Sandhu Group | Global Health and Populations

Sandhu Group | Global Health and Populations

Sandhu Group

sandhugroup2.jpgSanger Institute, Genome Research Limited

Our Research and Approach

Our research focuses on global health and populations, assessing human diversity and its impact on the burden and aetiology of infectious and non-communicable diseases. Our approach integrates principles and procedures underlying epidemiology, genomics and public health.

We have a strong interest in exploring epidemiological transitions in low and middle-income countries, particularly in sub-Saharan Africa and South-East Asia. We use genome-wide genotyping and sequencing technologies to understand human genetic diversity and population history, as well as the biological mechanisms underlying the development of complex diseases and traits among those populations.

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People

Sandhu, Manjinder
Manjinder Sandhu
Group Leader

Manj’s research seeks to improve our understanding of genomic diversity and the development of and susceptibility to complex diseases by integrating population genetics, epidemiology and genomic wide technologies.

Key Projects, Collaborations, Tools & Data

Research Programmes

Partners and Funders

Internal Partners

Publications

  • The African Genome Variation Project shapes medical genetics in Africa.

    Gurdasani D, Carstensen T, Tekola-Ayele F, Pagani L, Tachmazidou I et al.

    Nature 2015;517;7534;327-32

  • Urbanicity and lifestyle risk factors for cardiometabolic diseases in rural Uganda: a cross-sectional study.

    Riha J, Karabarinde A, Ssenyomo G, Allender S, Asiki G et al.

    PLoS medicine 2014;11;7;e1001683

  • A general approach for haplotype phasing across the full spectrum of relatedness.

    O'Connell J, Gurdasani D, Delaneau O, Pirastu N, Ulivi S et al.

    PLoS genetics 2014;10;4;e1004234

  • Discovery and refinement of loci associated with lipid levels.

    Global Lipids Genetics Consortium, Willer CJ, Schmidt EM, Sengupta S, Peloso GM et al.

    Nature genetics 2013;45;11;1274-83

  • Common variants associated with plasma triglycerides and risk for coronary artery disease.

    Do R, Willer CJ, Schmidt EM, Sengupta S, Gao C et al.

    Nature genetics 2013;45;11;1345-52

  • Effect modification by population dietary folate on the association between MTHFR genotype, homocysteine, and stroke risk: a meta-analysis of genetic studies and randomised trials.

    Holmes MV, Newcombe P, Hubacek JA, Sofat R, Ricketts SL et al.

    Lancet (London, England) 2011;378;9791;584-94

  • Biological, clinical and population relevance of 95 loci for blood lipids.

    Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM et al.

    Nature 2010;466;7307;707-13

  • 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

  • LDL-cholesterol concentrations: a genome-wide association study.

    Sandhu MS, Waterworth DM, Debenham SL, Wheeler E, Papadakis K et al.

    Lancet (London, England) 2008;371;9611;483-91

  • Common variants in WFS1 confer risk of type 2 diabetes.

    Sandhu MS, Weedon MN, Fawcett KA, Wasson J, Debenham SL et al.

    Nature genetics 2007;39;8;951-3

  • Apolipoprotein(a) isoform size, lipoprotein(a) concentration, and coronary artery disease: a mendelian randomisation analysis.

    Saleheen D, Haycock PC, Zhao W, Rasheed A, Taleb A et al.

    The lancet. Diabetes & endocrinology 2017

  • Polymorphisms of large effect explain the majority of the host genetic contribution to variation of HIV-1 virus load.

    McLaren PJ, Coulonges C, Bartha I, Lenz TL, Deutsch AJ et al.

    Proceedings of the National Academy of Sciences of the United States of America 2015;112;47;14658-63

  • POPULATION GENETICS. Genomic evidence for the Pleistocene and recent population history of Native Americans.

    Raghavan M, Steinrücken M, Harris K, Schiffels S, Rasmussen S et al.

    Science (New York, N.Y.) 2015;349;6250;aab3884

  • The African Genome Variation Project shapes medical genetics in Africa.

    Gurdasani D, Carstensen T, Tekola-Ayele F, Pagani L, Tachmazidou I et al.

    Nature 2015;517;7534;327-32

  • Prevalence of dyslipidaemia and associated risk factors in a rural population in South-Western Uganda: a community based survey.

    Asiki G, Murphy GA, Baisley K, Nsubuga RN, Karabarinde A et al.

