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McGurk KA, Zhang X, Theotokis P, Thomson K, Harper A, Buchan RJ, Mazaika E, Ormondroyd E, Wright WT, Macaya D, Pua CJ, Funke B, MacArthur DG, Prasad SK, Cook SA, Allouba M, Aguib Y, Yacoub MH, O'Regan DP, Barton PJR, Watkins H, Bottolo L, Ware JS. The penetrance of rare variants in cardiomyopathy-associated genes: A cross-sectional approach to estimating penetrance for secondary findings. Am J Hum Genet 2023; 110:1482-1495. [PMID: 37652022 PMCID: PMC10502871 DOI: 10.1016/j.ajhg.2023.08.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [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: 03/15/2023] [Revised: 07/21/2023] [Accepted: 08/04/2023] [Indexed: 09/02/2023] Open
Abstract
Understanding the penetrance of pathogenic variants identified as secondary findings (SFs) is of paramount importance with the growing availability of genetic testing. We estimated penetrance through large-scale analyses of individuals referred for diagnostic sequencing for hypertrophic cardiomyopathy (HCM; 10,400 affected individuals, 1,332 variants) and dilated cardiomyopathy (DCM; 2,564 affected individuals, 663 variants), using a cross-sectional approach comparing allele frequencies against reference populations (293,226 participants from UK Biobank and gnomAD). We generated updated prevalence estimates for HCM (1:543) and DCM (1:220). In aggregate, the penetrance by late adulthood of rare, pathogenic variants (23% for HCM, 35% for DCM) and likely pathogenic variants (7% for HCM, 10% for DCM) was substantial for dominant cardiomyopathy (CM). Penetrance was significantly higher for variant subgroups annotated as loss of function or ultra-rare and for males compared to females for variants in HCM-associated genes. We estimated variant-specific penetrance for 316 recurrent variants most likely to be identified as SFs (found in 51% of HCM- and 17% of DCM-affected individuals). 49 variants were observed at least ten times (14% of affected individuals) in HCM-associated genes. Median penetrance was 14.6% (±14.4% SD). We explore estimates of penetrance by age, sex, and ancestry and simulate the impact of including future cohorts. This dataset reports penetrance of individual variants at scale and will inform the management of individuals undergoing genetic screening for SFs. While most variants had low penetrance and the costs and harms of screening are unclear, some individuals with highly penetrant variants may benefit from SFs.
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Affiliation(s)
- Kathryn A McGurk
- National Heart and Lung Institute, Imperial College London, London, UK; MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Xiaolei Zhang
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Pantazis Theotokis
- National Heart and Lung Institute, Imperial College London, London, UK; MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Kate Thomson
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine and the Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Andrew Harper
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine and the Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Rachel J Buchan
- National Heart and Lung Institute, Imperial College London, London, UK; MRC London Institute of Medical Sciences, Imperial College London, London, UK; Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Erica Mazaika
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Elizabeth Ormondroyd
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine and the Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - William T Wright
- Northern Ireland Regional Genetics Centre, Belfast Health and Social Care Trust, Belfast City Hospital, Belfast, Northern Ireland, UK
| | | | - Chee Jian Pua
- National Heart Research Institute Singapore and Duke-National University of Singapore, Singapore, Singapore
| | - Birgit Funke
- Laboratory for Molecular Medicine, Partners Healthcare Center for Personalized Genetic Medicine, Boston, MA, USA
| | - Daniel G MacArthur
- Centre for Population Genomics, Garvan Institute of Medical Research and UNSW, Sydney, NSW, Australia; Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Sanjay K Prasad
- National Heart and Lung Institute, Imperial College London, London, UK; Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Stuart A Cook
- MRC London Institute of Medical Sciences, Imperial College London, London, UK; National Heart Research Institute Singapore and Duke-National University of Singapore, Singapore, Singapore
| | - Mona Allouba
- National Heart and Lung Institute, Imperial College London, London, UK; Aswan Heart Centre, Aswan, Egypt
| | - Yasmine Aguib
- National Heart and Lung Institute, Imperial College London, London, UK; Aswan Heart Centre, Aswan, Egypt
| | - Magdi H Yacoub
- National Heart and Lung Institute, Imperial College London, London, UK; Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK; Aswan Heart Centre, Aswan, Egypt
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Paul J R Barton
- National Heart and Lung Institute, Imperial College London, London, UK; MRC London Institute of Medical Sciences, Imperial College London, London, UK; Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Hugh Watkins
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine and the Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Leonardo Bottolo
- Department of Medical Genetics, University of Cambridge, Cambridge, UK; The Alan Turing Institute, London, UK; MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - James S Ware
- National Heart and Lung Institute, Imperial College London, London, UK; MRC London Institute of Medical Sciences, Imperial College London, London, UK; Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK.
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Zuber V, Lewin A, Levin MG, Haglund A, Ben-Aicha S, Emanueli C, Damrauer S, Burgess S, Gill D, Bottolo L. Multi-response Mendelian randomization: Identification of shared and distinct exposures for multimorbidity and multiple related disease outcomes. Am J Hum Genet 2023; 110:1177-1199. [PMID: 37419091 PMCID: PMC10357504 DOI: 10.1016/j.ajhg.2023.06.005] [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: 01/30/2023] [Revised: 06/11/2023] [Accepted: 06/11/2023] [Indexed: 07/09/2023] Open
Abstract
The existing framework of Mendelian randomization (MR) infers the causal effect of one or multiple exposures on one single outcome. It is not designed to jointly model multiple outcomes, as would be necessary to detect causes of more than one outcome and would be relevant to model multimorbidity or other related disease outcomes. Here, we introduce multi-response Mendelian randomization (MR2), an MR method specifically designed for multiple outcomes to identify exposures that cause more than one outcome or, conversely, exposures that exert their effect on distinct responses. MR2 uses a sparse Bayesian Gaussian copula regression framework to detect causal effects while estimating the residual correlation between summary-level outcomes, i.e., the correlation that cannot be explained by the exposures, and vice versa. We show both theoretically and in a comprehensive simulation study how unmeasured shared pleiotropy induces residual correlation between outcomes irrespective of sample overlap. We also reveal how non-genetic factors that affect more than one outcome contribute to their correlation. We demonstrate that by accounting for residual correlation, MR2 has higher power to detect shared exposures causing more than one outcome. It also provides more accurate causal effect estimates than existing methods that ignore the dependence between related responses. Finally, we illustrate how MR2 detects shared and distinct causal exposures for five cardiovascular diseases in two applications considering cardiometabolic and lipidomic exposures and uncovers residual correlation between summary-level outcomes reflecting known relationships between cardiovascular diseases.
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Affiliation(s)
- Verena Zuber
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK; UK Dementia Research Institute, Imperial College London, London, UK.
| | - Alex Lewin
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Michael G Levin
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Corporal Michael J. Crescenz VA Medical Center, Philadelphia, USA
| | - Alexander Haglund
- Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK
| | - Soumaya Ben-Aicha
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Costanza Emanueli
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Scott Damrauer
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Corporal Michael J. Crescenz VA Medical Center, Philadelphia, USA
| | - Stephen Burgess
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK; Cardiovascular Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; Chief Scientific Advisor Office, Research and Early Development, Novo Nordisk, Copenhagen, Denmark
| | - Leonardo Bottolo
- Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, UK; Alan Turing Institute, London, UK; MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
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Bilo L, Ochoa E, Lee S, Dey D, Kurth I, Kraft F, Rodger F, Docquier F, Toribio A, Bottolo L, Binder G, Fekete G, Elbracht M, Maher ER, Begemann M, Eggermann T. Molecular characterisation of 36 multilocus imprinting disturbance (MLID) patients: a comprehensive approach. Clin Epigenetics 2023; 15:35. [PMID: 36859312 PMCID: PMC9979536 DOI: 10.1186/s13148-023-01453-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 02/20/2023] [Indexed: 03/03/2023] Open
Abstract
BACKGROUND Imprinting disorders (ImpDis) comprise diseases which are caused by aberrant regulation of monoallelically and parent-of-origin-dependent expressed genes. A characteristic molecular change in ImpDis patients is aberrant methylation signatures at disease-specific loci, without an obvious DNA change at the specific differentially methylated region (DMR). However, there is a growing number of reports on multilocus imprinting disturbances (MLIDs), i.e. aberrant methylation at different DMRs in the same patient. These MLIDs account for a significant number of patients with specific ImpDis, and several reports indicate a central role of pathogenic maternal effect variants in their aetiology by affecting the maturation of the oocyte and the early embryo. Though several studies on the prevalence and the molecular causes of MLID have been conducted, homogeneous datasets comprising both genomic and methylation data are still lacking. RESULTS Based on a cohort of 36 MLID patients, we here present both methylation data obtained from next-generation sequencing (NGS, ImprintSeq) approaches and whole-exome sequencing (WES). The compilation of methylation data did not reveal a disease-specific MLID episignature, and a predisposition for the phenotypic modification was not obvious as well. In fact, this lack of epigenotype-phenotype correlation might be related to the mosaic distribution of imprinting defects and their functional relevance in specific tissues. CONCLUSIONS Due to the higher sensitivity of NGS-based approaches, we suggest that ImprintSeq might be offered at reference centres in case of ImpDis patients with unusual phenotypes but MLID negative by conventional tests. By WES, additional MLID causes than the already known maternal effect variants could not be identified, neither in the patients nor in the maternal exomes. In cases with negative WES results, it is currently unclear to what extent either environmental factors or undetected genetic variants contribute to MLID.
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Affiliation(s)
- Larissa Bilo
- Medical Faculty, Institute for Human Genetics and Genome Medicine, RWTH Aachen University, Pauwelsstr. 30, 52074, Aachen, Germany
| | - Eguzkine Ochoa
- Department of Medical Genetics, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Sunwoo Lee
- Department of Medical Genetics, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Daniela Dey
- Medical Faculty, Institute for Human Genetics and Genome Medicine, RWTH Aachen University, Pauwelsstr. 30, 52074, Aachen, Germany
| | - Ingo Kurth
- Medical Faculty, Institute for Human Genetics and Genome Medicine, RWTH Aachen University, Pauwelsstr. 30, 52074, Aachen, Germany
| | - Florian Kraft
- Medical Faculty, Institute for Human Genetics and Genome Medicine, RWTH Aachen University, Pauwelsstr. 30, 52074, Aachen, Germany
| | - Fay Rodger
- Department of Medical Genetics, University of Cambridge, Cambridge, CB2 0QQ, UK
- Stratified Medicine Core Laboratory NGS Hub, Department of Medical Genetics, University of Cambridge, Cambridge, UK
| | - France Docquier
- Department of Medical Genetics, University of Cambridge, Cambridge, CB2 0QQ, UK
- Stratified Medicine Core Laboratory NGS Hub, Department of Medical Genetics, University of Cambridge, Cambridge, UK
| | - Ana Toribio
- Department of Medical Genetics, University of Cambridge, Cambridge, CB2 0QQ, UK
- Stratified Medicine Core Laboratory NGS Hub, Department of Medical Genetics, University of Cambridge, Cambridge, UK
| | - Leonardo Bottolo
- Department of Medical Genetics, University of Cambridge, Cambridge, CB2 0QQ, UK
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - Gerhard Binder
- Pediatric Endocrinology, University Children's Hospital, Universiy of Tuebingen, Tuebingen, Germany
| | - György Fekete
- Department of Pediatrics, Semmelweis University, Budapest, Hungary
| | - Miriam Elbracht
- Medical Faculty, Institute for Human Genetics and Genome Medicine, RWTH Aachen University, Pauwelsstr. 30, 52074, Aachen, Germany
| | - Eamonn R Maher
- Department of Medical Genetics, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Matthias Begemann
- Medical Faculty, Institute for Human Genetics and Genome Medicine, RWTH Aachen University, Pauwelsstr. 30, 52074, Aachen, Germany
| | - Thomas Eggermann
- Medical Faculty, Institute for Human Genetics and Genome Medicine, RWTH Aachen University, Pauwelsstr. 30, 52074, Aachen, Germany.
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Ochoa E, Lee S, Lan-Leung B, Dias RP, Ong KK, Radley JA, Pérez de Nanclares G, Martinez R, Clark G, Martin E, Castaño L, Bottolo L, Maher ER. ImprintSeq, a novel tool to interrogate DNA methylation at human imprinted regions and diagnose multilocus imprinting disturbance. Genet Med 2022; 24:463-474. [PMID: 34906518 DOI: 10.1016/j.gim.2021.10.011] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 10/19/2021] [Indexed: 01/14/2023] Open
Abstract
PURPOSE Disruptions of genomic imprinting are associated with congenital imprinting disorders (CIDs) and other disease states, including cancer. CIDs are most often associated with altered methylation at imprinted differentially methylated regions (iDMRs). In some cases, multiple iDMRs are affected causing multilocus imprinting disturbances (MLIDs). The availability of accurate, quantitative, and scalable high-throughput methods to interrogate multiple iDMRs simultaneously would enhance clinical diagnostics and research. METHODS We report the development of a custom targeted methylation sequencing panel that covered most relevant 63 iDMRs for CIDs and the detection of MLIDs. We tested it in 70 healthy controls and 147 individuals with CIDs. We distinguished loss and gain of methylation per differentially methylated region and classified high and moderate methylation alterations. RESULTS Across a range of CIDs with a variety of molecular mechanisms, ImprintSeq performed at 98.4% sensitivity, 99.9% specificity, and 99.9% accuracy (when compared with previous diagnostic testing). ImprintSeq was highly sensitive for detecting MLIDs and enabled diagnostic criteria for MLID to be proposed. In a child with extreme MLID profile a probable genetic cause was identified. CONCLUSION ImprintSeq provides a novel assay for clinical diagnostic and research studies of CIDs, MLIDs, and the role of disordered imprinting in human disease states.
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Affiliation(s)
- Eguzkine Ochoa
- Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Sunwoo Lee
- Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Benoit Lan-Leung
- Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Renuka P Dias
- Department of Endocrinology and Diabetes, Birmingham Children's Hospital, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, United Kingdom; Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Ken K Ong
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom; Department of Paediatrics, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Jessica A Radley
- West Midlands Regional Clinical Genetics Service and Birmingham Health Partners, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, United Kingdom; London North West Regional Genetics Service, St. Mark's and Northwick Park hospitals, Harrow, Middlesex, United Kingdom
| | - Gustavo Pérez de Nanclares
- Biocruces Bizkaia Health Research Institute, Hospital Universitario Cruces, CIBERDEM, CIBERER, Endo-ERN, University of the Basque Country (UPV-EHU), Bizkaia, Spain
| | - Rosa Martinez
- Biocruces Bizkaia Health Research Institute, Hospital Universitario Cruces, CIBERDEM, CIBERER, Endo-ERN, University of the Basque Country (UPV-EHU), Bizkaia, Spain
| | - Graeme Clark
- Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom; Stratified Medicine Core Laboratory NGS Hub, Department of Medical Genetics, University of Cambridge, Cambridge, United Kingdom
| | - Ezequiel Martin
- Stratified Medicine Core Laboratory NGS Hub, Department of Medical Genetics, University of Cambridge, Cambridge, United Kingdom
| | - Luis Castaño
- Biocruces Bizkaia Health Research Institute, Hospital Universitario Cruces, CIBERDEM, CIBERER, Endo-ERN, University of the Basque Country (UPV-EHU), Bizkaia, Spain
| | - Leonardo Bottolo
- Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom; The Alan Turing Institute, London, United Kingdom; MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Eamonn R Maher
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom.
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Ochoa E, Zuber V, Bottolo L. Accurate Measurement of DNA Methylation: Challenges and Bias Correction. Methods Mol Biol 2022; 2432:25-47. [PMID: 35505205 DOI: 10.1007/978-1-0716-1994-0_3] [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] [Indexed: 06/14/2023]
Abstract
DNA methylation is a key epigenetic modification involved in gene regulation whose contribution to disease susceptibility is still not fully understood. As the cost of genome sequencing technologies continues to drop, it will soon become commonplace to perform genome-wide quantification of DNA methylation at a single base-pair resolution. However, the demand for its accurate quantification might vary across studies. When the scope of the analysis is to detect regions of the genome with different methylation patterns between two or more conditions, e.g., case vs control; treatments vs placebo, accuracy is not crucial. This is the case in epigenome-wide association studies used as genome-wide screening of methylation changes to detect new candidate genes and regions associated with a specific disease or condition. If the aim of the analysis is to use DNA methylation measurements as a biomarker for diseases diagnosis and treatment (Laird, Nat Rev Cancer 3:253-266, 2003; Bock, Epigenomics 1:99-110, 2009), it is instead recommended to produce accurate methylation measurements. Furthermore, if the objective is the detection of DNA methylation in subclonal tumor cell populations or in circulating tumor DNA or in any case of mosaicism, the importance of accuracy becomes critical. The aim of this chapter is to describe the factors that could affect the precise measurement of methylation levels and a recent Bayesian statistical method called MethylCal and its extension that have been proposed to minimize this problem.
