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Koprulu M, Zhao Y, Wheeler E, Dong L, Rocha N, Li C, Griffin JD, Patel S, Van de Streek M, Glastonbury CA, Stewart ID, Day FR, Luan J, Bowker N, Wittemans LBL, Kerrison ND, Cai L, Lucarelli DME, Barroso I, McCarthy MI, Scott RA, Saudek V, Small KS, Wareham NJ, Semple RK, Perry JRB, O’Rahilly S, Lotta LA, Langenberg C, Savage DB. Identification of Rare Loss-of-Function Genetic Variation Regulating Body Fat Distribution. J Clin Endocrinol Metab 2022; 107:1065-1077. [PMID: 34875679 PMCID: PMC8947777 DOI: 10.1210/clinem/dgab877] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Indexed: 11/25/2022]
Abstract
CONTEXT Biological and translational insights from large-scale, array-based genetic studies of fat distribution, a key determinant of metabolic health, have been limited by the difficulty in linking predominantly noncoding variants to specific gene targets. Rare coding variant analyses provide greater confidence that a specific gene is involved, but do not necessarily indicate whether gain or loss of function (LoF) would be of most therapeutic benefit. OBJECTIVE This work aimed to identify genes/proteins involved in determining fat distribution. METHODS We combined the power of genome-wide analysis of array-based rare, nonsynonymous variants in 450 562 individuals in the UK Biobank with exome-sequence-based rare LoF gene burden testing in 184 246 individuals. RESULTS The data indicate that the LoF of 4 genes (PLIN1 [LoF variants, P = 5.86 × 10-7], INSR [LoF variants, P = 6.21 × 10-7], ACVR1C [LoF + moderate impact variants, P = 1.68 × 10-7; moderate impact variants, P = 4.57 × 10-7], and PDE3B [LoF variants, P = 1.41 × 10-6]) is associated with a beneficial effect on body mass index-adjusted waist-to-hip ratio and increased gluteofemoral fat mass, whereas LoF of PLIN4 (LoF variants, P = 5.86 × 10-7 adversely affects these parameters. Phenotypic follow-up suggests that LoF of PLIN1, PDE3B, and ACVR1C favorably affects metabolic phenotypes (eg, triglycerides [TGs] and high-density lipoprotein [HDL] cholesterol concentrations) and reduces the risk of cardiovascular disease, whereas PLIN4 LoF has adverse health consequences. INSR LoF is associated with lower TG and HDL levels but may increase the risk of type 2 diabetes. CONCLUSION This study robustly implicates these genes in the regulation of fat distribution, providing new and in some cases somewhat counterintuitive insight into the potential consequences of targeting these molecules therapeutically.
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Affiliation(s)
- Mine Koprulu
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Yajie Zhao
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Eleanor Wheeler
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Liang Dong
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Nuno Rocha
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Chen Li
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - John D Griffin
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development, and Medical, Cambridge, Massachusetts 02139, USA
| | - Satish Patel
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Marcel Van de Streek
- Department of Twin Research and Genetic Epidemiology, King’s College London, St Thomas’ Campus, London, SE1 7EH, UK
| | | | - Isobel D Stewart
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Felix R Day
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Jian’an Luan
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Nicholas Bowker
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Laura B L Wittemans
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, OX3 7LF, UK
- Nuffield Department of Women’s and Reproductive Health, Medical Sciences Division, University of Oxford, Oxford, OX3 9DU, UK
| | - Nicola D Kerrison
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Lina Cai
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Debora M E Lucarelli
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
- D.M.E.L. is currently an employee of Enhanc3D Genomics Ltd
| | - Inês Barroso
- Exeter Centre of Excellence for Diabetes Research (EXCEED), Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, EX1 2HZ, UK
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
- M.McM.’s current address is Genentech, 1 DNA Way, South San Francisco, CA 94080, USA
| | - Robert A Scott
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Vladimir Saudek
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King’s College London, St Thomas’ Campus, London, SE1 7EH, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Robert K Semple
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, EH16 4TJ, UK
| | - John R B Perry
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Stephen O’Rahilly
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Luca A Lotta
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
- Computational Medicine, Berlin Institute of Health at Charité–Universitätsmedizin Berlin, 10117 Berlin, Germany
- Correspondence: Claudia Langenberg, MD, Dr Med, PhD, MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Box 285, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
| | - David B Savage
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
- David B. Savage, MBCHB, PhD, University of Cambridge Metabolic Research Laboratories, Wellcome Trust–MRC Institute of Metabolic Science, Box 289, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
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2
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Bovijn J, Krebs K, Chen CY, Boxall R, Censin JC, Ferreira T, Pulit SL, Glastonbury CA, Laber S, Millwood IY, Lin K, Li L, Chen Z, Milani L, Smith GD, Walters RG, Mägi R, Neale BM, Lindgren CM, Holmes MV. Response to comment on "Evaluating the cardiovascular safety of sclerostin inhibition using evidence from meta-analysis of clinical trials and human genetics". Sci Transl Med 2021; 13:eabf4530. [PMID: 34851696 DOI: 10.1126/scitranslmed.abf4530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Triangulation of evidence from clinical trials and human genetics suggests a potential excess risk of cardiovascular disease events arising from therapeutic inhibition of sclerostin.
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Affiliation(s)
- Jonas Bovijn
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Kristi Krebs
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Chia-Yen Chen
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ruth Boxall
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Jenny C Censin
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Teresa Ferreira
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
| | - Sara L Pulit
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Genetics, University Medical Center Utrecht, 3584 CX Utrecht, Netherlands
| | - Craig A Glastonbury
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
| | - Samantha Laber
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Iona Y Millwood
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Kuang Lin
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, Peking University Health Science Centre, Peking University, Beijing 100191, China
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Lili Milani
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Robin G Walters
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Reedik Mägi
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Benjamin M Neale
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Cecilia M Lindgren
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Michael V Holmes
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
- National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford OX3 9DU, UK
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3
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Morales LD, Cromack DT, Tripathy D, Fourcaudot M, Kumar S, Curran JE, Carless M, Göring HHH, Hu SL, Lopez-Alvarenga JC, Garske KM, Pajukanta P, Small KS, Glastonbury CA, Das SK, Langefeld C, Hanson RL, Hsueh WC, Norton L, Arya R, Mummidi S, Blangero J, DeFronzo RA, Duggirala R, Jenkinson CP. Further evidence supporting a potential role for ADH1B in obesity. Sci Rep 2021; 11:1932. [PMID: 33479282 PMCID: PMC7820614 DOI: 10.1038/s41598-020-80563-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Accepted: 12/06/2020] [Indexed: 01/22/2023] Open
Abstract
Insulin is an essential hormone that regulates glucose homeostasis and metabolism. Insulin resistance (IR) arises when tissues fail to respond to insulin, and it leads to serious health problems including Type 2 Diabetes (T2D). Obesity is a major contributor to the development of IR and T2D. We previously showed that gene expression of alcohol dehydrogenase 1B (ADH1B) was inversely correlated with obesity and IR in subcutaneous adipose tissue of Mexican Americans. In the current study, a meta-analysis of the relationship between ADH1B expression and BMI in Mexican Americans, African Americans, Europeans, and Pima Indians verified that BMI was increased with decreased ADH1B expression. Using established human subcutaneous pre-adipocyte cell lines derived from lean (BMI < 30 kg m−2) or obese (BMI ≥ 30 kg m−2) donors, we found that ADH1B protein expression increased substantially during differentiation, and overexpression of ADH1B inhibited fatty acid binding protein expression. Mature adipocytes from lean donors expressed ADH1B at higher levels than obese donors. Insulin further induced ADH1B protein expression as well as enzyme activity. Knockdown of ADH1B expression decreased insulin-stimulated glucose uptake. Our findings suggest that ADH1B is involved in the proper development and metabolic activity of adipose tissues and this function is suppressed by obesity.