    PloS one 2015;10;5;e0126166

  • Open-source electronic data capture system offered increased accuracy and cost-effectiveness compared with paper methods in Africa.

    Dillon DG, Pirie F, Rice S, Pomilla C, Sandhu MS et al.

    Journal of clinical epidemiology 2014;67;12;1358-63

  • Genetic characterization of Greek population isolates reveals strong genetic drift at missense and trait-associated variants.

    Panoutsopoulou K, Hatzikotoulas K, Xifara DK, Colonna V, Farmaki AE et al.

    Nature communications 2014;5;5345

  • Response to comment on Ye et al. The association between circulating lipoprotein(a) and type 2 diabetes: is it causal? Diabetes 2014;63:332-342.

    Ye Z, Sandhu MS and Forouhi NG

    Diabetes 2014;63;8;e15

  • Urbanicity and lifestyle risk factors for cardiometabolic diseases in rural Uganda: a cross-sectional study.

    Riha J, Karabarinde A, Ssenyomo G, Allender S, Asiki G et al.

    PLoS medicine 2014;11;7;e1001683

  • Lipoprotein(a) levels, genotype, and incident aortic valve stenosis: a prospective Mendelian randomization study and replication in a case-control cohort.

    Arsenault BJ, Boekholdt SM, Dubé MP, Rhéaume E, Wareham NJ et al.

    Circulation. Cardiovascular genetics 2014;7;3;304-10

  • A general approach for haplotype phasing across the full spectrum of relatedness.

    O'Connell J, Gurdasani D, Delaneau O, Pirastu N, Ulivi S et al.

    PLoS genetics 2014;10;4;e1004234

  • The use of anthropometric measures for cardiometabolic risk identification in a rural African population.

    Murphy GA, Asiki G, Nsubuga RN, Young EH, Seeley J et al.

    Diabetes care 2014;37;4;e64-5

  • A systematic review of definitions of extreme phenotypes of HIV control and progression.

    Gurdasani D, Iles L, Dillon DG, Young EH, Olson AD et al.

    AIDS (London, England) 2014;28;2;149-62

  • The association between circulating lipoprotein(a) and type 2 diabetes: is it causal?

    Ye Z, Haycock PC, Gurdasani D, Pomilla C, Boekholdt SM et al.

    Diabetes 2014;63;1;332-342

  • Association of HIV and ART with cardiometabolic traits in sub-Saharan Africa: a systematic review and meta-analysis.

    Dillon DG, Gurdasani D, Riha J, Ekoru K, Asiki G et al.

    International journal of epidemiology 2013;42;6;1754-71

  • Sociodemographic distribution of non-communicable disease risk factors in rural Uganda: a cross-sectional study.

    Murphy GA, Asiki G, Ekoru K, Nsubuga RN, Nakiyingi-Miiro J et al.

    International journal of epidemiology 2013;42;6;1740-53

  • Discovery and refinement of loci associated with lipid levels.

    Global Lipids Genetics Consortium, Willer CJ, Schmidt EM, Sengupta S, Peloso GM et al.

    Nature genetics 2013;45;11;1274-83

  • Cardiometabolic risk in a rural Ugandan population.

    Murphy GA, Asiki G, Young EH, Seeley J, Nsubuga RN et al.

    Diabetes care 2013;36;9;e143

  • Lipoprotein(a) and risk of coronary, cerebrovascular, and peripheral artery disease: the EPIC-Norfolk prospective population study.

    Gurdasani D, Sjouke B, Tsimikas S, Hovingh GK, Luben RN et al.

    Arteriosclerosis, thrombosis, and vascular biology 2012;32;12;3058-65

  • Common variants at 10 genomic loci influence hemoglobin A₁(C) levels via glycemic and nonglycemic pathways.

    Soranzo N, Sanna S, Wheeler E, Gieger C, Radke D et al.

    Diabetes 2010;59;12;3229-39

  • Family history of premature coronary heart disease and risk prediction in the EPIC-Norfolk prospective population study.

    Sivapalaratnam S, Boekholdt SM, Trip MD, Sandhu MS, Luben R et al.

    Heart (British Cardiac Society) 2010;96;24;1985-9

  • Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index.

    Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G et al.

    Nature genetics 2010;42;11;937-48

  • Genetic variants influencing circulating lipid levels and risk of coronary artery disease.

    Waterworth DM, Ricketts SL, Song K, Chen L, Zhao JH et al.