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Affiliation(s)
- Eguzkine Ochoa
- Department of Medical Genetics, University of Cambridge, Cambridge, UK
- Cambridge NIHR Biomedical Research Centre, Cambridge, UK
| | - Verena Zuber
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Leonardo Bottolo
- Department of Medical Genetics, University of Cambridge, Cambridge, UK.
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
- The Alan Turing Institute, London, UK.
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Zuber V, Cameron A, Myserlis EP, Bottolo L, Fernandez-Cadenas I, Burgess S, Anderson CD, Dawson J, Gill D. Leveraging Genetic Data to Elucidate the Relationship Between COVID-19 and Ischemic Stroke. J Am Heart Assoc 2021; 10:e022433. [PMID: 34755518 PMCID: PMC8751930 DOI: 10.1161/jaha.121.022433] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [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] [Indexed: 01/06/2023]
Abstract
Background The relationship between COVID‐19 and ischemic stroke is poorly understood due to potential unmeasured confounding and reverse causation. We aimed to leverage genetic data to triangulate reported associations. Methods and Results Analyses primarily focused on critical COVID‐19, defined as hospitalization with COVID‐19 requiring respiratory support or resulting in death. Cross‐trait linkage disequilibrium score regression was used to estimate genetic correlations of critical COVID‐19 with ischemic stroke, other related cardiovascular outcomes, and risk factors common to both COVID‐19 and cardiovascular disease (body mass index, smoking and chronic inflammation, estimated using C‐reactive protein). Mendelian randomization analysis was performed to investigate whether liability to critical COVID‐19 was associated with increased risk of any cardiovascular outcome for which genetic correlation was identified. There was evidence of genetic correlation between critical COVID‐19 and ischemic stroke (rg=0.29, false discovery rate [FDR]=0.012), body mass index (rg=0.21, FDR=0.00002), and C‐reactive protein (rg=0.20, FDR=0.00035), but no other trait investigated. In Mendelian randomization, liability to critical COVID‐19 was associated with increased risk of ischemic stroke (odds ratio [OR] per logOR increase in genetically predicted critical COVID‐19 liability 1.03, 95% CI 1.00–1.06, P‐value=0.03). Similar estimates were obtained for ischemic stroke subtypes. Consistent estimates were also obtained when performing statistical sensitivity analyses more robust to the inclusion of pleiotropic variants, including multivariable Mendelian randomization analyses adjusting for potential genetic confounding through body mass index, smoking, and chronic inflammation. There was no evidence to suggest that genetic liability to ischemic stroke increased the risk of critical COVID‐19. Conclusions These data support that liability to critical COVID‐19 is associated with an increased risk of ischemic stroke. The host response predisposing to severe COVID‐19 is likely to increase the risk of ischemic stroke, independent of other potentially mitigating risk factors.
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Affiliation(s)
- Verena Zuber
- Department of Epidemiology and Biostatistics School of Public Health Imperial College London London UK.,Dementia Research Institute at Imperial College London London UK
| | - Alan Cameron
- Institute of Cardiovascular and Medical SciencesUniversity of Glasgow UK
| | - Evangelos P Myserlis
- Center for Genomic Medicine Massachusetts General Hospital Boston MA.,McCance Center for Brain Health Massachusetts General Hospital Boston MA.,Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA
| | - Leonardo Bottolo
- Department of Medical Genetics School of Clinical Medicine University of Cambridge UK.,The Alan Turing Institute London UK.,Medical Research Council Biostatistics Unit University of Cambridge UK
| | | | - Stephen Burgess
- Medical Research Council Biostatistics Unit University of Cambridge UK.,Department of Public Health and Primary Care Cardiovascular Epidemiology Unit University of Cambridge UK
| | - Christopher D Anderson
- McCance Center for Brain Health Massachusetts General Hospital Boston MA.,Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA.,Department of Neurology Brigham and Women's Hospital Boston MA
| | - Jesse Dawson
- Institute of Cardiovascular and Medical SciencesUniversity of Glasgow UK
| | - Dipender Gill
- Department of Epidemiology and Biostatistics School of Public Health Imperial College London London UK.,Clinical Pharmacology and Therapeutics Section Institute of Medical and Biomedical Education and Institute for Infection and ImmunitySt George'sUniversity of London London UK.,Clinical Pharmacology Group, Pharmacy and Medicines Directorate St George's University Hospitals NHS Foundation Trust London UK.,Novo Nordisk Research Centre Oxford Oxford UK
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7
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Levin MG, Zuber V, Walker VM, Klarin D, Lynch J, Malik R, Aday AW, Bottolo L, Pradhan AD, Dichgans M, Chang KM, Rader DJ, Tsao PS, Voight BF, Gill D, Burgess S, Damrauer SM. Prioritizing the Role of Major Lipoproteins and Subfractions as Risk Factors for Peripheral Artery Disease. Circulation 2021; 144:353-364. [PMID: 34139859 PMCID: PMC8323712 DOI: 10.1161/circulationaha.121.053797] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Accepted: 05/16/2021] [Indexed: 12/24/2022]
Abstract
BACKGROUND Lipoprotein-related traits have been consistently identified as risk factors for atherosclerotic cardiovascular disease, largely on the basis of studies of coronary artery disease (CAD). The relative contributions of specific lipoproteins to the risk of peripheral artery disease (PAD) have not been well defined. We leveraged large-scale genetic association data to investigate the effects of circulating lipoprotein-related traits on PAD risk. METHODS Genome-wide association study summary statistics for circulating lipoprotein-related traits were used in the mendelian randomization bayesian model averaging framework to prioritize the most likely causal major lipoprotein and subfraction risk factors for PAD and CAD. Mendelian randomization was used to estimate the effect of apolipoprotein B (ApoB) lowering on PAD risk using gene regions proxying lipid-lowering drug targets. Genes relevant to prioritized lipoprotein subfractions were identified with transcriptome-wide association studies. RESULTS ApoB was identified as the most likely causal lipoprotein-related risk factor for both PAD (marginal inclusion probability, 0.86; P=0.003) and CAD (marginal inclusion probability, 0.92; P=0.005). Genetic proxies for ApoB-lowering medications were associated with reduced risk of both PAD (odds ratio,0.87 per 1-SD decrease in ApoB [95% CI, 0.84-0.91]; P=9×10-10) and CAD (odds ratio,0.66 [95% CI, 0.63-0.69]; P=4×10-73), with a stronger predicted effect of ApoB lowering on CAD (ratio of effects, 3.09 [95% CI, 2.29-4.60]; P<1×10-6). Extra-small very-low-density lipoprotein particle concentration was identified as the most likely subfraction associated with PAD risk (marginal inclusion probability, 0.91; P=2.3×10-4), whereas large low-density lipoprotein particle concentration was the most likely subfraction associated with CAD risk (marginal inclusion probability, 0.95; P=0.011). Genes associated with extra-small very-low-density lipoprotein particle and large low-density lipoprotein particle concentration included canonical ApoB pathway components, although gene-specific effects were variable. Lipoprotein(a) was associated with increased risk of PAD independently of ApoB (odds ratio, 1.04 [95% CI, 1.03-1.04]; P=1.0×10-33). CONCLUSIONS ApoB was prioritized as the major lipoprotein fraction causally responsible for both PAD and CAD risk. However, ApoB-lowering drug targets and ApoB-containing lipoprotein subfractions had diverse associations with atherosclerotic cardiovascular disease, and distinct subfraction-associated genes suggest possible differences in the role of lipoproteins in the pathogenesis of PAD and CAD.
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Affiliation(s)
- Michael G. Levin
- Division of Cardiovascular Medicine (M.G.L.), University of Pennsylvania Perelman School of Medicine, Philadelphia
- Department of Medicine (M.G.L., K.-M.C., D.J.R.), University of Pennsylvania Perelman School of Medicine, Philadelphia
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA (M.G.L., K.-M.C., B.F.V., S.M.D.)
| | - Verena Zuber
- MRC Biostatistics Unit (V.Z., S.B.), School of Clinical Medicine, University of Cambridge, UK
- Department of Epidemiology and Biostatistics (V.Z.), Imperial College London, UK
- Dementia Research Institute (V.Z.), Imperial College London, UK
| | - Venexia M. Walker
- Department of Surgery (V.M.W., S.M.D.), University of Pennsylvania Perelman School of Medicine, Philadelphia
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, UK (V.M.W.)
| | - Derek Klarin
- Malcolm Randall VA Medical Center, Gainesville, FL (D.K.)
- Department of Surgery, University of Florida, Gainesville (D.K.)
| | - Julie Lynch
- VA Informatics and Computing Infrastructure, Department of Veterans Affairs, Salt Lake City Health Care System, UT (J.L.)
- University of Utah School of Medicine, Salt Lake City (J.L.)
| | - Rainer Malik
- Institute for Stroke and Dementia Research, University Hospital of Ludwig-Maximilians-University, Munich, Germany (R.M.)
| | - Aaron W. Aday
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, TN (A.W.A.)
| | - Leonardo Bottolo
- Department of Medical Genetics (L.B.), School of Clinical Medicine, University of Cambridge, UK
- The Alan Turing Institute, London, UK (L.B.)
| | - Aruna D. Pradhan
- Division of Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA (A.D.P.)
- Division of Cardiovascular Medicine, VA Boston Medical Center, MA (A.D.P.)
| | - Martin Dichgans
- Institute for Stroke and Dementia Research, University Hospital of Ludwig-Maximilians-University, Munich, Germany (M.D.)
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany (M.D.)
| | - Kyong-Mi Chang
- Department of Medicine (M.G.L., K.-M.C., D.J.R.), University of Pennsylvania Perelman School of Medicine, Philadelphia
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA (M.G.L., K.-M.C., B.F.V., S.M.D.)
| | - Daniel J. Rader
- Department of Medicine (M.G.L., K.-M.C., D.J.R.), University of Pennsylvania Perelman School of Medicine, Philadelphia
- Institute for Translational Medicine and Therapeutics (D.J.R., B.F.V.), University of Pennsylvania Perelman School of Medicine, Philadelphia
- Department of Genetics (D.J.R., B.V.F.), University of Pennsylvania Perelman School of Medicine, Philadelphia
- Munich Cluster for Systems Neurology (SyNergy), Germany (D.J.R., B.F.V.)
| | - Philip S. Tsao
- Palo Alto VA Healthcare System, CA (P.S.T.)
- Department of Medicine, Division of Cardiovascular Medicine, and Stanford Cardiovascular Institute, Stanford University, Palo Alto, CA (P.S.T.)
| | - Benjamin F. Voight
- Institute for Translational Medicine and Therapeutics (D.J.R., B.F.V.), University of Pennsylvania Perelman School of Medicine, Philadelphia
- Department of Genetics (D.J.R., B.V.F.), University of Pennsylvania Perelman School of Medicine, Philadelphia
- Department of Systems Pharmacology and Translational Therapeutics (B.V.F.), University of Pennsylvania Perelman School of Medicine, Philadelphia
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA (M.G.L., K.-M.C., B.F.V., S.M.D.)
- Munich Cluster for Systems Neurology (SyNergy), Germany (D.J.R., B.F.V.)
| | - Dipender Gill
- Department of Epidemiology and Biostatistics (D.G.), Imperial College London, UK
- Clinical Pharmacology and Therapeutics Section, Institute for Infection and Immunity, St. George’s, University of London, UK (D.G.)
- Novo Nordisk Research Centre Oxford, Old Road Campus, UK (D.G.)
| | - Stephen Burgess
- MRC Biostatistics Unit (V.Z., S.B.), School of Clinical Medicine, University of Cambridge, UK
- BHF Cardiovascular Epidemiology Unit (S.B.), School of Clinical Medicine, University of Cambridge, UK
| | - Scott M. Damrauer
- Department of Surgery (V.M.W., S.M.D.), University of Pennsylvania Perelman School of Medicine, Philadelphia
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA (M.G.L., K.-M.C., B.F.V., S.M.D.)
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8
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Bottolo L, Banterle M, Richardson S, Ala-Korpela M, Järvelin MR, Lewin A. A computationally efficient Bayesian seemingly unrelated regressions model for high-dimensional quantitative trait loci discovery. J R Stat Soc Ser C Appl Stat 2021; 70:886-908. [PMID: 35001978 PMCID: PMC7612194 DOI: 10.1111/rssc.12490] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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] [Indexed: 01/02/2023]
Abstract
Our work is motivated by the search for metabolite quantitative trait loci (QTL) in a cohort of more than 5000 people. There are 158 metabolites measured by NMR spectroscopy in the 31-year follow-up of the Northern Finland Birth Cohort 1966 (NFBC66). These metabolites, as with many multivariate phenotypes produced by high-throughput biomarker technology, exhibit strong correlation structures. Existing approaches for combining such data with genetic variants for multivariate QTL analysis generally ignore phenotypic correlations or make restrictive assumptions about the associations between phenotypes and genetic loci. We present a computationally efficient Bayesian seemingly unrelated regressions model for high-dimensional data, with cell-sparse variable selection and sparse graphical structure for covariance selection. Cell sparsity allows different phenotype responses to be associated with different genetic predictors and the graphical structure is used to represent the conditional dependencies between phenotype variables. To achieve feasible computation of the large model space, we exploit a factorisation of the covariance matrix. Applying the model to the NFBC66 data with 9000 directly genotyped single nucleotide polymorphisms, we are able to simultaneously estimate genotype-phenotype associations and the residual dependence structure among the metabolites. The R package BayesSUR with full documentation is available at https://cran.r-project.org/web/packages/BayesSUR/.
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Affiliation(s)
- Leonardo Bottolo
- Department of Medical Genetics, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
- MRC Biostatistics Unit, Cambridge, UK
| | - Marco Banterle
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Sylvia Richardson
- The Alan Turing Institute, London, UK
- MRC Biostatistics Unit, Cambridge, UK
| | - Mika Ala-Korpela
- Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Marjo-Riitta Järvelin
- Center for Life Course Health Research, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- Department of Life Sciences, Brunel University London, Uxbridge, UK
| | - Alex Lewin
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
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9
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Zuber V, Gill D, Ala-Korpela M, Langenberg C, Butterworth A, Bottolo L, Burgess S. High-throughput multivariable Mendelian randomization analysis prioritizes apolipoprotein B as key lipid risk factor for coronary artery disease. Int J Epidemiol 2021; 50:893-901. [PMID: 33130851 PMCID: PMC8271202 DOI: 10.1093/ije/dyaa216] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [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] [Accepted: 09/30/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Genetic variants can be used to prioritize risk factors as potential therapeutic targets via Mendelian randomization (MR). An agnostic statistical framework using Bayesian model averaging (MR-BMA) can disentangle the causal role of correlated risk factors with shared genetic predictors. Here, our objective is to identify lipoprotein measures as mediators between lipid-associated genetic variants and coronary artery disease (CAD) for the purpose of detecting therapeutic targets for CAD. METHODS As risk factors we consider 30 lipoprotein measures and metabolites derived from a high-throughput metabolomics study including 24 925 participants. We fit multivariable MR models of genetic associations with CAD estimated in 453 595 participants (including 113 937 cases) regressed on genetic associations with the risk factors. MR-BMA assigns to each combination of risk factors a model score quantifying how well the genetic associations with CAD are explained. Risk factors are ranked by their marginal score and selected using false-discovery rate (FDR) criteria. We perform supplementary and sensitivity analyses varying the dataset for genetic associations with CAD. RESULTS In the main analysis, the top combination of risk factors ranked by the model score contains apolipoprotein B (ApoB) only. ApoB is also the highest ranked risk factor with respect to the marginal score (FDR <0.005). Additionally, ApoB is selected in all sensitivity analyses. No other measure of cholesterol or triglyceride is consistently selected otherwise. CONCLUSIONS Our agnostic genetic investigation prioritizes ApoB across all datasets considered, suggesting that ApoB, representing the total number of hepatic-derived lipoprotein particles, is the primary lipid determinant of CAD.
