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Abraham A, Cule M, Thanaj M, Basty N, Hashemloo MA, Sorokin EP, Whitcher B, Burgess S, Bell JD, Sattar N, Thomas EL, Yaghootkar H. Genetic evidence for distinct biological mechanisms that link adiposity to type 2 diabetes: towards precision medicine. Diabetes 2024:db231005. [PMID: 38530928 DOI: 10.2337/db23-1005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 03/22/2024] [Indexed: 03/28/2024]
Abstract
We aimed to unravel the mechanisms connecting adiposity to type 2 diabetes. We employed MR-Clust to cluster independent genetic variants associated with body fat percentage (388 variants) and BMI (540 variants) based on their impact on type 2 diabetes. We identified five clusters of adiposity-increasing alleles associated with higher type 2 diabetes risk (unfavorable adiposity) and three clusters associated with lower risk (favorable adiposity). We then characterized each cluster based on various biomarkers, metabolites and Magnetic Resonance Imaging-based measures of fat distribution and muscle quality. Analyzing the metabolic signatures of these clusters revealed two primary mechanisms connecting higher adiposity to reduced type 2 diabetes risk. The first involves higher adiposity in subcutaneous tissues (abdomen and thigh), lower liver fat, improved insulin sensitivity, and decreased risk of cardiometabolic diseases and diabetes complications. The second mechanism is characterized by increased body size, enhanced muscle quality, with no impact on cardiometabolic outcomes. Furthermore, our findings unveil diverse mechanisms linking higher adiposity to higher disease risk, such as cholesterol pathways or inflammation. These results reinforce the existence of adiposity-related mechanisms that may act as protective factors against type 2 diabetes and its complications, especially when accompanied by reduced ectopic liver fat.
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Affiliation(s)
- Angela Abraham
- College of Health and Science, University of Lincoln, Joseph Banks Laboratories, Green Lane, Lincoln, UK
| | | | - Marjola Thanaj
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - M Amin Hashemloo
- Department of Life Sciences, Brunel University London, Uxbridge, United Kingdom
| | | | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
- Department of Radiology, MRI Unit, The Royal Marsden NHS Foundation Trust, London, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Naveed Sattar
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Hanieh Yaghootkar
- College of Health and Science, University of Lincoln, Joseph Banks Laboratories, Green Lane, Lincoln, UK
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Thanaj M, Basty N, Whitcher B, Sorokin EP, Liu Y, Srinivasan R, Cule M, Thomas EL, Bell JD. Precision MRI phenotyping of muscle volume and quality at a population scale. Front Physiol 2024; 15:1288657. [PMID: 38370011 PMCID: PMC10869600 DOI: 10.3389/fphys.2024.1288657] [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] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 01/09/2024] [Indexed: 02/20/2024] Open
Abstract
Introduction: Magnetic resonance imaging (MRI) enables direct measurements of muscle volume and quality, allowing for an in-depth understanding of their associations with anthropometric traits, and health conditions. However, it is unclear which muscle volume measurements: total muscle volume, regional measurements, measurements of muscle quality: intermuscular adipose tissue (IMAT) or proton density fat fraction (PDFF), are most informative and associate with relevant health conditions such as dynapenia and frailty. Methods: We have measured image-derived phenotypes (IDPs) including total and regional muscle volumes and measures of muscle quality, derived from the neck-to-knee Dixon images in 44,520 UK Biobank participants. We further segmented paraspinal muscle from 2D quantitative MRI to quantify muscle PDFF and iron concentration. We defined dynapenia based on grip strength below sex-specific cut-off points and frailty based on five criteria (weight loss, exhaustion, grip strength, low physical activity and slow walking pace). We used logistic regression to investigate the association between muscle volume and quality measurements and dynapenia and frailty. Results: Muscle volumes were significantly higher in male compared with female participants, even after correcting for height while, IMAT (corrected for muscle volume) and paraspinal muscle PDFF were significantly higher in female compared with male participants. From the overall cohort, 7.6% (N = 3,261) were identified with dynapenia, and 1.1% (N = 455) with frailty. Dynapenia and frailty were positively associated with age and negatively associated with physical activity levels. Additionally, reduced muscle volume and quality measurements were associated with both dynapenia and frailty. In dynapenia, muscle volume IDPs were most informative, particularly total muscle exhibiting odds ratios (OR) of 0.392, while for frailty, muscle quality was found to be most informative, in particular thigh IMAT volume indexed to height squared (OR = 1.396), both with p-values below the Bonferroni-corrected threshold (p < 8.8 × 10 - 5 ). Conclusion: Our fully automated method enables the quantification of muscle volumes and quality suitable for large population-based studies. For dynapenia, muscle volumes particularly those including greater body coverage such as total muscle are the most informative, whilst, for frailty, markers of muscle quality were the most informative IDPs. These results suggest that different measurements may have varying diagnostic values for different health conditions.
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Affiliation(s)
- Marjola Thanaj
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Elena P. Sorokin
- Calico Life Sciences LLC, South San Francisco, CA, United States
| | - Yi Liu
- Calico Life Sciences LLC, South San Francisco, CA, United States
| | | | - Madeleine Cule
- Calico Life Sciences LLC, South San Francisco, CA, United States
| | - E. Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Jimmy D. Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
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Thanaj M, Basty N, Cule M, Sorokin EP, Whitcher B, Bell JD, Thomas EL. Liver shape analysis using statistical parametric maps at population scale. BMC Med Imaging 2024; 24:15. [PMID: 38195400 PMCID: PMC10775563 DOI: 10.1186/s12880-023-01149-5] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 10/31/2023] [Indexed: 01/11/2024] Open
Abstract
BACKGROUND Morphometric image analysis enables the quantification of differences in the shape and size of organs between individuals. METHODS Here we have applied morphometric methods to the study of the liver by constructing surface meshes from liver segmentations from abdominal MRI images in 33,434 participants in the UK Biobank. Based on these three dimensional mesh vertices, we evaluated local shape variations and modelled their association with anthropometric, phenotypic and clinical conditions, including liver disease and type-2 diabetes. RESULTS We found that age, body mass index, hepatic fat and iron content, as well as, health traits were significantly associated with regional liver shape and size. Interaction models in groups with specific clinical conditions showed that the presence of type-2 diabetes accelerates age-related changes in the liver, while presence of liver fat further increased shape variations in both type-2 diabetes and liver disease. CONCLUSIONS The results suggest that this novel approach may greatly benefit studies aiming at better categorisation of pathologies associated with acute and chronic clinical conditions.