    Arteriosclerosis, thrombosis, and vascular biology 2010;30;11;2264-76

  • Biological, clinical and population relevance of 95 loci for blood lipids.

    Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM et al.

    Nature 2010;466;7307;707-13

  • Chemokine ligand 2 genetic variants, serum monocyte chemoattractant protein-1 levels, and the risk of coronary artery disease.

    van Wijk DF, van Leuven SI, Sandhu MS, Tanck MW, Hutten BA et al.

    Arteriosclerosis, thrombosis, and vascular biology 2010;30;7;1460-6

  • Detailed investigation of the role of common and low-frequency WFS1 variants in type 2 diabetes risk.

    Fawcett KA, Wheeler E, Morris AP, Ricketts SL, Hallmans G et al.

    Diabetes 2010;59;3;741-6

  • New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk.

    Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N et al.

    Nature genetics 2010;42;2;105-16

  • Genome-wide association scan meta-analysis identifies three Loci influencing adiposity and fat distribution.

    Lindgren CM, Heid IM, Randall JC, Lamina C, Steinthorsdottir V et al.

    PLoS genetics 2009;5;6;e1000508

  • Genome-wide association study identifies eight loci associated with blood pressure.

    Newton-Cheh C, Johnson T, Gateva V, Tobin MD, Bochud M et al.

    Nature genetics 2009;41;6;666-76

  • Meta-analysis of genome-wide scans for human adult stature identifies novel Loci and associations with measures of skeletal frame size.

    Soranzo N, Rivadeneira F, Chinappen-Horsley U, Malkina I, Richards JB et al.

    PLoS genetics 2009;5;4;e1000445

  • Six new loci associated with body mass index highlight a neuronal influence on body weight regulation.

    Willer CJ, Speliotes EK, Loos RJ, Li S, Lindgren CM et al.

    Nature genetics 2009;41;1;25-34

  • Variants in MTNR1B influence fasting glucose levels.

    Prokopenko I, Langenberg C, Florez JC, Saxena R, Soranzo N et al.

    Nature genetics 2009;41;1;77-81

  • Common variants near MC4R are associated with fat mass, weight and risk of obesity.

    Loos RJ, Lindgren CM, Li S, Wheeler E, Zhao JH et al.

    Nature genetics 2008;40;6;768-75

  • Genome-wide association analysis identifies 20 loci that influence adult height.

    Weedon MN, Lango H, Lindgren CM, Wallace C, Evans DM et al.

    Nature genetics 2008;40;5;575-83

  • Replication of the association between variants in WFS1 and risk of type 2 diabetes in European populations.

    Franks PW, Rolandsson O, Debenham SL, Fawcett KA, Payne F et al.

    Diabetologia 2008;51;3;458-63

  • Testing of diabetes-associated WFS1 polymorphisms in the Diabetes Prevention Program.

    Florez JC, Jablonski KA, McAteer J, Sandhu MS, Wareham NJ et al.

    Diabetologia 2008;51;3;451-7

  • LDL-cholesterol concentrations: a genome-wide association study.

    Sandhu MS, Waterworth DM, Debenham SL, Wheeler E, Papadakis K et al.

    Lancet (London, England) 2008;371;9611;483-91

  • Mendelian randomisation studies of type 2 diabetes: future prospects.

    Sandhu MS, Debenham SL, Barroso I and Loos RJ

    Diabetologia 2008;51;2;211-3

  • The V103I polymorphism of the MC4R gene and obesity: population based studies and meta-analysis of 29 563 individuals.

    Young EH, Wareham NJ, Farooqi S, Hinney A, Hebebrand J et al.

    International journal of obesity (2005) 2007;31;9;1437-41

  • Common variants in WFS1 confer risk of type 2 diabetes.

    Sandhu MS, Weedon MN, Fawcett KA, Wasson J, Debenham SL et al.

    Nature genetics 2007;39;8;951-3

  • PARL Leu262Val is not associated with fasting insulin levels in UK populations.

    Fawcett KA, Wareham NJ, Luan J, Syddall H, Cooper C et al.

    Diabetologia 2006;49;11;2649-52

  • Meta-analysis of the Gly482Ser variant in PPARGC1A in type 2 diabetes and related phenotypes.

    Barroso I, Luan J, Sandhu MS, Franks PW, Crowley V et al.

    Diabetologia 2006;49;3;501-5