Work

Identifying lineage effects when controlling for population structure improves power in bacterial association studies

Year: 2016

Type: article

Source: Nature Microbiology

DOI: 10.1038/nmicrobiol.2016.41

Authors: Sarah G. Earle, Chieh-Hsi Wu, Jane Charlesworth, Nicole Stoesser, N. Claire Gordon, Timothy M. Walker, Chris C. A. Spencer, Zamin Iqbal, David A. Clifton, Katie L. Hopkins, Neil Woodford, E. Grace Smith, Nazir Ismail, Martin J. Llewelyn, Tim E. Peto, Derrick W. Crook, Gil McVean, A. Sarah Walker, Daniel J. Wilson

Institutions: John Radcliffe Hospital, University of Oxford, Centre for Human Genetics, Public Health England, Heartlands Hospital, National Institute for Communicable Diseases, University of Pretoria, Royal Sussex County Hospital, University Hospitals Sussex NHS Foundation Trust

Language: English

Topic: Genetic Associations and Epidemiology

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

Funders: Wellcome Trust, United Kingdom Clinical Research Collaboration, National Institute for Health and Care Research, Royal Academy of Engineering, Directorate for Biological Sciences, Medical Research Council, Engineering and Physical Sciences Research Council

Awards: Whole Genome Sequencing as a unified platform for outbreak identification, resistance prediction and virulence profiling in Staphylococcus aureus, The Genetic Analysis of Populations., G1628705071, NIHR Senior Investigator Award, Statistical methods for understanding the genetics of human disease phenotypes., G2381902757, G2581641997, G4204630404, Modernising Medical Microbiology: Establishing how new technologies can be optimally integrated into microbiology., Modernising Medical Microbiology: Establishing how new technologies can be optimally integrated into microbiology., NIHR Senior Investigator Award, Statistical methods for analyzing complex genomic variation in human pathogens., Statistical methods for analyzing complex genomic variation in human pathogens., G7855966497, Statistical methods for understanding the genetics of human disease phenotypes., Statistical Methods for Whole Genome Phenotype Mapping in Bacterial Populations., G9000046247, G9000090404, G9000226936

OpenAlex ID: W2237956683

FWCI: 16.73

Cites: 77

Cited by: 288

Related to: 10