with an increased risk of thyroid dysfunction, which underlines the clinical importance of our findings.

In conclusion, this first GWAS for TPOAbs identified five newly associated loci, three of which were also associated with clinical thyroid disease. Furthermore, we show that carriage of multiple risk variants is not only associated with a substantial increased risk of TPOAb-positivity, but also with a higher risk of increased TSH levels (including subclinical and overt hypothyroidism) and a lower risk of goiter. These genetic markers not only help to identify large groups in the general population with an increased risk of TPOAb-positivity, but may also predict which TPOAb-positive persons are particularly at risk of developing clinical thyroid disease.

Materials and Methods

Study cohorts

For the TPOAb GWAS stage 1 and 2 analyses, and the hypothyroidism, hyperthyroidism and goiter analyses, individuals were recruited from 16 independent community-based and family studies. For the Graves' disease analyses, cases were recruited from the United Kingdom Graves' disease cohort and controls from the British 1958 Birth Cohort. Thyroid cancer cases and controls were recruited from the Nijmegen and Ohio thyroid cancer cohorts. A detailed description of the original cohorts contributing samples is provided in Table 1 and in the Supplementary Material. All participants provided written informed consent and protocols were approved by the institutional review boards or research ethics committees at the respective institutions, and conducted according to the Declaration of Helsinki.

Phenotype definitions

Serum TPOAb levels were determined with a range of assays. TPOAb-positives were defined as subjects with TPOAb levels above the assay-specific TPOAb-positivity cut-off, as defined by the manufacturer (Table 1). Serum TSH and free thyroxine (FT4) levels were determined using a range of assays (Table 1). Assay-specific TSH and FT4 reference ranges were used, as provided by the manufacturer (Table 1). Overt hypothyroidism was defined as a high TSH (i.e., a TSH level above the TSH reference range) and a low FT4. Increased TSH was defined as a high TSH, including persons with overt hypothyroidism or subclinical hypothyroidism (i.e., high TSH with a normal FT4). Overt hyperthyroidism was defined as a low TSH and a high FT4. Decreased TSH was defined as a low TSH, including persons with subclinical or overt hyperthyroidism.

The diagnosis of goiter is described in the Supplementary Material, and the diagnosis of Graves' disease and thyroid cancer in the respective cohorts have been described previously [41].

Genotyping

Samples were genotyped with a range of GWAS genotyping arrays (Supplementary Table S1). Sample and SNP quality control procedures were undertaken within each study. For each GWAS, over 2.5 million SNPs were imputed using CEU samples from Phase 2 of the International HapMap project (www.hapmap.org). Genotyping procedures in the stage 2, Graves' disease and thyroid cancer populations are described in the Supplementary Material.

Association analyses

The heritabilities of TPOAb-positivity and serum TPOAb levels were estimated, as described in the Supplementary Material.

In stage 1, we performed a GWAS on TPOAb-positivity as well as a GWAS on continuous TPOAb levels. Persons taking thyroid medication were excluded. Each SNP was tested for association with TPOAb-positivity using logistic regression analyses, adjusting for age and sex. For cohorts with family structure, we approximated the probability of being affected with a linear mixed model adjusting for age and sex. The produced model was used to predict the expected proportion of “risk” (effective) alleles in cases and controls, hence giving the means to estimate odds ratios. Only unrelated individuals were considered for the SardiNIA cohort. For the GWAS of continuous TPOAb levels, samples with a TPOAb level lower than the minimum TPOAb assay detection limit (Table 1) were excluded. TPOAb levels were natural log-transformed, and sex-specific, age adjusted standardized residuals were calculated. Each SNP was tested for association with these TPOAb level residuals using linear regression analyses (additive model), correcting for relatedness in studies with family structure. See Supplementary Table S1 for the software used for these analyses.

Before meta-analysis, SNPs with a minor allele frequency (MAF) <1% or a low imputation quality were excluded (Supplementary Material), after which the results of each GWAS were combined in a population size weighted z-score based meta-analysis using METAL [71]. Genomic control was applied to individual studies if λ>1.0.