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Affiliation(s)
- Liza D Morales
- South Texas Diabetes and Obesity Institute Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Edinburg/Harlingen/Brownsville, TX, USA.
| | | | - Devjit Tripathy
- South Texas Veterans Health Care System, San Antonio, TX, USA.,Department of Medicine, University of Texas Health San Antonio, San Antonio, TX, USA
| | - Marcel Fourcaudot
- Department of Medicine, University of Texas Health San Antonio, San Antonio, TX, USA
| | - Satish Kumar
- South Texas Diabetes and Obesity Institute Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Edinburg/Harlingen/Brownsville, TX, USA
| | - Joanne E Curran
- South Texas Diabetes and Obesity Institute Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Edinburg/Harlingen/Brownsville, TX, USA
| | - Melanie Carless
- Department of Population Health, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Harald H H Göring
- South Texas Diabetes and Obesity Institute Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Edinburg/Harlingen/Brownsville, TX, USA
| | - Shirley L Hu
- University of Texas Health Houston, School of Public Health, Brownsville, TX, USA
| | - Juan Carlos Lopez-Alvarenga
- South Texas Diabetes and Obesity Institute Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Edinburg/Harlingen/Brownsville, TX, USA
| | - Kristina M Garske
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Päivi Pajukanta
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | | | | | - Swapan K Das
- Internal Medicine-Endocrinology and Metabolism, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Carl Langefeld
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Robert L Hanson
- Phoenix Epidemiology and Clinical Research Branch, NIDDK, Phoenix, AZ, USA
| | - Wen-Chi Hsueh
- Phoenix Epidemiology and Clinical Research Branch, NIDDK, Phoenix, AZ, USA
| | - Luke Norton
- Department of Medicine, University of Texas Health San Antonio, San Antonio, TX, USA
| | - Rector Arya
- South Texas Diabetes and Obesity Institute Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Edinburg/Harlingen/Brownsville, TX, USA
| | - Srinivas Mummidi
- South Texas Diabetes and Obesity Institute Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Edinburg/Harlingen/Brownsville, TX, USA
| | - John Blangero
- South Texas Diabetes and Obesity Institute Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Edinburg/Harlingen/Brownsville, TX, USA
| | - Ralph A DeFronzo
- South Texas Veterans Health Care System, San Antonio, TX, USA.,Department of Medicine, University of Texas Health San Antonio, San Antonio, TX, USA
| | - Ravindranath Duggirala
- South Texas Diabetes and Obesity Institute Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Edinburg/Harlingen/Brownsville, TX, USA
| | - Christopher P Jenkinson
- South Texas Diabetes and Obesity Institute Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Edinburg/Harlingen/Brownsville, TX, USA.
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4
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Glastonbury CA, Pulit SL, Honecker J, Censin JC, Laber S, Yaghootkar H, Rahmioglu N, Pastel E, Kos K, Pitt A, Hudson M, Nellåker C, Beer NL, Hauner H, Becker CM, Zondervan KT, Frayling TM, Claussnitzer M, Lindgren CM. Machine Learning based histology phenotyping to investigate the epidemiologic and genetic basis of adipocyte morphology and cardiometabolic traits. PLoS Comput Biol 2020; 16:e1008044. [PMID: 32797044 PMCID: PMC7449405 DOI: 10.1371/journal.pcbi.1008044] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Revised: 08/26/2020] [Accepted: 06/11/2020] [Indexed: 12/29/2022] Open
Abstract
Genetic studies have recently highlighted the importance of fat distribution, as well as overall adiposity, in the pathogenesis of obesity-associated diseases. Using a large study (n = 1,288) from 4 independent cohorts, we aimed to investigate the relationship between mean adipocyte area and obesity-related traits, and identify genetic factors associated with adipocyte cell size. To perform the first large-scale study of automatic adipocyte phenotyping using both histological and genetic data, we developed a deep learning-based method, the Adipocyte U-Net, to rapidly derive mean adipocyte area estimates from histology images. We validate our method using three state-of-the-art approaches; CellProfiler, Adiposoft and floating adipocytes fractions, all run blindly on two external cohorts. We observe high concordance between our method and the state-of-the-art approaches (Adipocyte U-net vs. CellProfiler: R2visceral = 0.94, P < 2.2 × 10-16, R2subcutaneous = 0.91, P < 2.2 × 10-16), and faster run times (10,000 images: 6mins vs 3.5hrs). We applied the Adipocyte U-Net to 4 cohorts with histology, genetic, and phenotypic data (total N = 820). After meta-analysis, we found that mean adipocyte area positively correlated with body mass index (BMI) (Psubq = 8.13 × 10-69, βsubq = 0.45; Pvisc = 2.5 × 10-55, βvisc = 0.49; average R2 across cohorts = 0.49) and that adipocytes in subcutaneous depots are larger than their visceral counterparts (Pmeta = 9.8 × 10-7). Lastly, we performed the largest GWAS and subsequent meta-analysis of mean adipocyte area and intra-individual adipocyte variation (N = 820). Despite having twice the number of samples than any similar study, we found no genome-wide significant associations, suggesting that larger sample sizes and a homogenous collection of adipose tissue are likely needed to identify robust genetic associations.
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Affiliation(s)
- Craig A. Glastonbury
- Big Data Institute, University of Oxford, Oxford, United Kingdom
- BenevolentAI, London, United Kingdom
| | - Sara L. Pulit
- Big Data Institute, University of Oxford, Oxford, United Kingdom
| | - Julius Honecker
- Else Kröner-Fresenius-Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Jenny C. Censin
- Big Data Institute, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Human Genetics (WCHG), Oxford, United Kingdom
| | - Samantha Laber
- Big Data Institute, University of Oxford, Oxford, United Kingdom
- Broad Institute of MIT and Harvard, Cambridge Massachusetts, United States of America
| | - Hanieh Yaghootkar
- Genetics of Complex Traits, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, United Kingdom
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Nilufer Rahmioglu
- Wellcome Centre for Human Genetics (WCHG), Oxford, United Kingdom
- Endometriosis CaRe Centre Oxford, Nuffield Department of Women’s and Reproductive Health, University of Oxford, Oxford, United Kingdom
| | - Emilie Pastel
- Genetics of Complex Traits, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, United Kingdom
| | - Katerina Kos
- Genetics of Complex Traits, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, United Kingdom
| | - Andrew Pitt
- NIHR Exeter Clinical Research Facility, University of Exeter Medical School, University of Exeter and Royal Devon and Exeter NHS Foundation Trust Exeter, United Kingdom
| | - Michelle Hudson
- NIHR Exeter Clinical Research Facility, University of Exeter Medical School, University of Exeter and Royal Devon and Exeter NHS Foundation Trust Exeter, United Kingdom
| | - Christoffer Nellåker
- Big Data Institute, University of Oxford, Oxford, United Kingdom
- Endometriosis CaRe Centre Oxford, Nuffield Department of Women’s and Reproductive Health, University of Oxford, Oxford, United Kingdom
| | - Nicola L. Beer
- Novo Nordisk Research Centre Oxford (NNRCO), Oxford, United Kingdom
| | - Hans Hauner
- Else Kröner-Fresenius-Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany
- Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, Munich
- German Center of Diabetes Research, Helmholtz Center Munich, Neuherberg, Germany
| | - Christian M. Becker
- Endometriosis CaRe Centre Oxford, Nuffield Department of Women’s and Reproductive Health, University of Oxford, Oxford, United Kingdom
| | - Krina T. Zondervan
- Wellcome Centre for Human Genetics (WCHG), Oxford, United Kingdom
- Endometriosis CaRe Centre Oxford, Nuffield Department of Women’s and Reproductive Health, University of Oxford, Oxford, United Kingdom
| | - Timothy M. Frayling
- Genetics of Complex Traits, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, United Kingdom
- NIHR Exeter Clinical Research Facility, University of Exeter Medical School, University of Exeter and Royal Devon and Exeter NHS Foundation Trust Exeter, United Kingdom
| | - Melina Claussnitzer
- Broad Institute of MIT and Harvard, Cambridge Massachusetts, United States of America
- University of Hohenheim, Stuttgart, Germany
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Cecilia M. Lindgren
- Big Data Institute, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Human Genetics (WCHG), Oxford, United Kingdom
- Broad Institute of MIT and Harvard, Cambridge Massachusetts, United States of America
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5
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Bovijn J, Krebs K, Chen CY, Boxall R, Censin JC, Ferreira T, Pulit SL, Glastonbury CA, Laber S, Millwood IY, Lin K, Li L, Chen Z, Milani L, Smith GD, Walters RG, Mägi R, Neale BM, Lindgren CM, Holmes MV. Evaluating the cardiovascular safety of sclerostin inhibition using evidence from meta-analysis of clinical trials and human genetics. Sci Transl Med 2020; 12:eaay6570. [PMID: 32581134 PMCID: PMC7116615 DOI: 10.1126/scitranslmed.aay6570] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 11/26/2019] [Accepted: 05/26/2020] [Indexed: 12/23/2022]
Abstract
Inhibition of sclerostin is a therapeutic approach to lowering fracture risk in patients with osteoporosis. However, data from phase 3 randomized controlled trials (RCTs) of romosozumab, a first-in-class monoclonal antibody that inhibits sclerostin, suggest an imbalance of serious cardiovascular events, and regulatory agencies have issued marketing authorizations with warnings of cardiovascular disease. Here, we meta-analyze published and unpublished cardiovascular outcome trial data of romosozumab and investigate whether genetic variants that mimic therapeutic inhibition of sclerostin are associated with higher risk of cardiovascular disease. Meta-analysis of up to three RCTs indicated a probable higher risk of cardiovascular events with romosozumab. Scaled to the equivalent dose of romosozumab (210 milligrams per month; 0.09 grams per square centimeter of higher bone mineral density), the SOST genetic variants were associated with lower risk of fracture and osteoporosis (commensurate with the therapeutic effect of romosozumab) and with a higher risk of myocardial infarction and/or coronary revascularization and major adverse cardiovascular events. The same variants were also associated with increased risk of type 2 diabetes mellitus and higher systolic blood pressure and central adiposity. Together, our findings indicate that inhibition of sclerostin may elevate cardiovascular risk, warranting a rigorous evaluation of the cardiovascular safety of romosozumab and other sclerostin inhibitors.