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Affiliation(s)
- Marjola Thanaj
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | | | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
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Thanaj M, Basty N, Cule M, Sorokin EP, Whitcher B, Srinivasan R, Lennon R, Bell JD, Thomas EL. Kidney shape statistical analysis: associations with disease and anthropometric factors. BMC Nephrol 2023; 24:362. [PMID: 38057740 PMCID: PMC10698953 DOI: 10.1186/s12882-023-03407-8] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 11/22/2023] [Indexed: 12/08/2023] Open
Abstract
BACKGROUND Organ measurements derived from magnetic resonance imaging (MRI) have the potential to enhance our understanding of the precise phenotypic variations underlying many clinical conditions. METHODS We applied morphometric methods to study the kidneys by constructing surface meshes from kidney segmentations from abdominal MRI data in 38,868 participants in the UK Biobank. Using mesh-based analysis techniques based on statistical parametric maps (SPMs), we were able to detect variations in specific regions of the kidney and associate those with anthropometric traits as well as disease states including chronic kidney disease (CKD), type-2 diabetes (T2D), and hypertension. Statistical shape analysis (SSA) based on principal component analysis was also used within the disease population and the principal component scores were used to assess the risk of disease events. RESULTS We show that CKD, T2D and hypertension were associated with kidney shape. Age was associated with kidney shape consistently across disease groups. Body mass index (BMI) and waist-to-hip ratio (WHR) were also associated with kidney shape for the participants with T2D. Using SSA, we were able to capture kidney shape variations, relative to size, angle, straightness, width, length, and thickness of the kidneys, within disease populations. We identified significant associations between both left and right kidney length and width and incidence of CKD (hazard ratio (HR): 0.74, 95% CI: 0.61-0.90, p < 0.05, in the left kidney; HR: 0.76, 95% CI: 0.63-0.92, p < 0.05, in the right kidney) and hypertension (HR: 1.16, 95% CI: 1.03-1.29, p < 0.05, in the left kidney; HR: 0.87, 95% CI: 0.79-0.96, p < 0.05, in the right kidney). CONCLUSIONS The results suggest that shape-based analysis of the kidneys can augment studies aiming at the better categorisation of pathologies associated with chronic kidney conditions.
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Affiliation(s)
- Marjola Thanaj
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | | | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | - Rachel Lennon
- Wellcome Centre for Cell-Matrix Research, Division of Cell-Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
- Department of Paediatric Nephrology, Royal Manchester Children's Hospital, Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
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Sun BB, Chiou J, Traylor M, Benner C, Hsu YH, Richardson TG, Surendran P, Mahajan A, Robins C, Vasquez-Grinnell SG, Hou L, Kvikstad EM, Burren OS, Davitte J, Ferber KL, Gillies CE, Hedman ÅK, Hu S, Lin T, Mikkilineni R, Pendergrass RK, Pickering C, Prins B, Baird D, Chen CY, Ward LD, Deaton AM, Welsh S, Willis CM, Lehner N, Arnold M, Wörheide MA, Suhre K, Kastenmüller G, Sethi A, Cule M, Raj A, Burkitt-Gray L, Melamud E, Black MH, Fauman EB, Howson JMM, Kang HM, McCarthy MI, Nioi P, Petrovski S, Scott RA, Smith EN, Szalma S, Waterworth DM, Mitnaul LJ, Szustakowski JD, Gibson BW, Miller MR, Whelan CD. Plasma proteomic associations with genetics and health in the UK Biobank. Nature 2023; 622:329-338. [PMID: 37794186 PMCID: PMC10567551 DOI: 10.1038/s41586-023-06592-6] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/31/2023] [Indexed: 10/06/2023]
Abstract
The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public-private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics1.
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Affiliation(s)
- Benjamin B Sun
- Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA.
| | - Joshua Chiou
- Internal Medicine Research Unit, Worldwide Research, Development and Medical, Pfizer, Cambridge, MA, USA
| | - Matthew Traylor
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | | | | | - Tom G Richardson
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
- Genomic Sciences, GlaxoSmithKline, Stevenage, UK
| | | | | | - Chloe Robins
- Genomic Sciences, GlaxoSmithKline, Collegeville, PA, USA
| | | | - Liping Hou
- Population Analytics, Janssen Research & Development, Spring House, PA, USA
| | | | - Oliver S Burren
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | | | - Kyle L Ferber
- Biostatistics, Research and Development, Biogen, Cambridge, MA, USA
| | | | - Åsa K Hedman
- External Science and Innovation Target Sciences, Worldwide Research, Development and Medical, Pfizer, Stockholm, Sweden
| | - Sile Hu
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Tinchi Lin
- Analytics and Data Sciences, Biogen, Cambridge, MA, USA
| | - Rajesh Mikkilineni
- Data Science Institute, Takeda Development Center Americas, Cambridge, MA, USA
| | | | | | - Bram Prins
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Denis Baird
- Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA
| | - Chia-Yen Chen
- Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA
| | - Lucas D Ward
- Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA
| | - Aimee M Deaton
- Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA
| | | | - Carissa M Willis
- Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA
| | - Nick Lehner
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Maria A Wörheide
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | | | | | - Anil Raj
- Calico Life Sciences, San Francisco, CA, USA
| | | | | | - Mary Helen Black
- Population Analytics, Janssen Research & Development, Spring House, PA, USA
| | - Eric B Fauman
- Internal Medicine Research Unit, Worldwide Research, Development and Medical, Pfizer, Cambridge, MA, USA
| | - Joanna M M Howson
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | | | | | - Paul Nioi
- Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA
| | - Slavé Petrovski
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
- Department of Medicine, University of Melbourne, Austin Health, Melbourne, Victoria, Australia
| | | | - Erin N Smith
- Takeda Development Center Americas, San Diego, CA, USA
| | - Sándor Szalma
- Takeda Development Center Americas, San Diego, CA, USA
| | | | | | | | | | - Melissa R Miller
- Internal Medicine Research Unit, Worldwide Research, Development and Medical, Pfizer, Cambridge, MA, USA
| | - Christopher D Whelan
- Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA.
- Neuroscience Data Science, Janssen Research & Development, Cambridge, MA, USA.
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Basty N, Sorokin EP, Thanaj M, Srinivasan R, Whitcher B, Bell JD, Cule M, Thomas EL. Abdominal imaging associates body composition with COVID-19 severity. PLoS One 2023; 18:e0283506. [PMID: 37053189 PMCID: PMC10101472 DOI: 10.1371/journal.pone.0283506] [Citation(s) in RCA: 1] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 03/10/2023] [Indexed: 04/14/2023] Open
Abstract
The main drivers of COVID-19 disease severity and the impact of COVID-19 on long-term health after recovery are yet to be fully understood. Medical imaging studies investigating COVID-19 to date have mostly been limited to small datasets and post-hoc analyses of severe cases. The UK Biobank recruited recovered SARS-CoV-2 positive individuals (n = 967) and matched controls (n = 913) who were extensively imaged prior to the pandemic and underwent follow-up scanning. In this study, we investigated longitudinal changes in body composition, as well as the associations of pre-pandemic image-derived phenotypes with COVID-19 severity. Our longitudinal analysis, in a population of mostly mild cases, associated a decrease in lung volume with SARS-CoV-2 positivity. We also observed that increased visceral adipose tissue and liver fat, and reduced muscle volume, prior to COVID-19, were associated with COVID-19 disease severity. Finally, we trained a machine classifier with demographic, anthropometric and imaging traits, and showed that visceral fat, liver fat and muscle volume have prognostic value for COVID-19 disease severity beyond the standard demographic and anthropometric measurements. This combination of image-derived phenotypes from abdominal MRI scans and ensemble learning to predict risk may have future clinical utility in identifying populations at-risk for a severe COVID-19 outcome.
