Whole Genome Association Study of the Plasma Metabolome Identifies Metabolites Linked to Cardiometabolic Disease in Black Individuals

Abstract

Integrating genetic information with metabolomics has provided new insights into genes affecting human metabolism. However, gene-metabolite integration has been primarily studied in individuals of European Ancestry, limiting the opportunity to leverage genomic diversity for discovery. In addition, these analyses have principally involved known metabolites, with the majority of the profiled peaks left unannotated. Here, we perform a whole genome association study of 2,291 metabolite peaks (known and unknown features) in 2,466 Black individuals from the Jackson Heart Study. We identify 519 locus-metabolite associations for 427 metabolite peaks and validate our findings in two multi-ethnic cohorts. A significant proportion of these associations are in ancestry specific alleles including findings in APOE, TTR and CD36. We leverage tandem mass spectrometry to annotate unknown metabolites, providing new insight into hereditary diseases including transthyretin amyloidosis and sickle cell disease. Our integrative omics approach leverages genomic diversity to provide novel insights into diverse cardiometabolic diseases.

Fig. 1. Whole Genome Association Study of known and unknown metabolites in the Jackson Heart Study.

Flow diagram detailing whole genome association study of the metabolome, main results, and subsequent bioinformatic pipeline for unknown metabolite identification. Rare minor allele frequency is defined as <1% in NFE using gnomAD. Confirmation of metabolite identities was limited to commercially available metabolite standards. WGAS whole genome association study, MS mass spectrometry; NFE non-Finish Europeans, GNPS global natural product social networking.

Fig. 2. Phenogram of 519 locus-metabolite relationships in the Jackson Heart Study.

118 loci-metabolite associations are for known metabolites. 401 associations are for unknown metabolite features. The most common metabolite class includes amino acids, peptides, and analogs. Highlighted are sentinel genes with ≧4 locus-metabolite associations.

Fig. 3. Genetic architecture of metabolite-WGAS associations.

A Number of metabolites associated with each locus; B Absolute distance from mQTL position to transcription start site; C Minor allele frequency and effect size; D Frequency of mQTL sentinel allele in non-Finnish European Individuals vs African individuals; E Location of mQTL. WGAS whole genome association study, mQTL metabolite quantitative loci.

Fig. 4. Ancestry-specific alleles reveal novel associations of TTR and APOE with retinol species.

A Association of V122I in TTR with an unknown metabolite (QI722; m/z 269.226); B Correlation between QI722 and retinol-binding protein; C Association of rs769455 missense variant in APOE with unknown metabolite (QI176; m/z 269.226); D TTR associated unknown metabolite matching spectra with trans-retinol; APOE associated unknown metabolite with identical molecular mass but earlier retention time indicating it’s a cis-isomer of retinol. Additional isomers tested without compound match include 9 and 11-cis retinol.

Fig. 5. Unknown metabolite annotation pipeline using bioinformatic tools leveraging MS/MS spectra.

Unknown metabolite identification with initial clustering of features to elucidate adducts and fragments of primary features or major ions. Subsequent implementation of tools leveraging MS/MS data, including SIRIUS, GNPS, and CANOPUS. Metabolite ID validation is limited to commercially available standards.

Fig. 6. GNPS molecular network identifies carotenoid metabolites linked with genomic loci.

A Molecular network of unknown features matching beta-carotene (m/z 536.4354; identified using MS/MS database) and carotene-related compounds using the Global Natural Products Social Molecular Networking. Nodes represent MS/MS spectra obtained at either discreet collision energies ranging from 10 to 50 V or stepped (SV) collision energies. The circular node shape illustrates whether features are representative ions (highest mean abundance) in clusters of co-eluting features with abundances correlating with Spearman coefficients >0.80. Conversely, square nodes correspond to features that based on correlation with co-eluting compounds, are potentially redundant fragments or adducts. Edges represent the cosine similarity among MS/MS spectra and formulas. Zeaxanthin is the predicted metabolite at m/z 568.427 based on m/z differences and association with BCO1, which catalyzes the conversion of carotenoids to retinal and ISX, which regulates the expression of BCO1. B Spectral comparison of plasma unknowns matching carotene and zeaxanthin MS/MS obtained using stepped and discrete collision energy, respectively, illustrating an edge cosine similarity score of 0.88. C Validation of compound identities for zeaxanthin and carotene confirming the retention time match of authentic standards with the unknown features in plasma (D) as well as their MS/MS spectrum match.