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Affiliation(s)
- Jonas Bovijn
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK.
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Kristi Krebs
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Chia-Yen Chen
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ruth Boxall
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Jenny C Censin
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Teresa Ferreira
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
| | - Sara L Pulit
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Genetics, University Medical Center Utrecht, 3584 CX Utrecht, Netherlands
| | - Craig A Glastonbury
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
| | - Samantha Laber
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Iona Y Millwood
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Kuang Lin
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, Peking University Health Science Centre, Peking University, Beijing 100191, China
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Lili Milani
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Robin G Walters
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Reedik Mägi
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Benjamin M Neale
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Cecilia M Lindgren
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK.
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Michael V Holmes
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK.
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
- National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford OX3 9DU, UK
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6
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Glastonbury CA, Couto Alves A, El-Sayed Moustafa JS, Small KS. Cell-Type Heterogeneity in Adipose Tissue Is Associated with Complex Traits and Reveals Disease-Relevant Cell-Specific eQTLs. Am J Hum Genet 2019; 104:1013-1024. [PMID: 31130283 PMCID: PMC6556877 DOI: 10.1016/j.ajhg.2019.03.025] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 03/26/2019] [Indexed: 12/31/2022] Open
Abstract
Adipose tissue is an important endocrine organ with a role in many cardiometabolic diseases. It is comprised of a heterogeneous collection of cell types that can differentially impact disease phenotypes. Cellular heterogeneity can also confound -omic analyses but is rarely taken into account in analysis of solid-tissue transcriptomes. Here, we investigate cell-type heterogeneity in two population-level subcutaneous adipose-tissue RNA-seq datasets (TwinsUK, n = 766 and the Genotype-Tissue Expression project [GTEx], n = 326) by estimating the relative proportions of four distinct cell types (adipocytes, macrophages, CD4+ T cells, and micro-vascular endothelial cells). We find significant cellular heterogeneity within and between the TwinsUK and GTEx adipose datasets. We find that adipose cell-type composition is heritable and confirm the positive association between adipose-resident macrophage proportion and obesity (high BMI), but we find a stronger BMI-independent association with dual-energy X-ray absorptiometry (DXA) derived body-fat distribution traits. We benchmark the impact of adipose-tissue cell composition on a range of standard analyses, including phenotype-gene expression association, co-expression networks, and cis-eQTL discovery. Our results indicate that it is critical to account for cell-type composition when combining adipose transcriptome datasets in co-expression analysis and in differential expression analysis with obesity-related traits. We applied gene expression by cell-type proportion interaction models (G × Cell) to identify 26 cell-type-specific expression quantitative trait loci (eQTLs) in 20 genes, including four autoimmune disease genome-wide association study (GWAS) loci. These results identify cell-specific eQTLs and demonstrate the potential of in silico deconvolution of bulk tissue to identify cell-type-restricted regulatory variants.
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Affiliation(s)
- Craig A Glastonbury
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK.
| | | | | | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK.
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7
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Bovijn J, Jackson L, Censin J, Chen CY, Laisk T, Laber S, Ferreira T, Pulit SL, Glastonbury CA, Smoller JW, Harrison JW, Ruth KS, Beaumont RN, Jones SE, Tyrrell J, Wood AR, Weedon MN, Mägi R, Neale B, Lindgren CM, Murray A, Holmes MV. GWAS Identifies Risk Locus for Erectile Dysfunction and Implicates Hypothalamic Neurobiology and Diabetes in Etiology. Am J Hum Genet 2019; 104:157-163. [PMID: 30583798 PMCID: PMC6323625 DOI: 10.1016/j.ajhg.2018.11.004] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Accepted: 11/07/2018] [Indexed: 12/03/2022] Open
Abstract
Erectile dysfunction (ED) is a common condition affecting more than 20% of men over 60 years, yet little is known about its genetic architecture. We performed a genome-wide association study of ED in 6,175 case subjects among 223,805 European men and identified one locus at 6q16.3 (lead variant rs57989773, OR 1.20 per C-allele; p = 5.71 × 10−14), located between MCHR2 and SIM1. In silico analysis suggests SIM1 to confer ED risk through hypothalamic dysregulation. Mendelian randomization provides evidence that genetic risk of type 2 diabetes mellitus is a cause of ED (OR 1.11 per 1-log unit higher risk of type 2 diabetes). These findings provide insights into the biological underpinnings and the causes of ED and may help prioritize the development of future therapies for this common disorder.
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8
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Pulit SL, Stoneman C, Morris AP, Wood AR, Glastonbury CA, Tyrrell J, Yengo L, Ferreira T, Marouli E, Ji Y, Yang J, Jones S, Beaumont R, Croteau-Chonka DC, Winkler TW, Hattersley AT, Loos RJF, Hirschhorn JN, Visscher PM, Frayling TM, Yaghootkar H, Lindgren CM. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum Mol Genet 2019; 28:166-174. [PMID: 30239722 PMCID: PMC6298238 DOI: 10.1093/hmg/ddy327] [Citation(s) in RCA: 590] [Impact Index Per Article: 118.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Revised: 07/27/2018] [Accepted: 09/03/2018] [Indexed: 01/08/2023] Open
Abstract
More than one in three adults worldwide is either overweight or obese. Epidemiological studies indicate that the location and distribution of excess fat, rather than general adiposity, are more informative for predicting risk of obesity sequelae, including cardiometabolic disease and cancer. We performed a genome-wide association study meta-analysis of body fat distribution, measured by waist-to-hip ratio (WHR) adjusted for body mass index (WHRadjBMI), and identified 463 signals in 346 loci. Heritability and variant effects were generally stronger in women than men, and we found approximately one-third of all signals to be sexually dimorphic. The 5% of individuals carrying the most WHRadjBMI-increasing alleles were 1.62 times more likely than the bottom 5% to have a WHR above the thresholds used for metabolic syndrome. These data, made publicly available, will inform the biology of body fat distribution and its relationship with disease.