In stage 2, we followed-up stage 1 GWAS significant SNPs, as well as promising SNPs not reaching GWAS significance, in an attempt to reach GWAS significant associations by increasing sample size (Supplementary Material). Results from stage 1 and 2 were combined in a population size weighted z-score based meta-analysis using METAL [71]. A z-score based meta-analysis was used to reduce bias that might be induced by different assays. As this method does not provide betas, and we wanted to provide a rough estimate of the actual effect sizes for convenience, we calculated betas using the fixed effects (inverse variance based) meta-analysis method. Heterogeneity was tested, applying bonferroni based P-value thresholds of P = 0.004 for the TPOAb-positivity analyses and P = 0.005 for the TPOAb level analyses.

All studies assessed and, if present, corrected for population stratification using principal-component analysis (PCA) and/or multidimensional-scaling (MDS), with the exception of SardiNIA and ValBorbera where the high isolation substantiates a lack of stratification (Table S1) [72], [73]. Lambda values were all ∼1, indicating that population stratification was overall properly accounted for (Table S1). To fully remove residual effects, we applied genomic correction to studies were lambda was >1. The final meta-analyses reported a lambda of 1.01 for both the TPOAb-positivity and the TPOAb level GWAS, thus no genomic correction was applied.

The variances explained by the GWAS significant SNPs were calculated. We subsequently studied the individual as well as the combined effects of the GWAS significant SNPs on the risk of clinical thyroid disease, as specified in the Supplementary Material. In short, to study combined effects, a genetic risk score was calculated for every person as the weighted sum of TPOAb risk alleles. The associations between the individual SNPs, genetic risk scores and the risk of abnormal thyroid function tests were studied using logistic regression analyses. Logistic regression analyses were used to study the associations with goiter, Graves' disease and thyroid cancer (Supplementary Material). The results of each study were combined in a population size weighted z-score based meta-analysis using METAL [71].

Various bioinformatic tools were searched for evidence for functional relevance of the GWAS significant SNPs and pathway analyses were performed on the Stage 1 lead SNPs (see Supplementary Material).

Supporting Information

Figure S1.

TPOAb level distributions in persons with detectable TPOAb levels in stage 1 and 2 populations.

https://doi.org/10.1371/journal.pgen.1004123.s001

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Figure S2.

Quantile-quantile (QQ) plots for the TPOAb-positivity and TPOAb level stage 1 meta-analyses.

https://doi.org/10.1371/journal.pgen.1004123.s002

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Figure S3.

Manhattan plots for stage 1 meta-analyses for TPOAb-positivity (a) and TPOAb levels (b). SNPs are plotted on the x-axis according to their chromosomal position against TPOAb-positivity (a) or TPOAb levels (b) (shown as – log10 P value) on the y-axis. The horizontal grey line indicates the threshold for genome-wide statistical significance (P<5×10−8). Genome-wide significant associations were observed near TPO (Chr 2p25; P = 1.5×10−12), at ATXN2 (Chr 12q24.1; P = 1.6×10−9) and near HCP5 (Chr 6p21.3; P = 4.1×10−8) for TPOAb-positivity, and near TPO (Chr 2p25; P = 5.4×10−13) and at ATXN2 (Chr 12q24.1; P = 1.1×10−8) for TPOAb levels.

https://doi.org/10.1371/journal.pgen.1004123.s003

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Figure S4.

Regional association plots of stage 1 lead loci for TPOAb-positivity (panels a-m). The y-axis on the left indicates the – log10 P value for the association with TPOAb –positivity. SNPs are plotted on the x-axis according to their chromosomal position. The most significant stage 1 SNP is indicated in purple. The combined stage 1 and 2 result of this SNP is indicated in yellow. The SNPs surrounding the most significant SNP are color-coded to reflect their LD with this SNP. Symbols reflect functional genomic annotation, as indicated in the legend. The blue y-axes on the right of each plot indicate the estimated recombination rates (based on HapMap Phase II); the bottom of each panel shows the respective annotated genes at the locus and their transcriptional direction. Mb, megabases.

https://doi.org/10.1371/journal.pgen.1004123.s004

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Figure S5.