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Affiliation(s)
- Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Elena P. Sorokin
- Calico Life Sciences LLC, South San Francisco, California, United States of America
| | - Marjola Thanaj
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | | | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Jimmy D. Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Madeleine Cule
- Calico Life Sciences LLC, South San Francisco, California, United States of America
| | - E. Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
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Basty N, Thanaj M, Cule M, Sorokin EP, Liu Y, Thomas EL, Bell JD, Whitcher B. Artifact-free fat-water separation in Dixon MRI using deep learning. J Big Data 2023; 10:4. [PMID: 36686622 PMCID: PMC9835035 DOI: 10.1186/s40537-022-00677-1] [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] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 12/25/2022] [Indexed: 06/17/2023]
Abstract
Chemical-shift encoded MRI (CSE-MRI) is a widely used technique for the study of body composition and metabolic disorders, where derived fat and water signals enable the quantification of adipose tissue and muscle. The UK Biobank is acquiring whole-body Dixon MRI (a specific implementation of CSE-MRI) for over 100,000 participants. Current processing methods associated with large whole-body volumes are time intensive and prone to artifacts during fat-water separation performed by the scanner, making quantitative analysis challenging. The most common artifacts are fat-water swaps, where the labels are inverted at the voxel level. It is common for researchers to discard swapped data (generally around 10%), which is wasteful and may lead to unintended biases. Given the large number of whole-body Dixon MRI acquisitions in the UK Biobank, thousands of swaps are expected to be present in the fat and water volumes from image reconstruction performed on the scanner. If they go undetected, errors will propagate into processes such as organ segmentation, and dilute the results in population-based analyses. There is a clear need for a robust method to accurately separate fat and water volumes in big data collections like the UK Biobank. We formulate fat-water separation as a style transfer problem, where swap-free fat and water volumes are predicted from the acquired Dixon MRI data using a conditional generative adversarial network, and introduce a new loss function for the generator model. Our method is able to predict highly accurate fat and water volumes free from artifacts in the UK Biobank. We show that our model separates fat and water volumes using either single input (in-phase only) or dual input (in-phase and opposed-phase) data, with the latter producing superior results. Our proposed method enables faster and more accurate downstream analysis of body composition from Dixon MRI in population studies by eliminating the need for visual inspection or discarding data due to fat-water swaps. Supplementary Information The online version contains supplementary material available at 10.1186/s40537-022-00677-1.
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Affiliation(s)
- Nicolas Basty
- Research Centre for Optimal Health, University of Westminster, London, UK
| | - Marjola Thanaj
- Research Centre for Optimal Health, University of Westminster, London, UK
| | | | | | - Yi Liu
- Calico Life Sciences LLC, South San Francisco, USA
| | - E. Louise Thomas
- Research Centre for Optimal Health, University of Westminster, London, UK
| | - Jimmy D. Bell
- Research Centre for Optimal Health, University of Westminster, London, UK
| | - Brandon Whitcher
- Research Centre for Optimal Health, University of Westminster, London, UK
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Sorokin EP, Basty N, Whitcher B, Liu Y, Bell JD, Cohen RL, Cule M, Thomas EL. Analysis of MRI-derived spleen iron in the UK Biobank identifies genetic variation linked to iron homeostasis and hemolysis. Am J Hum Genet 2022; 109:1092-1104. [PMID: 35568031 PMCID: PMC9247824 DOI: 10.1016/j.ajhg.2022.04.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 04/19/2022] [Indexed: 11/25/2022] Open
Abstract
The spleen plays a key role in iron homeostasis. It is the largest filter of the blood and performs iron reuptake from old or damaged erythrocytes. Despite this role, spleen iron concentration has not been measured in a large, population-based cohort. In this study, we quantify spleen iron in 41,764 participants of the UK Biobank by using magnetic resonance imaging and provide a reference range for spleen iron in an unselected population. Through genome-wide association study, we identify associations between spleen iron and regulatory variation at two hereditary spherocytosis genes, ANK1 and SPTA1. Spherocytosis-causing coding mutations in these genes are associated with lower reticulocyte volume and increased reticulocyte percentage, while these common alleles are associated with increased expression of ANK1 and SPTA1 in blood and with larger reticulocyte volume and reduced reticulocyte percentage. As genetic modifiers, these common alleles may explain mild spherocytosis phenotypes that have been observed clinically. Our genetic study also identifies a signal that co-localizes with a splicing quantitative trait locus for MS4A7, and we show this gene is abundantly expressed in the spleen and in macrophages. The combination of deep learning and efficient image processing enables non-invasive measurement of spleen iron and, in turn, characterization of genetic factors related to the lytic phase of the erythrocyte life cycle and iron reuptake in the spleen.
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Affiliation(s)
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Yi Liu
- Calico Life Sciences LLC, South San Francisco, CA, USA
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | | | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
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Whitcher B, Thanaj M, Cule M, Liu Y, Basty N, Sorokin EP, Bell JD, Thomas EL. Precision MRI phenotyping enables detection of small changes in body composition for longitudinal cohorts. Sci Rep 2022; 12:3748. [PMID: 35260612 PMCID: PMC8904801 DOI: 10.1038/s41598-022-07556-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 02/18/2022] [Indexed: 12/23/2022] Open
Abstract
Longitudinal studies provide unique insights into the impact of environmental factors and lifespan issues on health and disease. Here we investigate changes in body composition in 3088 free-living participants, part of the UK Biobank in-depth imaging study. All participants underwent neck-to-knee MRI scans at the first imaging visit and after approximately two years (second imaging visit). Image-derived phenotypes for each participant were extracted using a fully-automated image processing pipeline, including volumes of several tissues and organs: liver, pancreas, spleen, kidneys, total skeletal muscle, iliopsoas muscle, visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue, as well as fat and iron content in liver, pancreas and spleen. Overall, no significant changes were observed in BMI, body weight, or waist circumference over the scanning interval, despite some large individual changes. A significant decrease in grip strength was observed, coupled to small, but statistically significant, decrease in all skeletal muscle measurements. Significant increases in VAT and intermuscular fat in the thighs were also detected in the absence of changes in BMI, waist circumference and ectopic-fat deposition. Adjusting for disease status at the first imaging visit did not have an additional impact on the changes observed. In summary, we show that even after a relatively short period of time significant changes in body composition can take place, probably reflecting the obesogenic environment currently inhabited by most of the general population in the United Kingdom.