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Affiliation(s)
- Verena Zuber
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.,MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Mika Ala-Korpela
- Computational Medicine, Faculty of Medicine, University of Oulu & Biocenter Oulu, Oulu, Finland.,NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Claudia Langenberg
- MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Adam Butterworth
- Department of Public Health and Primary Care, British Heart Foundation Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK.,British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK.,National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK.,National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK.,Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.,Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Leonardo Bottolo
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.,Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, UK.,Alan Turing Institute, London, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.,Department of Public Health and Primary Care, British Heart Foundation Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK
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10
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Witte F, Ruiz-Orera J, Mattioli CC, Blachut S, Adami E, Schulz JF, Schneider-Lunitz V, Hummel O, Patone G, Mücke MB, Šilhavý J, Heinig M, Bottolo L, Sanchis D, Vingron M, Chekulaeva M, Pravenec M, Hubner N, van Heesch S. A trans locus causes a ribosomopathy in hypertrophic hearts that affects mRNA translation in a protein length-dependent fashion. Genome Biol 2021; 22:191. [PMID: 34183069 PMCID: PMC8240307 DOI: 10.1186/s13059-021-02397-w] [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] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 06/02/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Little is known about the impact of trans-acting genetic variation on the rates with which proteins are synthesized by ribosomes. Here, we investigate the influence of such distant genetic loci on the efficiency of mRNA translation and define their contribution to the development of complex disease phenotypes within a panel of rat recombinant inbred lines. RESULTS We identify several tissue-specific master regulatory hotspots that each control the translation rates of multiple proteins. One of these loci is restricted to hypertrophic hearts, where it drives a translatome-wide and protein length-dependent change in translational efficiency, altering the stoichiometric translation rates of sarcomere proteins. Mechanistic dissection of this locus across multiple congenic lines points to a translation machinery defect, characterized by marked differences in polysome profiles and misregulation of the small nucleolar RNA SNORA48. Strikingly, from yeast to humans, we observe reproducible protein length-dependent shifts in translational efficiency as a conserved hallmark of translation machinery mutants, including those that cause ribosomopathies. Depending on the factor mutated, a pre-existing negative correlation between protein length and translation rates could either be enhanced or reduced, which we propose to result from mRNA-specific imbalances in canonical translation initiation and reinitiation rates. CONCLUSIONS We show that distant genetic control of mRNA translation is abundant in mammalian tissues, exemplified by a single genomic locus that triggers a translation-driven molecular mechanism. Our work illustrates the complexity through which genetic variation can drive phenotypic variability between individuals and thereby contribute to complex disease.
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Affiliation(s)
- Franziska Witte
- Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany
- Present Address: NUVISAN ICB GmbH, Lead Discovery-Structrual Biology, 13353, Berlin, Germany
| | - Jorge Ruiz-Orera
- Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany
| | - Camilla Ciolli Mattioli
- Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 10115, Berlin, Germany
- Present Address: Department of Biological Regulation, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Susanne Blachut
- Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany
| | - Eleonora Adami
- Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany
- Present Address: Program in Cardiovascular and Metabolic Disorders, Duke-National University of Singapore, Singapore, 169857, Singapore
| | - Jana Felicitas Schulz
- Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany
| | - Valentin Schneider-Lunitz
- Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany
| | - Oliver Hummel
- Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany
| | - Giannino Patone
- Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany
| | - Michael Benedikt Mücke
- Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, 13347, Berlin, Germany
- Charité-Universitätsmedizin, 10117, Berlin, Germany
| | - Jan Šilhavý
- Institute of Physiology of the Czech Academy of Sciences, 4, 142 20, Praha, Czech Republic
| | - Matthias Heinig
- Institute of Computational Biology (ICB), HMGU, Ingolstaedter Landstr. 1, 85764 Neuherberg, Munich, Germany
- Department of Informatics, Technische Universitaet Muenchen (TUM), Boltzmannstr. 3, 85748 Garching, Munich, Germany
| | - Leonardo Bottolo
- Department of Medical Genetics, University of Cambridge, Cambridge, CB2 0QQ, UK
- The Alan Turing Institute, London, NW1 2DB, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, CB2 0SR, UK
| | - Daniel Sanchis
- Institut de Recerca Biomedica de Lleida (IRBLLEIDA), Universitat de Lleida, Edifici Biomedicina-I. Av. Rovira Roure, 80, 25198, Lleida, Spain
| | - Martin Vingron
- Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, 14195, Berlin, Germany
| | - Marina Chekulaeva
- Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 10115, Berlin, Germany
| | - Michal Pravenec
- Institute of Physiology of the Czech Academy of Sciences, 4, 142 20, Praha, Czech Republic
| | - Norbert Hubner
- Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany.
- DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, 13347, Berlin, Germany.
- Charité-Universitätsmedizin, 10117, Berlin, Germany.
| | - Sebastiaan van Heesch
- Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany.
- Present Address: The Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands.
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11
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Ruffieux H, Fairfax BP, Nassiri I, Vigorito E, Wallace C, Richardson S, Bottolo L. EPISPOT: An epigenome-driven approach for detecting and interpreting hotspots in molecular QTL studies. Am J Hum Genet 2021; 108:983-1000. [PMID: 33909991 PMCID: PMC8206410 DOI: 10.1016/j.ajhg.2021.04.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 04/08/2021] [Indexed: 12/27/2022] Open
Abstract
We present EPISPOT, a fully joint framework which exploits large panels of epigenetic annotations as variant-level information to enhance molecular quantitative trait locus (QTL) mapping. Thanks to a purpose-built Bayesian inferential algorithm, EPISPOT accommodates functional information for both cis and trans actions, including QTL hotspot effects. It effectively couples simultaneous QTL analysis of thousands of genetic variants and molecular traits with hypothesis-free selection of biologically interpretable annotations which directly contribute to the QTL effects. This unified, epigenome-aided learning boosts statistical power and sheds light on the regulatory basis of the uncovered hits; EPISPOT therefore marks an essential step toward improving the challenging detection and functional interpretation of trans-acting genetic variants and hotspots. We illustrate the advantages of EPISPOT in simulations emulating real-data conditions and in a monocyte expression QTL study, which confirms known hotspots and finds other signals, as well as plausible mechanisms of action. In particular, by highlighting the role of monocyte DNase-I sensitivity sites from >150 epigenetic annotations, we clarify the mediation effects and cell-type specificity of major hotspots close to the lysozyme gene. Our approach forgoes the daunting and underpowered task of one-annotation-at-a-time enrichment analyses for prioritizing cis and trans QTL hits and is tailored to any transcriptomic, proteomic, or metabolomic QTL problem. By enabling principled epigenome-driven QTL mapping transcriptome-wide, EPISPOT helps progress toward a better functional understanding of genetic regulation.
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Affiliation(s)
- Hélène Ruffieux
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK.
| | - Benjamin P Fairfax
- Department of Oncology, MRC Weatherall Institute for Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DS, UK
| | - Isar Nassiri
- Department of Oncology, MRC Weatherall Institute for Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DS, UK
| | - Elena Vigorito
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK
| | - Chris Wallace
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK; Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge CB2 0AW, UK
| | - Sylvia Richardson
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK; The Alan Turing Institute, London NW1 2DB, UK
| | - Leonardo Bottolo
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK; The Alan Turing Institute, London NW1 2DB, UK; Department of Medical Genetics, University of Cambridge, Cambridge CB2 0QQ, UK
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12
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Zuber V, Cameron A, Myserlis EP, Bottolo L, Fernandez-Cadenas I, Burgess S, Anderson CD, Dawson J, Gill D. Leveraging genetic data to elucidate the relationship between Covid-19 and ischemic stroke. medRxiv 2021:2021.02.25.21252441. [PMID: 33688662 PMCID: PMC7941632 DOI: 10.1101/2021.02.25.21252441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
BACKGROUND The relationship between coronavirus disease 2019 (Covid-19) and ischemic stroke is poorly defined. We aimed to leverage genetic data to investigate reported associations. METHODS Genetic association estimates for liability to Covid-19 and cardiovascular traits were obtained from large-scale consortia. Analyses primarily focused on critical Covid-19, defined as hospitalization with Covid-19 requiring respiratory support or resulting in death. Cross-trait linkage disequilibrium score regression was used to estimate genetic correlations of critical Covid-19 with ischemic stroke, other related cardiovascular outcomes, and risk factors common to both Covid-19 and cardiovascular disease (body mass index, smoking and chronic inflammation, estimated using C-reactive protein). Mendelian randomization analysis was performed to investigate whether liability to critical Covid-19 was associated with increased risk of any of the cardiovascular outcomes for which genetic correlation was identified. RESULTS There was evidence of genetic correlation between critical Covid-19 and ischemic stroke (r g =0.29, FDR p -value=4.65×10 -3 ), body mass index (r g =0.21, FDR- p -value = 6.26×10 -6 ) and C-reactive protein (r g =0.20, FDR- p -value=1.35×10 -4 ), but none of the other considered traits. In Mendelian randomization analysis, liability to critical Covid-19 was associated with increased risk of ischemic stroke (odds ratio [OR] per logOR increase in genetically predicted critical Covid-19 liability 1.03, 95% confidence interval 1.00-1.06, p -value=0.03). Similar estimates were obtained when considering ischemic stroke subtypes. Consistent estimates were also obtained when performing statistical sensitivity analyses more robust to the inclusion of pleiotropic variants, including multivariable Mendelian randomization analyses adjusting for potential genetic confounding through body mass index, smoking and chronic inflammation. There was no evidence to suggest that genetic liability to ischemic stroke increased the risk of critical Covid-19. CONCLUSIONS These data support that liability to critical Covid-19 is associated with an increased risk of ischemic stroke. The host response predisposing to severe Covid-19 is likely to increase the risk of ischemic stroke, independent of other potentially mitigating risk factors.
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Affiliation(s)
- Verena Zuber
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Dementia Research Institute at Imperial College London, London, UK
| | - Alan Cameron
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Evangelos P. Myserlis
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Leonardo Bottolo
- Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | | | - Stephen Burgess
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Christopher D. Anderson
- McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Jesse Dawson
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Clinical Pharmacology and Therapeutics Section, Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George’s, University of London, London, UK
- Clinical Pharmacology Group, Pharmacy and Medicines Directorate, St George’s University Hospitals NHS Foundation Trust, London, UK
- Novo Nordisk Research Centre Oxford, Old Road Campus, Oxford, UK
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13
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Zhang X, Walsh R, Whiffin N, Buchan R, Midwinter W, Wilk A, Govind R, Li N, Ahmad M, Mazzarotto F, Roberts A, Theotokis PI, Mazaika E, Allouba M, de Marvao A, Pua CJ, Day SM, Ashley E, Colan SD, Michels M, Pereira AC, Jacoby D, Ho CY, Olivotto I, Gunnarsson GT, Jefferies JL, Semsarian C, Ingles J, O'Regan DP, Aguib Y, Yacoub MH, Cook SA, Barton PJR, Bottolo L, Ware JS. Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions. Genet Med 2021; 23:69-79. [PMID: 33046849 PMCID: PMC7790749 DOI: 10.1038/s41436-020-00972-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [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: 06/20/2020] [Revised: 09/08/2020] [Accepted: 09/09/2020] [Indexed: 11/18/2022] Open
Abstract
PURPOSE Accurate discrimination of benign and pathogenic rare variation remains a priority for clinical genome interpretation. State-of-the-art machine learning variant prioritization tools are imprecise and ignore important parameters defining gene-disease relationships, e.g., distinct consequences of gain-of-function versus loss-of-function variants. We hypothesized that incorporating disease-specific information would improve tool performance. METHODS We developed a disease-specific variant classifier, CardioBoost, that estimates the probability of pathogenicity for rare missense variants in inherited cardiomyopathies and arrhythmias. We assessed CardioBoost's ability to discriminate known pathogenic from benign variants, prioritize disease-associated variants, and stratify patient outcomes. RESULTS CardioBoost has high global discrimination accuracy (precision recall area under the curve [AUC] 0.91 for cardiomyopathies; 0.96 for arrhythmias), outperforming existing tools (4-24% improvement). CardioBoost obtains excellent accuracy (cardiomyopathies 90.2%; arrhythmias 91.9%) for variants classified with >90% confidence, and increases the proportion of variants classified with high confidence more than twofold compared with existing tools. Variants classified as disease-causing are associated with both disease status and clinical severity, including a 21% increased risk (95% confidence interval [CI] 11-29%) of severe adverse outcomes by age 60 in patients with hypertrophic cardiomyopathy. CONCLUSIONS A disease-specific variant classifier outperforms state-of-the-art genome-wide tools for rare missense variants in inherited cardiac conditions ( https://www.cardiodb.org/cardioboost/ ), highlighting broad opportunities for improved pathogenicity prediction through disease specificity.
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Affiliation(s)
- Xiaolei Zhang
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
| | - Roddy Walsh
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
| | - Nicola Whiffin
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
| | - Rachel Buchan
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
| | - William Midwinter
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
| | - Alicja Wilk
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
| | - Risha Govind
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
| | - Nicholas Li
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | - Mian Ahmad
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
| | - Francesco Mazzarotto
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiomyopathy Unit, Careggi University Hospital, Florence, Italy
- Department of Clinical and Experimental Medicine, University of Florence, Florence, Italy
| | - Angharad Roberts
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
| | - Pantazis I Theotokis
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
| | - Erica Mazaika
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
| | - Mona Allouba
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Aswan Heart Centre, Magdi Yacoub Heart Foundation, Aswan, Egypt
| | - Antonio de Marvao
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | | | - Sharlene M Day
- Division of Cardiovascular Medicine and Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Euan Ashley
- Division of Cardiovascular Medicine, Stanford University Medical Center, Stanford, CA, USA
| | - Steven D Colan
- Department of Cardiology, Boston Children's Hospital, Boston, MA, USA
| | - Michelle Michels
- Department of Cardiology, Thoraxcenter, Erasmus MC Rotterdam, Rotterdam, Netherlands
| | - Alexandre C Pereira
- Heart Institute (InCor), University of Sao Paulo Medical School, Sao Paulo, Brazil
| | - Daniel Jacoby
- Department of Internal Medicine, Yale University, New Haven, CT, USA
| | - Carolyn Y Ho
- Cardiovascular Division, Brigham and Women's Hospital, Boston, MA, USA
| | - Iacopo Olivotto
- Cardiomyopathy Unit, Careggi University Hospital, Florence, Italy
| | | | - John L Jefferies
- The Cardiovascular Institute, University of Tennessee, Memphis, TN, USA
| | - Chris Semsarian
- Centenary Institute, The University of Sydney, Sydney, Australia
- Department of Cardiology, Royal Prince Alfred Hospital, Sydney, Australia
| | - Jodie Ingles
- Centenary Institute, The University of Sydney, Sydney, Australia
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | - Yasmine Aguib
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Aswan Heart Centre, Magdi Yacoub Heart Foundation, Aswan, Egypt
| | - Magdi H Yacoub
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Aswan Heart Centre, Magdi Yacoub Heart Foundation, Aswan, Egypt
| | - Stuart A Cook
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
- National Heart Centre, Singapore, Singapore
- Duke-National University of Singapore, Singapore, Singapore
| | - Paul J R Barton
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
| | - Leonardo Bottolo
- Department of Medical Genetics, University of Cambridge, Cambridge, United Kingdom.
- Alan Turing Institute, London, United Kingdom.
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom.
| | - James S Ware
- National Heart and Lung Institute, Imperial College London, London, United Kingdom.
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom.
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom.
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14
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Zhao Z, Banterle M, Bottolo L, Richardson S, Lewin A, Zucknick M. BayesSUR: An R Package for High-Dimensional Multivariate Bayesian Variable and Covariance Selection in Linear Regression. J Stat Softw 2021. [DOI: 10.18637/jss.v100.i11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
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15
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Abstract
We develop a novel Bayesian method to select important predictors in regression models with multiple responses of diverse types. A sparse Gaussian copula regression model is used to account for the multivariate dependencies between any combination of discrete and/or continuous responses and their association with a set of predictors. We utilize the parameter expansion for data augmentation strategy to construct a Markov chain Monte Carlo algorithm for the estimation of the parameters and the latent variables of the model. Based on a centered parametrization of the Gaussian latent variables, we design a fixed-dimensional proposal distribution to update jointly the latent binary vectors of important predictors and the corresponding non-zero regression coefficients. For Gaussian responses and for outcomes that can be modeled as a dependent version of a Gaussian response, this proposal leads to a Metropolis-Hastings step that allows an efficient exploration of the predictors' model space. The proposed strategy is tested on simulated data and applied to real data sets in which the responses consist of low-intensity counts, binary, ordinal and continuous variables.
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Affiliation(s)
| | - Leonardo Bottolo
- Department of Medical Genetics, University of Cambridge, UK
- The Alan Turing Institute, London, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
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16
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Lawler K, Huang-Doran I, Sonoyama T, Collet TH, Keogh JM, Henning E, O’Rahilly S, Bottolo L, Farooqi IS. Leptin-Mediated Changes in the Human Metabolome. J Clin Endocrinol Metab 2020; 105:dgaa251. [PMID: 32392278 PMCID: PMC7282709 DOI: 10.1210/clinem/dgaa251] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 05/06/2020] [Indexed: 02/08/2023]
Abstract
CONTEXT While severe obesity due to congenital leptin deficiency is rare, studies in patients before and after treatment with leptin can provide unique insights into the role that leptin plays in metabolic and endocrine function. OBJECTIVE The aim of this study was to characterize changes in peripheral metabolism in people with congenital leptin deficiency undergoing leptin replacement therapy, and to investigate the extent to which these changes are explained by reduced caloric intake. DESIGN Ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) was used to measure 661 metabolites in 6 severely obese people with congenital leptin deficiency before, and within 1 month after, treatment with recombinant leptin. Data were analyzed using unsupervised and hypothesis-driven computational approaches and compared with data from a study of acute caloric restriction in healthy volunteers. RESULTS Leptin replacement was associated with class-wide increased levels of fatty acids and acylcarnitines and decreased phospholipids, consistent with enhanced lipolysis and fatty acid oxidation. Primary and secondary bile acids increased after leptin treatment. Comparable changes were observed after acute caloric restriction. Branched-chain amino acids and steroid metabolites decreased after leptin, but not after acute caloric restriction. Individuals with severe obesity due to leptin deficiency and other genetic obesity syndromes shared a metabolomic signature associated with increased BMI. CONCLUSION Leptin replacement was associated with changes in lipolysis and substrate utilization that were consistent with negative energy balance. However, leptin's effects on branched-chain amino acids and steroid metabolites were independent of reduced caloric intake and require further exploration.
