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Constantinescu AE, Bull CJ, Goudswaard LJ, Zheng J, Elsworth B, Timpson NJ, Moore SF, Hers I, Vincent EE. A phenome-wide approach to identify causal risk factors for deep vein thrombosis. BMC Med Genomics 2023; 16:284. [PMID: 37951941 PMCID: PMC10640748 DOI: 10.1186/s12920-023-01710-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 10/20/2023] [Indexed: 11/14/2023] Open
Abstract
Deep vein thrombosis (DVT) is the formation of a blood clot in a deep vein. DVT can lead to a venous thromboembolism (VTE), the combined term for DVT and pulmonary embolism, a leading cause of death and disability worldwide. Despite the prevalence and associated morbidity of DVT, the underlying causes are not well understood. Our aim was to leverage publicly available genetic summary association statistics to identify causal risk factors for DVT. We conducted a Mendelian randomization phenome-wide association study (MR-PheWAS) using genetic summary association statistics for 973 exposures and DVT (6,767 cases and 330,392 controls in UK Biobank). There was evidence for a causal effect of 57 exposures on DVT risk, including previously reported risk factors (e.g. body mass index-BMI and height) and novel risk factors (e.g. hyperthyroidism and varicose veins). As the majority of identified risk factors were adiposity-related, we explored the molecular link with DVT by undertaking a two-sample MR mediation analysis of BMI-associated circulating proteins on DVT risk. Our results indicate that circulating neurogenic locus notch homolog protein 1 (NOTCH1), inhibin beta C chain (INHBC) and plasminogen activator inhibitor 1 (PAI-1) influence DVT risk, with PAI-1 mediating the BMI-DVT relationship. Using a phenome-wide approach, we provide putative causal evidence that hyperthyroidism, varicose veins and BMI enhance the risk of DVT. Furthermore, the circulating protein PAI-1 has a causal role in DVT aetiology and is involved in mediating the BMI-DVT relationship.
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Affiliation(s)
- Andrei-Emil Constantinescu
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK.
- Bristol Medical School, Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK.
- School of Translational Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK.
| | - Caroline J Bull
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
- School of Translational Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
- Health Data Research UK. Registered Office, 215 Euston Road, London, NW1 2BE, UK
| | - Lucy J Goudswaard
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
- School of Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol, UK
| | - Jie Zheng
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Benjamin Elsworth
- Our Future Health Ltd. Registered office: 2 New Bailey, 6 Stanley Street, Manchester, M3 5GS, UK
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - Samantha F Moore
- School of Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol, UK
- UKRI Medical Research Council, Swindon, UK
| | - Ingeborg Hers
- School of Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol, UK
| | - Emma E Vincent
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
- School of Translational Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
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Zheng J, Zhang Y, Zhao H, Liu Y, Baird D, Karim MA, Ghoussaini M, Schwartzentruber J, Dunham I, Elsworth B, Roberts K, Compton H, Miller-Molloy F, Liu X, Wang L, Zhang H, Smith GD, Gaunt TR. Multi-ancestry Mendelian randomization of omics traits revealing drug targets of COVID-19 severity. EBioMedicine 2022; 81:104112. [PMID: 35772218 PMCID: PMC9235320 DOI: 10.1016/j.ebiom.2022.104112] [Citation(s) in RCA: 0] [Impact Index Per Article: 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: 03/08/2022] [Revised: 05/16/2022] [Accepted: 05/28/2022] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Recent omic studies prioritised several drug targets associated with coronavirus disease 2019 (COVID-19) severity. However, little evidence was provided to systematically estimate the effect of drug targets on COVID-19 severity in multiple ancestries. METHODS In this study, we applied Mendelian randomization (MR) and colocalization approaches to understand the putative causal effects of 16,059 transcripts and 1608 proteins on COVID-19 severity in European and effects of 610 proteins on COVID-19 severity in African ancestry. We further integrated genetics, clinical and literature evidence to prioritise drug targets. Additional sensitivity analyses including multi-trait colocalization and phenome-wide MR were conducted to test for MR assumptions. FINDINGS MR and colocalization prioritized four protein targets, FCRL3, ICAM5, ENTPD5 and OAS1 that showed effect on COVID-19 severity in European ancestry. One protein target, SERPINA1 showed a stronger effect in African ancestry but much weaker effect in European ancestry (odds ratio [OR] in Africans=0.369, 95%CI=0.203 to 0.668, P = 9.96 × 10-4; OR in Europeans=1.021, 95%CI=0.901 to 1.157, P = 0.745), which suggested that increased level of SERPINA1 will reduce COVID-19 risk in African ancestry. One protein, ICAM1 showed suggestive effect on COVID-19 severity in both ancestries (OR in Europeans=1.152, 95%CI=1.063 to 1.249, P = 5.94 × 10-4; OR in Africans=1.481, 95%CI=1.008 to 2.176; P = 0.045). The OAS1, SERPINA1 and ICAM1 effects were replicated using updated COVID-19 severity data in the two ancestries respectively, where alternative splicing events in OAS1 and ICAM1 also showed marginal effects on COVID-19 severity in Europeans. The phenome-wide MR of the prioritised targets on 622 complex traits provided information on potential beneficial effects on other diseases and suggested little evidence of adverse effects on major complications. INTERPRETATION Our study identified six proteins as showing putative causal effects on COVID-19 severity. OAS1 and SERPINA1 were targets of existing drugs in trials as potential COVID-19 treatments. ICAM1, ICAM5 and FCRL3 are related to the immune system. Across the six targets, OAS1 has no reliable instrument in African ancestry; SERPINA1, FCRL3, ICAM5 and ENTPD5 showed a different level of putative causal evidence in European and African ancestries, which highlights the importance of more powerful ancestry-specific GWAS and value of multi-ancestry MR in informing the effects of drug targets on COVID-19 across different populations. This study provides a first step towards clinical investigation of beneficial and adverse effects of COVID-19 drug targets. FUNDING No.
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Affiliation(s)
- Jie Zheng
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom.
| | - Yuemiao Zhang
- Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Key Laboratory of Renal Disease, Ministry of Health of China, Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing, People's Republic of China
| | - Huiling Zhao
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom
| | - Yi Liu
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom
| | - Denis Baird
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom
| | - Mohd Anisul Karim
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SA, United Kingdom; Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, United Kingdom
| | - Maya Ghoussaini
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SA, United Kingdom; Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, United Kingdom
| | - Jeremy Schwartzentruber
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SA, United Kingdom; Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, United Kingdom
| | - Ian Dunham
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SA, United Kingdom; Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, United Kingdom; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, United Kingdom
| | - Benjamin Elsworth
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom
| | - Katherine Roberts
- Bristol Medical School, University of Bristol, 5 Tyndall Avenue, Bristol, BS8 1UD, United Kingdom
| | - Hannah Compton
- Bristol Medical School, University of Bristol, 5 Tyndall Avenue, Bristol, BS8 1UD, United Kingdom
| | - Felix Miller-Molloy
- Bristol Medical School, University of Bristol, 5 Tyndall Avenue, Bristol, BS8 1UD, United Kingdom
| | - Xingzi Liu
- Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Key Laboratory of Renal Disease, Ministry of Health of China, Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing, People's Republic of China
| | - Lin Wang
- Department of Microbiology and Infectious Disease Centre, School of Basic Medical Sciences, Peking University Health Science Centre, Beijing, China
| | - Hong Zhang
- Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Key Laboratory of Renal Disease, Ministry of Health of China, Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing, People's Republic of China
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom; NIHR Biomedical Research Centre at the University Hospitals Bristol NHS Foundation Trust and the University of Bristol, United Kingdom
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom; NIHR Biomedical Research Centre at the University Hospitals Bristol NHS Foundation Trust and the University of Bristol, United Kingdom.