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Affiliation(s)
- Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Marjola Thanaj
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Madeleine Cule
- Calico Life Sciences LLC, South San Francisco, California, USA
| | - Yi Liu
- Calico Life Sciences LLC, South San Francisco, California, USA
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Elena P Sorokin
- Calico Life Sciences LLC, South San Francisco, California, USA
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
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10
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Martin S, Sorokin EP, Thomas EL, Sattar N, Cule M, Bell JD, Yaghootkar H. Estimating the Effect of Liver and Pancreas Volume and Fat Content on Risk of Diabetes: A Mendelian Randomization Study. Diabetes Care 2022; 45:460-468. [PMID: 34983059 DOI: 10.2337/dc21-1262] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [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] [Received: 06/16/2021] [Accepted: 11/05/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Fat content and volume of liver and pancreas are associated with risk of diabetes in observational studies; whether these associations are causal is unknown. We conducted a Mendelian randomization (MR) study to examine causality of such associations. RESEARCH DESIGN AND METHODS We used genetic variants associated (P < 5 × 10-8) with the exposures (liver and pancreas volume and fat content) using MRI scans of UK Biobank participants (n = 32,859). We obtained summary-level data for risk of type 1 (9,358 cases) and type 2 (55,005 cases) diabetes from the largest available genome-wide association studies. We performed inverse-variance weighted MR as main analysis and several sensitivity analyses to assess pleiotropy and to exclude variants with potential pleiotropic effects. RESULTS Observationally, liver fat and volume were associated with type 2 diabetes (odds ratio per 1 SD higher exposure 2.16 [2.02, 2.31] and 2.11 [1.96, 2.27], respectively). Pancreatic fat was associated with type 2 diabetes (1.42 [1.34, 1.51]) but not type 1 diabetes, and pancreas volume was negatively associated with type 1 diabetes (0.42 [0.36, 0.48]) and type 2 diabetes (0.73 [0.68, 0.78]). MR analysis provided evidence only for a causal role of liver fat and pancreas volume in risk of type 2 diabetes (1.27 [1.08, 1.49] or 27% increased risk and 0.76 [0.62, 0.94] or 24% decreased risk per 1SD, respectively) and no causal associations with type 1 diabetes. CONCLUSIONS Our findings assist in understanding the causal role of ectopic fat in the liver and pancreas and of organ volume in the pathophysiology of type 1 and type 2 diabetes.
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Affiliation(s)
- Susan Martin
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, U.K
| | | | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K
| | - Naveed Sattar
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, U.K
| | | | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K
| | - Hanieh Yaghootkar
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, U.K.,Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K.,Department of Life Sciences, Centre for Inflammation Research and Translational Medicine, Brunel University London, London, U.K
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11
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Sethi A, Taylor DL, Ruby JG, Venkataraman J, Sorokin E, Cule M, Melamud E. Calcification of the abdominal aorta is an under-appreciated cardiovascular disease risk factor in the general population. Front Cardiovasc Med 2022; 9:1003246. [PMID: 36277789 PMCID: PMC9582957 DOI: 10.3389/fcvm.2022.1003246] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 09/13/2022] [Indexed: 12/05/2022] Open
Abstract
Calcification of large arteries is a high-risk factor in the development of cardiovascular diseases, however, due to the lack of routine monitoring, the pathology remains severely under-diagnosed and prevalence in the general population is not known. We have developed a set of machine learning methods to quantitate levels of abdominal aortic calcification (AAC) in the UK Biobank imaging cohort and carried out the largest to-date analysis of genetic, biochemical, and epidemiological risk factors associated with the pathology. In a genetic association study, we identified three novel loci associated with AAC (FGF9, NAV9, and APOE), and replicated a previously reported association at the TWIST1/HDAC9 locus. We find that AAC is a highly prevalent pathology, with ~ 1 in 10 adults above the age of 40 showing significant levels of hydroxyapatite build-up (Kauppila score > 3). Presentation of AAC was strongly predictive of future cardiovascular events including stenosis of precerebral arteries (HR~1.5), myocardial infarction (HR~1.3), ischemic heart disease (HR~1.3), as well as other diseases such as chronic obstructive pulmonary disease (HR~1.3). Significantly, we find that the risk for myocardial infarction from elevated AAC (HR ~1.4) was comparable to the risk of hypercholesterolemia (HR~1.4), yet most people who develop AAC are not hypercholesterolemic. Furthermore, the overwhelming majority (98%) of individuals who develop pathology do so in the absence of known pre-existing risk conditions such as chronic kidney disease and diabetes (0.6% and 2.7% respectively). Our findings indicate that despite the high cardiovascular risk, calcification of large arteries remains a largely under-diagnosed lethal condition, and there is a clear need for increased awareness and monitoring of the pathology in the general population.
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Affiliation(s)
- Anurag Sethi
- Calico Life Sciences LLC, South San Francisco, CA, United States
| | - D Leland Taylor
- Calico Life Sciences LLC, South San Francisco, CA, United States
| | - J Graham Ruby
- Calico Life Sciences LLC, South San Francisco, CA, United States
| | | | - Elena Sorokin
- Calico Life Sciences LLC, South San Francisco, CA, United States
| | - Madeleine Cule
- Calico Life Sciences LLC, South San Francisco, CA, United States
| | - Eugene Melamud
- Calico Life Sciences LLC, South San Francisco, CA, United States
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12
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Martin S, Cule M, Basty N, Tyrrell J, Beaumont RN, Wood AR, Frayling TM, Sorokin E, Whitcher B, Liu Y, Bell JD, Thomas EL, Yaghootkar H. Genetic Evidence for Different Adiposity Phenotypes and Their Opposing Influences on Ectopic Fat and Risk of Cardiometabolic Disease. Diabetes 2021; 70:1843-1856. [PMID: 33980691 DOI: 10.2337/db21-0129] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 05/06/2021] [Indexed: 11/13/2022]
Abstract
To understand the causal role of adiposity and ectopic fat in type 2 diabetes and cardiometabolic diseases, we aimed to identify two clusters of adiposity genetic variants: one with "adverse" metabolic effects (UFA) and the other with, paradoxically, "favorable" metabolic effects (FA). We performed a multivariate genome-wide association study using body fat percentage and metabolic biomarkers from UK Biobank and identified 38 UFA and 36 FA variants. Adiposity-increasing alleles were associated with an adverse metabolic profile, higher risk of disease, higher CRP, and higher fat in subcutaneous and visceral adipose tissue, liver, and pancreas for UFA and a favorable metabolic profile, lower risk of disease, higher CRP and higher subcutaneous adipose tissue but lower liver fat for FA. We detected no sexual dimorphism. The Mendelian randomization studies provided evidence for a risk-increasing effect of UFA and protective effect of FA for type 2 diabetes, heart disease, hypertension, stroke, nonalcoholic fatty liver disease, and polycystic ovary syndrome. FA is distinct from UFA by its association with lower liver fat and protection from cardiometabolic diseases; it was not associated with visceral or pancreatic fat. Understanding the difference in FA and UFA may lead to new insights in preventing, predicting, and treating cardiometabolic diseases.
