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

Abstract

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.

Fig 1. Study design.

(a) Genotyping and metabotyping of 3,861 SHIP-0 study participants. One-dimensional ¹H NMR spectra of the urine samples were recorded to derive targeted and non-targeted metabolic traits. (b) Two-staged mGWAS. First stage: genome-wide association tests using genotyped SNPs and 15,379 targeted and non-targeted traits. Second stage: fine mapping of regions with potentially significant associations using imputed SNPs. (c) Replication and interpretation. Genome-wide significantly associated SNPs were assigned to one of 23 distinct genetic loci. The loci and the significantly associated non-targeted traits were annotated using algorithmic approaches. 22 of the 23 loci could be replicated using genotype and metabotype data from 1,691 KORA F4 participants.

Fig 2. Manhattan plot of genetic associations to targeted and non-targeted traits.

SNPs are plotted according to chromosomal location and the-log₁₀ transformed P-value of the strongest association with targeted traits (top) and non-targeted traits (bottom). In case of associations with ratios, only associations with P-gain exceeding 15,180 (targeted metabolic traits) or 138,610 (non-targeted traits) were considered. Associations of genome-wide significance (P < 3.25×10⁻¹²) are plotted in red. Triangles indicate associations with P < 1.0×10⁻¹⁰⁰. Significant associations within a physical distance of 1 Mb were assigned to a locus labeled after the most likely causative gene (as determined using an evidence-based approach for the identification of candidate genes; see Methods).

Fig 3. Loci with associated urinary metabolic traits and their overlap with previous mGWAS in blood and urine.

We identified and replicated genome-wide significant associations between metabolic traits and genetic variants in 22 genetic loci (named after the most likely causative gene). Three loci could only be identified using targeted metabolic traits, while 7 loci were exclusively discovered with non-targeted traits. 12 loci were identified using both targeted and non-targeted approaches. Loci with hitherto unknown associations with urinary metabolic traits are highlighted (totaling 15). We identified and replicated significant associations in 7 of the 11 loci that were reported in previous mGWAS in urine [, –12]. We also discovered significant associations of the ABO locus (marked with an asterisk) with non-targeted traits, but this locus could not be replicated in KORA F4. When compared to previous mGWAS in blood, we find 14 loci that display associations with metabolic traits in both urine and blood [, , –, –24].