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Affiliation(s)
- Katherine Lawler
- University of Cambridge Metabolic Research Laboratories and NIHR Cambridge Biomedical Research Centre, Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK
| | - Isabel Huang-Doran
- University of Cambridge Metabolic Research Laboratories and NIHR Cambridge Biomedical Research Centre, Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK
| | - Takuhiro Sonoyama
- University of Cambridge Metabolic Research Laboratories and NIHR Cambridge Biomedical Research Centre, Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK
| | - Tinh-Hai Collet
- University of Cambridge Metabolic Research Laboratories and NIHR Cambridge Biomedical Research Centre, Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK
- Service of Endocrinology, Diabetes and Metabolism, Department of Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Julia M Keogh
- University of Cambridge Metabolic Research Laboratories and NIHR Cambridge Biomedical Research Centre, Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK
| | - Elana Henning
- University of Cambridge Metabolic Research Laboratories and NIHR Cambridge Biomedical Research Centre, Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK
| | - Stephen O’Rahilly
- University of Cambridge Metabolic Research Laboratories and NIHR Cambridge Biomedical Research Centre, Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK
| | - Leonardo Bottolo
- University Department of Medical Genetics, Addenbrooke’s Hospital, Cambridge, UK
- The Alan Turing Institute, London, UK
- MRC Biostatistics Unit, University of Cambridge, Robinson Way, Cambridge, UK
| | - I Sadaf Farooqi
- University of Cambridge Metabolic Research Laboratories and NIHR Cambridge Biomedical Research Centre, Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK
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17
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Ruffieux H, Davison AC, Hager J, Inshaw J, Fairfax BP, Richardson S, Bottolo L. A Global-Local Approach for Detecting Hotspots in Multiple-Response Regression. Ann Appl Stat 2020; 14:905-928. [PMID: 34992707 PMCID: PMC7612176 DOI: 10.1214/20-aoas1332] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
We tackle modelling and inference for variable selection in regression problems with many predictors and many responses. We focus on detecting hotspots, that is, predictors associated with several responses. Such a task is critical in statistical genetics, as hotspot genetic variants shape the architecture of the genome by controlling the expression of many genes and may initiate decisive functional mechanisms underlying disease endpoints. Existing hierarchical regression approaches designed to model hotspots suffer from two limitations: their discrimination of hotspots is sensitive to the choice of top-level scale parameters for the propensity of predictors to be hotspots, and they do not scale to large predictor and response vectors, for example, of dimensions 103-105 in genetic applications. We address these shortcomings by introducing a flexible hierarchical regression framework that is tailored to the detection of hotspots and scalable to the above dimensions. Our proposal implements a fully Bayesian model for hotspots based on the horseshoe shrinkage prior. Its global-local formulation shrinks noise globally and, hence, accommodates the highly sparse nature of genetic analyses while being robust to individual signals, thus leaving the effects of hotspots unshrunk. Inference is carried out using a fast variational algorithm coupled with a novel simulated annealing procedure that allows efficient exploration of multimodal distributions.
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Affiliation(s)
| | | | | | - Jamie Inshaw
- Wellcome Centre for Human Genetics, Oxford, University of Oxford
| | - Benjamin P. Fairfax
- Department of Oncology, MRC Weatherall Institute for Molecular Medicine, University of Oxford
| | - Sylvia Richardson
- MRC Biostatistics Unit, University of Cambridge
- Alan Turing Institute
| | - Leonardo Bottolo
- MRC Biostatistics Unit, University of Cambridge
- Alan Turing Institute
- Department of Medical Genetics, University of Cambridge
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18
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Bottolo L, Miller S, Johnson SR. Sphingolipid, fatty acid and phospholipid metabolites are associated with disease severity and mTOR inhibition in lymphangioleiomyomatosis. Thorax 2020; 75:679-688. [PMID: 32467337 DOI: 10.1136/thoraxjnl-2019-214241] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 03/26/2020] [Accepted: 04/16/2020] [Indexed: 01/22/2023]
Abstract
BACKGROUND Lymphangioleiomyomatosis (LAM) is a rare multisystem disease almost exclusively affecting women which causes loss of lung function, lymphatic abnormalities and angiomyolipomas. LAM occurs sporadically and in people with tuberous sclerosis complex (TSC). Loss of TSC gene function leads to dysregulated mechanistic target of rapamycin (mTOR) signalling. As mTOR is a regulator of lipid and nucleotide synthesis, we hypothesised that the serum metabolome would be altered in LAM and related to disease severity and activity. METHODS Ultrahigh performance liquid chromatography-tandem mass spectroscopy was used to examine the serum metabolome of 79 closely phenotyped women with LAM, including 29 receiving treatment with an mTOR inhibitor and 43 healthy control women. RESULTS Sphingolipid, fatty acid and phospholipid metabolites were associated with FEV1 in women with LAM (eg, behenoyl sphingomyelin adjusted (adj.) p=8.10 × 10-3). Those with higher disease-burden scores had abnormalities in fatty acid, phospholipid and lysolipids. Rate of loss of FEV1 was associated with differences in acyl-carnitine, acyl-glycines, acyl-glutamine, fatty acids, endocanbinoids and sphingolipids (eg, myristoleoylcarnitine adj. p=0.07). In TSC-LAM, rapamycin affected modules of interrelated metabolites which comprised linoleic acid, the tricarboxylic acid cycle, aminoacyl-tRNA biosynthesis, cysteine, methionine, arginine and proline metabolism. Metabolomic pathway analysis within modules reiterated the importance of glycerophospholipid metabolites (adj. p=0.047). CONCLUSIONS Women with LAM have altered lipid metabolism. The associations between these metabolites, multiple markers of disease activity and their potential biological roles in cell survival and signalling, suggest that lipid species may be both disease-relevant biomarkers and potential therapeutic targets for LAM.
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Affiliation(s)
- Leonardo Bottolo
- Department of Medical Genetics, University of Cambridge, Cambridge, UK.,The Alan Turing Institute, London, UK.,MRC Biostatistics Unit, Cambridge, UK
| | - Suzanne Miller
- Division of Respiratory Medicine, NIHR Biomedical Research Centre, Biodiscovery Institute and Nottingham Molecular Pathology Node, University of Nottingham, Nottingham, UK
| | - Simon R Johnson
- Division of Respiratory Medicine, NIHR Biomedical Research Centre, Biodiscovery Institute and Nottingham Molecular Pathology Node, University of Nottingham, Nottingham, UK .,National Centre for Lymphangioleiomyomatosis, Nottingham University Hospitals NHS Trust, Nottingham, UK
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19
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Steele H, Gomez‐Duran A, Pyle A, Hopton S, Newman J, Stefanetti RJ, Charman SJ, Parikh JD, He L, Viscomi C, Jakovljevic DG, Hollingsworth KG, Robinson AJ, Taylor RW, Bottolo L, Horvath R, Chinnery PF. Metabolic effects of bezafibrate in mitochondrial disease. EMBO Mol Med 2020; 12:e11589. [PMID: 32107855 PMCID: PMC7059007 DOI: 10.15252/emmm.201911589] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [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: 10/10/2019] [Revised: 01/29/2020] [Accepted: 01/29/2020] [Indexed: 12/16/2022] Open
Abstract
Mitochondrial disorders affect 1/5,000 and have no cure. Inducing mitochondrial biogenesis with bezafibrate improves mitochondrial function in animal models, but there are no comparable human studies. We performed an open-label observational experimental medicine study of six patients with mitochondrial myopathy caused by the m.3243A>G MTTL1 mutation. Our primary aim was to determine the effects of bezafibrate on mitochondrial metabolism, whilst providing preliminary evidence of safety and efficacy using biomarkers. The participants received 600-1,200 mg bezafibrate daily for 12 weeks. There were no clinically significant adverse events, and liver function was not affected. We detected a reduction in the number of complex IV-immunodeficient muscle fibres and improved cardiac function. However, this was accompanied by an increase in serum biomarkers of mitochondrial disease, including fibroblast growth factor 21 (FGF-21), growth and differentiation factor 15 (GDF-15), plus dysregulation of fatty acid and amino acid metabolism. Thus, although potentially beneficial in short term, inducing mitochondrial biogenesis with bezafibrate altered the metabolomic signature of mitochondrial disease, raising concerns about long-term sequelae.
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Affiliation(s)
- Hannah Steele
- Institute of Genetic MedicineNewcastle UniversityNewcastle upon TyneUK
| | - Aurora Gomez‐Duran
- Department of Clinical NeurosciencesUniversity of Cambridge, Cambridge Biomedical CampusCambridgeUK
- MRC Mitochondrial Biology UnitUniversity of CambridgeCambridgeUK
| | - Angela Pyle
- Institute of Genetic MedicineNewcastle UniversityNewcastle upon TyneUK
- Wellcome Centre for Mitochondrial ResearchNewcastle UniversityNewcastle upon TyneUK
| | - Sila Hopton
- Wellcome Centre for Mitochondrial ResearchNewcastle UniversityNewcastle upon TyneUK
- NHS Highly Specialised Service for Rare Mitochondrial Disorders of Adults and ChildrenNewcastle upon Tyne Hospitals NHS Foundation TrustNewcastle upon TyneUK
| | - Jane Newman
- Wellcome Centre for Mitochondrial ResearchNewcastle UniversityNewcastle upon TyneUK
- Institute of NeuroscienceNewcastle UniversityNewcastle upon TyneUK
| | | | - Sarah J Charman
- Institute of Cellular MedicineNewcastle UniversityNewcastle upon TyneUK
| | - Jehill D Parikh
- Institute of Cellular MedicineNewcastle UniversityNewcastle upon TyneUK
| | - Langping He
- Wellcome Centre for Mitochondrial ResearchNewcastle UniversityNewcastle upon TyneUK
- NHS Highly Specialised Service for Rare Mitochondrial Disorders of Adults and ChildrenNewcastle upon Tyne Hospitals NHS Foundation TrustNewcastle upon TyneUK
| | - Carlo Viscomi
- MRC Mitochondrial Biology UnitUniversity of CambridgeCambridgeUK
| | | | | | - Alan J Robinson
- MRC Mitochondrial Biology UnitUniversity of CambridgeCambridgeUK
| | - Robert W Taylor
- Wellcome Centre for Mitochondrial ResearchNewcastle UniversityNewcastle upon TyneUK
- NHS Highly Specialised Service for Rare Mitochondrial Disorders of Adults and ChildrenNewcastle upon Tyne Hospitals NHS Foundation TrustNewcastle upon TyneUK
- Institute of NeuroscienceNewcastle UniversityNewcastle upon TyneUK
| | - Leonardo Bottolo
- Department of Medical GeneticsUniversity of CambridgeCambridgeUK
- The Alan Turing InstituteLondonUK
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
| | - Rita Horvath
- Department of Clinical NeurosciencesUniversity of Cambridge, Cambridge Biomedical CampusCambridgeUK
| | - Patrick F Chinnery
- Department of Clinical NeurosciencesUniversity of Cambridge, Cambridge Biomedical CampusCambridgeUK
- MRC Mitochondrial Biology UnitUniversity of CambridgeCambridgeUK
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20
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Chen H, Moreno-Moral A, Pesce F, Devapragash N, Mancini M, Heng EL, Rotival M, Srivastava PK, Harmston N, Shkura K, Rackham OJL, Yu WP, Sun XM, Tee NGZ, Tan ELS, Barton PJR, Felkin LE, Lara-Pezzi E, Angelini G, Beltrami C, Pravenec M, Schafer S, Bottolo L, Hubner N, Emanueli C, Cook SA, Petretto E. Author Correction: WWP2 regulates pathological cardiac fibrosis by modulating SMAD2 signaling. Nat Commun 2019; 10:4085. [PMID: 31501434 PMCID: PMC6733793 DOI: 10.1038/s41467-019-12060-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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Affiliation(s)
- Huimei Chen
- Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore, 169857, Republic of Singapore
| | - Aida Moreno-Moral
- Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore, 169857, Republic of Singapore
| | - Francesco Pesce
- Department of Emergency and Organ Transplantation (DETO), University of Bari, 70124, Bari, Italy
| | - Nithya Devapragash
- Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore, 169857, Republic of Singapore
| | - Massimiliano Mancini
- SOC di Anatomia Patologica, Ospedale San Giovanni di Dio, 50123, Florence, Italy
| | - Ee Ling Heng
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
| | - Maxime Rotival
- Unit of Human Evolutionary Genetics, Institute Pasteur, 75015, Paris, France
| | - Prashant K Srivastava
- Division of Brain Sciences, Imperial College Faculty of Medicine, London, W12 0NN, UK
| | - Nathan Harmston
- Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore, 169857, Republic of Singapore
| | - Kirill Shkura
- Division of Brain Sciences, Imperial College Faculty of Medicine, London, W12 0NN, UK
| | - Owen J L Rackham
- Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore, 169857, Republic of Singapore
| | - Wei-Ping Yu
- Animal Gene Editing Laboratory, BRC, A*STAR20 Biopolis Way, Singapore, 138668, Republic of Singapore
- Institute of Molecular and Cell Biology, A*STAR, 61 Biopolis Drive, Singapore, 138673, Republic of Singapore
| | - Xi-Ming Sun
- MRC London Institute of Medical Sciences (LMC), Imperial College, London, W12 0NN, UK
| | | | - Elisabeth Li Sa Tan
- Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore, 169857, Republic of Singapore
| | - Paul J R Barton
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS Trust, London, SW3 6NP, UK
| | - Leanne E Felkin
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS Trust, London, SW3 6NP, UK
| | - Enrique Lara-Pezzi
- Centro Nacional de Investigaciones Cardiovasculares - CNIC, 28029, Madrid, Spain
| | - Gianni Angelini
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol, BS2 89HW, UK
| | - Cristina Beltrami
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
| | - Michal Pravenec
- Institute of Physiology, Czech Academy of Sciences, 142 00, Praha 4, Czech Republic
| | - Sebastian Schafer
- Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore, 169857, Republic of Singapore
- National Heart Centre Singapore, Singapore, 169609, Republic of Singapore
| | - Leonardo Bottolo
- Department of Medical Genetics, University of Cambridge, Cambridge, CB2 0QQ, UK
- The Alan Turing Institute, London, NW1 2DB, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, CB2 0SR, UK
| | - Norbert Hubner
- Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, 13347, Berlin, Germany
- Charité-Universitätsmedizin, 10117, Berlin, Germany
- Berlin Institute of Health (BIH), 10178, Berlin, Germany
| | - Costanza Emanueli
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS Trust, London, SW3 6NP, UK
| | - Stuart A Cook
- Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore, 169857, Republic of Singapore
- MRC London Institute of Medical Sciences (LMC), Imperial College, London, W12 0NN, UK
- National Heart Centre Singapore, Singapore, 169609, Republic of Singapore
| | - Enrico Petretto
- Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore, 169857, Republic of Singapore.
- MRC London Institute of Medical Sciences (LMC), Imperial College, London, W12 0NN, UK.
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21
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Ochoa E, Zuber V, Fernandez-Jimenez N, Bilbao JR, Clark GR, Maher ER, Bottolo L. MethylCal: Bayesian calibration of methylation levels. Nucleic Acids Res 2019; 47:e81. [PMID: 31049595 PMCID: PMC6698668 DOI: 10.1093/nar/gkz325] [Citation(s) in RCA: 5] [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: 10/16/2018] [Revised: 03/24/2019] [Accepted: 04/20/2019] [Indexed: 12/16/2022] Open
Abstract
Bisulfite amplicon sequencing has become the primary choice for single-base methylation quantification of multiple targets in parallel. The main limitation of this technology is a preferential amplification of an allele and strand in the PCR due to methylation state. This effect, known as 'PCR bias', causes inaccurate estimation of the methylation levels and calibration methods based on standard controls have been proposed to correct for it. Here, we present a Bayesian calibration tool, MethylCal, which can analyse jointly all CpGs within a CpG island (CGI) or a Differentially Methylated Region (DMR), avoiding 'one-at-a-time' CpG calibration. This enables more precise modeling of the methylation levels observed in the standard controls. It also provides accurate predictions of the methylation levels not considered in the controlled experiment, a feature that is paramount in the derivation of the corrected methylation degree. We tested the proposed method on eight independent assays (two CpG islands and six imprinting DMRs) and demonstrated its benefits, including the ability to detect outliers. We also evaluated MethylCal's calibration in two practical cases, a clinical diagnostic test on 18 patients potentially affected by Beckwith-Wiedemann syndrome, and 17 individuals with celiac disease. The calibration of the methylation levels obtained by MethylCal allows a clearer identification of patients undergoing loss or gain of methylation in borderline cases and could influence further clinical or treatment decisions.