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Richardson TG, Leyden GM, Wang Q, Bell JA, Elsworth B, Davey Smith G, Holmes MV. Characterising metabolomic signatures of lipid-modifying therapies through drug target mendelian randomisation. PLoS Biol 2022; 20:e3001547. [PMID: 35213538 PMCID: PMC8906647 DOI: 10.1371/journal.pbio.3001547] [Citation(s) in RCA: 57] [Impact Index Per Article: 28.5] [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: 10/26/2021] [Revised: 03/09/2022] [Accepted: 01/19/2022] [Indexed: 12/02/2022] Open
Abstract
Large-scale molecular profiling and genotyping provide a unique opportunity to systematically compare the genetically predicted effects of therapeutic targets on the human metabolome. We firstly constructed genetic risk scores for 8 drug targets on the basis that they primarily modify low-density lipoprotein (LDL) cholesterol (HMGCR, PCKS9, and NPC1L1), high-density lipoprotein (HDL) cholesterol (CETP), or triglycerides (APOC3, ANGPTL3, ANGPTL4, and LPL). Conducting mendelian randomisation (MR) provided strong evidence of an effect of drug-based genetic scores on coronary artery disease (CAD) risk with the exception of ANGPTL3. We then systematically estimated the effects of each score on 249 metabolic traits derived using blood samples from an unprecedented sample size of up to 115,082 UK Biobank participants. Genetically predicted effects were generally consistent among drug targets, which were intended to modify the same lipoprotein lipid trait. For example, the linear fit for the MR estimates on all 249 metabolic traits for genetically predicted inhibition of LDL cholesterol lowering targets HMGCR and PCSK9 was r2 = 0.91. In contrast, comparisons between drug classes that were designed to modify discrete lipoprotein traits typically had very different effects on metabolic signatures (for instance, HMGCR versus each of the 4 triglyceride targets all had r2 < 0.02). Furthermore, we highlight this discrepancy for specific metabolic traits, for example, finding that LDL cholesterol lowering therapies typically had a weak effect on glycoprotein acetyls, a marker of inflammation, whereas triglyceride modifying therapies assessed provided evidence of a strong effect on lowering levels of this inflammatory biomarker. Our findings indicate that genetically predicted perturbations of these drug targets on the blood metabolome can drastically differ, despite largely consistent effects on risk of CAD, with potential implications for biomarkers in clinical development and measuring treatment response.
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Affiliation(s)
- Tom G. Richardson
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
- Novo Nordisk Research Centre, Headington, Oxford, United Kingdom
- * E-mail:
| | - Genevieve M. Leyden
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
- Bristol Medical School: Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, United Kingdom
| | - Qin Wang
- MRC Population Health Research Unit (PHRU), Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Joshua A. Bell
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
| | - Benjamin Elsworth
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
| | - Michael V. Holmes
- MRC Population Health Research Unit (PHRU), Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
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4
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Zheng J, Zhang Y, Rasheed H, Walker V, Sugawara Y, Li J, Leng Y, Elsworth B, Wootton RE, Fang S, Yang Q, Burgess S, Haycock PC, Borges MC, Cho Y, Carnegie R, Howell A, Robinson J, Thomas LF, Brumpton BM, Hveem K, Hallan S, Franceschini N, Morris AP, Köttgen A, Pattaro C, Wuttke M, Yamamoto M, Kashihara N, Akiyama M, Kanai M, Matsuda K, Kamatani Y, Okada Y, Walters R, Millwood IY, Chen Z, Davey Smith G, Barbour S, Yu C, Åsvold BO, Zhang H, Gaunt TR. Trans-ethnic Mendelian-randomization study reveals causal relationships between cardiometabolic factors and chronic kidney disease. Int J Epidemiol 2022; 50:1995-2010. [PMID: 34999880 PMCID: PMC8743120 DOI: 10.1093/ije/dyab203] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [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: 07/03/2021] [Accepted: 09/01/2021] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND This study was to systematically test whether previously reported risk factors for chronic kidney disease (CKD) are causally related to CKD in European and East Asian ancestries using Mendelian randomization. METHODS A total of 45 risk factors with genetic data in European ancestry and 17 risk factors in East Asian participants were identified as exposures from PubMed. We defined the CKD by clinical diagnosis or by estimated glomerular filtration rate of <60 ml/min/1.73 m2. Ultimately, 51 672 CKD cases and 958 102 controls of European ancestry from CKDGen, UK Biobank and HUNT, and 13 093 CKD cases and 238 118 controls of East Asian ancestry from Biobank Japan, China Kadoorie Biobank and Japan-Kidney-Biobank/ToMMo were included. RESULTS Eight risk factors showed reliable evidence of causal effects on CKD in Europeans, including genetically predicted body mass index (BMI), hypertension, systolic blood pressure, high-density lipoprotein cholesterol, apolipoprotein A-I, lipoprotein(a), type 2 diabetes (T2D) and nephrolithiasis. In East Asians, BMI, T2D and nephrolithiasis showed evidence of causality on CKD. In two independent replication analyses, we observed that increased hypertension risk showed reliable evidence of a causal effect on increasing CKD risk in Europeans but in contrast showed a null effect in East Asians. Although liability to T2D showed consistent effects on CKD, the effects of glycaemic phenotypes on CKD were weak. Non-linear Mendelian randomization indicated a threshold relationship between genetically predicted BMI and CKD, with increased risk at BMI of >25 kg/m2. CONCLUSIONS Eight cardiometabolic risk factors showed causal effects on CKD in Europeans and three of them showed causality in East Asians, providing insights into the design of future interventions to reduce the burden of CKD.
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Affiliation(s)
- Jie Zheng
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - Yuemiao Zhang
- Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Key Laboratory of Renal Disease, Ministry of Health of China, Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing, P. R. China
| | - Humaira Rasheed
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Venexia Walker
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Yuka Sugawara
- Division of Nephrology and Endocrinology, The University of Tokyo Hospital, Tokyo, Japan
| | - Jiachen Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, P. R. China
| | - Yue Leng
- Department of Psychiatry, University of California, San Francisco, CA, USA
| | - Benjamin Elsworth
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - Robyn E Wootton
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - Si Fang
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - Qian Yang
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, UK
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Philip C Haycock
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - Yoonsu Cho
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - Rebecca Carnegie
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - Amy Howell
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - Jamie Robinson
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - Laurent F Thomas
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Clinical and Molecular Medicine, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ben Michael Brumpton
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Thoracic Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Stein Hallan
- Department of Clinical and Molecular Medicine, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Nephrology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Andrew P Morris
- Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center–University of Freiburg, Freiburg, Germany
| | - Cristian Pattaro
- Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy
| | - Matthias Wuttke
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center–University of Freiburg, Freiburg, Germany
| | - Masayuki Yamamoto
- Tohoku Medical Megabank Organization and Tohoku University Graduate School of Medicine, Tohoku University, Sendai, Miyagi, Japan
| | - Naoki Kashihara
- Department of Nephrology and Hypertension, Kawasaki Medical School, Kurashiki, Okayama, Japan
| | - Masato Akiyama
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Masahiro Kanai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Koichi Matsuda
- Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, the University of Tokyo, Tokyo, Japan
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, the University of Tokyo, Tokyo, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
| | - Robin Walters
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Iona Y Millwood
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
- NIHR Biomedical Research Centre at the University Hospitals Bristol NHS Foundation Trust and the University of Bristol, UK
| | - Sean Barbour
- Division of Nephrology, University of British Columbia, Vancouver, British Columbia, Canada
- British Columbia Provincial Renal Agency, Vancouver, British Columbia, Canada
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, P. R. China
| | - Bjørn Olav Åsvold
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Hong Zhang
- Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Key Laboratory of Renal Disease, Ministry of Health of China, Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing, P. R. China
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
- NIHR Biomedical Research Centre at the University Hospitals Bristol NHS Foundation Trust and the University of Bristol, UK
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5
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Liu Y, Elsworth B, Erola P, Haberland V, Hemani G, Lyon M, Zheng J, Lloyd O, Vabistsevits M, Gaunt TR. EpiGraphDB: a database and data mining platform for health data science. Bioinformatics 2021; 37:1304-1311. [PMID: 33165574 PMCID: PMC8189674 DOI: 10.1093/bioinformatics/btaa961] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [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: 07/22/2020] [Revised: 10/03/2020] [Accepted: 11/04/2020] [Indexed: 12/30/2022] Open
Abstract
