Figure 1

Flowchart of the spectral GWAS [7]. For each individual, genome-wide SNP data and blood plasma samples were available. Each blood plasma sample was then assayed with two different metabolomics platforms (mass spectrometry and proton NMR spectroscopy). The chemical shifts in the NMR spectra were then analyzed using a sliding window to create bins that quantified the amount of each molecule(s) that contributed to that bin in each sample. Traditionally, metabolite concentrations are extracted from NMR spectra using known profiles, but the use of bins allowed the authors [7] to take a hypothesis-free data mining approach. The authors then performed a two-stage GWAS, first identifying the 500 bins with the strongest genetic signals, determining the ratios between each pair of them, and then adding all unique ratios of the top bins in a second GWAS. The phenotype associations of the detected loci could then be interpreted using the mass spectrometry metabolomics data from the same blood plasma samples.