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Affiliation(s)
- Susan Martin
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, U.K
| | | | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K
| | - Jessica Tyrrell
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, U.K
| | - Robin N Beaumont
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, U.K
| | - Andrew R Wood
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, U.K
| | - Timothy M Frayling
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, U.K
| | | | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K
| | - Yi Liu
- Calico Life Sciences LLC, South San Francisco, CA
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K
| | - Hanieh Yaghootkar
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, U.K.
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K
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13
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Liu Y, Basty N, Whitcher B, Bell JD, Sorokin EP, van Bruggen N, Thomas EL, Cule M. Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning. eLife 2021; 10:e65554. [PMID: 34128465 PMCID: PMC8205492 DOI: 10.7554/elife.65554] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [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: 12/08/2020] [Accepted: 05/09/2021] [Indexed: 12/24/2022] Open
Abstract
Cardiometabolic diseases are an increasing global health burden. While socioeconomic, environmental, behavioural, and genetic risk factors have been identified, a better understanding of the underlying mechanisms is required to develop more effective interventions. Magnetic resonance imaging (MRI) has been used to assess organ health, but biobank-scale studies are still in their infancy. Using over 38,000 abdominal MRI scans in the UK Biobank, we used deep learning to quantify volume, fat, and iron in seven organs and tissues, and demonstrate that imaging-derived phenotypes reflect health status. We show that these traits have a substantial heritable component (8-44%) and identify 93 independent genome-wide significant associations, including four associations with liver traits that have not previously been reported. Our work demonstrates the tractability of deep learning to systematically quantify health parameters from high-throughput MRI across a range of organs and tissues, and use the largest-ever study of its kind to generate new insights into the genetic architecture of these traits.
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Affiliation(s)
- Yi Liu
- Calico Life Sciences LLCSouth San FranciscoUnited States
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of WestminsterLondonUnited Kingdom
| | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of WestminsterLondonUnited Kingdom
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of WestminsterLondonUnited Kingdom
| | | | | | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of WestminsterLondonUnited Kingdom
| | - Madeleine Cule
- Calico Life Sciences LLCSouth San FranciscoUnited States
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14
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Melamud E, Taylor DL, Sethi A, Cule M, Baryshnikova A, Saleheen D, van Bruggen N, FitzGerald GA. The promise and reality of therapeutic discovery from large cohorts. J Clin Invest 2020; 130:575-581. [PMID: 31929188 PMCID: PMC6994121 DOI: 10.1172/jci129196] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Technological advances in rapid data acquisition have transformed medical biology into a data mining field, where new data sets are routinely dissected and analyzed by statistical models of ever-increasing complexity. Many hypotheses can be generated and tested within a single large data set, and even small effects can be statistically discriminated from a sea of noise. On the other hand, the development of therapeutic interventions moves at a much slower pace. They are determined from carefully randomized and well-controlled experiments with explicitly stated outcomes as the principal mechanism by which a single hypothesis is tested. In this paradigm, only a small fraction of interventions can be tested, and an even smaller fraction are ultimately deemed therapeutically successful. In this Review, we propose strategies to leverage large-cohort data to inform the selection of targets and the design of randomized trials of novel therapeutics. Ultimately, the incorporation of big data and experimental medicine approaches should aim to reduce the failure rate of clinical trials as well as expedite and lower the cost of drug development.
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Affiliation(s)
- Eugene Melamud
- Calico Life Sciences LLC, South San Francisco, California, USA
| | | | - Anurag Sethi
- Calico Life Sciences LLC, South San Francisco, California, USA
| | - Madeleine Cule
- Calico Life Sciences LLC, South San Francisco, California, USA
| | | | | | | | - Garret A. FitzGerald
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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15
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Greenside P, Zook J, Salit M, Cule M, Poplin R, DePristo M. CrowdVariant: a crowdsourcing approach to classify copy number variants. Pac Symp Biocomput 2019; 24:224-235. [PMID: 30864325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Copy number variants (CNVs) are an important type of genetic variation that play a causal role in many diseases. The ability to identify high quality CNVs is of substantial clinical relevance. However, CNVs are notoriously difficult to identify accurately from array-based methods and next-generation sequencing (NGS) data, particularly for small (< 10kbp) CNVs. Manual curation by experts widely remains the gold standard but cannot scale with the pace of sequencing, particularly in fast-growing clinical applications. We present the first proof-of-principle study demonstrating high throughput manual curation of putative CNVs by non-experts. We developed a crowdsourcing framework, called CrowdVariant, that leverages Google's high-throughput crowdsourcing platform to create a high confidence set of deletions for NA24385 (NIST HG002/RM 8391), an Ashkenazim reference sample developed in partnership with the Genome In A Bottle (GIAB) Consortium. We show that non-experts tend to agree both with each other and with experts on putative CNVs. We show that crowdsourced non-expert classifications can be used to accurately assign copy number status to putative CNV calls and identify 1,781 high confidence deletions in a reference sample. Multiple lines of evidence suggest these calls are a substantial improvement over existing CNV callsets and can also be useful in benchmarking and improving CNV calling algorithms. Our crowdsourcing methodology takes the first step toward showing the clinical potential for manual curation of CNVs at scale and can further guide other crowdsourcing genomics applications.
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Affiliation(s)
- Peyton Greenside
- Biomedical Informatics, Stanford University, Stanford, CA 94305, USA,
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16
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Cule M, Donnelly P. Stochastic modelling and inference in electronic hospital databases for the spread of infections: Clostridium difficile transmission in Oxfordshire hospitals 2007-2010. Ann Appl Stat 2017; 11:655-679. [PMID: 31105805 PMCID: PMC6520235 DOI: 10.1214/16-aoas1011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [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: 12/18/2022]
Abstract
The combination of genetic information with electronic patient records promises to provide a powerful new resource for understanding human disease and its treatment. Here we develop and apply a novel stochastic compartmental model to a large dataset on Clostridium difficile infection (CDI) in three Oxfordshire hospitals over a 2.5 year period which combines genetic information on 858 confirmed cases of CDI with a database of 750,000 patient records. C. difficile is a major cause of healthcare-associated diarrhoea and is responsible for substantial mortality and morbidity, with relatively little known about its biology or its transmission epidemiology. Bayesian analysis of our model, via Markov chain Monte Carlo, provides new information about the biology of CDI, including genetic heterogeneity in infectiousness across different sequence types, and evidence for ward contamination as a significant mode of transmission, and allows inferences about the contribution of particular individuals, wards, or hospitals to transmission of the bacterium, and assessment of changes in these over time following changes in hospital practice. Our work demonstrates the value of using statistical modelling and computational inference on large-scale hospital patient databases and genetic data.