MOTIVATION The wealth of data resources on human phenotypes, risk factors, molecular traits and therapeutic interventions presents new opportunities for population health sciences. These opportunities are paralleled by a growing need for data integration, curation and mining to increase research efficiency, reduce mis-inference and ensure reproducible research. RESULTS We developed EpiGraphDB (https://epigraphdb.org/), a graph database containing an array of different biomedical and epidemiological relationships and an analytical platform to support their use in human population health data science. In addition, we present three case studies that illustrate the value of this platform. The first uses EpiGraphDB to evaluate potential pleiotropic relationships, addressing mis-inference in systematic causal analysis. In the second case study, we illustrate how protein-protein interaction data offer opportunities to identify new drug targets. The final case study integrates causal inference using Mendelian randomization with relationships mined from the biomedical literature to 'triangulate' evidence from different sources. AVAILABILITY AND IMPLEMENTATION The EpiGraphDB platform is openly available at https://epigraphdb.org. Code for replicating case study results is available at https://github.com/MRCIEU/epigraphdb as Jupyter notebooks using the API, and https://mrcieu.github.io/epigraphdb-r using the R package. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yi Liu
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Benjamin Elsworth
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Pau Erola
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Valeriia Haberland
- Cancer Genetics, Norwich Medical School, University of East Anglia, Norwich, UK
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Matt Lyon
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol, UK
| | - Jie Zheng
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Oliver Lloyd
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Marina Vabistsevits
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol, UK
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Elsworth B, Gaunt TR. MELODI Presto: a fast and agile tool to explore semantic triples derived from biomedical literature. Bioinformatics 2021; 37:583-585. [PMID: 32810207 PMCID: PMC8088324 DOI: 10.1093/bioinformatics/btaa726] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [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] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 07/09/2020] [Accepted: 08/11/2020] [Indexed: 12/05/2022] Open
Abstract
SUMMARY The field of literature-based discovery is growing in step with the volume of literature being produced. From modern natural language processing algorithms to high quality entity tagging, the methods and their impact are developing rapidly. One annotation object that arises from these approaches, the subject-predicate-object triple, is proving to be very useful in representing knowledge. We have implemented efficient search methods and an application programming interface, to create fast and convenient functions to utilize triples extracted from the biomedical literature by SemMedDB. By refining these data, we have identified a set of triples that focus on the mechanistic aspects of the literature, and provide simple methods to explore both enriched triples from single queries, and overlapping triples across two query lists. AVAILABILITY AND IMPLEMENTATION https://melodi-presto.mrcieu.ac.uk/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Benjamin Elsworth
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK
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7
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Liu Y, Elsworth B, Erola P, Haberland V, Hemani G, Lyon M, Zheng J, Lloyd O, Vabistsevits M, Gaunt TR. Erratum to: EpiGraphDB: a database and data mining platform for health data science. Bioinformatics 2021; 37:288. [PMID: 33693535 PMCID: PMC8055219 DOI: 10.1093/bioinformatics/btab104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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8
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Ellingjord-Dale M, Papadimitriou N, Katsoulis M, Yee C, Dimou N, Gill D, Aune D, Ong JS, MacGregor S, Elsworth B, Lewis SJ, Martin RM, Riboli E, Tsilidis KK. Coffee consumption and risk of breast cancer: A Mendelian randomization study. PLoS One 2021; 16:e0236904. [PMID: 33465101 PMCID: PMC7815134 DOI: 10.1371/journal.pone.0236904] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [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: 07/13/2020] [Accepted: 01/03/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Observational studies have reported either null or weak protective associations for coffee consumption and risk of breast cancer. METHODS We conducted a two-sample Mendelian randomization (MR) analysis to evaluate the relationship between coffee consumption and breast cancer risk using 33 single-nucleotide polymorphisms (SNPs) associated with coffee consumption from a genome-wide association (GWA) study on 212,119 female UK Biobank participants of White British ancestry. Risk estimates for breast cancer were retrieved from publicly available GWA summary statistics from the Breast Cancer Association Consortium (BCAC) on 122,977 cases (of which 69,501 were estrogen receptor (ER)-positive, 21,468 ER-negative) and 105,974 controls of European ancestry. Random-effects inverse variance weighted (IVW) MR analyses were performed along with several sensitivity analyses to assess the impact of potential MR assumption violations. RESULTS One cup per day increase in genetically predicted coffee consumption in women was not associated with risk of total (IVW random-effects; odds ratio (OR): 0.91, 95% confidence intervals (CI): 0.80-1.02, P: 0.12, P for instrument heterogeneity: 7.17e-13), ER-positive (OR = 0.90, 95% CI: 0.79-1.02, P: 0.09) and ER-negative breast cancer (OR: 0.88, 95% CI: 0.75-1.03, P: 0.12). Null associations were also found in the sensitivity analyses using MR-Egger (total breast cancer; OR: 1.00, 95% CI: 0.80-1.25), weighted median (OR: 0.97, 95% CI: 0.89-1.05) and weighted mode (OR: 1.00, CI: 0.93-1.07). CONCLUSIONS The results of this large MR study do not support an association of genetically predicted coffee consumption on breast cancer risk, but we cannot rule out existence of a weak association.
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Affiliation(s)
- Merete Ellingjord-Dale
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Nikos Papadimitriou
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon, France
| | - Michail Katsoulis
- Institute of Health Informatics Research, Faculty of Population Health Sciences, University College London, London, United Kingdom
| | - Chew Yee
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Niki Dimou
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon, France
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Dagfinn Aune
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- Department of Nutrition, Bjørknes University College, Oslo, Norway
- Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway
| | - Jue-Sheng Ong
- Statistical Genetics, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Stuart MacGregor
- Statistical Genetics, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Benjamin Elsworth
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Sarah J. Lewis
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Richard M. Martin
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- National Institute for Health Research (NIHR) Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and the University of Bristol, Bristol, United Kingdom
| | - Elio Riboli
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Konstantinos K. Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, Ioannina, Greece
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9
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Baird DA, Liu JZ, Zheng J, Sieberts SK, Perumal T, Elsworth B, Richardson TG, Chen CY, Carrasquillo MM, Allen M, Reddy JS, De Jager PL, Ertekin-Taner N, Mangravite LM, Logsdon B, Estrada K, Haycock PC, Hemani G, Runz H, Smith GD, Gaunt TR. Identifying drug targets for neurological and psychiatric disease via genetics and the brain transcriptome. PLoS Genet 2021; 17:e1009224. [PMID: 33417599 PMCID: PMC7819609 DOI: 10.1371/journal.pgen.1009224] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [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] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 01/21/2021] [Accepted: 10/26/2020] [Indexed: 11/26/2022] Open
Abstract
Discovering drugs that efficiently treat brain diseases has been challenging. Genetic variants that modulate the expression of potential drug targets can be utilized to assess the efficacy of therapeutic interventions. We therefore employed Mendelian Randomization (MR) on gene expression measured in brain tissue to identify drug targets involved in neurological and psychiatric diseases. We conducted a two-sample MR using cis-acting brain-derived expression quantitative trait loci (eQTLs) from the Accelerating Medicines Partnership for Alzheimer’s Disease consortium (AMP-AD) and the CommonMind Consortium (CMC) meta-analysis study (n = 1,286) as genetic instruments to predict the effects of 7,137 genes on 12 neurological and psychiatric disorders. We conducted Bayesian colocalization analysis on the top MR findings (using P<6x10-7 as evidence threshold, Bonferroni-corrected for 80,557 MR tests) to confirm sharing of the same causal variants between gene expression and trait in each genomic region. We then intersected the colocalized genes with known monogenic disease genes recorded in Online Mendelian Inheritance in Man (OMIM) and with genes annotated as drug targets in the Open Targets platform to identify promising drug targets. 80 eQTLs showed MR evidence of a causal effect, from which we prioritised 47 genes based on colocalization with the trait. We causally linked the expression of 23 genes with schizophrenia and a single gene each with anorexia, bipolar disorder and major depressive disorder within the psychiatric diseases and 9 genes with Alzheimer’s disease, 6 genes with Parkinson’s disease, 4 genes with multiple sclerosis and two genes with amyotrophic lateral sclerosis within the neurological diseases we tested. From these we identified five genes (ACE, GPNMB, KCNQ5, RERE and SUOX) as attractive drug targets that may warrant follow-up in functional studies and clinical trials, demonstrating the value of this study design for discovering drug targets in neuropsychiatric diseases. Genetic association studies have been successful in identifying many genetic variants associated with disease risk, but it has been far more challenging to determine the genes through which these act. This is important, because such genes may encode effective drug targets for these diseases. We used Mendelian randomization (MR) and colocalization, two methods which in combination exploit these genetic variants to estimate the causal effects of individual genes. We applied this approach to 12 neurological and psychiatric diseases using data from the AMP-AD and CMC brain expression quantitative locus dataset, which is large enough to provide robust evidence for the relationship between genetic variants and gene expression. We found a causal relationship between the change in expression of 47 genes and increased disease risk across the 12 diseases we tested. As drug targets with human genetic evidence are far more likely to be approved in clinical trials, these findings provide a valuable list of potential therapeutic targets, including the ACE, GPNMB, KCNQ5, RERE and SUOX genes.
