Danesh Group | Health Data Research UK Cambridge Hub

Danesh Group | Health Data Research UK Cambridge Hub

Danesh Group

Our Research and Approach

The Danesh group works as part of the Health Data Research (HDR) UK Cambridge site, using genomic, molecular and electronic health record data at population scale to understand disease at a deeper than ever biological level. 

Work in this area aims to advance understanding of disease prediction, causation, and progression through the integration of molecular data and other intermediate phenotypes with routine clinical data. We seek to gain novel insights into biology and disease aetiology by integration of information, at scale, on genomics, other biomolecular traits, and high-resolution electronic health records (EHRs). This multi-dimensional framework should afford an aetiological “systems genomics” approach to address complex biomedical problems. Our ultimate vision, therefore, is to create new informatics infrastructures and data science methods that help achieve a deep integration of biology, biomedicine, and population health science.

The Danesh group also combines clinical epidemiology and genomics to focus on understanding the underlying biology of cardiovascular disease and health. This work will help clinicans and researchers to better understand the roots and progression of heart- and blood system-related diseases, paving the way for more effective, personalised treatment for individual patients.

People

Danesh, John
Professor John Danesh
Group Leader

As a Faculty member Danesh works to further understand cardiometabolic traits through combining cardiovascular epidemiology with genomic research techniques. Danesh is Director of HDR UK Cambridge, a consortium comprising the Wellcome Sanger Institute, EMBL-EBI, the University of Cambridge and its hospitals, which aims to understand disease at a deeper than ever biological level, to enable us to better predict the onset and progression of ill-health and tailor medicines for sub-types of disease, as well as predict patients’ reaction to medicines.

Chapman, Michael

Chapman, Michael
Dr Michael Chapman
Director of Health Informatics, Health Data Research UK Cambridge

Key Projects, Collaborations, Tools & Data

Programmes, Associate Research Programmes and Facilities

Partners and Funders

Internal Partners
External Partners and Funders

Publications

  • Genomic risk score offers predictive performance comparable to clinical risk factors for ischaemic stroke.

    Abraham G, Malik R, Yonova-Doing E, Salim A, Wang T et al.

    Nature communications 2019;10;1;5819

  • Lipoprotein signatures of cholesteryl ester transfer protein and HMG-CoA reductase inhibition.

    Kettunen J, Holmes MV, Allara E, Anufrieva O, Ohukainen P et al.

    PLoS biology 2019;17;12;e3000572

  • PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations.

    Kamat MA, Blackshaw JA, Young R, Surendran P, Burgess S et al.

    Bioinformatics (Oxford, England) 2019;35;22;4851-4853

  • Longer-term efficiency and safety of increasing the frequency of whole blood donation (INTERVAL): extension study of a randomised trial of 20 757 blood donors.

    Kaptoge S, Di Angelantonio E, Moore C, Walker M, Armitage J et al.

    The Lancet. Haematology 2019;6;10;e510-e520

  • World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions.

    WHO CVD Risk Chart Working Group

    The Lancet. Global health 2019;7;10;e1332-e1345

  • Association of menopausal characteristics and risk of coronary heart disease: a pan-European case-cohort analysis.

    Dam V, van der Schouw YT, Onland-Moret NC, Groenwold RHH, Peters SAE et al.

    International journal of epidemiology 2019;48;4;1275-1285

  • Genetically modulated educational attainment and coronary disease risk.

    Zeng L, Ntalla I, Kessler T, Kastrati A, Erdmann J et al.

    European heart journal 2019;40;29;2413-2420

  • Consumption of Meat, Fish, Dairy Products, and Eggs and Risk of Ischemic Heart Disease.

    Key TJ, Appleby PN, Bradbury KE, Sweeting M, Wood A et al.

    Circulation 2019;139;25;2835-2845

  • Genetic Risk Score for Coronary Disease Identifies Predispositions to Cardiovascular and Noncardiovascular Diseases.

    Ntalla I, Kanoni S, Zeng L, Giannakopoulou O, Danesh J et al.

    Journal of the American College of Cardiology 2019;73;23;2932-2942

  • An Unbiased Lipid Phenotyping Approach To Study the Genetic Determinants of Lipids and Their Association with Coronary Heart Disease Risk Factors.

    Harshfield EL, Koulman A, Ziemek D, Marney L, Fauman EB et al.

    Journal of proteome research 2019;18;6;2397-2410

  • A catalog of genetic loci associated with kidney function from analyses of a million individuals.

    Wuttke M, Li Y, Li M, Sieber KB, Feitosa MF et al.

    Nature genetics 2019;51;6;957-972

  • Author Correction: New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries.

    Shrine N, Guyatt AL, Erzurumluoglu AM, Jackson VE, Hobbs BD et al.

