Comparison of DNA quantification methodology used in the DNA extraction protocol for the UK Biobank cohort

Abstract

Background: UK Biobank is a large prospective cohort study in the UK established by the Medical Research Council (MRC) and the Wellcome Trust to enable approved researchers to investigate the role of genetic factors, environmental exposures and lifestyle in the causes of major diseases of late and middle age. A wide range of phenotypic data has been collected at recruitment and has recently been enhanced by the UK Biobank Genotyping Project. All UK Biobank participants (500,000) have been genotyped on either the UK Biobank Axiom® Array or the Affymetrix UK BiLEVE Axiom® Array and the workflow for preparing samples for genotyping is described. The genetic data is hoped to provide further insight into the genetics of disease. All data, including the genetic data, is available for access to approved researchers. Data for two methods of DNA quantification (ultraviolet-visible spectroscopy [UV/Vis]) measured on the Trinean DropSense™ 96 and PicoGreen®) were compared by two laboratories (UK Biobank and Affymetrix). Results: The sample processing workflow established at UK Biobank, for genotyping on the custom Affymetrix Axiom® array, resulted in high quality DNA (average DNA concentration 38.13 ng/μL, average 260/280 absorbance 1.91). The DNA generated high quality genotype data (average call rate 99.48% and pass rate 99.45%). The DNA concentration measured on the Trinean DropSense™ 96 at UK Biobank correlated well with DNA concentration measured by PicoGreen® at Affymetrix (r = 0.85). Conclusions: The UK Biobank Genotyping Project demonstrated that the high throughput DNA extraction protocol described generates high quality DNA suitable for genotyping on the Affymetrix Axiom array. The correlation between DNA concentration derived from UV/Vis and PicoGreen® quantification methods suggests, in large-scale genetic studies involving two laboratories, it may be possible to remove the DNA quantification step in one laboratory without affecting downstream analyses. This would result in reductions in cost and time to complete the project, allowing generation of genetic data faster and cheaper.