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Affiliation(s)
- Eguzkine Ochoa
- Department of Medical Genetics, University of Cambridge, Cambridge CB2 0QQ, UK
- Cambridge NIHR Biomedical Research Centre, Cambridge CB2 0QQ, UK
| | - Verena Zuber
- Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK
| | - Nora Fernandez-Jimenez
- Department of Genetics, Physical Anthropology and Animal Physiology, Biocruces-Bizkaia Health Research Institute, University of the Basque Country (UPV/EHU), Leioa, Bizkaia, Spain
| | - Jose Ramon Bilbao
- Department of Genetics, Physical Anthropology and Animal Physiology, Biocruces-Bizkaia Health Research Institute, University of the Basque Country (UPV/EHU), Leioa, Bizkaia, Spain
- CIBERDEM Diabetes and Associated Metabolic Diseases, Spain
| | - Graeme R Clark
- Department of Medical Genetics, University of Cambridge, Cambridge CB2 0QQ, UK
- Cambridge NIHR Biomedical Research Centre, Cambridge CB2 0QQ, UK
| | - Eamonn R Maher
- Department of Medical Genetics, University of Cambridge, Cambridge CB2 0QQ, UK
- Cambridge NIHR Biomedical Research Centre, Cambridge CB2 0QQ, UK
| | - Leonardo Bottolo
- Department of Medical Genetics, University of Cambridge, Cambridge CB2 0QQ, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK
- The Alan Turing Institute, London NW1 2DB, UK
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22
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Chen H, Moreno-Moral A, Pesce F, Devapragash N, Mancini M, Heng EL, Rotival M, Srivastava PK, Harmston N, Shkura K, Rackham OJL, Yu WP, Sun XM, Tee NGZ, Tan ELS, Barton PJR, Felkin LE, Lara-Pezzi E, Angelini G, Beltrami C, Pravenec M, Schafer S, Bottolo L, Hubner N, Emanueli C, Cook SA, Petretto E. WWP2 regulates pathological cardiac fibrosis by modulating SMAD2 signaling. Nat Commun 2019; 10:3616. [PMID: 31399586 PMCID: PMC6689010 DOI: 10.1038/s41467-019-11551-9] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 07/19/2019] [Indexed: 01/03/2023] Open
Abstract
Cardiac fibrosis is a final common pathology in inherited and acquired heart diseases that causes cardiac electrical and pump failure. Here, we use systems genetics to identify a pro-fibrotic gene network in the diseased heart and show that this network is regulated by the E3 ubiquitin ligase WWP2, specifically by the WWP2-N terminal isoform. Importantly, the WWP2-regulated pro-fibrotic gene network is conserved across different cardiac diseases characterized by fibrosis: human and murine dilated cardiomyopathy and repaired tetralogy of Fallot. Transgenic mice lacking the N-terminal region of the WWP2 protein show improved cardiac function and reduced myocardial fibrosis in response to pressure overload or myocardial infarction. In primary cardiac fibroblasts, WWP2 positively regulates the expression of pro-fibrotic markers and extracellular matrix genes. TGFβ1 stimulation promotes nuclear translocation of the WWP2 isoforms containing the N-terminal region and their interaction with SMAD2. WWP2 mediates the TGFβ1-induced nucleocytoplasmic shuttling and transcriptional activity of SMAD2.
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Affiliation(s)
- Huimei Chen
- Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore, 169857, Republic of Singapore
| | - Aida Moreno-Moral
- Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore, 169857, Republic of Singapore
| | - Francesco Pesce
- Department of Emergency and Organ Transplantation (DETO), University of Bari, 70124, Bari, Italy
| | - Nithya Devapragash
- Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore, 169857, Republic of Singapore
| | - Massimiliano Mancini
- SOC di Anatomia Patologica, Ospedale San Giovanni di Dio, 50123, Florence, Italy
| | - Ee Ling Heng
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
| | - Maxime Rotival
- Unit of Human Evolutionary Genetics, Institute Pasteur, 75015, Paris, France
| | - Prashant K Srivastava
- Division of Brain Sciences, Imperial College Faculty of Medicine, London, W12 0NN, UK
| | - Nathan Harmston
- Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore, 169857, Republic of Singapore
| | - Kirill Shkura
- Division of Brain Sciences, Imperial College Faculty of Medicine, London, W12 0NN, UK
| | - Owen J L Rackham
- Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore, 169857, Republic of Singapore
| | - Wei-Ping Yu
- Animal Gene Editing Laboratory, BRC, A*STAR20 Biopolis Way, Singapore, 138668, Republic of Singapore
- Institute of Molecular and Cell Biology, A*STAR, 61 Biopolis Drive, Singapore, 138673, Republic of Singapore
| | - Xi-Ming Sun
- MRC London Institute of Medical Sciences (LMC), Imperial College, London, W12 0NN, UK
| | | | - Elisabeth Li Sa Tan
- Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore, 169857, Republic of Singapore
| | - Paul J R Barton
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS Trust, London, SW3 6NP, UK
| | - Leanne E Felkin
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS Trust, London, SW3 6NP, UK
| | - Enrique Lara-Pezzi
- Centro Nacional de Investigaciones Cardiovasculares - CNIC, 28029, Madrid, Spain
| | - Gianni Angelini
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol, BS2 89HW, UK
| | - Cristina Beltrami
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
| | - Michal Pravenec
- Institute of Physiology, Czech Academy of Sciences, 142 00, Praha 4, Czech Republic
| | - Sebastian Schafer
- Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore, 169857, Republic of Singapore
- National Heart Centre Singapore, Singapore, 169609, Republic of Singapore
| | - Leonardo Bottolo
- Department of Medical Genetics, University of Cambridge, Cambridge, CB2 0QQ, UK
- The Alan Turing Institute, London, NW1 2DB, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, CB2 0SR, UK
| | - Norbert Hubner
- Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, 13347, Berlin, Germany
- Charité-Universitätsmedizin, 10117, Berlin, Germany
- Berlin Institute of Health (BIH), 10178, Berlin, Germany
| | - Costanza Emanueli
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS Trust, London, SW3 6NP, UK
| | - Stuart A Cook
- Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore, 169857, Republic of Singapore
- MRC London Institute of Medical Sciences (LMC), Imperial College, London, W12 0NN, UK
- National Heart Centre Singapore, Singapore, 169609, Republic of Singapore
| | - Enrico Petretto
- Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore, 169857, Republic of Singapore.
- MRC London Institute of Medical Sciences (LMC), Imperial College, London, W12 0NN, UK.
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Affiliation(s)
- L Bottolo
- Department of Medical Genetics, University of Cambridge, J. J. Thomson Avenue, Cambridge, U.K
| | - S Richardson
- MRC Biostatistics Unit, University of Cambridge, Robinson Way, Cambridge, U.K
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Schon K, van Os NJ, Oscroft N, Baxendale H, Scoffings D, Ray J, Suri M, Whitehouse WP, Mehta PR, Everett N, Bottolo L, van de Warrenburg BP, Byrd PJ, Weemaes C, Willemsen MA, Tischkowitz M, Taylor AM, Hensiek AE. Genotype, extrapyramidal features, and severity of variant ataxia-telangiectasia. Ann Neurol 2019; 85:170-180. [PMID: 30549301 PMCID: PMC6590299 DOI: 10.1002/ana.25394] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [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: 06/26/2018] [Revised: 12/09/2018] [Accepted: 12/10/2018] [Indexed: 12/24/2022]
Abstract
OBJECTIVE Variant ataxia-telangiectasia is caused by mutations that allow some retained ataxia telangiectasia-mutated (ATM) kinase activity. Here, we describe the clinical features of the largest established cohort of individuals with variant ataxia-telangiectasia and explore genotype-phenotype correlations. METHODS Cross-sectional data were collected retrospectively. Patients were classified as variant ataxia-telangiectasia based on retained ATM kinase activity. RESULTS The study includes 57 individuals. Mean age at assessment was 37.5 years. Most had their first symptoms by age 10 (81%). There was a diagnostic delay of more than 10 years in 68% and more than 20 years in one third of probands. Disease severity was mild in one third of patients, and 43% were still ambulant 20 years after disease onset. Only one third had predominant ataxia, and 18% had a pure extrapyramidal presentation. Individuals with extrapyramidal presentations had milder neurological disease severity. There were no significant respiratory or immunological complications, but 25% of individuals had a history of malignancy. Missense mutations were associated with milder neurological disease severity, but with a higher risk of malignancy, compared to leaky splice site mutations. INTERPRETATION Individuals with variant ataxia-telangiectasia require malignancy surveillance and tailored management. However, our data suggest the condition may sometimes be mis- or underdiagnosed because of atypical features, including exclusive extrapyramidal symptoms, normal eye movements, and normal alpha-fetoprotein levels in some individuals. Missense mutations are associated with milder neurological presentations, but a particularly high malignancy risk, and it is important for clinicians to be aware of these phenotypes. ANN NEUROL 2019;85:170-180.
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Affiliation(s)
- Katherine Schon
- East Anglian Medical Genetics ServiceCambridge University Hospitals NHS Foundation TrustCambridgeUnited Kingdom
| | - Nienke J.H. van Os
- Departments of Neurology & Pediatric Neurology, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CentreNijmegenThe Netherlands
| | - Nicholas Oscroft
- Ataxia Telangiectasia Service, Respiratory Support and Sleep CentrePapworth HospitalCambridgeUnited Kingdom
| | - Helen Baxendale
- Ataxia Telangiectasia Service, Respiratory Support and Sleep CentrePapworth HospitalCambridgeUnited Kingdom
| | - Daniel Scoffings
- Department of RadiologyCambridge University Hospitals NHS Foundation TrustCambridgeUnited Kingdom
| | - Julian Ray
- Department of NeurophysiologyCambridge University Hospitals NHS Foundation TrustCambridgeUnited Kingdom
| | - Mohnish Suri
- Nottingham Clinical Genetics ServiceNational Paediatric Ataxia‐Telangiectasia ClinicNottinghamUnited Kingdom
| | - William P. Whitehouse
- School of MedicineUniversity of Nottingham, Queen's Medical CentreNottinghamUnited Kingdom
- Department of Paediatric NeurologyNottingham Children's Hospital, Nottingham University Hospitals NHS TrustNottinghamUnited Kingdom
| | - Puja R. Mehta
- Ataxia Telangiectasia Service, Respiratory Support and Sleep CentrePapworth HospitalCambridgeUnited Kingdom
| | - Natasha Everett
- Ataxia Telangiectasia Service, Respiratory Support and Sleep CentrePapworth HospitalCambridgeUnited Kingdom
| | - Leonardo Bottolo
- Department of Medical GeneticsCambridge Biomedical CampusCambridgeUnited Kingdom
- The Alan Turing InstituteBritish LibraryLondonUnited Kingdom
- MRC Biostatistics UnitUniversity of Cambridge, Cambridge Biomedical CampusCambridgeUnited Kingdom
| | - Bart P. van de Warrenburg
- Departments of Neurology & Pediatric Neurology, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CentreNijmegenThe Netherlands
| | - Philip J. Byrd
- Institute of Cancer and Genomic Sciences, College of Medical and Dental SciencesUniversity of BirminghamBirminghamUnited Kingdom
| | - Corry Weemaes
- Department of PediatricsRadboudumc Amalia Children's HospitalNijmegenThe Netherlands
| | - Michel A. Willemsen
- Departments of Neurology & Pediatric Neurology, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CentreNijmegenThe Netherlands
| | - Marc Tischkowitz
- East Anglian Medical Genetics ServiceCambridge University Hospitals NHS Foundation TrustCambridgeUnited Kingdom
- Department of Medical GeneticsCambridge Biomedical CampusCambridgeUnited Kingdom
| | - A. Malcolm Taylor
- Institute of Cancer and Genomic Sciences, College of Medical and Dental SciencesUniversity of BirminghamBirminghamUnited Kingdom
| | - Anke E. Hensiek
- Ataxia Telangiectasia Service, Respiratory Support and Sleep CentrePapworth HospitalCambridgeUnited Kingdom
- Department of NeurologyCambridge University Hospitals NHS Foundation TrustCambridgeUnited Kingdom
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Adamowicz M, Morgan CC, Haubner BJ, Noseda M, Collins MJ, Abreu Paiva M, Srivastava PK, Gellert P, Razzaghi B, O’Gara P, Raina P, Game L, Bottolo L, Schneider MD, Harding SE, Penninger J, Aitman TJ. Functionally Conserved Noncoding Regulators of Cardiomyocyte Proliferation and Regeneration in Mouse and Human. Circ: Genomic and Precision Medicine 2018; 11:e001805. [DOI: 10.1161/circgen.117.001805] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background:
The adult mammalian heart has little regenerative capacity after myocardial infarction (MI), whereas neonatal mouse heart regenerates without scarring or dysfunction. However, the underlying pathways are poorly defined. We sought to derive insights into the pathways regulating neonatal development of the mouse heart and cardiac regeneration post-MI.
Methods and Results:
Total RNA-seq of mouse heart through the first 10 days of postnatal life (referred to as P3, P5, P10) revealed a previously unobserved transition in microRNA (miRNA) expression between P3 and P5 associated specifically with altered expression of protein-coding genes on the focal adhesion pathway and cessation of cardiomyocyte cell division. We found profound changes in the coding and noncoding transcriptome after neonatal MI, with evidence of essentially complete healing by P10. Over two-thirds of each of the messenger RNAs, long noncoding RNAs, and miRNAs that were differentially expressed in the post-MI heart were differentially expressed during normal postnatal development, suggesting a common regulatory pathway for normal cardiac development and post-MI cardiac regeneration. We selected exemplars of miRNAs implicated in our data set as regulators of cardiomyocyte proliferation. Several of these showed evidence of a functional influence on mouse cardiomyocyte cell division. In addition, a subset of these miRNAs, miR-144-3p, miR-195a-5p, miR-451a, and miR-6240 showed evidence of functional conservation in human cardiomyocytes.
Conclusions:
The sets of messenger RNAs, miRNAs, and long noncoding RNAs that we report here merit further investigation as gatekeepers of cell division in the postnatal heart and as targets for extension of the period of cardiac regeneration beyond the neonatal period.