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Affiliation(s)
- Sara L Pulit
- Big Data Institute, Li Ka Shing Center for Health Information and Discovery, Oxford University, Oxford, UK
- Department of Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
- Program in Medical and Population Genetics, Broad Institute, Boston, MA, USA
| | - Charli Stoneman
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
| | - Andrew P Morris
- Biostatistics Department, University of Liverpool, Liverpool, UK
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Andrew R Wood
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
| | - Craig A Glastonbury
- Big Data Institute, Li Ka Shing Center for Health Information and Discovery, Oxford University, Oxford, UK
| | - Jessica Tyrrell
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
| | - Loïc Yengo
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Teresa Ferreira
- Big Data Institute, Li Ka Shing Center for Health Information and Discovery, Oxford University, Oxford, UK
| | - Eirini Marouli
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Yingjie Ji
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
| | - Jian Yang
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Samuel Jones
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
| | - Robin Beaumont
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
| | - Damien C Croteau-Chonka
- Channing Division of Network Medicine, Department of MedicineBrigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Thomas W Winkler
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | | | - Andrew T Hattersley
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, The Mindich Child Health and Development Institute, the Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joel N Hirschhorn
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Timothy M Frayling
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
| | - Hanieh Yaghootkar
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
| | - Cecilia M Lindgren
- Big Data Institute, Li Ka Shing Center for Health Information and Discovery, Oxford University, Oxford, UK
- Program in Medical and Population Genetics, Broad Institute, Boston, MA, USA
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
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9
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Tsai PC, Glastonbury CA, Eliot MN, Bollepalli S, Yet I, Castillo-Fernandez JE, Carnero-Montoro E, Hardiman T, Martin TC, Vickers A, Mangino M, Ward K, Pietiläinen KH, Deloukas P, Spector TD, Viñuela A, Loucks EB, Ollikainen M, Kelsey KT, Small KS, Bell JT. Smoking induces coordinated DNA methylation and gene expression changes in adipose tissue with consequences for metabolic health. Clin Epigenetics 2018; 10:126. [PMID: 30342560 PMCID: PMC6196025 DOI: 10.1186/s13148-018-0558-0] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2018] [Accepted: 10/03/2018] [Indexed: 12/14/2022] Open
Abstract
Background Tobacco smoking is a risk factor for multiple diseases, including cardiovascular disease and diabetes. Many smoking-associated signals have been detected in the blood methylome, but the extent to which these changes are widespread to metabolically relevant tissues, and impact gene expression or metabolic health, remains unclear. Methods We investigated smoking-associated DNA methylation and gene expression variation in adipose tissue biopsies from 542 healthy female twins. Replication, tissue specificity, and longitudinal stability of the smoking-associated effects were explored in additional adipose, blood, skin, and lung samples. We characterized the impact of adipose tissue smoking methylation and expression signals on metabolic disease risk phenotypes, including visceral fat. Results We identified 42 smoking-methylation and 42 smoking-expression signals, where five genes (AHRR, CYP1A1, CYP1B1, CYTL1, F2RL3) were both hypo-methylated and upregulated in current smokers. CYP1A1 gene expression achieved 95% prediction performance of current smoking status. We validated and replicated a proportion of the signals in additional primary tissue samples, identifying tissue-shared effects. Smoking leaves systemic imprints on DNA methylation after smoking cessation, with stronger but shorter-lived effects on gene expression. Metabolic disease risk traits such as visceral fat and android-to-gynoid ratio showed association with methylation at smoking markers with functional impacts on expression, such as CYP1A1, and at tissue-shared smoking signals, such as NOTCH1. At smoking-signals, BHLHE40 and AHRR DNA methylation and gene expression levels in current smokers were predictive of future gain in visceral fat upon smoking cessation. Conclusions Our results provide the first comprehensive characterization of coordinated DNA methylation and gene expression markers of smoking in adipose tissue. The findings relate to human metabolic health and give insights into understanding the widespread health consequence of smoking outside of the lung. Electronic supplementary material The online version of this article (10.1186/s13148-018-0558-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Pei-Chien Tsai
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK. .,Department of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan. .,Division of Allergy, Asthma, and Rheumatology, Department of Pediatrics, Chang Gung Memorial Hospital, Linkou, Taiwan.
| | - Craig A Glastonbury
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK.,Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, OX3 7LF, UK
| | - Melissa N Eliot
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, 02912, USA
| | - Sailalitha Bollepalli
- Institute for Molecular Medicine Finland (FIMM) and Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Idil Yet
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK.,Department of Bioinformatics, Institute of Health Sciences, Hacettepe University, 06100, Ankara, Turkey
| | | | - Elena Carnero-Montoro
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK.,Pfizer - University of Granada - Andalusian Government Center for Genomics and Oncological Research (GENYO), Granada, Spain
| | - Thomas Hardiman
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK.,Division of Cancer Studies, King's College London, London, SE1 9RT, UK
| | - Tiphaine C Martin
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK.,Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, 10029, USA.,The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York City, NY, 10029, USA
| | - Alice Vickers
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK.,Centre for Stem Cells and Regenerative Medicine, King's College London, Floor 28, Tower Wing, Guy's Hospital, Great Maze Pond, London, SE1 9RT, UK
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK.,NIHR Biomedical Research Centre at Guy's and St Thomas' Foundation Trust, London, SE1 9RT, UK
| | - Kirsten Ward
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Kirsi H Pietiläinen
- Research Programs Unit, Diabetes and Obesity, Obesity Research Unit, University of Helsinki, Helsinki, Finland.,Endocrinology, Abdominal Center, Helsinki University Hospital, Helsinki, Finland
| | - Panos Deloukas
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK.,Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah, Saudi Arabia
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Ana Viñuela
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK.,Department of Genetic Medicine and Development, University of Geneva Medical School, 1211, Geneva, Switzerland.,Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, 1211, Geneva, Switzerland.,Swiss Institute of Bioinformatics, 1211, Geneva, Switzerland
| | - Eric B Loucks
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, 02912, USA
| | - Miina Ollikainen
- Institute for Molecular Medicine Finland (FIMM) and Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Karl T Kelsey
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, 02912, USA.,Department of Laboratory Medicine & Pathology, Brown University, Providence, RI, 02912, USA
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK.
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10
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Couto Alves A, Glastonbury CA, El-Sayed Moustafa JS, Small KS. Fasting and time of day independently modulate circadian rhythm relevant gene expression in adipose and skin tissue. BMC Genomics 2018; 19:659. [PMID: 30193568 PMCID: PMC6129005 DOI: 10.1186/s12864-018-4997-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2018] [Accepted: 08/07/2018] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND Intermittent fasting and time-restricted diets are associated with lower risk biomarkers for cardio-metabolic disease. The shared mechanisms underpinning the similar physiological response to these events is not established, but circadian rhythm could be involved. Here we investigated the transcriptional response to fasting in a large cross-sectional study of adipose and skin tissue from healthy volunteers (N = 625) controlling for confounders of circadian rhythm: time of day and season. RESULTS We identified 367 genes in adipose and 79 in skin whose expression levels were associated (FDR < 5%) with hours of fasting conditionally independent of time of day and season, with 19 genes common to both tissues. Among these genes, we replicated 38 in human, 157 in non-human studies, and 178 are novel associations. Fasting-responsive genes were enriched for regulation of and response to circadian rhythm. We identified 99 genes in adipose and 54 genes in skin whose expression was associated to time of day; these genes were also enriched for circadian rhythm processes. In genes associated to both exposures the effect of time of day was stronger and in an opposite direction to that of hours fasted. We also investigated the relationship between fasting and genetic regulation of gene expression, including GxE eQTL analysis to identify personal responses to fasting. CONCLUSION This study robustly implicates circadian rhythm genes in the response to hours fasting independently of time of day, seasonality, age and BMI. We identified tissue-shared and tissue-specific differences in the transcriptional response to fasting in a large sample of healthy volunteers.
