Genome-wide characterization of circulating metabolic biomarkers

Abstract

Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1–7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8–11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases.

Metabolome-wide association of carotid intima media thickness identifies FDX1 as a determinant of cholesterol metabolism and cardiovascular risk in Asian populations

“…rs10488763 is also not associated with non-cardiovascular diseases in these datasets. There was also little evidence for association of rs10488763 with nuclear magnetic resonance (NMR) spectroscopy-based serum metabolomic profile in 12,120 Asian and 456,571 European participants from the UK Biobank study 26 ( Supplementary Table 10 ). Genetic variants in FDX1 thus appear to have a specific effect on cardiovascular disease, that is independent of, and not mediated by, traditional risk factors.…”

“…We tested association of 168 NMR-based metabolite biomarkers with the candidate SNP, rs10488763, in the full UK Biobank within principal component-assigned ancestry groups 26 . Biomarker values were log(1 + x) transformed, and values more than 4 standard deviations from the mean were removed.…”

“…We obtained NMR-based metabolome-wide GWAS summary data from the UK Biobank Study 26 and performed a fixed-effects meta-analysis of EAS and SAS population-specific summary statistics using the R package ‘metafor’ to generate combined meta-analyzed results for “Asian” population. A P-value threshold of 0.0003 was used after correcting for the 168 metabolites tested.…”

Insights into the metabolic consequences of type 2 diabetes

“…Conflicting conclusions exist for other metabolites, and little is known about the role of genetics in mediating these relationships. The genetic regulation of metabolite profiles is increasingly described, for example through the latest meta-analysis reporting over 400 independent loci associated with 233 metabolite levels 19 . These metabolite QTLs, also called mQTLs, are usually reported in the general population, and studies are starting to emerge on how they are modified by factors such as diet 20 .…”

Biomarker discovery and metabolic profiling in serum of cardiovascular disease patients with untargeted metabolomics and machine learning

“…Metabolomics emerges as an enabling technique to identify circulating metabolites as potential disease biomarkers, including cardiovascular diseases (CVD). 1 Although dysregulation of lipid metabolism has been implicated in CVD, yet a comprehensive analysis of their underlying metabolomic profiles and disease‐specific metabolic biomarkers is lacking, especially for polar metabolites. 2 In a large cohort of 10741samples, Zeller et al.…”