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Affiliation(s)
- Denis A. Baird
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, University of Bristol, Bristol, United Kingdom
- * E-mail: (DAB); (TRG)
| | - Jimmy Z. Liu
- Translational Biology, Research and Development, Cambridge, Massachusetts, United States of America
| | - Jie Zheng
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, University of Bristol, Bristol, United Kingdom
| | | | | | - Benjamin Elsworth
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, University of Bristol, Bristol, United Kingdom
| | - Tom G. Richardson
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, University of Bristol, Bristol, United Kingdom
| | - Chia-Yen Chen
- Translational Biology, Research and Development, Cambridge, Massachusetts, United States of America
| | - Minerva M. Carrasquillo
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, Florida, United States of America
| | - Mariet Allen
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, Florida, United States of America
| | - Joseph S. Reddy
- Department of Health Sciences Research, Mayo Clinic Florida, Jacksonville, Florida, United States of America
| | - Philip L. De Jager
- Centre for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Medical Centre, New York, New York, United States of America
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Centre, New York, New York, United States of America
| | - Nilufer Ertekin-Taner
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, Florida, United States of America
- Department of Neurology, Mayo Clinic Florida, Jacksonville, Florida, United States of America
| | | | - Ben Logsdon
- Sage Bionetworks, Seattle, Washington, United States of America
| | - Karol Estrada
- Translational Biology, Research and Development, Cambridge, Massachusetts, United States of America
- BioMarin Pharmaceuticals, San Rafael, California, United States of America
| | - Philip C. Haycock
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, University of Bristol, Bristol, United Kingdom
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, University of Bristol, Bristol, United Kingdom
| | - Heiko Runz
- Translational Biology, Research and Development, Cambridge, Massachusetts, United States of America
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, University of Bristol, Bristol, United Kingdom
- NIHR Bristol Biomedical Research Centre, Oakfield House, University of Bristol, Bristol, United Kingdom
| | - Tom R. Gaunt
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, University of Bristol, Bristol, United Kingdom
- NIHR Bristol Biomedical Research Centre, Oakfield House, University of Bristol, Bristol, United Kingdom
- * E-mail: (DAB); (TRG)
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10
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Zheng J, Haberland V, Baird D, Walker V, Haycock PC, Hurle MR, Gutteridge A, Erola P, Liu Y, Luo S, Robinson J, Richardson TG, Staley JR, Elsworth B, Burgess S, Sun BB, Danesh J, Runz H, Maranville JC, Martin HM, Yarmolinsky J, Laurin C, Holmes MV, Liu JZ, Estrada K, Santos R, McCarthy L, Waterworth D, Nelson MR, Smith GD, Butterworth AS, Hemani G, Scott RA, Gaunt TR. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. Nat Genet 2020; 52:1122-1131. [PMID: 32895551 PMCID: PMC7610464 DOI: 10.1038/s41588-020-0682-6] [Citation(s) in RCA: 131] [Impact Index Per Article: 32.8] [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: 04/08/2019] [Accepted: 07/24/2020] [Indexed: 01/23/2023]
Abstract
The human proteome is a major source of therapeutic targets. Recent genetic association analyses of the plasma proteome enable systematic evaluation of the causal consequences of variation in plasma protein levels. Here we estimated the effects of 1,002 proteins on 225 phenotypes using two-sample Mendelian randomization (MR) and colocalization. Of 413 associations supported by evidence from MR, 130 (31.5%) were not supported by results of colocalization analyses, suggesting that genetic confounding due to linkage disequilibrium is widespread in naïve phenome-wide association studies of proteins. Combining MR and colocalization evidence in cis-only analyses, we identified 111 putatively causal effects between 65 proteins and 52 disease-related phenotypes ( https://www.epigraphdb.org/pqtl/ ). Evaluation of data from historic drug development programs showed that target-indication pairs with MR and colocalization support were more likely to be approved, evidencing the value of this approach in identifying and prioritizing potential therapeutic targets.
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Affiliation(s)
- Jie Zheng
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK.
- Proteome MR writing group, .
| | - Valeriia Haberland
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- Proteome MR writing group
| | - Denis Baird
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- Proteome MR writing group
| | - Venexia Walker
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- Proteome MR writing group
| | - Philip C Haycock
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- Proteome MR writing group
| | - Mark R Hurle
- Human Genetics, GlaxoSmithKline, Collegeville, PA, USA
| | | | - Pau Erola
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - Yi Liu
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - Shan Luo
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, Hong Kong, China
| | - Jamie Robinson
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - James R Staley
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Benjamin Elsworth
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - Stephen Burgess
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Benjamin B Sun
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - John Danesh
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- BHF Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, Cambridge, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- NIHR Cambridge Biomedical Research Centre, School of Clinical Medicine, Addenbrooke's Hospital, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Heiko Runz
- Translational Biology, Biogen, Cambridge, MA, USA
| | - Joseph C Maranville
- Informatics and Predictive Sciences, Celgene Corporation, Cambridge, MA, USA
| | - Hannah M Martin
- School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | - James Yarmolinsky
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - Charles Laurin
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - Michael V Holmes
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- National Institute for Health Research, Oxford Biomedical Research Centre, Oxford University Hospital, Oxford, UK
| | - Jimmy Z Liu
- Translational Biology, Biogen, Cambridge, MA, USA
| | | | - Rita Santos
- Functional Genomics, GlaxoSmithKline, Stevenage, Hertfordshire, UK
| | | | | | | | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- Proteome MR writing group
- NIHR Bristol Biomedical Research Centre, Bristol, UK
| | - Adam S Butterworth
- Proteome MR writing group
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- BHF Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, Cambridge, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- NIHR Cambridge Biomedical Research Centre, School of Clinical Medicine, Addenbrooke's Hospital, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- Proteome MR writing group
| | - Robert A Scott
- Proteome MR writing group, .
- Human Genetics, GlaxoSmithKline, Stevenage, UK.
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK.
- Proteome MR writing group, .
- NIHR Bristol Biomedical Research Centre, Bristol, UK.
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11
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Richardson TG, Sanderson E, Elsworth B, Tilling K, Davey Smith G. Use of genetic variation to separate the effects of early and later life adiposity on disease risk: mendelian randomisation study. BMJ 2020; 369:m1203. [PMID: 32376654 PMCID: PMC7201936 DOI: 10.1136/bmj.m1203] [Citation(s) in RCA: 124] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
OBJECTIVE To evaluate whether body size in early life has an independent effect on risk of disease in later life or whether its influence is mediated by body size in adulthood. DESIGN Two sample univariable and multivariable mendelian randomisation. SETTING The UK Biobank prospective cohort study and four large scale genome-wide association studies (GWAS) consortiums. PARTICIPANTS 453 169 participants enrolled in UK Biobank and a combined total of more than 700 000 people from different GWAS consortiums. EXPOSURES Measured body mass index during adulthood (mean age 56.5) and self-reported perceived body size at age 10. MAIN OUTCOME MEASURES Coronary artery disease, type 2 diabetes, breast cancer, and prostate cancer. RESULTS Having a larger genetically predicted body size in early life was associated with an increased odds of coronary artery disease (odds ratio 1.49 for each change in body size category unless stated otherwise, 95% confidence interval 1.33 to 1.68) and type 2 diabetes (2.32, 1.76 to 3.05) based on univariable mendelian randomisation analyses. However, little evidence was found of a direct effect (ie, not through adult body size) based on multivariable mendelian randomisation estimates (coronary artery disease: 1.02, 0.86 to 1.22; type 2 diabetes:1.16, 0.74 to 1.82). In the multivariable mendelian randomisation analysis of breast cancer risk, strong evidence was found of a protective direct effect for larger body size in early life (0.59, 0.50 to 0.71), with less evidence of a direct effect of adult body size on this outcome (1.08, 0.93 to 1.27). Including age at menarche as an additional exposure provided weak evidence of a total causal effect (univariable mendelian randomisation odds ratio 0.98, 95% confidence interval 0.91 to 1.06) but strong evidence of a direct causal effect, independent of early life and adult body size (multivariable mendelian randomisation odds ratio 0.90, 0.85 to 0.95). No strong evidence was found of a causal effect of either early or later life measures on prostate cancer (early life body size odds ratio 1.06, 95% confidence interval 0.81 to 1.40; adult body size 0.87, 0.70 to 1.08). CONCLUSIONS The findings suggest that the positive association between body size in childhood and risk of coronary artery disease and type 2 diabetes in adulthood can be attributed to individuals remaining large into later life. However, having a smaller body size during childhood might increase the risk of breast cancer regardless of body size in adulthood, with timing of puberty also putatively playing a role.
