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

By Clare Bycroft, C. Freeman, Desislava Petkova, G. Band, L. Elliott, Kevin Sharp, A. Motyer, Damjan Vukcevic, Olivier Delaneau, Jared O’Connell and 6 more. bioRxiv, 2017

696 citations recorded by the source corpus.

Subfields: Cancer Research, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9fa966db39dc751ce716e79ef0c69363a9811d2d

How researchers see this paper

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In Cancer Research

1. Stephen Burgess
2. George Davey Smith

In Genetics

1. J. Bowden
2. George Davey Smith
3. Gibran Hemani
4. Stephen Burgess
5. Deborah Lawlor
6. S. Thompson
7. J. Thompson
8. E. Sanderson

In Molecular Biology

1. Reedik Mägi
2. Brian L. Browning
3. Sharon R. Browning
4. Yingxiao Zhou
5. Petr Danecek
6. J. Bowden
7. Jian Yang
8. Zhili Zheng

Citations in the corpus

• Genomic Risk Prediction of Coronary Artery Disease in 480,000 Adults Implications for Primary Prevention (2019, methodology): “using the genetic principal components (PCs) supplied by UKB (16).”
• Polygenic prediction via Bayesian regression and continuous shrinkage priors (2018, methodology): “We conduct simulation studies using the UK Biobank genetic data [Bycroft et al., 2017; Sudlow et al., 2015], and demonstrate that PRS-CS dramatically improves the predictive performance of PRS over existing methods across a wide range of genetic architectures, especially when the training sample…”
• Genome-wide association analyses of chronotype in 697,828 individuals provides insights into circadian rhythms (2019, methodology): “Further details on the UK Biobank genotyping, quality control and imputation procedures can be found elsewhere 28 .”
• Very low-depth whole-genome sequencing in complex trait association studies (2017, methodology): “This parallelization allowed processing the 1239 MANOLIS samples in under a month, and as imputation software continue to grow more efficient (Bycroft et al., 2017), future pipelines should greatly simplify postprocessing of very low-depth sequencing data.”
• Development and Standardization of an Improved Type 1 Diabetes Genetic Risk Score for Use in Newborn Screening and Incident Diagnosis (2019, methodology): “We used data which was centrally imputed by UK BioBank to a combination of the Haplotype Reference Consortium (HRC) panel and the UK10K + 1000 Genomes panels (23).”
• A one penny imputed genome from next generation reference panels (2018, background): “5k – 10M) and target sample sizes (10-1000) considered.”
• The MR-Base platform supports systematic causal inference across the human phenome (2018, methodology): “The analysed traits included 605 traits generated using the UK Biobank resource ( Millard et al., 2017 ; Bycroft et al., 2017 ; Churchhouse and Neale, 2017 ; GIANT consortium et al., 2016 ; Jones et al., 2016 ; Pilling et al., 2016 ), 575 metabolomic traits from two studies ( Shin et al., 2014 ;…”