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Affiliation(s)
- Alexessander Couto Alves
- Department of Twin Research and Genetic Epidemiology, King’s College London, St Thomas’ Campus, Westminster Bridge Road, London, SE1 7EH UK
| | - Craig A. Glastonbury
- Department of Twin Research and Genetic Epidemiology, King’s College London, St Thomas’ Campus, Westminster Bridge Road, London, SE1 7EH UK
| | - Julia S. El-Sayed Moustafa
- Department of Twin Research and Genetic Epidemiology, King’s College London, St Thomas’ Campus, Westminster Bridge Road, London, SE1 7EH UK
| | - Kerrin S. Small
- Department of Twin Research and Genetic Epidemiology, King’s College London, St Thomas’ Campus, Westminster Bridge Road, London, SE1 7EH UK
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11
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Small KS, Todorčević M, Civelek M, El-Sayed Moustafa JS, Wang X, Simon MM, Fernandez-Tajes J, Mahajan A, Horikoshi M, Hugill A, Glastonbury CA, Quaye L, Neville MJ, Sethi S, Yon M, Pan C, Che N, Vinuela A, Tsai PC, Nag A, Buil A, Thorleifsson G, Raghavan A, Ding Q, Morris AP, Bell JT, Thorsteinsdottir U, Stefansson K, Laakso M, Dahlman I, Arner P, Gloyn AL, Musunuru K, Lusis AJ, Cox RD, Karpe F, McCarthy MI. Author Correction: Regulatory variants at KLF14 influence type 2 diabetes risk via a female-specific effect on adipocyte size and body composition. Nat Genet 2018; 50:1342. [PMID: 30087441 DOI: 10.1038/s41588-018-0180-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
In the version of this article originally published, minus signs were missing from the three β-values for BMI given in Table 1. The errors have been corrected in the HTML and PDF versions of the article.
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Affiliation(s)
- Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
| | - Marijana Todorčević
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
| | - Mete Civelek
- Center for Public Health Genomics, Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.,Department of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Xiao Wang
- Cardiovascular Institute, Department of Medicine, Department of Genetics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Michelle M Simon
- Biocomputing, Medical Research Council Harwell Institute, Oxford, UK
| | | | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Momoko Horikoshi
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK.,Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Alison Hugill
- Genetics of Type 2 Diabetes, Medical Research Council Harwell Institute, Oxford, UK
| | - Craig A Glastonbury
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Lydia Quaye
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Matt J Neville
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK.,Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Siddharth Sethi
- Biocomputing, Medical Research Council Harwell Institute, Oxford, UK
| | - Marianne Yon
- Genetics of Type 2 Diabetes, Medical Research Council Harwell Institute, Oxford, UK
| | - Calvin Pan
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Nam Che
- Department of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Ana Vinuela
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Pei-Chien Tsai
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Abhishek Nag
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Alfonso Buil
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | | | | | - Qiurong Ding
- CAS Key Laboratory of Nutrition and Metabolism , Institute for Nutritional Sciences, Shanghai Institutes for Biological SciencesChinese Academy of Sciences, Shanghai, PR China
| | - Andrew P Morris
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.,Department of Biostatistics, University of Liverpool, Liverpool, UK
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Unnur Thorsteinsdottir
- deCODE Genetics, Reykjavik, Iceland.,Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Kari Stefansson
- deCODE Genetics, Reykjavik, Iceland.,Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Ingrid Dahlman
- Department of Medicine,Huddinge, Karolinska Institutet, Stockholm, Sweden
| | - Peter Arner
- Department of Medicine,Huddinge, Karolinska Institutet, Stockholm, Sweden
| | - Anna L Gloyn
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK.,Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.,Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Kiran Musunuru
- Cardiovascular Institute, Department of Medicine, Department of Genetics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Aldons J Lusis
- Department of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Roger D Cox
- Genetics of Type 2 Diabetes, Medical Research Council Harwell Institute, Oxford, UK
| | - Fredrik Karpe
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK.,Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Mark I McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK. .,Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK. .,Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK.
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12
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Pan DZ, Garske KM, Alvarez M, Bhagat YV, Boocock J, Nikkola E, Miao Z, Raulerson CK, Cantor RM, Civelek M, Glastonbury CA, Small KS, Boehnke M, Lusis AJ, Sinsheimer JS, Mohlke KL, Laakso M, Pajukanta P, Ko A. Author Correction: Integration of human adipocyte chromosomal interactions with adipose gene expression prioritizes obesity-related genes from GWAS. Nat Commun 2018; 9:3472. [PMID: 30135520 PMCID: PMC6105720 DOI: 10.1038/s41467-018-05849-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
In the original version of this Article, Supplementary Table 10 contained incorrect primer sequences for the mobility shift assay for SNP rs4776984. These errors have now been fixed and the corrected version of the Supplementary Information PDF is available to download from the HTML version of the Article.
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Affiliation(s)
- David Z Pan
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, 90095, USA
| | - Kristina M Garske
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
| | - Marcus Alvarez
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
| | - Yash V Bhagat
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
| | - James Boocock
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
| | - Elina Nikkola
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
| | - Zong Miao
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, 90095, USA
| | - Chelsea K Raulerson
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Rita M Cantor
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
| | - Mete Civelek
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22904, USA
| | | | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King's College, London, UK
| | - Michael Boehnke
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Aldons J Lusis
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
| | - Janet S Sinsheimer
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
- Department of Biomathematics, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, FI-70210, Finland
| | - Päivi Pajukanta
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, 90095, USA
- Molecular Biology Institute at UCLA, Los Angeles, CA, 90095, USA
| | - Arthur Ko
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA.
- Molecular Biology Institute at UCLA, Los Angeles, CA, 90095, USA.
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13
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Small KS, Todorčević M, Civelek M, El-Sayed Moustafa JS, Wang X, Simon MM, Fernandez-Tajes J, Mahajan A, Horikoshi M, Hugill A, Glastonbury CA, Quaye L, Neville MJ, Sethi S, Yon M, Pan C, Che N, Viñuela A, Tsai PC, Nag A, Buil A, Thorleifsson G, Raghavan A, Ding Q, Morris AP, Bell JT, Thorsteinsdottir U, Stefansson K, Laakso M, Dahlman I, Arner P, Gloyn AL, Musunuru K, Lusis AJ, Cox R, Karpe F, McCarthy MI. Regulatory variants at KLF14 influence type 2 diabetes risk via a female-specific effect on adipocyte size and body composition. Nat Genet 2018; 50:572-580. [PMID: 29632379 PMCID: PMC5935235 DOI: 10.1038/s41588-018-0088-x] [Citation(s) in RCA: 101] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Accepted: 02/15/2018] [Indexed: 12/30/2022]
Abstract
Individual risk of type 2 diabetes (T2D) is modified by perturbations to the mass, distribution and function of adipose tissue. To investigate the mechanisms underlying these associations, we explored the molecular, cellular and whole-body effects of T2D-associated alleles near KLF14. We show that KLF14 diabetes-risk alleles act in adipose tissue to reduce KLF14 expression and modulate, in trans, the expression of 385 genes. We demonstrate, in human cellular studies, that reduced KLF14 expression increases pre-adipocyte proliferation but disrupts lipogenesis, and in mice, that adipose tissue-specific deletion of Klf14 partially recapitulates the human phenotype of insulin resistance, dyslipidemia and T2D. We show that carriers of the KLF14 T2D risk allele shift body fat from gynoid stores to abdominal stores and display a marked increase in adipocyte cell size, and that these effects on fat distribution, and the T2D association, are female specific. The metabolic risk associated with variation at this imprinted locus depends on the sex both of the subject and of the parent from whom the risk allele derives.