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Affiliation(s)
- Tom G Richardson
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Eleanor Sanderson
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Benjamin Elsworth
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Kate Tilling
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
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12
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Zheng J, Maerz W, Gergei I, Kleber M, Drechsler C, Wanner C, Brandenburg V, Reppe S, Gautvik KM, Medina-Gomez C, Shevroja E, Gilly A, Park YC, Dedoussis G, Zeggini E, Lorentzon M, Henning P, Lerner UH, Nilsson KH, Movérare-Skrtic S, Baird D, Elsworth B, Falk L, Groom A, Capellini TD, Grundberg E, Nethander M, Ohlsson C, Davey Smith G, Tobias JH. Mendelian Randomization Analysis Reveals a Causal Influence of Circulating Sclerostin Levels on Bone Mineral Density and Fractures. J Bone Miner Res 2019; 34:1824-1836. [PMID: 31170332 PMCID: PMC6899787 DOI: 10.1002/jbmr.3803] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 05/14/2019] [Accepted: 05/23/2019] [Indexed: 12/21/2022]
Abstract
In bone, sclerostin is mainly osteocyte-derived and plays an important local role in adaptive responses to mechanical loading. Whether circulating levels of sclerostin also play a functional role is currently unclear, which we aimed to examine by two-sample Mendelian randomization (MR). A genetic instrument for circulating sclerostin, derived from a genomewide association study (GWAS) meta-analysis of serum sclerostin in 10,584 European-descent individuals, was examined in relation to femoral neck bone mineral density (BMD; n = 32,744) in GEFOS and estimated bone mineral density (eBMD) by heel ultrasound (n = 426,824) and fracture risk (n = 426,795) in UK Biobank. Our GWAS identified two novel serum sclerostin loci, B4GALNT3 (standard deviation [SD]) change in sclerostin per A allele (β = 0.20, p = 4.6 × 10-49 ) and GALNT1 (β = 0.11 per G allele, p = 4.4 × 10-11 ). B4GALNT3 is an N-acetyl-galactosaminyltransferase, adding a terminal LacdiNAc disaccharide to target glycocoproteins, found to be predominantly expressed in kidney, whereas GALNT1 is an enzyme causing mucin-type O-linked glycosylation. Using these two single-nucleotide polymorphisms (SNPs) as genetic instruments, MR revealed an inverse causal relationship between serum sclerostin and femoral neck BMD (β = -0.12, 95% confidence interval [CI] -0.20 to -0.05) and eBMD (β = -0.12, 95% CI -0.14 to -0.10), and a positive relationship with fracture risk (β = 0.11, 95% CI 0.01 to 0.21). Colocalization analysis demonstrated common genetic signals within the B4GALNT3 locus for higher sclerostin, lower eBMD, and greater B4GALNT3 expression in arterial tissue (probability >99%). Our findings suggest that higher sclerostin levels are causally related to lower BMD and greater fracture risk. Hence, strategies for reducing circulating sclerostin, for example by targeting glycosylation enzymes as suggested by our GWAS results, may prove valuable in treating osteoporosis. © 2019 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals, Inc.
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Affiliation(s)
- Jie Zheng
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - Winfried Maerz
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria.,SYNLAB Academy, SYNLAB Holding Deutschland GmbH, Mannheim, Germany.,Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Ingrid Gergei
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Marcus Kleber
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | | | - Christoph Wanner
- Department of Cardiology and Nephrology, Rhein-Maas-Klinikum Würselen, Germany
| | - Vincent Brandenburg
- Department of Cardiology and Nephrology, Rhein-Maas-Klinikum Würselen, Germany
| | - Sjur Reppe
- Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway.,Unger-Vetlesen Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - Kaare M Gautvik
- Unger-Vetlesen Institute, Lovisenberg Diaconal Hospital, Oslo, Norway.,Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Carolina Medina-Gomez
- Department of Internal Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Enisa Shevroja
- Department of Internal Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Arthur Gilly
- Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.,Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Young-Chan Park
- Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.,University of Cambridge, Cambridge, UK
| | - George Dedoussis
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
| | - Eleftheria Zeggini
- Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.,Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Mattias Lorentzon
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.,Geriatric Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.,Geriatric Medicine Clinic, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Petra Henning
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Ulf H Lerner
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Karin H Nilsson
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Sofia Movérare-Skrtic
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Denis Baird
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - Benjamin Elsworth
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - Louise Falk
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - Alix Groom
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK.,Bristol Bioresource Laboratories, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Terence D Capellini
- Human Evolutionary Biology, Harvard University, Boston, MA, USA.,Broad Institute of MIT and Harvard, Boston, MA, USA
| | - Elin Grundberg
- Department of Human Genetics, McGill University, Quebec, Canada.,Center for Pediatric Genomic Medicine, Children's Mercy, Kansas City, MO, USA
| | - Maria Nethander
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Claes Ohlsson
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - Jonathan H Tobias
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK.,Musculoskeletal Research Unit, University of Bristol, Bristol, UK
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13
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Dudding T, Haworth S, Lind PA, Sathirapongsasuti JF, Tung JY, Mitchell R, Colodro-Conde L, Medland SE, Gordon S, Elsworth B, Paternoster L, Franks PW, Thomas SJ, Martin NG, Timpson NJ. Genome wide analysis for mouth ulcers identifies associations at immune regulatory loci. Nat Commun 2019; 10:1052. [PMID: 30837455 PMCID: PMC6400940 DOI: 10.1038/s41467-019-08923-6] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [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: 10/12/2018] [Accepted: 02/05/2019] [Indexed: 12/23/2022] Open
Abstract
Mouth ulcers are the most common ulcerative condition and encompass several clinical diagnoses, including recurrent aphthous stomatitis (RAS). Despite previous evidence for heritability, it is not clear which specific genetic loci are implicated in RAS. In this genome-wide association study (n = 461,106) heritability is estimated at 8.2% (95% CI: 6.4%, 9.9%). This study finds 97 variants which alter the odds of developing non-specific mouth ulcers and replicate these in an independent cohort (n = 355,744) (lead variant after meta-analysis: rs76830965, near IL12A, OR 0.72 (95% CI: 0.71, 0.73); P = 4.4e−483). Additional effect estimates from three independent cohorts with more specific phenotyping and specific study characteristics support many of these findings. In silico functional analyses provide evidence for a role of T cell regulation in the aetiology of mouth ulcers. These results provide novel insight into the pathogenesis of a common, important condition. Oral ulcerations are sores of the mucous membrane of the mouth and highly prevalent in the population. Here, in a genome-wide association study, the authors identify 97 loci associated with mouth ulcers highlighting genes involved in T cell-mediated immunity and TH1 responses.
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Affiliation(s)
- Tom Dudding
- Medical Research Council Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK.,Bristol Dental School, University of Bristol, Bristol, BS1 2LY, UK
| | - Simon Haworth
- Medical Research Council Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK.,Bristol Dental School, University of Bristol, Bristol, BS1 2LY, UK
| | - Penelope A Lind
- Department of Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, 4006, Queensland, Australia
| | | | | | - Joyce Y Tung
- Research, 23andMe, Inc, Mountain View, 94041, CA, USA
| | - Ruth Mitchell
- Medical Research Council Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Lucía Colodro-Conde
- Department of Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, 4006, Queensland, Australia
| | - Sarah E Medland
- Department of Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, 4006, Queensland, Australia
| | - Scott Gordon
- Department of Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, 4006, Queensland, Australia
| | - Benjamin Elsworth
- Medical Research Council Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Lavinia Paternoster
- Medical Research Council Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Paul W Franks
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University, Malmö, 221 00, Sweden.,Department of Public Health & Clinical Medicine, Umeå University, Umeå, 901 87, Sweden.,Department of Nutrition, Harvard T.H. Chan School of Public Health, Harvard University, Boston, 02115, MA, USA
| | - Steven J Thomas
- Bristol Dental School, University of Bristol, Bristol, BS1 2LY, UK
| | - Nicholas G Martin
- Department of Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, 4006, Queensland, Australia
| | - Nicholas J Timpson
- Medical Research Council Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK.
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14
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Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, Laurin C, Burgess S, Bowden J, Langdon R, Tan VY, Yarmolinsky J, Shihab HA, Timpson NJ, Evans DM, Relton C, Martin RM, Davey Smith G, Gaunt TR, Haycock PC. The MR-Base platform supports systematic causal inference across the human phenome. eLife 2018. [PMID: 29846171 DOI: 10.7554/elife.34408s] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
Abstract
Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base (<ext-link ext-link-type="uri" xlink:href="http://www.mrbase.org">http://www.mrbase.org</ext-link>): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.