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Affiliation(s)
- Madeleine Cule
- Department of Statistics, 1 South Parks Road, Oxford OX1 3TG
| | - Peter Donnelly
- Department of Statistics, 1 South Parks Road, Oxford OX1 3TG
- Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford OX3 7BN
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17
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Miller RM, Price JR, Batty EM, Didelot X, Wyllie D, Golubchik T, Crook DW, Paul J, Peto TEA, Wilson DJ, Cule M, Ip CLC, Day NPJ, Moore CE, Bowden R, Llewelyn MJ. Healthcare-associated outbreak of meticillin-resistant Staphylococcus aureus bacteraemia: role of a cryptic variant of an epidemic clone. J Hosp Infect 2013; 86:83-9. [PMID: 24433924 PMCID: PMC3924019 DOI: 10.1016/j.jhin.2013.11.007] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [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: 02/28/2013] [Accepted: 11/20/2013] [Indexed: 12/21/2022]
Abstract
Background New strains of meticillin-resistant Staphylococcus aureus (MRSA) may be associated with changes in rates of disease or clinical presentation. Conventional typing techniques may not detect new clonal variants that underlie changes in epidemiology or clinical phenotype. Aim To investigate the role of clonal variants of MRSA in an outbreak of MRSA bacteraemia at a hospital in England. Methods Bacteraemia isolates of the major UK lineages (EMRSA-15 and -16) from before and after the outbreak were analysed by whole-genome sequencing in the context of epidemiological and clinical data. For comparison, EMRSA-15 and -16 isolates from another hospital in England were sequenced. A clonal variant of EMRSA-16 was identified at the outbreak hospital and a molecular signature test designed to distinguish variant isolates among further EMRSA-16 strains. Findings By whole-genome sequencing, EMRSA-16 isolates during the outbreak showed strikingly low genetic diversity (P < 1 × 10−6, Monte Carlo test), compared with EMRSA-15 and EMRSA-16 isolates from before the outbreak or the comparator hospital, demonstrating the emergence of a clonal variant. The variant was indistinguishable from the ancestral strain by conventional typing. This clonal variant accounted for 64/72 (89%) of EMRSA-16 bacteraemia isolates at the outbreak hospital from 2006. Conclusions Evolutionary changes in epidemic MRSA strains not detected by conventional typing may be associated with changes in disease epidemiology. Rapid and affordable technologies for whole-genome sequencing are becoming available with the potential to identify and track the emergence of variants of highly clonal organisms.
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Affiliation(s)
- R M Miller
- Nuffield Department of Clinical Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK; NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - J R Price
- Department of Microbiology and Infectious Diseases, Brighton and Sussex University Hospital NHS Trust, Brighton, UK
| | - E M Batty
- Department of Statistics, University of Oxford, Oxford, UK
| | - X Didelot
- Department of Statistics, University of Oxford, Oxford, UK
| | - D Wyllie
- Nuffield Department of Clinical Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK; NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - T Golubchik
- Department of Statistics, University of Oxford, Oxford, UK
| | - D W Crook
- Nuffield Department of Clinical Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK; NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - J Paul
- Public Health England, Royal Sussex County Hospital, Brighton, UK
| | - T E A Peto
- Nuffield Department of Clinical Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK; NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - D J Wilson
- Nuffield Department of Clinical Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK; Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - M Cule
- Department of Statistics, University of Oxford, Oxford, UK
| | - C L C Ip
- Department of Statistics, University of Oxford, Oxford, UK
| | - N P J Day
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Rajthevee, Bangkok, Thailand; Centre for Tropical Medicine, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - C E Moore
- Centre for Tropical Medicine, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK; Oxford University Collaborative Laboratory, Angkor Hospital for Children, Siem Reap, Cambodia
| | - R Bowden
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK; Department of Statistics, University of Oxford, Oxford, UK; Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - M J Llewelyn
- Department of Microbiology and Infectious Diseases, Brighton and Sussex University Hospital NHS Trust, Brighton, UK.
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18
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Batty EM, Wong THN, Trebes A, Argoud K, Attar M, Buck D, Ip CLC, Golubchik T, Cule M, Bowden R, Manganis C, Klenerman P, Barnes E, Walker AS, Wyllie DH, Wilson DJ, Dingle KE, Peto TEA, Crook DW, Piazza P. A modified RNA-Seq approach for whole genome sequencing of RNA viruses from faecal and blood samples. PLoS One 2013; 8:e66129. [PMID: 23762474 PMCID: PMC3677912 DOI: 10.1371/journal.pone.0066129] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2013] [Accepted: 05/02/2013] [Indexed: 12/12/2022] Open
Abstract
To date, very large scale sequencing of many clinically important RNA viruses has been complicated by their high population molecular variation, which creates challenges for polymerase chain reaction and sequencing primer design. Many RNA viruses are also difficult or currently not possible to culture, severely limiting the amount and purity of available starting material. Here, we describe a simple, novel, high-throughput approach to Norovirus and Hepatitis C virus whole genome sequence determination based on RNA shotgun sequencing (also known as RNA-Seq). We demonstrate the effectiveness of this method by sequencing three Norovirus samples from faeces and two Hepatitis C virus samples from blood, on an Illumina MiSeq benchtop sequencer. More than 97% of reference genomes were recovered. Compared with Sanger sequencing, our method had no nucleotide differences in 14,019 nucleotides (nt) for Noroviruses (from a total of 2 Norovirus genomes obtained with Sanger sequencing), and 8 variants in 9,542 nt for Hepatitis C virus (1 variant per 1,193 nt). The three Norovirus samples had 2, 3, and 2 distinct positions called as heterozygous, while the two Hepatitis C virus samples had 117 and 131 positions called as heterozygous. To confirm that our sample and library preparation could be scaled to true high-throughput, we prepared and sequenced an additional 77 Norovirus samples in a single batch on an Illumina HiSeq 2000 sequencer, recovering >90% of the reference genome in all but one sample. No discrepancies were observed across 118,757 nt compared between Sanger and our custom RNA-Seq method in 16 samples. By generating viral genomic sequences that are not biased by primer-specific amplification or enrichment, this method offers the prospect of large-scale, affordable studies of RNA viruses which could be adapted to routine diagnostic laboratory workflows in the near future, with the potential to directly characterize within-host viral diversity.