Regional association plots of stage 1 lead loci for TPOAb levels (panels a-j). The y-axis on the left indicates the – log10 P value for the association with TPOAb levels. SNPs are plotted on the x-axis according to their chromosomal position. The most significant stage 1 SNP is indicated in purple. The combined stage 1 and 2 result of this SNP is indicated in yellow. The SNPs surrounding the most significant SNP are color-coded to reflect their LD with this SNP. Symbols reflect functional genomic annotation, as indicated in the legend. The blue y-axes on the right of each plot indicate the estimated recombination rates (based on HapMap Phase II); the bottom of each panel shows the respective annotated genes at the locus and their transcriptional direction. Mb, megabases.

https://doi.org/10.1371/journal.pgen.1004123.s005

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Figure S6.

GRAIL results for the stage 1 TPOAb-positivity and TPOAb level lead SNPs. GRAIL circle plot of locus connectivity where each locus is plotted in a circle, where significant connections (P<0.05) based on PubMed abstracts are drawn spanning the circle. Analyses were based on the 20 stage 1 TPOAb-positivity and TPOAb level lead SNPs.

https://doi.org/10.1371/journal.pgen.1004123.s006

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Table S1.

Study sample genotyping, quality control and association analyses for stage 1 populations.

https://doi.org/10.1371/journal.pgen.1004123.s007

(DOCX)

Table S2.

Associations of stage 1 lead SNPs with TPOAb-positivity in stage 1 and 2.

https://doi.org/10.1371/journal.pgen.1004123.s008

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Table S3.

Associations of stage 1 lead SNPs with serum TPOAb levels in stage 1 and 2.

https://doi.org/10.1371/journal.pgen.1004123.s009

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Table S4.

Stage 1 TPOAb-positivity and TPOAb level meta-analyses results for GWAS significant SNPs reported in previous GWAS on thyroid related phenotypes.

https://doi.org/10.1371/journal.pgen.1004123.s010

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Table S5.

Genetic risk score and the risk of increased TSH levels.

https://doi.org/10.1371/journal.pgen.1004123.s011

(DOCX)

Table S6.

Newly identified TPOAb associated loci and the risk of thyroid cancer.

https://doi.org/10.1371/journal.pgen.1004123.s012

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Table S7.

Top IPA associated networks for the Stage 1 TPOAb-positivity and TPOAb level lead SNPs.

https://doi.org/10.1371/journal.pgen.1004123.s013

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Table S8.

Top IPA associated canonical pathways for the Stage 1 TPOAb-positivity and TPOAb level lead SNPs.

https://doi.org/10.1371/journal.pgen.1004123.s014

(DOCX)

Acknowledgments

We thank all study participants, volunteers and study personnel that made this work possible.

The Asklepios study is indebted to Femke van Hoeke, Bianca Leydens, and Caroline van Daele, and the residents and general practitioners of Erpe-Mere and Nieuwerkerken for their help in completing the study.

The Busselton Health Study thanks the Busselton Population Medical Research Foundation for approving the study. We thank Siemens Ltd. Australia and New Zealand Healthcare Sector for donating assay reagents.

The Rotterdam Study thanks Pascal Arp, Mila Jhamai, Marijn Verkerk, Lizbeth Herrera and Marjolein Peters for their help in creating the GWAS database, and Karol Estrada and Maksim V. Struchalin for their support in creation and analysis of imputed data. The authors are grateful to the study participants, the staff from the Rotterdam Study and the participating general practitioners and pharmacists. We would like to thank Karol Estrada, Dr. Fernando Rivadeneira, Dr. Tobias A. Knoch, Anis Abuseiris, Luc V. de Zeeuw, and Rob de Graaf (Erasmus MC Rotterdam, The Netherlands), for their help in creating GRIMP, and BigGRID, MediGRID, and Services@MediGRID/D-Grid for access to their grid computing resources. We would like to thank Symen Ligthart for his help with the IPA and GRIMP pathway analyses.