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Affiliation(s)
- Gibran Hemani
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Jie Zheng
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Benjamin Elsworth
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Kaitlin H Wade
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Valeriia Haberland
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Denis Baird
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Charles Laurin
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Stephen Burgess
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Jack Bowden
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Ryan Langdon
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Vanessa Y Tan
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - James Yarmolinsky
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Hashem A Shihab
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Nicholas J Timpson
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - David M Evans
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom.,University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Australia
| | - Caroline Relton
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Richard M Martin
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - George Davey Smith
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Tom R Gaunt
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Philip C Haycock
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
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15
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Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, Laurin C, Burgess S, Bowden J, Langdon R, Tan VY, Yarmolinsky J, Shihab HA, Timpson NJ, Evans DM, Relton C, Martin RM, Davey Smith G, Gaunt TR, Haycock PC. The MR-Base platform supports systematic causal inference across the human phenome. eLife 2018; 7:e34408. [PMID: 29846171 PMCID: PMC5976434 DOI: 10.7554/elife.34408] [Citation(s) in RCA: 2953] [Impact Index Per Article: 492.2] [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: 01/03/2018] [Accepted: 03/28/2018] [Indexed: 12/21/2022] Open
Abstract
Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base (http://www.mrbase.org): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.
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Affiliation(s)
- Gibran Hemani
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Jie Zheng
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Benjamin Elsworth
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Kaitlin H Wade
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Valeriia Haberland
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Denis Baird
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Charles Laurin
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Stephen Burgess
- Department of Public Health and Primary CareUniversity of CambridgeCambridgeUnited Kingdom
| | - Jack Bowden
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Ryan Langdon
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Vanessa Y Tan
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - James Yarmolinsky
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Hashem A Shihab
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Nicholas J Timpson
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - David M Evans
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
- University of Queensland Diamantina InstituteTranslational Research InstituteBrisbaneAustralia
| | - Caroline Relton
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Richard M Martin
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - George Davey Smith
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Tom R Gaunt
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Philip C Haycock
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
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16
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Hui MN, Cazet A, Elsworth B, Roden D, Cox T, Yang J, McFarland A, Deng N, Chan CL, O'Toole S, Swarbrick A. Targeting the Hedgehog signalling pathway in triple negative breast cancer. J Clin Oncol 2018. [DOI: 10.1200/jco.2018.36.15_suppl.e24216] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Mun Ngah Hui
- The Chris O'Brien Lifehouse, Camperdown, Australia
| | - Aurelie Cazet
- The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Darlinghurst, Australia
| | | | - Daniel Roden
- The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Darlinghurst, Australia
| | - Thomas Cox
- The Kinghorn Cancer Centre, Garvan Institute of Medical Reserach, Darlinghurst, Australia
| | - Jessica Yang
- The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Darlinghurst, Australia
| | - Andrea McFarland
- The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Darlinghurst, Australia
| | - NianTao Deng
- The Kinghorn Cancer Centre, Garvan Institute of Medical Reserach, Darlinghurst, Australia
| | - Chia-Ling Chan
- The Kinghorn Cancer Centre, Garvan Institute of Medical Reserach, Darlinghurst, Australia
| | - Sandra O'Toole
- The Kinghorn Cancer Centre, Garvan Institute of Medical Research, UNSW, Darlinghurst, Australia
| | - Alexander Swarbrick
- The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Darlinghurst, Australia
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17
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Sax MJ, Gasch C, Athota VR, Freeman R, Rasighaemi P, Westcott DE, Day CJ, Nikolic I, Elsworth B, Wei M, Rogers K, Swarbrick A, Mittal V, Pouliot N, Mellick AS. Cancer cell CCL5 mediates bone marrow independent angiogenesis in breast cancer. Oncotarget 2018; 7:85437-85449. [PMID: 27863423 PMCID: PMC5356747 DOI: 10.18632/oncotarget.13387] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2016] [Accepted: 10/27/2016] [Indexed: 01/03/2023] Open
Abstract
It has recently been suggested that the chemokine receptor (CCR5) is required for bone marrow (BM) derived endothelial progenitor cell (EPC) mediated angiogenesis. Here we show that suppression of either cancer cell produced CCL5, or host CCR5 leads to distinctive vascular and tumor growth defects in breast cancer. Surprisingly, CCR5 restoration in the BM alone was not sufficient to rescue the wild type phenotype, suggesting that impaired tumor growth associated with inhibiting CCL5/CCR5 is not due to defects in EPC biology. Instead, to promote angiogenesis cancer cell CCL5 may signal directly to endothelium in the tumor-stroma. In support of this hypothesis, we have also shown: (i) that endothelial cell CCR5 levels increases in response to tumor-conditioned media; (ii) that the amount of CCR5+ tumor vasculature correlates with invasive grade; and (iii) that inhibition of CCL5/CCR5 signaling impairs endothelial cell migration, associated with a decrease in activation of mTOR/AKT pathway members. Finally, we show that treatment with CCR5 antagonist results in less vasculature, impaired tumor growth, reduced metastases and improved survival. Taken as a whole, this work demonstrates that directly inhibiting CCR5 expressing vasculature constitutes a novel strategy for inhibiting angiogenesis and blocking metastatic progression in breast cancer.
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Affiliation(s)
- Michael John Sax
- School of Medical Science, Griffith University, Gold Coast, QLD, Australia
| | - Christin Gasch
- School of Medicine, Deakin University, Waurn Ponds, Victoria, Australia
| | - Vineel Rag Athota
- School of Medicine, Deakin University, Waurn Ponds, Victoria, Australia
| | - Ruth Freeman
- School of Medical Science, Griffith University, Gold Coast, QLD, Australia
| | - Parisa Rasighaemi
- School of Medicine, Deakin University, Waurn Ponds, Victoria, Australia
| | | | | | - Iva Nikolic
- Kinghorn Cancer Centre & Cancer Research Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.,St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Kensington NSW, Australia
| | - Benjamin Elsworth
- Kinghorn Cancer Centre & Cancer Research Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.,St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Kensington NSW, Australia
| | - Ming Wei
- School of Medical Science, Griffith University, Gold Coast, QLD, Australia
| | - Kelly Rogers
- Centre for Dynamic Imaging, Walter and Eliza Hall Institute for Medical Research, Parkville Victoria, Australia
| | - Alexander Swarbrick
- Kinghorn Cancer Centre & Cancer Research Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.,St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Kensington NSW, Australia
| | - Vivek Mittal
- Cardiothoracic Surgery and Neuberger Berman Lung Cancer Centre, Weill Cornell Medical College, New York, NY, USA
| | - Normand Pouliot
- Matrix Microenvironment & Metastasis Laboratory, Olivia Newton-John Cancer Research Institute, Heidelberg, Victoria, Australia.,School of Cancer Medicine, La Trobe University, Heidelberg, Victoria, Australia
| | - Albert Sleiman Mellick
- School of Medical Science, Griffith University, Gold Coast, QLD, Australia.,School of Medicine, Deakin University, Waurn Ponds, Victoria, Australia.,Faculty of Medicine, University of New South Wales, NSW, Australia.,School of Medicine, Western Sydney University, Campbelltown NSW, Australia.,Translational Oncology Unit, Ingham Institute for Applied Medical Research, Liverpool NSW, Australia
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18
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Nikolic I, Elsworth B, Dodson E, Wu SZ, Gould CM, Mestdagh P, Marshall GM, Horvath LG, Simpson KJ, Swarbrick A. Discovering cancer vulnerabilities using high-throughput micro-RNA screening. Nucleic Acids Res 2018; 45:12657-12670. [PMID: 29156009 PMCID: PMC5728403 DOI: 10.1093/nar/gkx1072] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [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: 11/07/2016] [Accepted: 10/19/2017] [Indexed: 12/20/2022] Open
Abstract
Micro-RNAs (miRNAs) are potent regulators of gene expression and cellular phenotype. Each miRNA has the potential to target hundreds of transcripts within the cell thus controlling fundamental cellular processes such as survival and proliferation. Here, we exploit this important feature of miRNA networks to discover vulnerabilities in cancer phenotype, and map miRNA-target relationships across different cancer types. More specifically, we report the results of a functional genomics screen of 1280 miRNA mimics and inhibitors in eight cancer cell lines, and its presentation in a sophisticated interactive data portal. This resource represents the most comprehensive survey of miRNA function in oncology, incorporating breast cancer, prostate cancer and neuroblastoma. A user-friendly web portal couples this experimental data with multiple tools for miRNA target prediction, pathway enrichment analysis and visualization. In addition, the database integrates publicly available gene expression and perturbation data enabling tailored and context-specific analysis of miRNA function in a particular disease. As a proof-of-principle, we use the database and its innovative features to uncover novel determinants of the neuroblastoma malignant phenotype.