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Affiliation(s)
- Kerrin S. Small
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK,Corresponding authors: Correspondence should be addressed to K.S.S. () or M.I.M ()
| | - Marijana Todorčević
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
| | - Mete Civelek
- Center for Public Health Genomics, Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA,Department of Medicine, University of California, Los Angeles, California, USA
| | | | - Xiao Wang
- Cardiovascular Institute, Department of Medicine, Department of Genetics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Michelle M. Simon
- Biocomputing, Medical Research Council Harwell Institute, Oxford, UK
| | | | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Momoko Horikoshi
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK,Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Alison Hugill
- Genetics of type 2 diabetes, Medical Research Council Harwell Institute, Oxford, UK
| | - Craig A. Glastonbury
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Lydia Quaye
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Matt J. Neville
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK,Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Siddharth Sethi
- Biocomputing, Medical Research Council Harwell Institute, Oxford, UK
| | - Marianne Yon
- Genetics of type 2 diabetes, Medical Research Council Harwell Institute, Oxford, UK
| | - Calvin Pan
- Department of Human Genetics, University of California, Los Angeles, California, USA
| | - Nam Che
- Department of Medicine, University of California, Los Angeles, California, USA
| | - Ana Viñuela
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Pei-Chien Tsai
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Abhishek Nag
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Alfonso Buil
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | | | | | - Qiurong Ding
- CAS Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, PR China
| | - Andrew P. Morris
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK,Department of Biostatistics, University of Liverpool, Liverpool, UK
| | - Jordana T. Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Unnur Thorsteinsdottir
- deCODE Genetics, Reykjavik, Iceland,Faculty of Medicine, University of Iceland, Reykjavik Iceland
| | - Kari Stefansson
- deCODE Genetics, Reykjavik, Iceland,Faculty of Medicine, University of Iceland, Reykjavik Iceland
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Ingrid Dahlman
- Department of Medicine, Huddinge, Karolinska Institutet, Stockholm, Sweden
| | - Peter Arner
- Department of Medicine, Huddinge, Karolinska Institutet, Stockholm, Sweden
| | - Anna L. Gloyn
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK,Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK,Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Kiran Musunuru
- Cardiovascular Institute, Department of Medicine, Department of Genetics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Aldons J. Lusis
- Department of Medicine, University of California, Los Angeles, California, USA,Department of Human Genetics, University of California, Los Angeles, California, USA,Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, California, USA
| | - Roger Cox
- Genetics of type 2 diabetes, Medical Research Council Harwell Institute, Oxford, UK
| | - Fredrik Karpe
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK,Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Mark I. McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK,Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK,Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK,Corresponding authors: Correspondence should be addressed to K.S.S. () or M.I.M ()
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14
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Abstract
Obesity, defined as a body mass index (BMI) ≥ 30 kg/m2, has reached epidemic proportions; people who are overweight (BMI > 25 kg/m2) or obese now comprise more than 25% of the world's population. Obese individuals have a higher risk of comorbidity development including type 2 diabetes, cardiovascular disease, cancer, and fertility complications. Areas covered: The study of monogenic and syndromic forms of obesity have revealed a small number of genes key to metabolic perturbations. Further, obesity and body shape in the general population are highly heritable phenotypes. Study of obesity at the population level, through genome-wide association studies of BMI and waist-to-hip ratio (WHR), have revealed > 150 genomic loci that associate with these traits, and highlight the role of adipose tissue and the central nervous system in obesity-related traits. Studies in animal models and cell lines have helped further elucidate the potential biological mechanisms underlying obesity. In particular, these studies implicate adipogenesis and expansion of adipose tissue as key biological pathways in obesity and weight gain. Expert commentary: Further work, including a focus on integrating genetic and additional genomic data types, as well as modeling obesity-like features in vitro, will be crucial in translating genome-wide association signals to the causal mechanisms driving disease.
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Affiliation(s)
- Sara L Pulit
- a Big Data Institute , Li Ka Shing Centre for Health Information and Discovery, University of Oxford , Oxford , UK
- b Department of Genetics , University Medical Center Utrecht , Utrecht , The Netherlands
- f Program in Medical and Population Genetics , Broad Institute , Cambridge , Massachusetts , USA
| | - Samantha Laber
- a Big Data Institute , Li Ka Shing Centre for Health Information and Discovery, University of Oxford , Oxford , UK
- c MRC Harwell Institute , Mammalian Genetics Unit , Harwell , Oxford , UK
- d Department of Physiology , Anatomy and Genetics, University of Oxford , Oxford , U.K
| | - Craig A Glastonbury
- a Big Data Institute , Li Ka Shing Centre for Health Information and Discovery, University of Oxford , Oxford , UK
- e Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine , University of Oxford , Oxford , UK
| | - Cecilia M Lindgren
- a Big Data Institute , Li Ka Shing Centre for Health Information and Discovery, University of Oxford , Oxford , UK
- e Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine , University of Oxford , Oxford , UK
- f Program in Medical and Population Genetics , Broad Institute , Cambridge , Massachusetts , USA
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15
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Xu H, Kaul S, Maruko E, Fatema K, Glastonbury CA, Small K, Thomas MJ, Sorci-Thomas M. Abstract 558: PCPE2 Promotes Adipocyte Maturation via Regulation of SR-BI Activity. Arterioscler Thromb Vasc Biol 2017. [DOI: 10.1161/atvb.37.suppl_1.558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Procollagen C-endopeptidase enhancer protein 2 (PCPE2) is an enhancer protein that enhances the cleavage of the C-termini of procollagen by bone morphogenetic protein 1. But the function of PCPE2 is not limited to collagen maturation. Recently our lab reported that PCPE2 increased HDL-associated cholesteryl ester uptake via scavenger receptor class B type 1 (SR-BI), an HDL cholesterol receptor highly expressed in adipose tissue. Interestingly, TwinsUK study in human provided data showing that PCPE2 mRNA is highly correlated with adipose tissue distribution. Further, our lab observed a reduced size of visceral fat pad in PCPE2 deficient mice despite its indifference in body weight compared to the wild type. To study the molecular and cellular mechanisms of how PCPE2 regulates SR-BI function and how it affects adipose tissue formation, we generated PCPE2 knockout (PCPE2
-/-
) cell line from murine preadipocyte 3T3-L1 cell using CRISPR-Cas9 technology. Induction of 3T3-L1 cell to differentiate into mature adipocyte increases the expression of both PCPE2 and SR-BI hand in hand around 3 fold, which parallels the process of lipid droplet formation. Immunofluorescence studies showed that PCPE2 is distributed all over the cell in mature adipocyte, while SR-BI is mostly distributed along the cytoplasmic and lipid droplet membranes, in addition to the perinuclear region. More interestingly, PCPE2
-/-
3T3-L1 cell shows an impairment in lipid droplet formation during the induction of differentiation, with less than 10% of cells generating lipid droplets, and the size of lipid droplet being only about 50% of that in wild type. Although PCPE2
-/-
3T3-L1 cell expresses SR-BI at a higher level compared with wild type cell, nearly 50% of the SR-BI in wild type cell are in homodimer structure while PCPE2
-/-
3T3-L1 cell has almost none SR-BI multimer, indicating that SR-BI in PCPE2
-/-
3T3-L1 cell is inactive. Taken together, we concluded that PCPE2 plays a critical role in keeping SR-BI in active conformation, which, in turn, controls adipocyte maturation. Whether this effect is associated with collagen maturation will be assessed in our future studies.
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Affiliation(s)
- Hao Xu
- Med College of Wisconsin, Milwaukee, WI
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16
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Maruko EC, Tschannen M, Liu P, Tsaih SW, Glastonbury CA, Small K, Kaul S, Xu H, Fatema K, Thomas MJ, Sorci-Thomas MG. Abstract 191: Adipocyte Procollagen C-endopeptidase Enhancer Protein 2 (PCPE2) Influences Lipid Metabolism Secondary to Changes in Expression and Function of Scavenger Receptor Class B Member 1 (SR-BI). Arterioscler Thromb Vasc Biol 2017. [DOI: 10.1161/atvb.37.suppl_1.191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Scavenger receptor class B member 1 (SR-BI) functions as a HDL receptor and stimulates reverse cholesterol transport (RCT) via selective uptake of HDL cholesterol esters (HDL-CE). Procollagen c-endopeptidase enhancer protein 2 (PCPE2) is an extracellular matrix glycoprotein encoded by the PCOLCE2 gene and most highly expressed in adipose tissue, heart, and aorta. PCPE2 has been suggested to be atheroprotective secondary to mechanisms involved in lipid metabolism. Our lab previously reported that SR-BI mediated cholesterol ester uptake is reduced in the absence of PCPE2 despite its increased expression levels. Subsequent HDL turnover studies suggested an overall reduction in catabolism, consistent with larger HDL particles observed, is responsible for the paradox. More recently, human data from the TwinsUK study showed strong correlations between PCOLCE2 and adipose tissue distribution with BMI included as a covariate. Furthermore, despite similar overall body weights, our lab observed a significant reduction in visceral fat pad development in PCPE2-deficient mice fed a Western diet for 25 weeks when compared to wild type mice. In addition, our lab has generated PCPE2 knockout cell lines from murine preadipocyte 3T3-L1 cells (PCPE2
-/-
3T3-L1) and Chinese hamster ovary (CHO) cells using CRISPR-Cas9 and siRNA technology, respectfully. In addition to both cell lines showing increased SR-BI expression, differentiated PCPE2
-/-
3T3-L1 cells have impaired lipid droplet formation. To elucidate the relationship between PCPE2 and SR-BI function and their effects on adipose tissue development, we performed RNA sequencing on visceral adipose tissue from PCPE2-deficient male mice fed a Western diet for 12 weeks. Genes exhibiting significant expression alterations (FDR <0.026) compared to wild type were further analyzed via Ingenuity Pathway Analysis (IPA). Differential expression analysis of diseases and functions related to changes in SR-BI expression highlighted permutations of a number of lipid modulating genes. Overall, changes in SR-BI related metabolic pathways secondary to PCPE2 deficiency appear to play a crucial role linking SR-BI mediated cholesterol ester uptake to adipocyte biology.