    Nature genetics 2019;51;6;1067

  • Rare Protein-Truncating Variants in APOB, Lower Low-Density Lipoprotein Cholesterol, and Protection Against Coronary Heart Disease.

    Peloso GM, Nomura A, Khera AV, Chaffin M, Won HH et al.

    Circulation. Genomic and precision medicine 2019;12;5;e002376

  • Association of Plasma Vitamin D Metabolites With Incident Type 2 Diabetes: EPIC-InterAct Case-Cohort Study.

    Zheng JS, Imamura F, Sharp SJ, van der Schouw YT, Sluijs I et al.

    The Journal of clinical endocrinology and metabolism 2019;104;4;1293-1303

  • Mendelian Randomization Study of ACLY and Cardiovascular Disease.

    Ference BA, Ray KK, Catapano AL, Ference TB, Burgess S et al.

    The New England journal of medicine 2019;380;11;1033-1042

  • Assessing the causal association of glycine with risk of cardio-metabolic diseases.

    Wittemans LBL, Lotta LA, Oliver-Williams C, Stewart ID, Surendran P et al.

    Nature communications 2019;10;1;1060

  • New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries.

    Shrine N, Guyatt AL, Erzurumluoglu AM, Jackson VE, Hobbs BD et al.

    Nature genetics 2019;51;3;481-493

  • Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution.

    Justice AE, Karaderi T, Highland HM, Young KL, Graff M et al.

    Nature genetics 2019;51;3;452-469

  • Interleukin-6 Receptor Signaling and Abdominal Aortic Aneurysm Growth Rates.

    Paige E, Clément M, Lareyre F, Sweeting M, Raffort J et al.

    Circulation. Genomic and precision medicine 2019;12;2;e002413

  • Genetic effects on promoter usage are highly context-specific and contribute to complex traits.

    Alasoo K, Rodrigues J, Danesh J, Freitag DF, Paul DS and Gaffney DJ

    eLife 2019;8

  • Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci.

    Erzurumluoglu AM, Liu M, Jackson VE, Barnes DR, Datta G et al.

    Molecular psychiatry 2019

  • DNA Sequence Variation in ACVR1C Encoding the Activin Receptor-Like Kinase 7 Influences Body Fat Distribution and Protects Against Type 2 Diabetes.

    Emdin CA, Khera AV, Aragam K, Haas M, Chaffin M et al.

    Diabetes 2019;68;1;226-234

  • Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program.

    Klarin D, Damrauer SM, Cho K, Sun YV, Teslovich TM et al.

    Nature genetics 2018;50;11;1514-1523

  • Genomic Risk Prediction of Coronary Artery Disease in 480,000 Adults: Implications for Primary Prevention.

    Inouye M, Abraham G, Nelson CP, Wood AM, Sweeting MJ et al.

    Journal of the American College of Cardiology 2018;72;16;1883-1893

  • Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits.

    Evangelou E, Warren HR, Mosen-Ansorena D, Mifsud B, Pazoki R et al.

    Nature genetics 2018;50;10;1412-1425

  • Genome-wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease.

    Yao C, Chen G, Song C, Keefe J, Mendelson M et al.

    Nature communications 2018;9;1;3268

  • Association of LPA Variants With Risk of Coronary Disease and the Implications for Lipoprotein(a)-Lowering Therapies: A Mendelian Randomization Analysis.

    Burgess S, Ference BA, Staley JR, Freitag DF, Mason AM et al.

    JAMA cardiology 2018;3;7;619-627

  • Automated typing of red blood cell and platelet antigens: a whole-genome sequencing study.

    Lane WJ, Westhoff CM, Gleadall NS, Aguad M, Smeland-Wagman R et al.

    The Lancet. Haematology 2018;5;6;e241-e251

  • Genomic atlas of the human plasma proteome.

    Sun BB, Maranville JC, Peters JE, Stacey D, Staley JR et al.

    Nature 2018;558;7708;73-79

  • Alcohol intake in relation to non-fatal and fatal coronary heart disease and stroke: EPIC-CVD case-cohort study.

    Ricci C, Wood A, Muller D, Gunter MJ, Agudo A et al.

    BMJ (Clinical research ed.) 2018;361;k934

  • Analysis of predicted loss-of-function variants in UK Biobank identifies variants protective for disease.

    Emdin CA, Khera AV, Chaffin M, Klarin D, Natarajan P et al.

    Nature communications 2018;9;1;1613

  • Big data from electronic health records for early and late translational cardiovascular research: challenges and potential.

    Hemingway H, Asselbergs FW, Danesh J, Dobson R, Maniadakis N et al.