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Affiliation(s)
- Martyna Adamowicz
- From the Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Scotland, United Kingdom (T.J.A.); National Heart and Lung Institute (M.A., C.C.M., M.N., M.A.P., P.O., M.D.S., S.E.H.), Department of Medicine (C.C.M., M.J.C., P.K.S., B.R., P.R., T.J.A.), Department of Mathematics (L.B.), Imperial College London, United Kingdom; IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna, Austria (B.J.H., J.P.)
| | - Claire C. Morgan
- From the Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Scotland, United Kingdom (T.J.A.); National Heart and Lung Institute (M.A., C.C.M., M.N., M.A.P., P.O., M.D.S., S.E.H.), Department of Medicine (C.C.M., M.J.C., P.K.S., B.R., P.R., T.J.A.), Department of Mathematics (L.B.), Imperial College London, United Kingdom; IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna, Austria (B.J.H., J.P.)
| | - Bernhard J. Haubner
- From the Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Scotland, United Kingdom (T.J.A.); National Heart and Lung Institute (M.A., C.C.M., M.N., M.A.P., P.O., M.D.S., S.E.H.), Department of Medicine (C.C.M., M.J.C., P.K.S., B.R., P.R., T.J.A.), Department of Mathematics (L.B.), Imperial College London, United Kingdom; IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna, Austria (B.J.H., J.P.)
| | - Michela Noseda
- From the Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Scotland, United Kingdom (T.J.A.); National Heart and Lung Institute (M.A., C.C.M., M.N., M.A.P., P.O., M.D.S., S.E.H.), Department of Medicine (C.C.M., M.J.C., P.K.S., B.R., P.R., T.J.A.), Department of Mathematics (L.B.), Imperial College London, United Kingdom; IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna, Austria (B.J.H., J.P.)
| | - Melissa J. Collins
- From the Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Scotland, United Kingdom (T.J.A.); National Heart and Lung Institute (M.A., C.C.M., M.N., M.A.P., P.O., M.D.S., S.E.H.), Department of Medicine (C.C.M., M.J.C., P.K.S., B.R., P.R., T.J.A.), Department of Mathematics (L.B.), Imperial College London, United Kingdom; IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna, Austria (B.J.H., J.P.)
| | - Marta Abreu Paiva
- From the Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Scotland, United Kingdom (T.J.A.); National Heart and Lung Institute (M.A., C.C.M., M.N., M.A.P., P.O., M.D.S., S.E.H.), Department of Medicine (C.C.M., M.J.C., P.K.S., B.R., P.R., T.J.A.), Department of Mathematics (L.B.), Imperial College London, United Kingdom; IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna, Austria (B.J.H., J.P.)
| | - Prashant K. Srivastava
- From the Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Scotland, United Kingdom (T.J.A.); National Heart and Lung Institute (M.A., C.C.M., M.N., M.A.P., P.O., M.D.S., S.E.H.), Department of Medicine (C.C.M., M.J.C., P.K.S., B.R., P.R., T.J.A.), Department of Mathematics (L.B.), Imperial College London, United Kingdom; IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna, Austria (B.J.H., J.P.)
| | - Pascal Gellert
- From the Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Scotland, United Kingdom (T.J.A.); National Heart and Lung Institute (M.A., C.C.M., M.N., M.A.P., P.O., M.D.S., S.E.H.), Department of Medicine (C.C.M., M.J.C., P.K.S., B.R., P.R., T.J.A.), Department of Mathematics (L.B.), Imperial College London, United Kingdom; IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna, Austria (B.J.H., J.P.)
| | - Bonnie Razzaghi
- From the Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Scotland, United Kingdom (T.J.A.); National Heart and Lung Institute (M.A., C.C.M., M.N., M.A.P., P.O., M.D.S., S.E.H.), Department of Medicine (C.C.M., M.J.C., P.K.S., B.R., P.R., T.J.A.), Department of Mathematics (L.B.), Imperial College London, United Kingdom; IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna, Austria (B.J.H., J.P.)
| | - Peter O’Gara
- From the Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Scotland, United Kingdom (T.J.A.); National Heart and Lung Institute (M.A., C.C.M., M.N., M.A.P., P.O., M.D.S., S.E.H.), Department of Medicine (C.C.M., M.J.C., P.K.S., B.R., P.R., T.J.A.), Department of Mathematics (L.B.), Imperial College London, United Kingdom; IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna, Austria (B.J.H., J.P.)
| | - Priyanka Raina
- From the Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Scotland, United Kingdom (T.J.A.); National Heart and Lung Institute (M.A., C.C.M., M.N., M.A.P., P.O., M.D.S., S.E.H.), Department of Medicine (C.C.M., M.J.C., P.K.S., B.R., P.R., T.J.A.), Department of Mathematics (L.B.), Imperial College London, United Kingdom; IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna, Austria (B.J.H., J.P.)
| | - Laurence Game
- From the Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Scotland, United Kingdom (T.J.A.); National Heart and Lung Institute (M.A., C.C.M., M.N., M.A.P., P.O., M.D.S., S.E.H.), Department of Medicine (C.C.M., M.J.C., P.K.S., B.R., P.R., T.J.A.), Department of Mathematics (L.B.), Imperial College London, United Kingdom; IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna, Austria (B.J.H., J.P.)
| | - Leonardo Bottolo
- From the Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Scotland, United Kingdom (T.J.A.); National Heart and Lung Institute (M.A., C.C.M., M.N., M.A.P., P.O., M.D.S., S.E.H.), Department of Medicine (C.C.M., M.J.C., P.K.S., B.R., P.R., T.J.A.), Department of Mathematics (L.B.), Imperial College London, United Kingdom; IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna, Austria (B.J.H., J.P.)
| | - Michael D. Schneider
- From the Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Scotland, United Kingdom (T.J.A.); National Heart and Lung Institute (M.A., C.C.M., M.N., M.A.P., P.O., M.D.S., S.E.H.), Department of Medicine (C.C.M., M.J.C., P.K.S., B.R., P.R., T.J.A.), Department of Mathematics (L.B.), Imperial College London, United Kingdom; IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna, Austria (B.J.H., J.P.)
| | - Sian E. Harding
- From the Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Scotland, United Kingdom (T.J.A.); National Heart and Lung Institute (M.A., C.C.M., M.N., M.A.P., P.O., M.D.S., S.E.H.), Department of Medicine (C.C.M., M.J.C., P.K.S., B.R., P.R., T.J.A.), Department of Mathematics (L.B.), Imperial College London, United Kingdom; IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna, Austria (B.J.H., J.P.)
| | - Josef Penninger
- From the Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Scotland, United Kingdom (T.J.A.); National Heart and Lung Institute (M.A., C.C.M., M.N., M.A.P., P.O., M.D.S., S.E.H.), Department of Medicine (C.C.M., M.J.C., P.K.S., B.R., P.R., T.J.A.), Department of Mathematics (L.B.), Imperial College London, United Kingdom; IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna, Austria (B.J.H., J.P.)
| | - Timothy J. Aitman
- From the Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Scotland, United Kingdom (T.J.A.); National Heart and Lung Institute (M.A., C.C.M., M.N., M.A.P., P.O., M.D.S., S.E.H.), Department of Medicine (C.C.M., M.J.C., P.K.S., B.R., P.R., T.J.A.), Department of Mathematics (L.B.), Imperial College London, United Kingdom; IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna, Austria (B.J.H., J.P.)
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Inshaw JRJ, Walker NM, Wallace C, Bottolo L, Todd JA. The chromosome 6q22.33 region is associated with age at diagnosis of type 1 diabetes and disease risk in those diagnosed under 5 years of age. Diabetologia 2018; 61:147-157. [PMID: 28983737 PMCID: PMC5719131 DOI: 10.1007/s00125-017-4440-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Accepted: 07/28/2017] [Indexed: 12/16/2022]
Abstract
AIMS/HYPOTHESIS The genetic risk of type 1 diabetes has been extensively studied. However, the genetic determinants of age at diagnosis (AAD) of type 1 diabetes remain relatively unexplained. Identification of AAD genes and pathways could provide insight into the earliest events in the disease process. METHODS Using ImmunoChip data from 15,696 cases, we aimed to identify regions in the genome associated with AAD. RESULTS Two regions were convincingly associated with AAD (p < 5 × 10-8): the MHC on 6p21, and 6q22.33. Fine-mapping of 6q22.33 identified two AAD-associated haplotypes in the region nearest to the genes encoding protein tyrosine phosphatase receptor kappa (PTPRK) and thymocyte-expressed molecule involved in selection (THEMIS). We examined the susceptibility to type 1 diabetes at these SNPs by performing a meta-analysis including 19,510 control participants. Although these SNPs were not associated with type 1 diabetes overall (p > 0.001), the SNP most associated with AAD, rs72975913, was associated with susceptibility to type 1 diabetes in those individuals diagnosed at less than 5 years old (p = 2.3 × 10-9). CONCLUSION/INTERPRETATION PTPRK and its neighbour THEMIS are required for early development of the thymus, which we can assume influences the initiation of autoimmunity. Non-HLA genes may only be detectable as risk factors for the disease in individuals diagnosed under the age 5 years because, after that period of immune development, their role in disease susceptibility has become redundant.
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Affiliation(s)
- Jamie R J Inshaw
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Wellcome Trust Centre for Human Genetics, University of Oxford, NIHR Oxford Biomedical Research Centre, Nuffield Department of Medicine, Roosevelt Drive, Oxford, OX3 7BN, UK.
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK.
| | - Neil M Walker
- Clinical Informatics, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Chris Wallace
- Department of Medicine, University of Cambridge, Cambridge, UK
- MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, UK
| | - Leonardo Bottolo
- MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, UK
- Department of Medical Genetics, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - John A Todd
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Wellcome Trust Centre for Human Genetics, University of Oxford, NIHR Oxford Biomedical Research Centre, Nuffield Department of Medicine, Roosevelt Drive, Oxford, OX3 7BN, UK.
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK.
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Abstract
Novel algorithmic and hardware techniques for fast SSM inference are proposed. New algorithm extends applicability of particle MCMC to multi-modal posteriors. FPGA architectures exploit particle and chain parallelism to accelerate sampling. 42x speedup vs. state-of-the-art CPU/GPU samplers is achieved for large problems.
Particle Markov Chain Monte Carlo (pMCMC) is a stochastic algorithm designed to generate samples from a probability distribution, when the density of the distribution does not admit a closed form expression. pMCMC is most commonly used to sample from the Bayesian posterior distribution in State-Space Models (SSMs), a class of probabilistic models used in numerous scientific applications. Nevertheless, this task is prohibitive when dealing with complex SSMs with massive data, due to the high computational cost of pMCMC and its poor performance when the posterior exhibits multi-modality. This paper aims to address both issues by: 1) Proposing a novel pMCMC algorithm (denoted ppMCMC), which uses multiple Markov chains (instead of the one used by pMCMC) to improve sampling efficiency for multi-modal posteriors, 2) Introducing custom, parallel hardware architectures, which are tailored for pMCMC and ppMCMC. The architectures are implemented on Field Programmable Gate Arrays (FPGAs), a type of hardware accelerator with massive parallelization capabilities. The new algorithm and the two FPGA architectures are evaluated using a large-scale case study from genetics. Results indicate that ppMCMC achieves 1.96x higher sampling efficiency than pMCMC when using sequential CPU implementations. The FPGA architecture of pMCMC is 12.1x and 10.1x faster than state-of-the-art, parallel CPU and GPU implementations of pMCMC and up to 53x more energy efficient; the FPGA architecture of ppMCMC increases these speedups to 34.9x and 41.8x respectively and is 173x more power efficient, bringing previously intractable SSM-based data analyses within reach.
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Affiliation(s)
- Grigorios Mingas
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, UK
| | - Leonardo Bottolo
- Department of Medical Genetics, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Christos-Savvas Bouganis
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, UK
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Abstract
The aim of expression Quantitative Trait Locus (eQTL) mapping is the identification of DNA sequence variants that explain variation in gene expression. Given the recent yield of trait-associated genetic variants identified by large-scale genome-wide association analyses (GWAS), eQTL mapping has become a useful tool to understand the functional context where these variants operate and eventually narrow down functional gene targets for disease. Despite its extensive application to complex (polygenic) traits and disease, the majority of eQTL studies still rely on univariate data modeling strategies, i.e., testing for association of all transcript-marker pairs. However these "one at-a-time" strategies are (1) unable to control the number of false-positives when an intricate Linkage Disequilibrium structure is present and (2) are often underpowered to detect the full spectrum of trans-acting regulatory effects. Here we present our viewpoint on the most recent advances on eQTL mapping approaches, with a focus on Bayesian methodology. We review the advantages of the Bayesian approach over frequentist methods and provide an empirical example of polygenic eQTL mapping to illustrate the different properties of frequentist and Bayesian methods. Finally, we discuss how multivariate eQTL mapping approaches have distinctive features with respect to detection of polygenic effects, accuracy, and interpretability of the results.
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Affiliation(s)
- Martha Imprialou
- Centre for Complement and Inflammation Research, Imperial College London, Hammersmith Hospital, Du Cane Road, London, W12 0NN, UK
| | - Enrico Petretto
- Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore.
| | - Leonardo Bottolo
- Department of Medical Genetics, University of Cambridge, Box 238, Lv 6 Addenbrooke's Treatment Centre, Addenbrooke's Hospital, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
- Department of Mathematics, Imperial College London, 180 Queen's Gate, London, SW7 2AZ, UK.
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29
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Liquet B, Bottolo L, Campanella G, Richardson S, Chadeau-Hyam M. R2GUESS: A Graphics Processing Unit-Based R Package for Bayesian Variable Selection Regression of Multivariate Responses. J Stat Softw 2016; 69. [PMID: 29568242 DOI: 10.18637/jss.v069.i02] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
Technological advances in molecular biology over the past decade have given rise to high dimensional and complex datasets offering the possibility to investigate biological associations between a range of genomic features and complex phenotypes. The analysis of this novel type of data generated unprecedented computational challenges which ultimately led to the definition and implementation of computationally efficient statistical models that were able to scale to genome-wide data, including Bayesian variable selection approaches. While extensive methodological work has been carried out in this area, only few methods capable of handling hundreds of thousands of predictors were implemented and distributed. Among these we recently proposed GUESS, a computationally optimised algorithm making use of graphics processing unit capabilities, which can accommodate multiple outcomes. In this paper we propose R2GUESS, an R package wrapping the original C++ source code. In addition to providing a user-friendly interface of the original code automating its parametrisation, and data handling, R2GUESS also incorporates many features to explore the data, to extend statistical inferences from the native algorithm (e.g., effect size estimation, significance assessment), and to visualize outputs from the algorithm. We first detail the model and its parametrisation, and describe in details its optimised implementation. Based on two examples we finally illustrate its statistical performances and flexibility.
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Affiliation(s)
- Benoît Liquet
- Laboratoire de Mathématiques et de leurs Applications, Université de Pau et des Pays de l'Adour, UMR CNRS 5142, Pau, France; ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology (QUT), Brisbane, Australia
| | | | | | | | - Marc Chadeau-Hyam
- Department of Epidemiology and Biostatistics, Imperial College London, St Mary's Hospital, Norfolk Place, London, W21PG, United Kingdom
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Lewin A, Saadi H, Peters JE, Moreno-Moral A, Lee JC, Smith KGC, Petretto E, Bottolo L, Richardson S. MT-HESS: an efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues. Bioinformatics 2015; 32:523-32. [PMID: 26504141 PMCID: PMC4743623 DOI: 10.1093/bioinformatics/btv568] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [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: 12/05/2014] [Accepted: 09/03/2015] [Indexed: 01/22/2023] Open
Abstract
MOTIVATION Analysing the joint association between a large set of responses and predictors is a fundamental statistical task in integrative genomics, exemplified by numerous expression Quantitative Trait Loci (eQTL) studies. Of particular interest are the so-called ': hotspots ': , important genetic variants that regulate the expression of many genes. Recently, attention has focussed on whether eQTLs are common to several tissues, cell-types or, more generally, conditions or whether they are specific to a particular condition. RESULTS We have implemented MT-HESS, a Bayesian hierarchical model that analyses the association between a large set of predictors, e.g. SNPs, and many responses, e.g. gene expression, in multiple tissues, cells or conditions. Our Bayesian sparse regression algorithm goes beyond ': one-at-a-time ': association tests between SNPs and responses and uses a fully multivariate model search across all linear combinations of SNPs, coupled with a model of the correlation between condition/tissue-specific responses. In addition, we use a hierarchical structure to leverage shared information across different genes, thus improving the detection of hotspots. We show the increase of power resulting from our new approach in an extensive simulation study. Our analysis of two case studies highlights new hotspots that would remain undetected by standard approaches and shows how greater prediction power can be achieved when several tissues are jointly considered. AVAILABILITY AND IMPLEMENTATION C[Formula: see text] source code and documentation including compilation instructions are available under GNU licence at http://www.mrc-bsu.cam.ac.uk/software/.