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Affiliation(s)
| | | | | | | | | | | | | | - Hao Xu
- Med College of Wisconsin, Milwaukee, WI
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17
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Pallister T, Jackson MA, Martin TC, Glastonbury CA, Jennings A, Beaumont M, Mohney RP, Small KS, MacGregor A, Steves CJ, Cassidy A, Spector TD, Menni C, Valdes AM. Untangling the relationship between diet and visceral fat mass through blood metabolomics and gut microbiome profiling. Int J Obes (Lond) 2017; 41:1106-1113. [PMID: 28293020 PMCID: PMC5504448 DOI: 10.1038/ijo.2017.70] [Citation(s) in RCA: 58] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2016] [Revised: 02/16/2017] [Accepted: 02/26/2017] [Indexed: 02/08/2023]
Abstract
BACKGROUND/OBJECTIVES Higher visceral fat mass (VFM) is associated with an increased risk for developing cardio-metabolic diseases. The mechanisms by which an unhealthy diet pattern may influence visceral fat (VF) development has yet to be examined through cutting-edge multi-omic methods. Therefore, our objective was to examine the dietary influences on VFM and identify gut microbiome and metabolite profiles that link food intakes to VFM. SUBJECTS/METHODS In 2218 twins with VFM, food intake and metabolomics data available we identified food intakes most strongly associated with VFM in 50% of the sample, then constructed and tested the 'VFM diet score' in the remainder of the sample. Using linear regression (adjusted for covariates, including body mass index and total fat mass), we investigated associations between the VFM diet score, the blood metabolomics profile and the fecal microbiome (n=889), and confirmed these associations with VFM. We replicated top findings in monozygotic (MZ) twins discordant (⩾1 s.d. apart) for VFM, matched for age, sex and the baseline genetic sequence. RESULTS Four metabolites were associated with the VFM diet score and VFM: hippurate, alpha-hydroxyisovalerate, bilirubin (Z,Z) and butyrylcarnitine. We replicated associations between VFM and the diet score (beta (s.e.): 0.281 (0.091); P=0.002), butyrylcarnitine (0.199 (0.087); P=0.023) and hippurate (-0.297 (0.095); P=0.002) in VFM-discordant MZ twins. We identified a single species, Eubacterium dolichum to be associated with the VFM diet score (0.042 (0.011), P=8.47 × 10-5), VFM (0.057 (0.019), P=2.73 × 10-3) and hippurate (-0.075 (0.032), P=0.021). Moreover, higher blood hippurate was associated with elevated adipose tissue expression neuroglobin, with roles in cellular oxygen homeostasis (0.016 (0.004), P=9.82x10-6). CONCLUSIONS We linked a dietary VFM score and VFM to E. dolichum and four metabolites in the blood. In particular, the relationship between hippurate, a metabolite derived from microbial metabolism of dietary polyphenols, and reduced VFM, the microbiome and increased adipose tissue expression of neuroglobin provides potential mechanistic insight into the influence of diet on VFM.
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Affiliation(s)
- T Pallister
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
| | - M A Jackson
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
| | - T C Martin
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
| | - C A Glastonbury
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
| | - A Jennings
- Department of Nutrition, Norwich Medical School, University of East Anglia, Norwich, UK
| | - M Beaumont
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
| | | | - K S Small
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
| | - A MacGregor
- Department of Nutrition, Norwich Medical School, University of East Anglia, Norwich, UK
| | - C J Steves
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
| | - A Cassidy
- Department of Nutrition, Norwich Medical School, University of East Anglia, Norwich, UK
| | - T D Spector
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
| | - C Menni
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
| | - A M Valdes
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK.,Academic Rheumatology Clinical Sciences Building, University of Nottingham, Nottingham City Hospital, Nottingham, UK
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18
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Menni C, Migaud M, Glastonbury CA, Beaumont M, Nikolaou A, Small KS, Brosnan MJ, Mohney RP, Spector TD, Valdes AM. Metabolomic profiling to dissect the role of visceral fat in cardiometabolic health. Obesity (Silver Spring) 2016; 24:1380-8. [PMID: 27129722 PMCID: PMC4914926 DOI: 10.1002/oby.21488] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Revised: 01/13/2016] [Accepted: 01/29/2016] [Indexed: 01/06/2023]
Abstract
OBJECTIVE Abdominal obesity is associated with increased risk of type 2 diabetes (T2D) and cardiovascular disease. The aim of this study was to assess whether metabolomic markers of T2D and blood pressure (BP) act on these traits via visceral fat (VF) mass. METHODS Metabolomic profiling of 280 fasting plasma metabolites was conducted on 2,401 women from TwinsUK. The overlap was assessed between published metabolites associated with T2D, insulin resistance, or BP and those that were identified to be associated with VF (after adjustment for covariates) measured by dual-energy X-ray absorptiometry. RESULTS In addition to glucose, six metabolites were strongly associated with both VF mass and T2D: lactate and branched-chain amino acids, all of them related to metabolism and the tricarboxylic acid cycle; on average, 38.5% of their association with insulin resistance was mediated by their association with VF mass. Five metabolites were associated with BP and VF mass including the inflammation-associated peptide HWESASXX, the steroid hormone androstenedione, lactate, and palmitate. On average, 29% of their effect on BP was mediated by their association with VF mass. CONCLUSIONS Little overlap was found between the metabolites associated with BP and those associated with insulin resistance via VF mass.
