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Smith HM, Moodie JE, Monterrubio-Gómez K, Gadd DA, Hillary RF, Chybowska AD, McCartney DL, Campbell A, Redmond P, Page D, Taylor A, Corley J, Harris SE, Valdés Hernández M, Muñoz Maniega S, Bastin ME, Wardlaw JM, Deary IJ, Boardman JP, Mullin DS, Russ TC, Cox SR, Marioni RE. Epigenetic scores of blood-based proteins as biomarkers of general cognitive function and brain health. Clin Epigenetics 2024; 16:46. [PMID: 38528588 PMCID: PMC10962132 DOI: 10.1186/s13148-024-01661-7] [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: 11/30/2023] [Accepted: 03/16/2024] [Indexed: 03/27/2024] Open
Abstract
BACKGROUND Epigenetic Scores (EpiScores) for blood protein levels have been associated with disease outcomes and measures of brain health, highlighting their potential usefulness as clinical biomarkers. They are typically derived via penalised regression, whereby a linear weighted sum of DNA methylation (DNAm) levels at CpG sites are predictive of protein levels. Here, we examine 84 previously published protein EpiScores as possible biomarkers of cross-sectional and longitudinal measures of general cognitive function and brain health, and incident dementia across three independent cohorts. RESULTS Using 84 protein EpiScores as candidate biomarkers, associations with general cognitive function (both cross-sectionally and longitudinally) were tested in three independent cohorts: Generation Scotland (GS), and the Lothian Birth Cohorts of 1921 and 1936 (LBC1921 and LBC1936, respectively). A meta-analysis of general cognitive functioning results in all three cohorts identified 18 EpiScore associations (absolute meta-analytic standardised estimates ranged from 0.03 to 0.14, median of 0.04, PFDR < 0.05). Several associations were also observed between EpiScores and global brain volumetric measures in the LBC1936. An EpiScore for the S100A9 protein (a known Alzheimer disease biomarker) was associated with general cognitive functioning (meta-analytic standardised beta: - 0.06, P = 1.3 × 10-9), and with time-to-dementia in GS (Hazard ratio 1.24, 95% confidence interval 1.08-1.44, P = 0.003), but not in LBC1936 (Hazard ratio 1.11, P = 0.32). CONCLUSIONS EpiScores might make a contribution to the risk profile of poor general cognitive function and global brain health, and risk of dementia, however these scores require replication in further studies.
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Affiliation(s)
- Hannah M Smith
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Joanna E Moodie
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Karla Monterrubio-Gómez
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Danni A Gadd
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Robert F Hillary
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Aleksandra D Chybowska
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Daniel L McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Paul Redmond
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Danielle Page
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Adele Taylor
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Janie Corley
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Sarah E Harris
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Maria Valdés Hernández
- Centre for Clinical Brain Sciences, and Edinburgh Centre in the UK Dementia Research Institute, Chancellor's Building, University of Edinburgh, Little France, Edinburgh, UK
| | - Susana Muñoz Maniega
- Centre for Clinical Brain Sciences, and Edinburgh Centre in the UK Dementia Research Institute, Chancellor's Building, University of Edinburgh, Little France, Edinburgh, UK
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, and Edinburgh Centre in the UK Dementia Research Institute, Chancellor's Building, University of Edinburgh, Little France, Edinburgh, UK
| | - Joanna M Wardlaw
- Centre for Clinical Brain Sciences, and Edinburgh Centre in the UK Dementia Research Institute, Chancellor's Building, University of Edinburgh, Little France, Edinburgh, UK
| | - Ian J Deary
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - James P Boardman
- Centre for Clinical Brain Sciences, and Edinburgh Centre in the UK Dementia Research Institute, Chancellor's Building, University of Edinburgh, Little France, Edinburgh, UK