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Affiliation(s)
| | - T. H. Nicholas Wong
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
- Oxford NIHR Biomedical Research Centre, John Radcliffe Hospital, Oxford, United Kingdom
| | - Amy Trebes
- Wellcome Trust Centre for Human Genetics, Oxford, United Kingdom
| | - Karène Argoud
- Wellcome Trust Centre for Human Genetics, Oxford, United Kingdom
| | - Moustafa Attar
- Wellcome Trust Centre for Human Genetics, Oxford, United Kingdom
| | - David Buck
- Wellcome Trust Centre for Human Genetics, Oxford, United Kingdom
| | - Camilla L. C. Ip
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Tanya Golubchik
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Madeleine Cule
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Rory Bowden
- Wellcome Trust Centre for Human Genetics, Oxford, United Kingdom
| | - Charis Manganis
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
| | - Paul Klenerman
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
| | - Eleanor Barnes
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
| | - A. Sarah Walker
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
- Oxford NIHR Biomedical Research Centre, John Radcliffe Hospital, Oxford, United Kingdom
| | - David H. Wyllie
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
- Oxford NIHR Biomedical Research Centre, John Radcliffe Hospital, Oxford, United Kingdom
| | - Daniel J. Wilson
- Wellcome Trust Centre for Human Genetics, Oxford, United Kingdom
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
| | - Kate E. Dingle
- Oxford NIHR Biomedical Research Centre, John Radcliffe Hospital, Oxford, United Kingdom
- Nuffield Department of Clinical Laboratory Sciences, Headley Way, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
| | - Tim E. A. Peto
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
- Oxford NIHR Biomedical Research Centre, John Radcliffe Hospital, Oxford, United Kingdom
| | - Derrick W. Crook
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
- Oxford NIHR Biomedical Research Centre, John Radcliffe Hospital, Oxford, United Kingdom
| | - Paolo Piazza
- Wellcome Trust Centre for Human Genetics, Oxford, United Kingdom
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19
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Golubchik T, Batty EM, Miller RR, Farr H, Young BC, Larner-Svensson H, Fung R, Godwin H, Knox K, Votintseva A, Everitt RG, Street T, Cule M, Ip CLC, Didelot X, Peto TEA, Harding RM, Wilson DJ, Crook DW, Bowden R. Within-host evolution of Staphylococcus aureus during asymptomatic carriage. PLoS One 2013; 8:e61319. [PMID: 23658690 PMCID: PMC3641031 DOI: 10.1371/journal.pone.0061319] [Citation(s) in RCA: 153] [Impact Index Per Article: 13.9] [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: 01/03/2013] [Accepted: 03/07/2013] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Staphylococcus aureus is a major cause of healthcare associated mortality, but like many important bacterial pathogens, it is a common constituent of the normal human body flora. Around a third of healthy adults are carriers. Recent evidence suggests that evolution of S. aureus during nasal carriage may be associated with progression to invasive disease. However, a more detailed understanding of within-host evolution under natural conditions is required to appreciate the evolutionary and mechanistic reasons why commensal bacteria such as S. aureus cause disease. Therefore we examined in detail the evolutionary dynamics of normal, asymptomatic carriage. Sequencing a total of 131 genomes across 13 singly colonized hosts using the Illumina platform, we investigated diversity, selection, population dynamics and transmission during the short-term evolution of S. aureus. PRINCIPAL FINDINGS We characterized the processes by which the raw material for evolution is generated: micro-mutation (point mutation and small insertions/deletions), macro-mutation (large insertions/deletions) and the loss or acquisition of mobile elements (plasmids and bacteriophages). Through an analysis of synonymous, non-synonymous and intergenic mutations we discovered a fitness landscape dominated by purifying selection, with rare examples of adaptive change in genes encoding surface-anchored proteins and an enterotoxin. We found evidence for dramatic, hundred-fold fluctuations in the size of the within-host population over time, which we related to the cycle of colonization and clearance. Using a newly-developed population genetics approach to detect recent transmission among hosts, we revealed evidence for recent transmission between some of our subjects, including a husband and wife both carrying populations of methicillin-resistant S. aureus (MRSA). SIGNIFICANCE This investigation begins to paint a picture of the within-host evolution of an important bacterial pathogen during its prevailing natural state, asymptomatic carriage. These results also have wider significance as a benchmark for future systematic studies of evolution during invasive S. aureus disease.
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Affiliation(s)
- Tanya Golubchik
- Department of Statistics, University of Oxford, Oxford, Oxfordshire, United Kingdom
| | - Elizabeth M. Batty
- Department of Statistics, University of Oxford, Oxford, Oxfordshire, United Kingdom
| | - Ruth R. Miller
- Experimental Medicine Division, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, Oxfordshire, United Kingdom
- National Institute for Health Research Oxford Biomedical Research Centre, Oxford, Oxfordshire, United Kingdom
| | - Helen Farr
- Experimental Medicine Division, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, Oxfordshire, United Kingdom
- National Institute for Health Research Oxford Biomedical Research Centre, Oxford, Oxfordshire, United Kingdom
| | - Bernadette C. Young
- Experimental Medicine Division, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, Oxfordshire, United Kingdom
| | - Hanna Larner-Svensson
- National Institute for Health Research Oxford Biomedical Research Centre, Oxford, Oxfordshire, United Kingdom
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, Oxfordshire, United Kingdom
| | - Rowena Fung
- Experimental Medicine Division, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, Oxfordshire, United Kingdom
- National Institute for Health Research Oxford Biomedical Research Centre, Oxford, Oxfordshire, United Kingdom
| | - Heather Godwin
- Oxford University Hospitals National Health Service Trust, Oxford, Oxfordshire, United Kingdom
| | - Kyle Knox
- Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxfordshire, United Kingdom
| | - Antonina Votintseva
- Experimental Medicine Division, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, Oxfordshire, United Kingdom
- National Institute for Health Research Oxford Biomedical Research Centre, Oxford, Oxfordshire, United Kingdom
| | - Richard G. Everitt
- Experimental Medicine Division, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, Oxfordshire, United Kingdom
| | - Teresa Street
- Department of Statistics, University of Oxford, Oxford, Oxfordshire, United Kingdom
| | - Madeleine Cule
- Department of Statistics, University of Oxford, Oxford, Oxfordshire, United Kingdom
| | - Camilla L. C. Ip
- Department of Statistics, University of Oxford, Oxford, Oxfordshire, United Kingdom
| | - Xavier Didelot
- Department of Statistics, University of Oxford, Oxford, Oxfordshire, United Kingdom
| | - Timothy E. A. Peto
- Experimental Medicine Division, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, Oxfordshire, United Kingdom
- National Institute for Health Research Oxford Biomedical Research Centre, Oxford, Oxfordshire, United Kingdom
| | - Rosalind M. Harding
- Department of Zoology, University of Oxford, Oxford, Oxfordshire, United Kingdom
| | - Daniel J. Wilson
- Experimental Medicine Division, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, Oxfordshire, United Kingdom
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, Oxfordshire, United Kingdom
| | - Derrick W. Crook
- Experimental Medicine Division, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, Oxfordshire, United Kingdom
- National Institute for Health Research Oxford Biomedical Research Centre, Oxford, Oxfordshire, United Kingdom
