Human metabolic profiles are stably controlled by genetic and environmental variation

George Nicholson(University of Oxford), Mattias Rantalainen(University of Oxford), Anthony D. Maher(Imperial College London), Jia V. Li(Imperial College London), Daniel Malmodin(Novo Nordisk (Denmark)), Kourosh R. Ahmadi(St Thomas' Hospital), Johan H. Faber(Novo Nordisk (Denmark)), Ingileif B. Hallgrímsdóttir(University of Oxford), Amy Barrett(University of Oxford), Henrik Toft(Novo Nordisk (Denmark)), Maria Krestyaninova(European Bioinformatics Institute), Juris Vīksna(Institute of Electronics and Computer Science), Sudeshna Guha Neogi(Addenbrooke's Hospital), Marc‐Emmanuel Dumas(Imperial College London), Uğis Sarkans(European Bioinformatics Institute), Bernard W. Silverman(Centre for Human Genetics), Peter Donnelly(Centre for Human Genetics), Jeremy K. Nicholson(Centre for Human Genetics), Maxine Allen(Imperial College London), Krina T. Zondervan(University of Oxford), John C. Lindon(Imperial College London), Tim D. Spector(St Thomas' Hospital), Mark I. McCarthy(St Thomas' Hospital), Elaine Holmes(Centre for Human Genetics), Dorrit Baunsgaard(Novo Nordisk (Denmark)), Chris Holmes(University of Oxford)
Molecular Systems Biology
August 30, 2011
Cited by 180Open Access
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Abstract

¹H Nuclear Magnetic Resonance spectroscopy (¹H NMR) is increasingly used to measure metabolite concentrations in sets of biological samples for top-down systems biology and molecular epidemiology. For such purposes, knowledge of the sources of human variation in metabolite concentrations is valuable, but currently sparse. We conducted and analysed a study to create such a resource. In our unique design, identical and non-identical twin pairs donated plasma and urine samples longitudinally. We acquired ¹H NMR spectra on the samples, and statistically decomposed variation in metabolite concentration into familial (genetic and common-environmental), individual-environmental, and longitudinally unstable components. We estimate that stable variation, comprising familial and individual-environmental factors, accounts on average for 60% (plasma) and 47% (urine) of biological variation in ¹H NMR-detectable metabolite concentrations. Clinically predictive metabolic variation is likely nested within this stable component, so our results have implications for the effective design of biomarker-discovery studies. We provide a power-calculation method which reveals that sample sizes of a few thousand should offer sufficient statistical precision to detect ¹H NMR-based biomarkers quantifying predisposition to disease.


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