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Affiliation(s)
- Cristina Menni
- Department of Twin Research and Genetic EpidemiologyKings College LondonLondonUK
| | - Marie Migaud
- School of PharmacyQueen's University BelfastBelfastUK
| | - Craig A. Glastonbury
- Department of Twin Research and Genetic EpidemiologyKings College LondonLondonUK
| | - Michelle Beaumont
- Department of Twin Research and Genetic EpidemiologyKings College LondonLondonUK
| | - Aikaterini Nikolaou
- Department of Twin Research and Genetic EpidemiologyKings College LondonLondonUK
| | - Kerrin S. Small
- Department of Twin Research and Genetic EpidemiologyKings College LondonLondonUK
| | - Mary Julia Brosnan
- Pfizer Worldwide Research and Development, Clinical Research StatisticsGroton, ConnecticutUSA
| | | | - Tim D. Spector
- Department of Twin Research and Genetic EpidemiologyKings College LondonLondonUK
| | - Ana M. Valdes
- Department of Twin Research and Genetic EpidemiologyKings College LondonLondonUK
- Academic Rheumatology Clinical Sciences Building, Nottingham City HospitalNottinghamUK
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19
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Bailey JNC, Loomis SJ, Kang JH, Allingham RR, Gharahkhani P, Khor CC, Burdon KP, Aschard H, Chasman DI, Igo RP, Hysi PG, Glastonbury CA, Ashley-Koch A, Brilliant M, Brown AA, Budenz DL, Buil A, Cheng CY, Choi H, Christen WG, Curhan G, De Vivo I, Fingert JH, Foster PJ, Fuchs C, Gaasterland D, Gaasterland T, Hewitt AW, Hu F, Hunter DJ, Khawaja AP, Lee RK, Li Z, Lichter PR, Mackey DA, McGuffin P, Mitchell P, Moroi SE, Perera SA, Pepper KW, Qi Q, Realini T, Richards JE, Ridker PM, Rimm E, Ritch R, Ritchie M, Schuman JS, Scott WK, Singh K, Sit AJ, Song YE, Tamimi RM, Topouzis F, Viswanathan AC, Verma SS, Vollrath D, Wang JJ, Weisschuh N, Wissinger B, Wollstein G, Wong TY, Yaspan BL, Zack DJ, Zhang K, Study ENE, Weinreb RN, Pericak-Vance MA, Small K, Hammond CJ, Aung T, Liu Y, Vithana EN, MacGregor S, Craig JE, Kraft P, Howell G, Hauser MA, Pasquale LR, Haines JL, Wiggs JL. Genome-wide association analysis identifies TXNRD2, ATXN2 and FOXC1 as susceptibility loci for primary open-angle glaucoma. Nat Genet 2016; 48:189-94. [PMID: 26752265 PMCID: PMC4731307 DOI: 10.1038/ng.3482] [Citation(s) in RCA: 170] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2015] [Accepted: 12/09/2015] [Indexed: 12/13/2022]
Abstract
Primary open angle glaucoma (POAG) is a leading cause of blindness world-wide. To identify new susceptibility loci, we meta-analyzed GWAS results from 8 independent studies from the United States (3,853 cases and 33,480 controls) and investigated the most significant SNPs in two Australian studies (1,252 cases and 2,592 controls), 3 European studies (875 cases and 4,107 controls) and a Singaporean Chinese study (1,037 cases and 2,543 controls). A meta-analysis of top SNPs identified three novel loci: rs35934224[T] within TXNRD2 (odds ratio (OR) = 0.78, P = 4.05×10−11 encoding a mitochondrial protein required for redox homeostasis; rs7137828[T] within ATXN2 (OR = 1.17, P = 8.73×10−10), and rs2745572[A] upstream of FOXC1 (OR = 1.17, P = 1.76×10−10). Using RT-PCR and immunohistochemistry, we show TXNRD2 and ATXN2 expression in retinal ganglion cells and the optic nerve head. These results identify new pathways underlying POAG susceptibility and suggest novel targets for preventative therapies.
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Affiliation(s)
- Jessica N Cooke Bailey
- Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | - Stephanie J Loomis
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
| | - Jae H Kang
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - R Rand Allingham
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina, USA
| | - Puya Gharahkhani
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Chiea Chuen Khor
- Division of Human Genetics, Genome Institute of Singapore, Singapore.,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Kathryn P Burdon
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia.,Department of Ophthalmology, Flinders University, Adelaide, South Australia, Australia
| | - Hugues Aschard
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Robert P Igo
- Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | - Pirro G Hysi
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Craig A Glastonbury
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Allison Ashley-Koch
- Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA
| | - Murray Brilliant
- Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA
| | - Andrew A Brown
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Donald L Budenz
- Department of Ophthalmology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Alfonso Buil
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Ching-Yu Cheng
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Eye Academic Clinical Program, Duke-National University of Singapore Graduate Medical School, Singapore
| | - Hyon Choi
- Section of Rheumatology and Clinical Epidemiology Unit, Boston University School of Medicine, Boston, Massachusetts, USA
| | - William G Christen
- Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Gary Curhan
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Renal Division, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Immaculata De Vivo
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - John H Fingert
- Department of Ophthalmology, University of Iowa, College of Medicine, Iowa City, Iowa, USA.,Department of Anatomy and Cell Biology, University of Iowa, College of Medicine, Iowa City, Iowa, USA
| | - Paul J Foster
- National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital, London, UK.,Department of Ophthalmology, University College London, London, UK
| | - Charles Fuchs
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Terry Gaasterland
- Scripps Genome Center, University of California at San Diego, San Diego, California, USA
| | - Alex W Hewitt
- Centre for Eye Research Australia, University of Melbourne, Melbourne, Victoria, Australia.,Department of Ophthalmology, Royal Victorian Eye and Ear Hospital, Melbourne, Victoria, Australia
| | - Frank Hu
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA.,Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, USA
| | - David J Hunter
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA.,Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Anthony P Khawaja
- Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Richard K Lee
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Zheng Li
- Division of Human Genetics, Genome Institute of Singapore, Singapore
| | - Paul R Lichter
- Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, USA
| | - David A Mackey
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia.,Centre for Ophthalmology and Visual Science, Lions Eye Institute, University of Western Australia, Perth, Western Australia, Australia
| | - Peter McGuffin
- Medical Research Council Social Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, King's College London, London, UK
| | - Paul Mitchell
- Centre for Vision Research, Westmead Millennium Institute, University of Sydney, Westmead, New South Wales, Australia
| | - Sayoko E Moroi
- Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, USA
| | - Shamira A Perera
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Duke-National University of Singapore Graduate Medical School, Singapore
| | | | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Tony Realini
- Department of Ophthalmology, West Virginia University Eye Institute, Morgantown, West Virginia, USA
| | - Julia E Richards
- Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, USA.,Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Paul M Ridker
- Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Eric Rimm
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA.,Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Robert Ritch
- Einhorn Clinical Research Center, Department of Ophthalmology, New York Eye and Ear Infirmary of Mount Sinai, New York, New York, USA
| | - Marylyn Ritchie
- Center for Systems Genomics, Pennsylvania State University, University Park, Pennsylvania, USA
| | - Joel S Schuman
- Department of Ophthalmology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - William K Scott
- Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Kuldev Singh
- Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, California, USA
| | - Arthur J Sit
- Department of Ophthalmology, Mayo Clinic, Rochester, Minnesota, USA
| | - Yeunjoo E Song
- Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | - Rulla M Tamimi
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Fotis Topouzis
- Department of Ophthalmology, School of Medicine, Aristotle University of Thessaloniki, AHEPA Hospital, Thessaloniki, Greece
| | - Ananth C Viswanathan
- National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital, London, UK
| | - Shefali Setia Verma
- Center for Systems Genomics, Pennsylvania State University, University Park, Pennsylvania, USA
| | - Douglas Vollrath
- Department of Genetics, Stanford University School of Medicine, Palo Alto, California, USA
| | - Jie Jin Wang
- Centre for Vision Research, Westmead Millennium Institute, University of Sydney, Westmead, New South Wales, Australia
| | - Nicole Weisschuh
- Institute for Ophthalmic Research, Centre for Ophthalmology, University of Tübingen, Tübingen, Germany
| | - Bernd Wissinger
- Institute for Ophthalmic Research, Centre for Ophthalmology, University of Tübingen, Tübingen, Germany
| | - Gadi Wollstein
- Department of Ophthalmology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Tien Y Wong
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | | | - Donald J Zack
- Wilmer Eye Institute, Johns Hopkins University Hospital, Baltimore, Maryland, USA
| | - Kang Zhang
- Hamilton Glaucoma Center, Shiley Eye Institute, University of California, San Diego, San Diego, California, USA
| | - Epic-Norfolk Eye Study
- Department of Cellular Biology and Anatomy, Georgia Regents University, Augusta, Georgia, USA
| | | | - Robert N Weinreb
- Hamilton Glaucoma Center, Shiley Eye Institute, University of California, San Diego, San Diego, California, USA
| | - Margaret A Pericak-Vance
- Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Kerrin Small
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Christopher J Hammond
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Eye Academic Clinical Program, Duke-National University of Singapore Graduate Medical School, Singapore
| | - Yutao Liu
- Department of Cellular Biology and Anatomy, Georgia Regents University, Augusta, Georgia, USA.,James and Jean Culver Vision Discovery Institute, Georgia Regents University, Augusta, Georgia, USA
| | - Eranga N Vithana
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Eye Academic Clinical Program, Duke-National University of Singapore Graduate Medical School, Singapore
| | - Stuart MacGregor
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Jamie E Craig
- Department of Ophthalmology, Flinders University, Adelaide, South Australia, Australia
| | - Peter Kraft
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA.,Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, Massachusetts, USA
| | | | - Michael A Hauser
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina, USA.,Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA
| | - Louis R Pasquale
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA.,Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jonathan L Haines
- Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | - Janey L Wiggs
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
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