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Affiliation(s)
- Alex Lewin
- Department of Mathematics, Brunel University London
| | - Habib Saadi
- Department of Epidemiology and Biostatistics, Imperial College London, London
| | - James E Peters
- Cambridge Institute for Medical Research, University of Cambridge, Cambridge, MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge
| | | | - James C Lee
- Cambridge Institute for Medical Research, University of Cambridge, Cambridge
| | - Kenneth G C Smith
- Cambridge Institute for Medical Research, University of Cambridge, Cambridge
| | - Enrico Petretto
- MRC Clinical Sciences Centre, Imperial College London, London, UK, Duke-NUS Graduate Medical School, Singapore, Singapore
| | - Leonardo Bottolo
- Department of Mathematics, Imperial College London, London, UK and Department of Medical Genetics, University of Cambridge
| | - Sylvia Richardson
- MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge
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Rackham OJL, Dellaportas P, Petretto E, Bottolo L. WGBSSuite: simulating whole-genome bisulphite sequencing data and benchmarking differential DNA methylation analysis tools. Bioinformatics 2015; 31:2371-3. [PMID: 25777524 PMCID: PMC4495289 DOI: 10.1093/bioinformatics/btv114] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [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: 12/23/2014] [Accepted: 02/17/2015] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION As the number of studies looking at differences between DNA methylation increases, there is a growing demand to develop and benchmark statistical methods to analyse these data. To date no objective approach for the comparison of these methods has been developed and as such it remains difficult to assess which analysis tool is most appropriate for a given experiment. As a result, there is an unmet need for a DNA methylation data simulator that can accurately reproduce a wide range of experimental setups, and can be routinely used to compare the performance of different statistical models. RESULTS We have developed WGBSSuite, a flexible stochastic simulation tool that generates single-base resolution DNA methylation data genome-wide. Several simulator parameters can be derived directly from real datasets provided by the user in order to mimic real case scenarios. Thus, it is possible to choose the most appropriate statistical analysis tool for a given simulated design. To show the usefulness of our simulator, we also report a benchmark of commonly used methods for differential methylation analysis. AVAILABILITY AND IMPLEMENTATION WGBS code and documentation are available under GNU licence at http://www.wgbssuite.org.uk/ CONTACT : owen.rackham@imperial.ac.uk or l.bottolo@imperial.ac.uk SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Owen J L Rackham
- Program in Cardiovascular & Metabolic Disorders and Centre for Computational Biology, Duke-NUS Graduate Medical School, Singapore, MRC Clinical Sciences Centre, Imperial College London, UK
| | - Petros Dellaportas
- Department of Statistics, Athens University of Economics and Business, Greece and
| | - Enrico Petretto
- Program in Cardiovascular & Metabolic Disorders and Centre for Computational Biology, Duke-NUS Graduate Medical School, Singapore, MRC Clinical Sciences Centre, Imperial College London, UK
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Johnson MR, Behmoaras J, Bottolo L, Krishnan ML, Pernhorst K, Santoscoy PLM, Rossetti T, Speed D, Srivastava PK, Chadeau-Hyam M, Hajji N, Dabrowska A, Rotival M, Razzaghi B, Kovac S, Wanisch K, Grillo FW, Slaviero A, Langley SR, Shkura K, Roncon P, De T, Mattheisen M, Niehusmann P, O'Brien TJ, Petrovski S, von Lehe M, Hoffmann P, Eriksson J, Coffey AJ, Cichon S, Walker M, Simonato M, Danis B, Mazzuferi M, Foerch P, Schoch S, De Paola V, Kaminski RM, Cunliffe VT, Becker AJ, Petretto E. Systems genetics identifies Sestrin 3 as a regulator of a proconvulsant gene network in human epileptic hippocampus. Nat Commun 2015; 6:6031. [PMID: 25615886 DOI: 10.1038/ncomms7031] [Citation(s) in RCA: 133] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2014] [Accepted: 12/04/2014] [Indexed: 01/20/2023] Open
Abstract
Gene-regulatory network analysis is a powerful approach to elucidate the molecular processes and pathways underlying complex disease. Here we employ systems genetics approaches to characterize the genetic regulation of pathophysiological pathways in human temporal lobe epilepsy (TLE). Using surgically acquired hippocampi from 129 TLE patients, we identify a gene-regulatory network genetically associated with epilepsy that contains a specialized, highly expressed transcriptional module encoding proconvulsive cytokines and Toll-like receptor signalling genes. RNA sequencing analysis in a mouse model of TLE using 100 epileptic and 100 control hippocampi shows the proconvulsive module is preserved across-species, specific to the epileptic hippocampus and upregulated in chronic epilepsy. In the TLE patients, we map the trans-acting genetic control of this proconvulsive module to Sestrin 3 (SESN3), and demonstrate that SESN3 positively regulates the module in macrophages, microglia and neurons. Morpholino-mediated Sesn3 knockdown in zebrafish confirms the regulation of the transcriptional module, and attenuates chemically induced behavioural seizures in vivo.
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Affiliation(s)
- Michael R Johnson
- Division of Brain Sciences, Imperial College London, Hammersmith Hospital Campus, Burlington Danes Building, London W12 0NN, UK
| | - Jacques Behmoaras
- Centre for Complement and Inflammation Research, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK
| | - Leonardo Bottolo
- Department of Mathematics, Imperial College London, 180 Queen's Gate, London SW7 2AZ, UK
| | - Michelle L Krishnan
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, St Thomas' Hospital, King's College London, London SE1 7EH, UK
| | - Katharina Pernhorst
- Section of Translational Epileptology, Department of Neuropathology, University of Bonn, Sigmund Freud Street 25, Bonn D-53127, Germany
| | - Paola L Meza Santoscoy
- Department of Biomedical Science, Bateson Centre, University of Sheffield, Firth Court, Western Bank, Sheffield S10 2TN, UK
| | - Tiziana Rossetti
- Medical Research Council (MRC) Clinical Sciences Centre, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK
| | - Doug Speed
- UCL Genetics Institute, University College London, Gower Street, London WC1E 6BT, UK
| | - Prashant K Srivastava
- Division of Brain Sciences, Imperial College London, Hammersmith Hospital Campus, Burlington Danes Building, London W12 0NN, UK.,Medical Research Council (MRC) Clinical Sciences Centre, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK
| | - Marc Chadeau-Hyam
- Department of Epidemiology and Biostatistics, School of Public Health, MRC/PHE Centre for Environment and Health, Imperial College London, St Mary's Hospital, Norfolk Place, W21PG London, UK
| | - Nabil Hajji
- Department of Medicine, Centre for Pharmacology and Therapeutics, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Aleksandra Dabrowska
- Department of Medicine, Centre for Pharmacology and Therapeutics, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Maxime Rotival
- Medical Research Council (MRC) Clinical Sciences Centre, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK
| | - Banafsheh Razzaghi
- Medical Research Council (MRC) Clinical Sciences Centre, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK
| | - Stjepana Kovac
- Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Klaus Wanisch
- Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Federico W Grillo
- Medical Research Council (MRC) Clinical Sciences Centre, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK
| | - Anna Slaviero
- Medical Research Council (MRC) Clinical Sciences Centre, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK
| | - Sarah R Langley
- Division of Brain Sciences, Imperial College London, Hammersmith Hospital Campus, Burlington Danes Building, London W12 0NN, UK.,Medical Research Council (MRC) Clinical Sciences Centre, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK
| | - Kirill Shkura
- Division of Brain Sciences, Imperial College London, Hammersmith Hospital Campus, Burlington Danes Building, London W12 0NN, UK.,Medical Research Council (MRC) Clinical Sciences Centre, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK
| | - Paolo Roncon
- Department of Medical Sciences, Section of Pharmacology and Neuroscience Center, University of Ferrara, 44121 Ferrara, Italy.,National Institute of Neuroscience, 44121 Ferrara, Italy
| | - Tisham De
- Medical Research Council (MRC) Clinical Sciences Centre, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK
| | - Manuel Mattheisen
- Department of Genomics, Life and Brain Center, University of Bonn, D-53127 Bonn, Germany.,Institute of Human Genetics, University of Bonn, D-53127 Bonn, Germany.,Institute for Genomic Mathematics, University of Bonn, D-53127 Bonn, Germany
| | - Pitt Niehusmann
- Section of Translational Epileptology, Department of Neuropathology, University of Bonn, Sigmund Freud Street 25, Bonn D-53127, Germany
| | - Terence J O'Brien
- Department of Medicine, RMH, University of Melbourne, Royal Melbourne Hospital, Royal Parade, Parkville, Victoria 3050, Australia
| | - Slave Petrovski
- Department of Neurology, Royal Melbourne Hospital, Melbourne, Parkville, Victoria 3050, Australia
| | - Marec von Lehe
- Department of Neurosurgery, University of Bonn Medical Center, Sigmund-Freud-Strasse 25, 53105 Bonn, Germany
| | - Per Hoffmann
- Institute of Human Genetics, University of Bonn, Sigmund-Freud-Strasse 25, 53127 Bonn, Germany.,Department of Biomedicine, University of Basel, Hebelstrasse 20, 4056 Basel, Switzerland
| | - Johan Eriksson
- Folkhälsan Research Centre, Topeliusgatan 20, 00250 Helsinki, Finland.,Helsinki University Central Hospital, Unit of General Practice, Haartmaninkatu 4, Helsinki 00290, Finland.,Department of General Practice and Primary Health Care, University of Helsinki, 407, PO Box 20, Tukholmankatu 8 B, Helsinki 00014, Finland
| | - Alison J Coffey
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK
| | - Sven Cichon
- Institute of Human Genetics, University of Bonn, Sigmund-Freud-Strasse 25, 53127 Bonn, Germany.,Department of Biomedicine, University of Basel, Hebelstrasse 20, 4056 Basel, Switzerland
| | - Matthew Walker
- Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Michele Simonato
- Department of Medical Sciences, Section of Pharmacology and Neuroscience Center, University of Ferrara, 44121 Ferrara, Italy.,National Institute of Neuroscience, 44121 Ferrara, Italy.,Laboratory for Technologies of Advanced Therapies (LTTA), University of Ferrara, 44121 Ferrara, Italy
| | - Bénédicte Danis
- Neuroscience TA, UCB Biopharma SPRL, Avenue de l'industrie, R9, B-1420 Braine l'Alleud, Belgium
| | - Manuela Mazzuferi
- Neuroscience TA, UCB Biopharma SPRL, Avenue de l'industrie, R9, B-1420 Braine l'Alleud, Belgium
| | - Patrik Foerch
- Neuroscience TA, UCB Biopharma SPRL, Avenue de l'industrie, R9, B-1420 Braine l'Alleud, Belgium
| | - Susanne Schoch
- Section of Translational Epileptology, Department of Neuropathology, University of Bonn, Sigmund Freud Street 25, Bonn D-53127, Germany.,Department of Epileptology, University of Bonn Medical Center, Sigmund-Freud-Strasse 25, Bonn D-53127, Germany
| | - Vincenzo De Paola
- Medical Research Council (MRC) Clinical Sciences Centre, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK
| | - Rafal M Kaminski
- Neuroscience TA, UCB Biopharma SPRL, Avenue de l'industrie, R9, B-1420 Braine l'Alleud, Belgium
| | - Vincent T Cunliffe
- Department of Biomedical Science, Bateson Centre, University of Sheffield, Firth Court, Western Bank, Sheffield S10 2TN, UK
| | - Albert J Becker
- Section of Translational Epileptology, Department of Neuropathology, University of Bonn, Sigmund Freud Street 25, Bonn D-53127, Germany
| | - Enrico Petretto
- Medical Research Council (MRC) Clinical Sciences Centre, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK.,Duke-NUS Graduate Medical School, 8 College Road, Singapore 169857, Singapore
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Xiao X, Moreno-Moral A, Rotival M, Bottolo L, Petretto E. Multi-tissue analysis of co-expression networks by higher-order generalized singular value decomposition identifies functionally coherent transcriptional modules. PLoS Genet 2014; 10:e1004006. [PMID: 24391511 PMCID: PMC3879165 DOI: 10.1371/journal.pgen.1004006] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [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: 05/09/2013] [Accepted: 10/22/2013] [Indexed: 12/27/2022] Open
Abstract
Recent high-throughput efforts such as ENCODE have generated a large body of genome-scale transcriptional data in multiple conditions (e.g., cell-types and disease states). Leveraging these data is especially important for network-based approaches to human disease, for instance to identify coherent transcriptional modules (subnetworks) that can inform functional disease mechanisms and pathological pathways. Yet, genome-scale network analysis across conditions is significantly hampered by the paucity of robust and computationally-efficient methods. Building on the Higher-Order Generalized Singular Value Decomposition, we introduce a new algorithmic approach for efficient, parameter-free and reproducible identification of network-modules simultaneously across multiple conditions. Our method can accommodate weighted (and unweighted) networks of any size and can similarly use co-expression or raw gene expression input data, without hinging upon the definition and stability of the correlation used to assess gene co-expression. In simulation studies, we demonstrated distinctive advantages of our method over existing methods, which was able to recover accurately both common and condition-specific network-modules without entailing ad-hoc input parameters as required by other approaches. We applied our method to genome-scale and multi-tissue transcriptomic datasets from rats (microarray-based) and humans (mRNA-sequencing-based) and identified several common and tissue-specific subnetworks with functional significance, which were not detected by other methods. In humans we recapitulated the crosstalk between cell-cycle progression and cell-extracellular matrix interactions processes in ventricular zones during neocortex expansion and further, we uncovered pathways related to development of later cognitive functions in the cortical plate of the developing brain which were previously unappreciated. Analyses of seven rat tissues identified a multi-tissue subnetwork of co-expressed heat shock protein (Hsp) and cardiomyopathy genes (Bag3, Cryab, Kras, Emd, Plec), which was significantly replicated using separate failing heart and liver gene expression datasets in humans, thus revealing a conserved functional role for Hsp genes in cardiovascular disease.
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Affiliation(s)
- Xiaolin Xiao
- Medical Research Council (MRC) Clinical Sciences Centre, Faculty of Medicine, Imperial College, London, United Kingdom
| | - Aida Moreno-Moral
- Medical Research Council (MRC) Clinical Sciences Centre, Faculty of Medicine, Imperial College, London, United Kingdom
| | - Maxime Rotival
- Medical Research Council (MRC) Clinical Sciences Centre, Faculty of Medicine, Imperial College, London, United Kingdom
| | - Leonardo Bottolo
- Department of Mathematics, Imperial College, London, United Kingdom
| | - Enrico Petretto
- Medical Research Council (MRC) Clinical Sciences Centre, Faculty of Medicine, Imperial College, London, United Kingdom
- * E-mail:
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Chadeau-Hyam M, Campanella G, Jombart T, Bottolo L, Portengen L, Vineis P, Liquet B, Vermeulen RCH. Deciphering the complex: methodological overview of statistical models to derive OMICS-based biomarkers. Environ Mol Mutagen 2013; 54:542-557. [PMID: 23918146 DOI: 10.1002/em.21797] [Citation(s) in RCA: 86] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2013] [Revised: 05/21/2013] [Accepted: 05/28/2013] [Indexed: 05/28/2023]
Abstract
Recent technological advances in molecular biology have given rise to numerous large-scale datasets whose analysis imposes serious methodological challenges mainly relating to the size and complex structure of the data. Considerable experience in analyzing such data has been gained over the past decade, mainly in genetics, from the Genome-Wide Association Study era, and more recently in transcriptomics and metabolomics. Building upon the corresponding literature, we provide here a nontechnical overview of well-established methods used to analyze OMICS data within three main types of regression-based approaches: univariate models including multiple testing correction strategies, dimension reduction techniques, and variable selection models. Our methodological description focuses on methods for which ready-to-use implementations are available. We describe the main underlying assumptions, the main features, and advantages and limitations of each of the models. This descriptive summary constitutes a useful tool for driving methodological choices while analyzing OMICS data, especially in environmental epidemiology, where the emergence of the exposome concept clearly calls for unified methods to analyze marginally and jointly complex exposure and OMICS datasets.
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Affiliation(s)
- Marc Chadeau-Hyam
- Department of Epidemiology and Biostatistics, MRC-HPA Centre for Environment and Health, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, United Kingdom.
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Langley SR, Bottolo L, Kunes J, Zicha J, Zidek V, Hubner N, Cook SA, Pravenec M, Aitman TJ, Petretto E. Systems-level approaches reveal conservation of trans-regulated genes in the rat and genetic determinants of blood pressure in humans. Cardiovasc Res 2012; 97:653-65. [PMID: 23118132 DOI: 10.1093/cvr/cvs329] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
AIMS Human genome-wide association studies (GWAS) of hypertension identified only few susceptibility loci with large effect that were replicated across populations. The vast majority of genes detected by GWAS has small effect and the regulatory mechanisms through which these genetic variants cause disease remain mostly unclear. Here, we used comparative genomics between human and an established rat model of hypertension to explore the transcriptional mechanisms mediating the effect of genes identified in 15 hypertension GWAS. METHODS AND RESULTS Time series analysis of radiotelemetric blood pressure (BP) was performed to assess 11 parameters of BP variation in recombinant inbred strains derived from the spontaneously hypertensive rat. BP data were integrated with ∼27 000 expression quantative trait loci (eQTLs) mapped across seven tissues, detecting >8000 significant associations between eQTL genes and BP variation in the rat. We then compiled a large catalogue of human genes from GWAS of hypertension and identified a subset of 2292 rat-human orthologous genes. Expression levels for 795 (34%) of these genes correlated with BP variation across rat tissues: 51 genes were cis-regulated, whereas 459 were trans-regulated and enriched for 'calcium signalling pathway' (P = 9.6 × 10(-6)) and 'ion channel' genes (P = 3.5 × 10(-7)), which are important determinants of hypertension. We identified 158 clusters of trans-eQTLs, annotated the underlying 'master regulator' genes and found significant over-representation in the human hypertension gene set (enrichment P = 5 × 10(-4)). CONCLUSION We showed extensive conservation of trans-regulated genes and their master regulators between rat and human hypertension. These findings reveal that small-effect genes associated with hypertension by human GWAS are likely to exert their action through coordinate regulation of pathogenic pathways.