| | - Rory Bowden
- Department of Statistics, University of Oxford, Oxford, Oxfordshire, United Kingdom
- National Institute for Health Research Oxford Biomedical Research Centre, Oxford, Oxfordshire, United Kingdom
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, Oxfordshire, United Kingdom
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Didelot X, Eyre DW, Cule M, Ip CLC, Ansari MA, Griffiths D, Vaughan A, O'Connor L, Golubchik T, Batty EM, Piazza P, Wilson DJ, Bowden R, Donnelly PJ, Dingle KE, Wilcox M, Walker AS, Crook DW, A Peto TE, Harding RM. Microevolutionary analysis of Clostridium difficile genomes to investigate transmission. Genome Biol 2012; 13:R118. [PMID: 23259504 PMCID: PMC4056369 DOI: 10.1186/gb-2012-13-12-r118] [Citation(s) in RCA: 159] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2012] [Revised: 11/08/2012] [Accepted: 12/21/2012] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND The control of Clostridium difficile infection is a major international healthcare priority, hindered by a limited understanding of transmission epidemiology for these bacteria. However, transmission studies of bacterial pathogens are rapidly being transformed by the advent of next generation sequencing. RESULTS Here we sequence whole C. difficile genomes from 486 cases arising over four years in Oxfordshire. We show that we can estimate the times back to common ancestors of bacterial lineages with sufficient resolution to distinguish whether direct transmission is plausible or not. Time depths were inferred using a within-host evolutionary rate that we estimated at 1.4 mutations per genome per year based on serially isolated genomes. The subset of plausible transmissions was found to be highly associated with pairs of patients sharing time and space in hospital. Conversely, the large majority of pairs of genomes matched by conventional typing and isolated from patients within a month of each other were too distantly related to be direct transmissions. CONCLUSIONS Our results confirm that nosocomial transmission between symptomatic C. difficile cases contributes far less to current rates of infection than has been widely assumed, which clarifies the importance of future research into other transmission routes, such as from asymptomatic carriers. With the costs of DNA sequencing rapidly falling and its use becoming more and more widespread, genomics will revolutionize our understanding of the transmission of bacterial pathogens.
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Affiliation(s)
- Xavier Didelot
- Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK
| | - David W Eyre
- Nuffield Department of Clinical Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK
- Oxford Biomedical Research Centre, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
| | - Madeleine Cule
- Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK
| | - Camilla LC Ip
- Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK
| | - M Azim Ansari
- Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK
| | - David Griffiths
- Nuffield Department of Clinical Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK
- Oxford Biomedical Research Centre, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
| | - Alison Vaughan
- Nuffield Department of Clinical Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK
- Oxford Biomedical Research Centre, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
| | - Lily O'Connor
- Oxford Biomedical Research Centre, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
| | - Tanya Golubchik
- Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK
| | - Elizabeth M Batty
- Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK
| | - Paolo Piazza
- Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford OX3 7BN, UK
| | - Daniel J Wilson
- Nuffield Department of Clinical Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK
- Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford OX3 7BN, UK
| | - Rory Bowden
- Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK
- Oxford Biomedical Research Centre, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
- Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford OX3 7BN, UK
| | - Peter J Donnelly
- Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK
- Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford OX3 7BN, UK
| | - Kate E Dingle
- Oxford Biomedical Research Centre, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
- Nuffield Department of Clinical Laboratory Sciences, Headley Way, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Mark Wilcox
- Department of Microbiology, The General Infirmary, Old Medical School, Great George Street, Leeds LS1 3EX, UK
- Leeds Institute of Molecular Medicine, University of Leeds, Beckett Street, Leeds LS9 7TF, UK
| | - A Sarah Walker
- Nuffield Department of Clinical Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK
- Oxford Biomedical Research Centre, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
- MRC Clinical Trials Unit, 125 Kingsway, London, WC2B 6NH, UK
| | - Derrick W Crook
- Nuffield Department of Clinical Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK
- Oxford Biomedical Research Centre, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
| | - Tim E A Peto
- Nuffield Department of Clinical Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK
- Oxford Biomedical Research Centre, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
| | - Rosalind M Harding
- Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK
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21
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van Schalkwyk C, Cule M, Welte A, van Helden P, van der Spuy G, Uys P. Towards eliminating bias in cluster analysis of TB genotyped data. PLoS One 2012; 7:e34109. [PMID: 22479534 PMCID: PMC3315507 DOI: 10.1371/journal.pone.0034109] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2012] [Accepted: 02/23/2012] [Indexed: 11/18/2022] Open
Abstract
The relative contributions of transmission and reactivation of latent infection to TB cases observed clinically has been reported in many situations, but always with some uncertainty. Genotyped data from TB organisms obtained from patients have been used as the basis for heuristic distinctions between circulating (clustered strains) and reactivated infections (unclustered strains). Naïve methods previously applied to the analysis of such data are known to provide biased estimates of the proportion of unclustered cases. The hypergeometric distribution, which generates probabilities of observing clusters of a given size as realized clusters of all possible sizes, is analyzed in this paper to yield a formal estimator for genotype cluster sizes. Subtle aspects of numerical stability, bias, and variance are explored. This formal estimator is seen to be stable with respect to the epidemiologically interesting properties of the cluster size distribution (the number of clusters and the number of singletons) though it does not yield satisfactory estimates of the number of clusters of larger sizes. The problem that even complete coverage of genotyping, in a practical sampling frame, will only provide a partial view of the actual transmission network remains to be explored.
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Affiliation(s)
- Cari van Schalkwyk
- The South African Department of Science and Technology/National Research Foundation (DST/NRF) Centre of Excellence in Epidemiological Modelling and Analysis, Faculty of Science, University of Stellenbosch, Stellenbosch, South Africa.
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Cule M, Samworth R. Theoretical properties of the log-concave maximum likelihood estimator of a multidimensional density. Electron J Stat 2010. [DOI: 10.1214/09-ejs505] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Cule M, Gramacy R, Samworth R. LogConcDEAD: An RPackage for Maximum Likelihood Estimation of a Multivariate Log-Concave Density. J Stat Softw 2009. [DOI: 10.18637/jss.v029.i02] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [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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