- Centre for Reproductive Health, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh, UK
| | - Donncha S Mullin
- Centre for Clinical Brain Sciences, Division of Psychiatry, University of Edinburgh, Edinburgh, UK
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK
| | - Tom C Russ
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
- Centre for Clinical Brain Sciences, and Edinburgh Centre in the UK Dementia Research Institute, Chancellor's Building, University of Edinburgh, Little France, Edinburgh, UK
| | - Simon R Cox
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
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Monterrubio-Gómez K, Constantine-Cooke N, Vallejos CA. A review on statistical and machine learning competing risks methods. Biom J 2024; 66:e2300060. [PMID: 38351217 DOI: 10.1002/bimj.202300060] [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: 02/23/2023] [Revised: 08/31/2023] [Accepted: 10/15/2023] [Indexed: 02/16/2024]
Abstract
When modeling competing risks (CR) survival data, several techniques have been proposed in both the statistical and machine learning literature. State-of-the-art methods have extended classical approaches with more flexible assumptions that can improve predictive performance, allow high-dimensional data and missing values, among others. Despite this, modern approaches have not been widely employed in applied settings. This article aims to aid the uptake of such methods by providing a condensed compendium of CR survival methods with a unified notation and interpretation across approaches. We highlight available software and, when possible, demonstrate their usage via reproducible R vignettes. Moreover, we discuss two major concerns that can affect benchmark studies in this context: the choice of performance metrics and reproducibility.
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Affiliation(s)
| | - Nathan Constantine-Cooke
- MRC Human Genetics Unit, University of Edinburgh, Edinburgh, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Catalina A Vallejos
- MRC Human Genetics Unit, University of Edinburgh, Edinburgh, UK
- The Alan Turing Institute, London, UK
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Constantine-Cooke N, Monterrubio-Gómez K, Plevris N, Derikx LAAP, Gros B, Jones GR, Marioni RE, Lees CW, Vallejos CA. Longitudinal Fecal Calprotectin Profiles Characterize Disease Course Heterogeneity in Crohn's Disease. Clin Gastroenterol Hepatol 2023; 21:2918-2927.e6. [PMID: 37004971 DOI: 10.1016/j.cgh.2023.03.026] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 03/06/2023] [Accepted: 03/21/2023] [Indexed: 04/04/2023]
Abstract
BACKGROUND AND AIMS The progressive nature of Crohn's disease is highly variable and hard to predict. In addition, symptoms correlate poorly with mucosal inflammation. There is therefore an urgent need to better characterize the heterogeneity of disease trajectories in Crohn's disease by utilizing objective markers of inflammation. We aimed to better understand this heterogeneity by clustering Crohn's disease patients with similar longitudinal fecal calprotectin profiles. METHODS We performed a retrospective cohort study at the Edinburgh IBD Unit, a tertiary referral center, and used latent class mixed models to cluster Crohn's disease subjects using fecal calprotectin observed within 5 years of diagnosis. Information criteria, alluvial plots, and cluster trajectories were used to decide the optimal number of clusters. Chi-square test, Fisher's exact test, and analysis of variance were used to test for associations with variables commonly assessed at diagnosis. RESULTS Our study cohort comprised 356 patients with newly diagnosed Crohn's disease and 2856 fecal calprotectin measurements taken within 5 years of diagnosis (median 7 per subject). Four distinct clusters were identified by characteristic calprotectin profiles: a cluster with consistently high fecal calprotectin and 3 clusters characterized by different downward longitudinal trends. Cluster membership was significantly associated with smoking (P = .015), upper gastrointestinal involvement (P < .001), and early biologic therapy (P < .001). CONCLUSIONS Our analysis demonstrates a novel approach to characterizing the heterogeneity of Crohn's disease by using fecal calprotectin. The group profiles do not simply reflect different treatment regimens and do not mirror classical disease progression endpoints.