    European heart journal 2018;39;16;1481-1495

  • Risk thresholds for alcohol consumption: combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies.

    Wood AM, Kaptoge S, Butterworth AS, Willeit P, Warnakula S et al.

    Lancet (London, England) 2018;391;10129;1513-1523

  • Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes.

    Malik R, Chauhan G, Traylor M, Sargurupremraj M, Okada Y et al.

    Nature genetics 2018;50;4;524-537

  • Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.

    Mahajan A, Wessel J, Willems SM, Zhao W, Robertson NR et al.

    Nature genetics 2018;50;4;559-571

  • NOX1 loss-of-function genetic variants in patients with inflammatory bowel disease.

    Schwerd T, Bryant RV, Pandey S, Capitani M, Meran L et al.

    Mucosal immunology 2018;11;2;562-574

  • Formalising recall by genotype as an efficient approach to detailed phenotyping and causal inference.

    Corbin LJ, Tan VY, Hughes DA, Wade KH, Paul DS et al.

    Nature communications 2018;9;1;711

  • Separate and combined associations of obesity and metabolic health with coronary heart disease: a pan-European case-cohort analysis.

    Lassale C, Tzoulaki I, Moons KGM, Sweeting M, Boer J et al.

    European heart journal 2018;39;5;397-406

  • Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

    Flannick J, Fuchsberger C, Mahajan A, Teslovich TM, Agarwala V et al.

    Scientific data 2018;5;180002

  • Genome-wide association study in 79,366 European-ancestry individuals informs the genetic architecture of 25-hydroxyvitamin D levels.

    Jiang X, O'Reilly PF, Aschard H, Hsu YH, Richards JB et al.

    Nature communications 2018;9;1;260

  • Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity.

    Turcot V, Lu Y, Highland HM, Schurmann C, Justice AE et al.

    Nature genetics 2018;50;1;26-41

  • PhenoScanner: a database of human genotype-phenotype associations.

    Staley JR, Blackshaw J, Kamat MA, Ellis S, Surendran P et al.

    Bioinformatics (Oxford, England) 2016;32;20;3207-3209

  • Association of Cardiometabolic Multimorbidity With Mortality.

    Emerging Risk Factors Collaboration, Di Angelantonio E, Kaptoge S, Wormser D, Willeit P et al.

    JAMA 2015;314;1;52-60

  • Glycated hemoglobin measurement and prediction of cardiovascular disease.

    Emerging Risk Factors Collaboration, Di Angelantonio E, Gao P, Khan H, Butterworth AS et al.

    JAMA 2014;311;12;1225-33

  • C-reactive protein, fibrinogen, and cardiovascular disease prediction.

    Emerging Risk Factors Collaboration, Kaptoge S, Di Angelantonio E, Pennells L, Wood AM et al.

    The New England journal of medicine 2012;367;14;1310-20

  • Lipid-related markers and cardiovascular disease prediction.

    Emerging Risk Factors Collaboration, Di Angelantonio E, Gao P, Pennells L, Kaptoge S et al.

    JAMA 2012;307;23;2499-506

  • Interleukin-6 receptor pathways in coronary heart disease: a collaborative meta-analysis of 82 studies.

    IL6R Genetics Consortium Emerging Risk Factors Collaboration, Sarwar N, Butterworth AS, Freitag DF, Gregson J et al.

    Lancet (London, England) 2012;379;9822;1205-13

  • Separate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studies.

    Emerging Risk Factors Collaboration, Wormser D, Kaptoge S, Di Angelantonio E, Wood AM et al.

    Lancet (London, England) 2011;377;9771;1085-95

  • Diabetes mellitus, fasting glucose, and risk of cause-specific death.

    Rao Kondapally Seshasai S, Kaptoge S, Thompson A, Di Angelantonio E, Gao P et al.

    The New England journal of medicine 2011;364;9;829-841

  • Triglyceride-mediated pathways and coronary disease: collaborative analysis of 101 studies.

    Triglyceride Coronary Disease Genetics Consortium and Emerging Risk Factors Collaboration, Sarwar N, Sandhu MS, Ricketts SL, Butterworth AS et al.

    Lancet (London, England) 2010;375;9726;1634-9

  • Lipoprotein-associated phospholipase A(2) and risk of coronary disease, stroke, and mortality: collaborative analysis of 32 prospective studies.

    Lp-PLA(2) Studies Collaboration, Thompson A, Gao P, Orfei L, Watson S et al.

    Lancet (London, England) 2010;375;9725;1536-44

  • C-reactive protein and other circulating markers of inflammation in the prediction of coronary heart disease.

    Danesh J, Wheeler JG, Hirschfield GM, Eda S, Eiriksdottir G et al.

    The New England journal of medicine 2004;350;14;1387-97