Genome-Wide Association Study with Targeted and Non-targeted NMR Metabolomics Identifies 15 Novel Loci of Urinary Human Metabolic Individuality.

Published online

Journal Article

Genome-wide association studies with metabolic traits (mGWAS) uncovered many genetic variants that influence human metabolism. These genetically influenced metabotypes (GIMs) contribute to our metabolic individuality, our capacity to respond to environmental challenges, and our susceptibility to specific diseases. While metabolic homeostasis in blood is a well investigated topic in large mGWAS with over 150 known loci, metabolic detoxification through urinary excretion has only been addressed by few small mGWAS with only 11 associated loci so far. Here we report the largest mGWAS to date, combining targeted and non-targeted 1H NMR analysis of urine samples from 3,861 participants of the SHIP-0 cohort and 1,691 subjects of the KORA F4 cohort. We identified and replicated 22 loci with significant associations with urinary traits, 15 of which are new (HIBCH, CPS1, AGXT, XYLB, TKT, ETNPPL, SLC6A19, DMGDH, SLC36A2, GLDC, SLC6A13, ACSM3, SLC5A11, PNMT, SLC13A3). Two-thirds of the urinary loci also have a metabolite association in blood. For all but one of the 6 loci where significant associations target the same metabolite in blood and urine, the genetic effects have the same direction in both fluids. In contrast, for the SLC5A11 locus, we found increased levels of myo-inositol in urine whereas mGWAS in blood reported decreased levels for the same genetic variant. This might indicate less effective re-absorption of myo-inositol in the kidneys of carriers. In summary, our study more than doubles the number of known loci that influence urinary phenotypes. It thus allows novel insights into the relationship between blood homeostasis and its regulation through excretion. The newly discovered loci also include variants previously linked to chronic kidney disease (CPS1, SLC6A13), pulmonary hypertension (CPS1), and ischemic stroke (XYLB). By establishing connections from gene to disease via metabolic traits our results provide novel hypotheses about molecular mechanisms involved in the etiology of diseases.

Full Text

Duke Authors

Cited Authors

  • Raffler, J; Friedrich, N; Arnold, M; Kacprowski, T; Rueedi, R; Altmaier, E; Bergmann, S; Budde, K; Gieger, C; Homuth, G; Pietzner, M; Römisch-Margl, W; Strauch, K; Völzke, H; Waldenberger, M; Wallaschofski, H; Nauck, M; Völker, U; Kastenmüller, G; Suhre, K

Published Date

  • September 2015

Published In

Volume / Issue

  • 11 / 9

Start / End Page

  • e1005487 -

PubMed ID

  • 26352407

Pubmed Central ID

  • 26352407

Electronic International Standard Serial Number (EISSN)

  • 1553-7404

Digital Object Identifier (DOI)

  • 10.1371/journal.pgen.1005487

Language

  • eng

Conference Location

  • United States