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Affiliation(s)
- Nathan Constantine-Cooke
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom; Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom.
| | - Karla Monterrubio-Gómez
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - Nikolas Plevris
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh IBD Unit, Western General Hospital, Edinburgh, United Kingdom
| | - Lauranne A A P Derikx
- Inflammatory Bowel Disease Center, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Beatriz Gros
- Edinburgh IBD Unit, Western General Hospital, Edinburgh, United Kingdom
| | - Gareth-Rhys Jones
- Edinburgh IBD Unit, Western General Hospital, Edinburgh, United Kingdom; Centre for Inflammation Research, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - Charlie W Lees
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh IBD Unit, Western General Hospital, Edinburgh, United Kingdom
| | - Catalina A Vallejos
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom; Alan Turing Institute, British Library, London, United Kingdom
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Cheng Y, Gadd DA, Gieger C, Monterrubio-Gómez K, Zhang Y, Berta I, Stam MJ, Szlachetka N, Lobzaev E, Wrobel N, Murphy L, Campbell A, Nangle C, Walker RM, Fawns-Ritchie C, Peters A, Rathmann W, Porteous DJ, Evans KL, McIntosh AM, Cannings TI, Waldenberger M, Ganna A, McCartney DL, Vallejos CA, Marioni RE. Development and validation of DNA methylation scores in two European cohorts augment 10-year risk prediction of type 2 diabetes. Nat Aging 2023; 3:450-458. [PMID: 37117793 DOI: 10.1038/s43587-023-00391-4] [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] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 02/27/2023] [Indexed: 04/30/2023]
Abstract
Type 2 diabetes mellitus (T2D) presents a major health and economic burden that could be alleviated with improved early prediction and intervention. While standard risk factors have shown good predictive performance, we show that the use of blood-based DNA methylation information leads to a significant improvement in the prediction of 10-year T2D incidence risk. Previous studies have been largely constrained by linear assumptions, the use of cytosine-guanine pairs one-at-a-time and binary outcomes. We present a flexible approach (via an R package, MethylPipeR) based on a range of linear and tree-ensemble models that incorporate time-to-event data for prediction. Using the Generation Scotland cohort (training set ncases = 374, ncontrols = 9,461; test set ncases = 252, ncontrols = 4,526) our best-performing model (area under the receiver operating characteristic curve (AUC) = 0.872, area under the precision-recall curve (PRAUC) = 0.302) showed notable improvement in 10-year onset prediction beyond standard risk factors (AUC = 0.839, precision-recall AUC = 0.227). Replication was observed in the German-based KORA study (n = 1,451, ncases = 142, P = 1.6 × 10-5).
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Affiliation(s)
- Yipeng Cheng
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Danni A Gadd
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Christian Gieger
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research, München-Neuherberg, Germany
| | - Karla Monterrubio-Gómez
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Yufei Zhang
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Imrich Berta
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Michael J Stam
- School of Informatics, University of Edinburgh, Edinburgh, UK
| | | | - Evgenii Lobzaev
- School of Informatics, University of Edinburgh, Edinburgh, UK
| | - Nicola Wrobel
- Edinburgh Clinical Research Facility, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Lee Murphy
- Edinburgh Clinical Research Facility, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Cliff Nangle
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Rosie M Walker
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh BioQuarter, Edinburgh, UK
| | - Chloe Fawns-Ritchie
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research, München-Neuherberg, Germany
- German Centre for Cardiovascular Research, Partner Site Munich Heart Alliance, München, Germany
| | - Wolfgang Rathmann
- German Center for Diabetes Research, München-Neuherberg, Germany
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Institute for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Kathryn L Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Andrew M McIntosh
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | | | - Melanie Waldenberger
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Andrea Ganna
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Daniel L McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Catalina A Vallejos
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
- The Alan Turing Institute, London, UK.
