Quality control and removal of technical variation of NMR metabolic biomarker data in ~120,000 UK Biobank participants

Funder: S.C.R was funded by the National Institute for Health and Care Research (NIHR) Cambridge Biomedical Research Centre (BRC) (BRC-1215-20014) and by the British Heart Foundation (BHF) programme grant (RG/18/13/33946).
Funder: Praveen Surendran was supported by a Rutherford Fund Fellowship from the Medical Research Council grant MR/S003746/1
Funder: Savita Karthikeyan was funded by a British Heart Foundation Programme Grant (RG/18/13/33946)
Funder: Samuel A. Lambert was supported by a Canadian Institutes of Health Research postdoctoral fellowship (MFE-171279)
Funder: Thomas Bolton was funded by the NIHR Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014-10024)
Funder: Lisa Pennells was funded by a British Heart Foundation Programme Grant (RG/18/13/33946)
Funder: John Danesh holds a British Heart Foundation Professorship and a NIHR Senior Investigator Award.
Funder: Michael Inouye is supported by the Munz Chair of Cardiovascular Prediction and Prevention and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014), and was also supported by the UK Economic and Social Research 878 Council (ES/T013192/1).
Metabolic biomarker data quantified by nuclear magnetic resonance (NMR) spectroscopy in approximately 121,000 UK Biobank participants has recently been released as a community resource, comprising absolute concentrations and ratios of 249 circulating metabolites, lipids, and lipoprotein sub-fractions. Here we identify and characterise additional sources of unwanted technical variation influencing individual biomarkers in the data available to download from UK Biobank. These included sample preparation time, shipping plate well, spectrometer batch effects, drift over time within spectrometer, and outlier shipping plates. We developed a procedure for removing this unwanted technical variation, and demonstrate that it increases signal for genetic and epidemiological studies of the NMR metabolic biomarker data in UK Biobank. We subsequently developed an R package, ukbnmr, which we make available to the wider research community to enhance the utility of the UK Biobank NMR metabolic biomarker data and to facilitate rapid analysis.