The SardiNIA study thanks the many individuals who generously participated in this study, Monsignore Piseddu, Bishop of Ogliastra, the mayors and citizens of the Sardinian towns (Lanusei, Ilbono, Arzana, and Elini), and the head of the Public Health Unit ASL4 for their volunteerism and cooperation; the team also thanks the physicians, Marco Orrù, Maria Grazia Pilia, Liana Ferreli, Francesco Loi, Stefano Angius, nurses Paola Loi, Monica Lai and Anna Cau who carried out participant physical exams, and the recruitment personnel Susanna Murino. We thank Francesco Cucca, PI of the SardiNIA study.

The SHIP study is grateful to the contribution of Florian Ernst, Anja Wiechert and Astrid Petersmann in generating the SNP data.

The SHIP–Trend study is grateful to Mario Stanke for the opportunity to use his Server Cluster for the SNP imputation as well as to Holger Prokisch and Thomas Meitinger (Helmholtz Zentrum München) for the genotyping of the SHIP-TREND cohort.

TwinsUK thanks the staff from the Genotyping Facilities at the Wellcome Trust Sanger Institute, UK, for sample preparation, quality control, and genotyping; Le Centre National de Génotypage, France, for genotyping; Duke University, NC, USA, for genotyping; and the Finnish Institute of Molecular Medicine, Finnish Genome Center, University of Helsinki. We thank the volunteer twins who made available their time.

The United Kingdom (UK) Graves' disease cohort would like to thank all principle investigators (Amit Allahabadia, Northern General Hospital; Sheffield, UK, Mary Armitage Royal Bournemouth Hospital, Bournemouth, UK; Krishna V. Chatterjee, University of Cambridge, Addenbrookes Hospital, Cambridge, UK; John H. Lazarus Centre for Endocrine and Diabetes Sciences, Cardiff University, Cardiff, UK; Simon H. Pearce, Institute of Human Genetics, Newcastle University, Newcastle-upon-Tyne, Newcastle, UK and Bijay Viadya, Royal Devon and Exeter Hospital, Exeter, UK), doctors and nurses for recruiting AITD subjects into the AITD National Collection.

Val Borbera thanks the inhabitants of the Val Borbera for participating in the study, the local administrations and the ASL-Novi Ligure for support and Fiammetta Viganò for technical help. We also thank Prof. Clara Camaschella, Prof Federico Caligaris-Cappio and the MDs of the Medicine Dept. of the San Raffaele Hospital for help with clinical data collection.

Author Contributions

Conceived and designed the experiments: MM SJB RAJ RR AA HJG ER JIR HH LC DTi BV TdM TJ JGE BMP AHo DS HW AdlC TMF AL KR LAK AGU JPW KS EWic CMe MdH TJV TDS SGW HV AC DTo SS SN RPP. Performed the experiments: MM EP GP AT LC SJB RAJ RR GLR TSP SHV JL MJS LLNH RMF BMS CG YSA AL TJV SS SN RPP. Analyzed the data: MM EP GP AT SJB RAJ RR GLR TSP SHV JL MJS LLNH RMF SLi BMS DP LC LB CG TC EK BT YET AA MvdB CMa TEG MT NP YSA AdlC RTNM SCLG JMK AL JWAS FR MdH SS RPP. Contributed reagents/materials/analysis tools: MM RR GLR TSP JL MJS LLNH BMS RN MGP CSa UV JBR FCS TIMK WEV ATH JK LC AHa WL GH ML SM NS MC MN CSp AR MH EML ER PJL SLa MV GA EWid AP AD APB DIWP JPB AM TF AJ JH HP EER PF SJF JIR AK DR GLS EB HH JAF BV TdM TJ JGE PCO ARH BMP TI AHo HW AdlC RTNM SCLG HMzS TMF AL FR AGU JPW CMe TJV TDS SGW HV AC DTo RPP. Wrote the paper: MM AT LC TJV SGW AC SS SN RPP.

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