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Genome-wide genetic data on ~500,000 UK Biobank participants

View ORCID ProfileClare Bycroft, Colin Freeman, Desislava Petkova, Gavin Band, View ORCID ProfileLloyd T. Elliott, View ORCID ProfileKevin Sharp, View ORCID ProfileAllan Motyer, View ORCID ProfileDamjan Vukcevic, Olivier Delaneau, Jared O’Connell, Adrian Cortes, Samantha Welsh, Gil McVean, View ORCID ProfileStephen Leslie, Peter Donnelly, View ORCID ProfileJonathan Marchini
doi: https://doi.org/10.1101/166298
Clare Bycroft
1Wellcome Trust Center for Human Genetics, University of Oxford, UK
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Colin Freeman
1Wellcome Trust Center for Human Genetics, University of Oxford, UK
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Desislava Petkova
1Wellcome Trust Center for Human Genetics, University of Oxford, UK
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Gavin Band
1Wellcome Trust Center for Human Genetics, University of Oxford, UK
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Lloyd T. Elliott
2Department of Statistics, University of Oxford, UK
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Kevin Sharp
2Department of Statistics, University of Oxford, UK
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Allan Motyer
3Centre for Systems Genomics and the Schools of Mathematics and Statistics, and BioSciences, The University of Melbourne, Parkville, Victoria, Australia
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Damjan Vukcevic
3Centre for Systems Genomics and the Schools of Mathematics and Statistics, and BioSciences, The University of Melbourne, Parkville, Victoria, Australia
4Murdoch Children’s Research Institute, Parkville, Victoria, Australia
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Olivier Delaneau
5Department of Genetic Medicine and Development, University of Geneva, 1 Michel Servet, Geneva, CH1211, Switzerland
6Swiss Institute of Bioinformatics, University of Geneva, 1 Michel Servet, Geneva, CH1211, Switzerland
7Institute of Genetics and Genomics in Geneva, University of Geneva, 1 Michel Servet, Geneva, CH1211, Switzerland
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Jared O’Connell
8Illumina Ltd, Chesterford Research Park, Little Chesterford, Essex, CB10 1XL, United Kingdom
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Adrian Cortes
1Wellcome Trust Center for Human Genetics, University of Oxford, UK
9Nuffield Department of Clinical Neurosciences, Division of Clinical Neurology, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, United Kingdom
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Samantha Welsh
10UK Biobank, Units 1-4 Spectrum Way, Adswood, Stockport, Cheshire, SK3 0SA, UK
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Gil McVean
1Wellcome Trust Center for Human Genetics, University of Oxford, UK
11Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
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Stephen Leslie
3Centre for Systems Genomics and the Schools of Mathematics and Statistics, and BioSciences, The University of Melbourne, Parkville, Victoria, Australia
4Murdoch Children’s Research Institute, Parkville, Victoria, Australia
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Peter Donnelly
1Wellcome Trust Center for Human Genetics, University of Oxford, UK
2Department of Statistics, University of Oxford, UK
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Jonathan Marchini
2Department of Statistics, University of Oxford, UK
1Wellcome Trust Center for Human Genetics, University of Oxford, UK
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  • For correspondence: marchini@stats.ox.ac.uk
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Abstract

The UK Biobank project is a large prospective cohort study of ~500,000 individuals from across the United Kingdom, aged between 40-69 at recruitment. A rich variety of phenotypic and health-related information is available on each participant, making the resource unprecedented in its size and scope. Here we describe the genome-wide genotype data (~805,000 markers) collected on all individuals in the cohort and its quality control procedures. Genotype data on this scale offers novel opportunities for assessing quality issues, although the wide range of ancestries of the individuals in the cohort also creates particular challenges. We also conducted a set of analyses that reveal properties of the genetic data – such as population structure and relatedness – that can be important for downstream analyses. In addition, we phased and imputed genotypes into the dataset, using computationally efficient methods combined with the Haplotype Reference Consortium (HRC) and UK10K haplotype resource. This increases the number of testable variants by over 100-fold to ~96 million variants. We also imputed classical allelic variation at 11 human leukocyte antigen (HLA) genes, and as a quality control check of this imputation, we replicate signals of known associations between HLA alleles and many common diseases. We describe tools that allow efficient genome-wide association studies (GWAS) of multiple traits and fast phenome-wide association studies (PheWAS), which work together with a new compressed file format that has been used to distribute the dataset. As a further check of the genotyped and imputed datasets, we performed a test-case genome-wide association scan on a well-studied human trait, standing height.

Footnotes

  • ↵† These authors jointly directed this work.

  • 1 Now part of Thermo Fisher Scientific.

  • 2 The genotyping batches in the UK Biobank project contain ~4,700 samples, so a MAF of, for example, 0.001 corresponds to an expected count (under HWE) of about 9 heterozygotes per batch.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted July 20, 2017.
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Genome-wide genetic data on ~500,000 UK Biobank participants
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, Gil McVean, Stephen Leslie, Peter Donnelly, Jonathan Marchini
bioRxiv 166298; doi: https://doi.org/10.1101/166298
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Genome-wide genetic data on ~500,000 UK Biobank participants
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, Gil McVean, Stephen Leslie, Peter Donnelly, Jonathan Marchini
bioRxiv 166298; doi: https://doi.org/10.1101/166298

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