Abstract
We have performed a metabolite quantitative trait locus (mQTL) study of the 1H nuclear magnetic resonance spectroscopy (1H NMR) metabolome in humans, building on recent targeted knowledge of genetic drivers of metabolic regulation. Urine and plasma samples were collected from two cohorts of individuals of European descent, with one cohort comprised of female twins donating samples longitudinally. Sample metabolite concentrations were quantified by 1H NMR and tested for association with genome-wide single-nucleotide polymorphisms (SNPs). Four metabolites' concentrations exhibited significant, replicable association with SNP variation (8.6×10−11<p<2.8×10−23). Three of these—trimethylamine, 3-amino-isobutyrate, and an N-acetylated compound—were measured in urine. The other—dimethylamine—was measured in plasma. Trimethylamine and dimethylamine mapped to a single genetic region (hence we report a total of three implicated genomic regions). Two of the three hit regions lie within haplotype blocks (at 2p13.1 and 10q24.2) that carry the genetic signature of strong, recent, positive selection in European populations. Genes NAT8 and PYROXD2, both with relatively uncharacterized functional roles, are good candidates for mediating the corresponding mQTL associations. The study's longitudinal twin design allowed detailed variance-components analysis of the sources of population variation in metabolite levels. The mQTLs explained 40%–64% of biological population variation in the corresponding metabolites' concentrations. These effect sizes are stronger than those reported in a recent, targeted mQTL study of metabolites in serum using the targeted-metabolomics Biocrates platform. By re-analysing our plasma samples using the Biocrates platform, we replicated the mQTL findings of the previous study and discovered a previously uncharacterized yet substantial familial component of variation in metabolite levels in addition to the heritability contribution from the corresponding mQTL effects.
Highlights
Expression quantitative trait loci studies have proved a powerful aid to functional genomics, with many thousand genetic loci highlighted that affect RNA transcription levels or splicing in human tissues [1]. eQTL studies have accelerated the characterization of biological mechanisms governing gene regulation [2,3,4,5], and genome-wide multi-tissue maps of known eQTLs have clarified the biological basis for a proportion of diseaseassociated [6,7] and positively selected [8] loci
We associate genome-wide genetic variation with concentrations of metabolites, small molecules involved in biochemical processes in living systems, which can be measured in samples such as biofluids and tissue extracts using 1H nuclear magnetic resonance spectroscopy (1H NMR) [15,16,17], or by the Biocrates platform. (For convenience, we use the term ‘Biocrates platform’ in the current paper to refer to the targeted-metabolomic platform using flow-injection tandem mass spectrometry—FIA-MS—developed by Biocrates Life Sciences [14,18].)
Our study demonstrates the existence of metabolite quantitative trait locus (mQTL) of larger effect size than those reported in [14] for the untargeted set of metabolites detectable by 1H NMR, in urine as well as plasma
Summary
Expression quantitative trait loci (eQTL) studies have proved a powerful aid to functional genomics, with many thousand genetic loci highlighted that affect RNA transcription levels or splicing in human tissues [1]. eQTL studies have accelerated the characterization of biological mechanisms governing gene regulation [2,3,4,5], and genome-wide multi-tissue maps of known eQTLs have clarified the biological basis for a proportion of diseaseassociated [6,7] and positively selected [8] loci (e.g. http://eqtl. uchicago.edu/cgi-bin/gbrowse/eqtl/). Expression quantitative trait loci (eQTL) studies have proved a powerful aid to functional genomics, with many thousand genetic loci highlighted that affect RNA transcription levels or splicing in human tissues [1]. We associate genome-wide genetic variation with concentrations of metabolites, small molecules involved in biochemical processes in living systems, which can be measured in samples such as biofluids and tissue extracts using 1H nuclear magnetic resonance spectroscopy (1H NMR) [15,16,17], or by the Biocrates platform. An mQTL study tests variation in each metabolite for association with genome-wide genetic variation. As such a large number of tests is performed, effect sizes must be substantially larger to be reach statistical significance. As well as being potentially rarer, mQTLs are typically more difficult to detect than eQTLs of equivalent effect size
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