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.

Original publication

DOI

10.1038/msb.2011.57

Type

Journal article

Journal

Molecular systems biology

Publication Date

30/08/2011

Volume

7

Addresses

Department of Statistics, University of Oxford, Oxford, UK. nicholso@stats.ox.ac.uk

Keywords

Humans, Nuclear Magnetic Resonance, Biomolecular, Models, Statistical, Sample Size, Systems Biology, Twins, Dizygotic, Twins, Monozygotic, Algorithms, Research Design, Databases, Genetic, Aged, Middle Aged, European Continental Ancestry Group, Female, Genetic Variation, Metabolome, Gene-Environment Interaction, Biomarkers