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Affiliation(s)
- Sarah R Langley
- MRC Clinical Sciences Centre, Faculty of Medicine, Imperial College London, Du Cane Road, London W12 0NN, UK
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El-Sayed Moustafa JS, Eleftherohorinou H, de Smith AJ, Andersson-Assarsson JC, Alves AC, Hadjigeorgiou E, Walters RG, Asher JE, Bottolo L, Buxton JL, Sladek R, Meyre D, Dina C, Visvikis-Siest S, Jacobson P, Sjöström L, Carlsson LMS, Walley A, Falchi M, Froguel P, Blakemore AIF, Coin LJM. Novel association approach for variable number tandem repeats (VNTRs) identifies DOCK5 as a susceptibility gene for severe obesity. Hum Mol Genet 2012; 21:3727-38. [PMID: 22595969 DOI: 10.1093/hmg/dds187] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Variable number tandem repeats (VNTRs) constitute a relatively under-examined class of genomic variants in the context of complex disease because of their sequence complexity and the challenges in assaying them. Recent large-scale genome-wide copy number variant mapping and association efforts have highlighted the need for improved methodology for association studies using these complex polymorphisms. Here we describe the in-depth investigation of a complex region on chromosome 8p21.2 encompassing the dedicator of cytokinesis 5 (DOCK5) gene. The region includes two VNTRs of complex sequence composition which flank a common 3975 bp deletion, all three of which were genotyped by polymerase chain reaction and fragment analysis in a total of 2744 subjects. We have developed a novel VNTR association method named VNTRtest, suitable for association analysis of multi-allelic loci with binary and quantitative outcomes, and have used this approach to show significant association of the DOCK5 VNTRs with childhood and adult severe obesity (P(empirical)= 8.9 × 10(-8) and P= 3.1 × 10(-3), respectively) which we estimate explains ~0.8% of the phenotypic variance. We also identified an independent association between the 3975 base pair (bp) deletion and obesity, explaining a further 0.46% of the variance (P(combined)= 1.6 × 10(-3)). Evidence for association between DOCK5 transcript levels and the 3975 bp deletion (P= 0.027) and both VNTRs (P(empirical)= 0.015) was also identified in adipose tissue from a Swedish family sample, providing support for a functional effect of the DOCK5 deletion and VNTRs. These findings highlight the potential role of DOCK5 in human obesity and illustrate a novel approach for analysis of the contribution of VNTRs to disease susceptibility through association studies.
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Affiliation(s)
- Julia S El-Sayed Moustafa
- Department of Genomics of Common Disease, School of Public Health Inperial College, London, W12 ONN, UK
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McDermott-Roe C, Ye J, Ahmed R, Sun XM, Serafín A, Ware J, Bottolo L, Muckett P, Cañas X, Zhang J, Rowe GC, Buchan R, Lu H, Braithwaite A, Mancini M, Hauton D, Martí R, García-Arumí E, Hubner N, Jacob H, Serikawa T, Zidek V, Papousek F, Kolar F, Cardona M, Ruiz-Meana M, García-Dorado D, Comella JX, Felkin LE, Barton PJR, Arany Z, Pravenec M, Petretto E, Sanchis D, Cook SA. Endonuclease G is a novel determinant of cardiac hypertrophy and mitochondrial function. Nature 2011; 478:114-8. [PMID: 21979051 PMCID: PMC3189541 DOI: 10.1038/nature10490] [Citation(s) in RCA: 115] [Impact Index Per Article: 8.8] [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: 03/06/2011] [Accepted: 08/17/2011] [Indexed: 12/31/2022]
Abstract
Left ventricular mass (LVM) is a highly heritable trait and an independent risk factor for all-cause mortality. So far, genome-wide association studies have not identified the genetic factors that underlie LVM variation, and the regulatory mechanisms for blood-pressure-independent cardiac hypertrophy remain poorly understood. Unbiased systems genetics approaches in the rat now provide a powerful complementary tool to genome-wide association studies, and we applied integrative genomics to dissect a highly replicated, blood-pressure-independent LVM locus on rat chromosome 3p. Here we identified endonuclease G (Endog), which previously was implicated in apoptosis but not hypertrophy, as the gene at the locus, and we found a loss-of-function mutation in Endog that is associated with increased LVM and impaired cardiac function. Inhibition of Endog in cultured cardiomyocytes resulted in an increase in cell size and hypertrophic biomarkers in the absence of pro-hypertrophic stimulation. Genome-wide network analysis unexpectedly implicated ENDOG in fundamental mitochondrial processes that are unrelated to apoptosis. We showed direct regulation of ENDOG by ERR-α and PGC1α (which are master regulators of mitochondrial and cardiac function), interaction of ENDOG with the mitochondrial genome and ENDOG-mediated regulation of mitochondrial mass. At baseline, the Endog-deleted mouse heart had depleted mitochondria, mitochondrial dysfunction and elevated levels of reactive oxygen species, which were associated with enlarged and steatotic cardiomyocytes. Our study has further established the link between mitochondrial dysfunction, reactive oxygen species and heart disease and has uncovered a role for Endog in maladaptive cardiac hypertrophy.
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Affiliation(s)
- Chris McDermott-Roe
- Medical Research Council Clinical Sciences Centre, Faculty of Medicine, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 ONN, UK
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Bottolo L, Chadeau-Hyam M, Hastie DI, Langley SR, Petretto E, Tiret L, Tregouet D, Richardson S. ESS++: a C++ objected-oriented algorithm for Bayesian stochastic search model exploration. ACTA ACUST UNITED AC 2011; 27:587-8. [PMID: 21233165 PMCID: PMC3035799 DOI: 10.1093/bioinformatics/btq684] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [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] [Indexed: 11/13/2022]
Abstract
SUMMARY ESS++ is a C++ implementation of a fully Bayesian variable selection approach for single and multiple response linear regression. ESS++ works well both when the number of observations is larger than the number of predictors and in the 'large p, small n' case. In the current version, ESS++ can handle several hundred observations, thousands of predictors and a few responses simultaneously. The core engine of ESS++ for the selection of relevant predictors is based on Evolutionary Monte Carlo. Our implementation is open source, allowing community-based alterations and improvements. AVAILABILITY C++ source code and documentation including compilation instructions are available under GNU licence at http://bgx.org.uk/software/ESS.html.
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Affiliation(s)
- Leonardo Bottolo
- Department of Epidemiology and Biostatistics, Imperial College London, UK.
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Petretto E, Bottolo L, Langley SR, Heinig M, McDermott-Roe C, Sarwar R, Pravenec M, Hübner N, Aitman TJ, Cook SA, Richardson S. New insights into the genetic control of gene expression using a Bayesian multi-tissue approach. PLoS Comput Biol 2010; 6:e1000737. [PMID: 20386736 PMCID: PMC2851562 DOI: 10.1371/journal.pcbi.1000737] [Citation(s) in RCA: 52] [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: 08/24/2009] [Accepted: 03/03/2010] [Indexed: 01/29/2023] Open
Abstract
The majority of expression quantitative trait locus (eQTL) studies have been carried out in single tissues or cell types, using methods that ignore information shared across tissues. Although global analysis of RNA expression in multiple tissues is now feasible, few integrated statistical frameworks for joint analysis of gene expression across tissues combined with simultaneous analysis of multiple genetic variants have been developed to date. Here, we propose Sparse Bayesian Regression models for mapping eQTLs within individual tissues and simultaneously across tissues. Testing these on a set of 2,000 genes in four tissues, we demonstrate that our methods are more powerful than traditional approaches in revealing the true complexity of the eQTL landscape at the systems-level. Highlighting the power of our method, we identified a two-eQTL model (cis/trans) for the Hopx gene that was experimentally validated and was not detected by conventional approaches. We showed common genetic regulation of gene expression across four tissues for ∼27% of transcripts, providing >5 fold increase in eQTLs detection when compared with single tissue analyses at 5% FDR level. These findings provide a new opportunity to uncover complex genetic regulatory mechanisms controlling global gene expression while the generality of our modelling approach makes it adaptable to other model systems and humans, with broad application to analysis of multiple intermediate and whole-body phenotypes. Integrated analysis of genome-wide genetic polymorphisms and gene expression profiles from different tissues or cell types has been highly successful in identifying genes modulating complex phenotypes in animal models and humans. However, an important limitation of the current approaches consists in their sole application to individual tissues, thus ignoring information shared across different tissues. To uncover complex genetic regulatory mechanisms controlling gene expression at the whole organism's level, it is essential to develop appropriate analytical methods for the analysis of genome-wide genetic polymorphisms and gene expression profiles simultaneously in multiple tissues. This paper presents a novel, fully integrated Bayesian approach for mapping the genetic components of gene expression within and across multiple tissues. In addition to increased power and enhanced mapping resolution when compared with traditional approaches, our model directly provides information on potential systemic effects on transcriptional profiles and co-existing local (cis) and distant (trans) genetic control of gene expression. We also discuss the possibility to extend our approach for the analysis of different phenotypes, and other study designs, thus providing an integrated computational tool to explore the genetic control underlying transcriptional regulation at the systems-level, beyond the single tissue resolution.
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Affiliation(s)
- Enrico Petretto
- Medical Research Council Clinical Sciences Centre, Faculty of Medicine, Imperial College London, London, United Kingdom
- Department of Epidemiology and Biostatistics, Faculty of Medicine, Imperial College, London, United Kingdom
| | - Leonardo Bottolo
- Medical Research Council Clinical Sciences Centre, Faculty of Medicine, Imperial College London, London, United Kingdom
- Department of Epidemiology and Biostatistics, Faculty of Medicine, Imperial College, London, United Kingdom
| | - Sarah R. Langley
- Medical Research Council Clinical Sciences Centre, Faculty of Medicine, Imperial College London, London, United Kingdom
| | | | - Chris McDermott-Roe
- Medical Research Council Clinical Sciences Centre, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Rizwan Sarwar
- Medical Research Council Clinical Sciences Centre, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Michal Pravenec
- Institute of Physiology, Czech Academy of Sciences and Centre for Applied Genomics, Prague, Czech Republic
- Charles University in Prague, Institute of Biology and Medical Genetics of the First Faculty of Medicine and General Teaching Hospital, Prague, Czech Republic
| | - Norbert Hübner
- Max-Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Timothy J. Aitman
- Medical Research Council Clinical Sciences Centre, Faculty of Medicine, Imperial College London, London, United Kingdom
- Section of Molecular Genetics and Rheumatology, Division and Faculty of Medicine, Imperial College, London, United Kingdom
| | - Stuart A. Cook
- Medical Research Council Clinical Sciences Centre, Faculty of Medicine, Imperial College London, London, United Kingdom
- National Heart and Lung Institute, Imperial College, London, United Kingdom
| | - Sylvia Richardson
- Department of Epidemiology and Biostatistics, Faculty of Medicine, Imperial College, London, United Kingdom
- * E-mail:
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Frazer KA, Ballinger DG, Cox DR, Hinds DA, Stuve LL, Gibbs RA, Belmont JW, Boudreau A, Hardenbol P, Leal SM, Pasternak S, Wheeler DA, Willis TD, Yu F, Yang H, Zeng C, Gao Y, Hu H, Hu W, Li C, Lin W, Liu S, Pan H, Tang X, Wang J, Wang W, Yu J, Zhang B, Zhang Q, Zhao H, Zhao H, Zhou J, Gabriel SB, Barry R, Blumenstiel B, Camargo A, Defelice M, Faggart M, Goyette M, Gupta S, Moore J, Nguyen H, Onofrio RC, Parkin M, Roy J, Stahl E, Winchester E, Ziaugra L, Altshuler D, Shen Y, Yao Z, Huang W, Chu X, He Y, Jin L, Liu Y, Shen Y, Sun W, Wang H, Wang Y, Wang Y, Xiong X, Xu L, Waye MMY, Tsui SKW, Xue H, Wong JTF, Galver LM, Fan JB, Gunderson K, Murray SS, Oliphant AR, Chee MS, Montpetit A, Chagnon F, Ferretti V, Leboeuf M, Olivier JF, Phillips MS, Roumy S, Sallée C, Verner A, Hudson TJ, Kwok PY, Cai D, Koboldt DC, Miller RD, Pawlikowska L, Taillon-Miller P, Xiao M, Tsui LC, Mak W, Song YQ, Tam PKH, Nakamura Y, Kawaguchi T, Kitamoto T, Morizono T, Nagashima A, Ohnishi Y, Sekine A, Tanaka T, Tsunoda T, Deloukas P, Bird CP, Delgado M, Dermitzakis ET, Gwilliam R, Hunt S, Morrison J, Powell D, Stranger BE, Whittaker P, Bentley DR, Daly MJ, de Bakker PIW, Barrett J, Chretien YR, Maller J, McCarroll S, Patterson N, Pe'er I, Price A, Purcell S, Richter DJ, Sabeti P, Saxena R, Schaffner SF, Sham PC, Varilly P, Altshuler D, Stein LD, Krishnan L, Smith AV, Tello-Ruiz MK, Thorisson GA, Chakravarti A, Chen PE, Cutler DJ, Kashuk CS, Lin S, Abecasis GR, Guan W, Li Y, Munro HM, Qin ZS, Thomas DJ, McVean G, Auton A, Bottolo L, Cardin N, Eyheramendy S, Freeman C, Marchini J, Myers S, Spencer C, Stephens M, Donnelly P, Cardon LR, Clarke G, Evans DM, Morris AP, Weir BS, Tsunoda T, Mullikin JC, Sherry ST, Feolo M, Skol A, Zhang H, Zeng C, Zhao H, Matsuda I, Fukushima Y, Macer DR, Suda E, Rotimi CN, Adebamowo CA, Ajayi I, Aniagwu T, Marshall PA, Nkwodimmah C, Royal CDM, Leppert MF, Dixon M, Peiffer A, Qiu R, Kent A, Kato K, Niikawa N, Adewole IF, Knoppers BM, Foster MW, Clayton EW, Watkin J, Gibbs RA, Belmont JW, Muzny D, Nazareth L, Sodergren E, Weinstock GM, Wheeler DA, Yakub I, Gabriel SB, Onofrio RC, Richter DJ, Ziaugra L, Birren BW, Daly MJ, Altshuler D, Wilson RK, Fulton LL, Rogers J, Burton J, Carter NP, Clee CM, Griffiths M, Jones MC, McLay K, Plumb RW, Ross MT, Sims SK, Willey DL, Chen Z, Han H, Kang L, Godbout M, Wallenburg JC, L'Archevêque P, Bellemare G, Saeki K, Wang H, An D, Fu H, Li Q, Wang Z, Wang R, Holden AL, Brooks LD, McEwen JE, Guyer MS, Wang VO, Peterson JL, Shi M, Spiegel J, Sung LM, Zacharia LF, Collins FS, Kennedy K, Jamieson R, Stewart J. A second generation human haplotype map of over 3.1 million SNPs. Nature 2007; 449:851-61. [PMID: 17943122 DOI: 10.1038/nature06258] [Citation(s) in RCA: 3275] [Impact Index Per Article: 192.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2007] [Accepted: 09/18/2007] [Indexed: 02/07/2023]
Abstract
We describe the Phase II HapMap, which characterizes over 3.1 million human single nucleotide polymorphisms (SNPs) genotyped in 270 individuals from four geographically diverse populations and includes 25-35% of common SNP variation in the populations surveyed. The map is estimated to capture untyped common variation with an average maximum r2 of between 0.9 and 0.96 depending on population. We demonstrate that the current generation of commercial genome-wide genotyping products captures common Phase II SNPs with an average maximum r2 of up to 0.8 in African and up to 0.95 in non-African populations, and that potential gains in power in association studies can be obtained through imputation. These data also reveal novel aspects of the structure of linkage disequilibrium. We show that 10-30% of pairs of individuals within a population share at least one region of extended genetic identity arising from recent ancestry and that up to 1% of all common variants are untaggable, primarily because they lie within recombination hotspots. We show that recombination rates vary systematically around genes and between genes of different function. Finally, we demonstrate increased differentiation at non-synonymous, compared to synonymous, SNPs, resulting from systematic differences in the strength or efficacy of natural selection between populations.
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Affiliation(s)
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- The Scripps Research Institute, 10550 North Torrey Pines Road MEM275, La Jolla, California 92037, USA
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Myers S, Spencer CCA, Auton A, Bottolo L, Freeman C, Donnelly P, McVean G. The distribution and causes of meiotic recombination in the human genome. Biochem Soc Trans 2006; 34:526-30. [PMID: 16856851 DOI: 10.1042/bst0340526] [Citation(s) in RCA: 96] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Using the statistical analysis of genetic variation, we have developed a high-resolution genetic map of recombination hotspots and recombination rate variation across the human genome. This map, which has a resolution several orders of magnitude greater than previous studies, identifies over 25,000 recombination hotspots and gives new insights into the distribution and determination of recombination. Wavelet-based analysis demonstrates scale-specific influences of base composition, coding context and DNA repeats on recombination rates, though, in contrast with other species, no association with DNase I hypersensitivity. We have also identified specific DNA motifs that are strongly associated with recombination hotspots and whose activity is influenced by local context. Comparative analysis of recombination rates in humans and chimpanzees demonstrates very high rates of evolution of the fine-scale structure of the recombination landscape. In the light of these observations, we suggest possible resolutions of the hotspot paradox.
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Affiliation(s)
- S Myers
- Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK
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Abstract
Genetic maps, which document the way in which recombination rates vary over a genome, are an essential tool for many genetic analyses. We present a high-resolution genetic map of the human genome, based on statistical analyses of genetic variation data, and identify more than 25,000 recombination hotspots, together with motifs and sequence contexts that play a role in hotspot activity. Differences between the behavior of recombination rates over large (megabase) and small (kilobase) scales lead us to suggest a two-stage model for recombination in which hotspots are stochastic features, within a framework in which large-scale rates are constrained.
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Affiliation(s)
- Simon Myers
- Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK
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