The UK Biobank resource with deep phenotyping and genomic data

Clare Bycroft, Colin Freeman, Desislava Petkova, Gavin Band, Lloyd T. Elliott, Kevin Sharp, Allan Motyer, Damjan Vukcevic, Olivier Delaneau, Jared O’Connell, Adrian Cortes, Samantha Welsh, Alan Young, Mark Effingham, Gil McVean, Stephen Leslie, Naomi Allen, Peter Donnelly and Jonathan Marchini ()
Additional contact information
Clare Bycroft: University of Oxford
Colin Freeman: University of Oxford
Desislava Petkova: University of Oxford
Gavin Band: University of Oxford
Lloyd T. Elliott: University of Oxford
Kevin Sharp: University of Oxford
Allan Motyer: The University of Melbourne, Parkville
Damjan Vukcevic: The University of Melbourne, Parkville
Olivier Delaneau: University of Geneva
Jared O’Connell: Illumina Ltd, Chesterford Research Park, Little Chesterford
Adrian Cortes: University of Oxford
Samantha Welsh: UK Biobank, Adswood, Stockport
Alan Young: University of Oxford
Mark Effingham: UK Biobank, Adswood, Stockport
Gil McVean: University of Oxford
Stephen Leslie: The University of Melbourne, Parkville
Naomi Allen: University of Oxford
Peter Donnelly: University of Oxford
Jonathan Marchini: University of Oxford

Nature, 2018, vol. 562, issue 7726, 203-209

Abstract: Abstract The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.

Keywords: Deep Phenotyping; Genotype Imputation; Genetic Investigation Of Anthropometric Traits (GIANT); Pseudo-autosomal Region (PAR); Acceptance Set (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (210)

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DOI: 10.1038/s41586-018-0579-z

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