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Affiliation(s)
- Iva Nikolic
- The Kinghorn Cancer Centre & Cancer Research Division, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia.,St Vincent's Clinical School, Faculty of Medicine, UNSW, Darlinghurst, NSW 2010, Australia.,Victorian Centre for Functional Genomics, Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia
| | - Benjamin Elsworth
- The Kinghorn Cancer Centre & Cancer Research Division, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia.,St Vincent's Clinical School, Faculty of Medicine, UNSW, Darlinghurst, NSW 2010, Australia
| | - Eoin Dodson
- The Kinghorn Cancer Centre & Cancer Research Division, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia.,St Vincent's Clinical School, Faculty of Medicine, UNSW, Darlinghurst, NSW 2010, Australia
| | - Sunny Z Wu
- The Kinghorn Cancer Centre & Cancer Research Division, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia.,St Vincent's Clinical School, Faculty of Medicine, UNSW, Darlinghurst, NSW 2010, Australia
| | - Cathryn M Gould
- The Kinghorn Cancer Centre & Cancer Research Division, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia.,Victorian Centre for Functional Genomics, Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia
| | - Pieter Mestdagh
- Center for Medical Genetics Ghent (CMGG), Ghent University, Ghent B-9000, Belgium.,Cancer Research Institute Ghent, Ghent University, Ghent B-9000, Belgium
| | - Glenn M Marshall
- Sydney Children's Hospital and Children's Cancer Institute, Sydney, NSW 2750, Australia
| | - Lisa G Horvath
- The Kinghorn Cancer Centre & Cancer Research Division, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia.,Chris O'Brien Lifehouse, Camperdown, NSW 2050, Australia.,University of Sydney, Camperdown, NSW 2050, Australia
| | - Kaylene J Simpson
- Victorian Centre for Functional Genomics, Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Alexander Swarbrick
- The Kinghorn Cancer Centre & Cancer Research Division, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia.,St Vincent's Clinical School, Faculty of Medicine, UNSW, Darlinghurst, NSW 2010, Australia
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19
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Elsworth B, Dawe K, Vincent EE, Langdon R, Lynch BM, Martin RM, Relton C, Higgins JPT, Gaunt TR. MELODI: Mining Enriched Literature Objects to Derive Intermediates. Int J Epidemiol 2018; 47:4803214. [PMID: 29342271 PMCID: PMC5913624 DOI: 10.1093/ije/dyx251] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Revised: 11/02/2017] [Accepted: 01/03/2018] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND The scientific literature contains a wealth of information from different fields on potential disease mechanisms. However, identifying and prioritizing mechanisms for further analytical evaluation presents enormous challenges in terms of the quantity and diversity of published research. The application of data mining approaches to the literature offers the potential to identify and prioritize mechanisms for more focused and detailed analysis. METHODS Here we present MELODI, a literature mining platform that can identify mechanistic pathways between any two biomedical concepts. RESULTS Two case studies demonstrate the potential uses of MELODI and how it can generate hypotheses for further investigation. First, an analysis of ETS-related gene ERG and prostate cancer derives the intermediate transcription factor SP1, recently confirmed to be physically interacting with ERG. Second, examining the relationship between a new potential risk factor for pancreatic cancer identifies possible mechanistic insights which can be studied in vitro. CONCLUSIONS We have demonstrated the possible applications of MELODI, including two case studies. MELODI has been implemented as a Python/Django web application, and is freely available to use at [www.melodi.biocompute.org.uk].
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Affiliation(s)
- Benjamin Elsworth
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Karen Dawe
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Emma E Vincent
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Ryan Langdon
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Brigid M Lynch
- Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, VIC, Australia
- Physical Activity Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Richard M Martin
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Caroline Relton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | | | - Tom R Gaunt
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
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20
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Phua YW, Nguyen A, Roden DL, Elsworth B, Deng N, Nikolic I, Yang J, Mcfarland A, Russell R, Kaplan W, Cowley MJ, Nair R, Zotenko E, O'Toole S, Tan SX, James DE, Clark SJ, Kouros-Mehr H, Swarbrick A. MicroRNA profiling of the pubertal mouse mammary gland identifies miR-184 as a candidate breast tumour suppressor gene. Breast Cancer Res 2015; 17:83. [PMID: 26070602 PMCID: PMC4504458 DOI: 10.1186/s13058-015-0593-0] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [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: 10/16/2014] [Accepted: 05/28/2015] [Indexed: 02/02/2023] Open
Abstract
INTRODUCTION The study of mammalian development has offered many insights into the molecular aetiology of cancer. We previously used analysis of mammary morphogenesis to discover a critical role for GATA-3 in mammary developmental and carcinogenesis. In recent years an important role for microRNAs (miRNAs) in a myriad of cellular processes in development and in oncogenesis has emerged. METHODS microRNA profiling was conducted on stromal and epithelial cellular subsets microdissected from the pubertal mouse mammary gland. miR-184 was reactivated by transient or stable overexpression in breast cancer cell lines and examined using a series of in vitro (proliferation, tumour-sphere and protein synthesis) assays. Orthotopic xenografts of breast cancer cells were used to assess the effect of miR-184 on tumourigenesis as well as distant metastasis. Interactions between miR-184 and its putative targets were assessed by quantitative PCR, microarray, bioinformatics and 3' untranslated region Luciferase reporter assay. The methylation status of primary patient samples was determined by MBD-Cap sequencing. Lastly, the clinical prognostic significance of miR-184 putative targets was assessed using publicly available datasets. RESULTS A large number of microRNA were restricted in their expression to specific tissue subsets. MicroRNA-184 (miR-184) was exclusively expressed in epithelial cells and markedly upregulated during differentiation of the proliferative, invasive cells of the pubertal terminal end bud (TEB) into ductal epithelial cells in vivo. miR-184 expression was silenced in mouse tumour models compared to non-transformed epithelium and in a majority of breast cancer cell line models. Ectopic reactivation of miR-184 inhibited the proliferation and self-renewal of triple negative breast cancer (TNBC) cell lines in vitro and delayed primary tumour formation and reduced metastatic burden in vivo. Gene expression studies uncovered multi-factorial regulation of genes in the AKT/mTORC1 pathway by miR-184. In clinical breast cancer tissues, expression of miR-184 is lost in primary TNBCs while the miR-184 promoter is methylated in a subset of lymph node metastases from TNBC patients. CONCLUSIONS These studies elucidate a new layer of regulation in the PI3K/AKT/mTOR pathway with relevance to mammary development and tumour progression and identify miR-184 as a putative breast tumour suppressor.
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Affiliation(s)
- Yu Wei Phua
- The Kinghorn Cancer Centre & Cancer Research Division, Garvan Institute of Medical Research, 370 Victoria Street, Darlinghurst, NSW, Sydney, Australia.
- St Vincent's Clinical School, Faculty of Medicine, Sydney, UNSW, Australia.
| | - Akira Nguyen
- The Kinghorn Cancer Centre & Cancer Research Division, Garvan Institute of Medical Research, 370 Victoria Street, Darlinghurst, NSW, Sydney, Australia.
- St Vincent's Clinical School, Faculty of Medicine, Sydney, UNSW, Australia.
| | - Daniel L Roden
- The Kinghorn Cancer Centre & Cancer Research Division, Garvan Institute of Medical Research, 370 Victoria Street, Darlinghurst, NSW, Sydney, Australia.
- St Vincent's Clinical School, Faculty of Medicine, Sydney, UNSW, Australia.
| | - Benjamin Elsworth
- The Kinghorn Cancer Centre & Cancer Research Division, Garvan Institute of Medical Research, 370 Victoria Street, Darlinghurst, NSW, Sydney, Australia.
- St Vincent's Clinical School, Faculty of Medicine, Sydney, UNSW, Australia.
| | - Niantao Deng
- The Kinghorn Cancer Centre & Cancer Research Division, Garvan Institute of Medical Research, 370 Victoria Street, Darlinghurst, NSW, Sydney, Australia.
- St Vincent's Clinical School, Faculty of Medicine, Sydney, UNSW, Australia.
| | - Iva Nikolic
- The Kinghorn Cancer Centre & Cancer Research Division, Garvan Institute of Medical Research, 370 Victoria Street, Darlinghurst, NSW, Sydney, Australia.
- St Vincent's Clinical School, Faculty of Medicine, Sydney, UNSW, Australia.
| | - Jessica Yang
- The Kinghorn Cancer Centre & Cancer Research Division, Garvan Institute of Medical Research, 370 Victoria Street, Darlinghurst, NSW, Sydney, Australia.
| | - Andrea Mcfarland
- The Kinghorn Cancer Centre & Cancer Research Division, Garvan Institute of Medical Research, 370 Victoria Street, Darlinghurst, NSW, Sydney, Australia.
| | - Roslin Russell
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, UK.
| | - Warren Kaplan
- The Kinghorn Cancer Centre & Cancer Research Division, Garvan Institute of Medical Research, 370 Victoria Street, Darlinghurst, NSW, Sydney, Australia.
| | - Mark J Cowley
- The Kinghorn Cancer Centre & Cancer Research Division, Garvan Institute of Medical Research, 370 Victoria Street, Darlinghurst, NSW, Sydney, Australia.
