Identification and MS-assisted interpretation of genetically influenced NMR signals in human plasma

Abstract

Nuclear magnetic resonance spectroscopy (NMR) provides robust readouts of many metabolic parameters in one experiment. However, identification of clinically relevant markers in 1H NMR spectra is a major challenge. Association of NMR-derived quantities with genetic variants can uncover biologically relevant metabolic traits. Using NMR data of plasma samples from 1,757 individuals from the KORA study together with 655,658 genetic variants, we show that ratios between NMR intensities at two chemical shift positions can provide informative and robust biomarkers. We report seven loci of genetic association with NMR-derived traits (APOA1, CETP, CPS1, GCKR, FADS1, LIPC, PYROXD2) and characterize these traits biochemically using mass spectrometry. These ratios may now be used in clinical studies.

Look, no hands! Spectral biomarkers from genetic association studies

“…In this issue of Genome Medicine , Suhre and colleagues [7] side-step a fundamental challenge in previous GWASs, the decomposition of nuclear magnetic resonance (NMR) spectra into known metabolite concentrations, to expand the power of spectral association studies. In doing so, they present a novel method for identifying previously uncharacterized spectral features that may prove to be important biomarkers of disease.…”

“…This lack of bias has enabled detection of previously unknown signals that would not have been found by methods such as candidate-gene studies. Analogously, the new study [7] shows the benefit of considering phenotypes in an unbiased way as well. Instead of searching for previously characterized metabolites in the NMR spectra and testing for association of these metabolites with genotype, the authors examined all available signals in the molecular spectra and associated each one with genotypes in a GWAS-style approach [7].…”

“…Analogously, the new study [7] shows the benefit of considering phenotypes in an unbiased way as well. Instead of searching for previously characterized metabolites in the NMR spectra and testing for association of these metabolites with genotype, the authors examined all available signals in the molecular spectra and associated each one with genotypes in a GWAS-style approach [7]. Similar to unbiased GWASs, the main premise of the unbiased NMR search is that by expanding testing beyond previously known metabolites, some novel classes and associations may be discovered and characterized.…”

“…To this end, Suhre and colleagues [7] used NMR measurements of plasma samples from more than 1,700 individuals in the KORA study [8]. A workflow of their study is presented in Figure 1.…”

“…However, exploring all unique pairs of bins for association with each SNP is computationally difficult. Therefore, the authors [7] took a two-stage approach: first all spectral features were examined for association with the SNPs, and then the top 500 spectral features were used to compute pairwise ratios, yielding a total of 133,350 phenotypes. The association between the genotypes and the NMR-based phenotypes (either spectral features or ratios thereof) was tested using a linear model adjusted for age and gender, followed by Bonferroni adjustment for multiple testing.…”