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
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5
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Taggart C, Monterrubio-Gómez K, Roos A, Boeddinghaus J, Kimenai DM, Kadesjo E, Bularga A, Wereski R, Ferry A, Lowry M, Anand A, Lee KK, Doudesis D, Manolopoulou I, Nestelberger T, Koechlin L, Lopez-Ayala P, Mueller C, Mills NL, Vallejos CA, Chapman AR. Improving Risk Stratification for Patients With Type 2 Myocardial Infarction. J Am Coll Cardiol 2023; 81:156-168. [PMID: 36631210 PMCID: PMC9841577 DOI: 10.1016/j.jacc.2022.10.025] [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] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/28/2022] [Accepted: 10/14/2022] [Indexed: 01/11/2023]
Abstract
BACKGROUND Despite poor cardiovascular outcomes, there are no dedicated, validated risk stratification tools to guide investigation or treatment in type 2 myocardial infarction. OBJECTIVES The goal of this study was to derive and validate a risk stratification tool for the prediction of death or future myocardial infarction in patients with type 2 myocardial infarction. METHODS The T2-risk score was developed in a prospective multicenter cohort of consecutive patients with type 2 myocardial infarction. Cox proportional hazards models were constructed for the primary outcome of myocardial infarction or death at 1 year using variables selected a priori based on clinical importance. Discrimination was assessed by area under the receiving-operating characteristic curve (AUC). Calibration was investigated graphically. The tool was validated in a single-center cohort of consecutive patients and in a multicenter cohort study from sites across Europe. RESULTS There were 1,121, 250, and 253 patients in the derivation, single-center, and multicenter validation cohorts, with the primary outcome occurring in 27% (297 of 1,121), 26% (66 of 250), and 14% (35 of 253) of patients, respectively. The T2-risk score incorporating age, ischemic heart disease, heart failure, diabetes mellitus, myocardial ischemia on electrocardiogram, heart rate, anemia, estimated glomerular filtration rate, and maximal cardiac troponin concentration had good discrimination (AUC: 0.76; 95% CI: 0.73-0.79) for the primary outcome and was well calibrated. Discrimination was similar in the consecutive patient (AUC: 0.83; 95% CI: 0.77-0.88) and multicenter (AUC: 0.74; 95% CI: 0.64-0.83) cohorts. T2-risk provided improved discrimination over the Global Registry of Acute Coronary Events 2.0 risk score in all cohorts. CONCLUSIONS The T2-risk score performed well in different health care settings and could help clinicians to prognosticate, as well as target investigation and preventative therapies more effectively. (High-Sensitivity Troponin in the Evaluation of Patients With Suspected Acute Coronary Syndrome [High-STEACS]; NCT01852123).
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Affiliation(s)
- Caelan Taggart
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Karla Monterrubio-Gómez
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - Andreas Roos
- Department of Medicine, Clinical Epidemiology Division, Karolinska Institute, Stockholm, Sweden; Department of Emergency and Reparative Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Jasper Boeddinghaus
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Dorien M Kimenai
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Erik Kadesjo
- Department of Medicine, Clinical Epidemiology Division, Karolinska Institute, Stockholm, Sweden; Department of Medicine, Karolinska Institute, Stockholm, Sweden
| | - Anda Bularga
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Ryan Wereski
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Amy Ferry
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Matthew Lowry
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Atul Anand
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Kuan Ken Lee
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Dimitrios Doudesis
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom; Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Ioanna Manolopoulou
- Department of Statistical Sciences, University College London, London, United Kingdom
| | - Thomas Nestelberger
- Cardiovascular Research Institute Basel (CRIB), University Hospital Basel, Basel, Switzerland
| | - Luca Koechlin
- Cardiovascular Research Institute Basel (CRIB), University Hospital Basel, Basel, Switzerland
| | - Pedro Lopez-Ayala
- Cardiovascular Research Institute Basel (CRIB), University Hospital Basel, Basel, Switzerland
| | - Christian Mueller
- Cardiovascular Research Institute Basel (CRIB), University Hospital Basel, Basel, Switzerland
| | - Nicholas L Mills
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom; Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Catalina A Vallejos
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom; The Alan Turing Institute, London, United Kingdom
| | - Andrew R Chapman
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom.
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Monterrubio-Gómez K, Roininen L, Wade S, Damoulas T, Girolami M. Posterior inference for sparse hierarchical non-stationary models. Comput Stat Data Anal 2020. [DOI: 10.1016/j.csda.2020.106954] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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