- St Vincent's Clinical School, Faculty of Medicine, Sydney, UNSW, Australia.
| | - Radhika Nair
- The Kinghorn Cancer Centre & Cancer Research Division, Garvan Institute of Medical Research, 370 Victoria Street, Darlinghurst, NSW, Sydney, Australia.
- St Vincent's Clinical School, Faculty of Medicine, Sydney, UNSW, Australia.
| | - Elena Zotenko
- The Kinghorn Cancer Centre & Cancer Research Division, Garvan Institute of Medical Research, 370 Victoria Street, Darlinghurst, NSW, Sydney, Australia.
- St Vincent's Clinical School, Faculty of Medicine, Sydney, UNSW, Australia.
| | - Sandra O'Toole
- The Kinghorn Cancer Centre & Cancer Research Division, Garvan Institute of Medical Research, 370 Victoria Street, Darlinghurst, NSW, Sydney, Australia. sandra.o'
- St Vincent's Clinical School, Faculty of Medicine, Sydney, UNSW, Australia. sandra.o'
- Department of Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia. sandra.o'
- Sydney Medical School, The University of Sydney, Camperdown, NSW, Australia. sandra.o'
| | - Shi-Xiong Tan
- Metabolism in Human Diseases Unit, Institute of Molecular and Cell Biology, A*STAR, 61 Biopolis Drive, Proteos, Singapore.
| | - David E James
- Sydney Medical School, The University of Sydney, Camperdown, NSW, Australia.
- The Charles Perkins Centre, School of Molecular Bioscience, University of Sydney, Camperdown, NSW, Australia.
| | - Susan J Clark
- The Kinghorn Cancer Centre & Cancer Research Division, Garvan Institute of Medical Research, 370 Victoria Street, Darlinghurst, NSW, Sydney, Australia.
- St Vincent's Clinical School, Faculty of Medicine, Sydney, UNSW, Australia.
| | - Hosein Kouros-Mehr
- Agensys, affiliate of Astellas Pharmaceuticals, 1800 Stewart St, Santa Monica, CA, 90403, USA.
| | - Alexander Swarbrick
- The Kinghorn Cancer Centre & Cancer Research Division, Garvan Institute of Medical Research, 370 Victoria Street, Darlinghurst, NSW, Sydney, Australia.
- St Vincent's Clinical School, Faculty of Medicine, Sydney, UNSW, Australia.
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Abstract
The diversity of biology in nematodes is reflected in the diversity of their genomes. Parasitic species in particular have evolved mechanisms to invade and outwit their hosts, and these offer opportunities for the development of control measures. Genomic analyses can reveal the molecular underpinnings of phenotypes such as parasitism and thus, initiate and support research programmes that explore the manipulation of host and parasite physiologies to achieve favourable outcomes. Wide sampling across nematode diversity allows phylogenetically informed formulation of research hypotheses, identification of core features shared by all species or important evolutionary novelties present in isolated clades. Many nematode species have been investigated through the use of the expressed sequence tag approach, which samples from the transcribed genome. Gene catalogues generated in this way can be explored to reveal the patterns of expression associated with parasitism and candidates for testing as drug targets or vaccine components. Analysis environments, such as NEMBASE facilitate exploitation of these data. The development of new high-throughput DNA-sequencing technologies has facilitated transcriptomic and genomic approaches to parasite biology. Whole genome sequencing offers more complete catalogues of genes and assists a systems approach to phenotype dissection. These efforts are being coordinated through the 959 Nematode Genomes initiative.
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Affiliation(s)
- M Blaxter
- Institute of Evolutionary Biology, The University of Edinburgh, Edinburgh EH9 3JT, UK.
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22
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Tyson T, O'Mahony Zamora G, Wong S, Skelton M, Daly B, Jones JT, Mulvihill ED, Elsworth B, Phillips M, Blaxter M, Burnell AM. A molecular analysis of desiccation tolerance mechanisms in the anhydrobiotic nematode Panagrolaimus superbus using expressed sequenced tags. BMC Res Notes 2012; 5:68. [PMID: 22281184 PMCID: PMC3296651 DOI: 10.1186/1756-0500-5-68] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [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: 11/22/2011] [Accepted: 01/26/2012] [Indexed: 11/13/2022] Open
Abstract
Background Some organisms can survive extreme desiccation by entering into a state of suspended animation known as anhydrobiosis. Panagrolaimus superbus is a free-living anhydrobiotic nematode that can survive rapid environmental desiccation. The mechanisms that P. superbus uses to combat the potentially lethal effects of cellular dehydration may include the constitutive and inducible expression of protective molecules, along with behavioural and/or morphological adaptations that slow the rate of cellular water loss. In addition, inducible repair and revival programmes may also be required for successful rehydration and recovery from anhydrobiosis. Results To identify constitutively expressed candidate anhydrobiotic genes we obtained 9,216 ESTs from an unstressed mixed stage population of P. superbus. We derived 4,009 unigenes from these ESTs. These unigene annotations and sequences can be accessed at http://www.nematodes.org/nembase4/species_info.php?species=PSC. We manually annotated a set of 187 constitutively expressed candidate anhydrobiotic genes from P. superbus. Notable among those is a putative lineage expansion of the lea (late embryogenesis abundant) gene family. The most abundantly expressed sequence was a member of the nematode specific sxp/ral-2 family that is highly expressed in parasitic nematodes and secreted onto the surface of the nematodes' cuticles. There were 2,059 novel unigenes (51.7% of the total), 149 of which are predicted to encode intrinsically disordered proteins lacking a fixed tertiary structure. One unigene may encode an exo-β-1,3-glucanase (GHF5 family), most similar to a sequence from Phytophthora infestans. GHF5 enzymes have been reported from several species of plant parasitic nematodes, with horizontal gene transfer (HGT) from bacteria proposed to explain their evolutionary origin. This P. superbus sequence represents another possible HGT event within the Nematoda. The expression of five of the 19 putative stress response genes tested was upregulated in response to desiccation. These were the antioxidants glutathione peroxidase, dj-1 and 1-Cys peroxiredoxin, an shsp sequence and an lea gene. Conclusions P. superbus appears to utilise a strategy of combined constitutive and inducible gene expression in preparation for entry into anhydrobiosis. The apparent lineage expansion of lea genes, together with their constitutive and inducible expression, suggests that LEA3 proteins are important components of the anhydrobiotic protection repertoire of P. superbus.
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Affiliation(s)
- Trevor Tyson
- Department of Biology, National University of Ireland Maynooth, Maynooth, Co, Kildare, Ireland.
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Elsworth B, Wasmuth J, Blaxter M. NEMBASE4: the nematode transcriptome resource. Int J Parasitol 2011; 41:881-94. [PMID: 21550347 DOI: 10.1016/j.ijpara.2011.03.009] [Citation(s) in RCA: 55] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2010] [Revised: 03/11/2011] [Accepted: 03/14/2011] [Indexed: 11/28/2022]
Abstract
Nematode parasites are of major importance in human health and agriculture, and free-living species deliver essential ecosystem services. The genomics revolution has resulted in the production of many datasets of expressed sequence tags (ESTs) from a phylogenetically wide range of nematode species, but these are not easily compared. NEMBASE4 presents a single portal into extensively functionally annotated, EST-derived transcriptomes from over 60 species of nematodes, including plant and animal parasites and free-living taxa. Using the PartiGene suite of tools, we have assembled the publicly available ESTs for each species into a high-quality set of putative transcripts. These transcripts have been translated to produce a protein sequence resource and each is annotated with functional information derived from comparison with well-studied nematode species such as Caenorhabditis elegans and other non-nematode resources. By cross-comparing the sequences within NEMBASE4, we have also generated a protein family assignment for each translation. The data are presented in an openly accessible, interactive database. To demonstrate the utility of NEMBASE4, we have used the database to examine the uniqueness of the transcriptomes of major clades of parasitic nematodes, identifying lineage-restricted genes that may underpin particular parasitic phenotypes, possible viral pathogens of nematodes, and nematode-unique protein families that may be developed as drug targets.
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Affiliation(s)
- Benjamin Elsworth
- Institute of Evolutionary Biology, The University of Edinburgh, Edinburgh EH9 3JT, UK
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