Genome-wide characterization of circulating metabolic biomarkers reveals substantial pleiotropy and novel disease pathways

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 of 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 predominantly population-based cohorts. We discover over 400 independent loci and assign likely causal genes at two-thirds of these using detailed manual curation of highly plausible biological candidates. We highlight the importance of sample- and participant characteristics, such as fasting status and sample type, that can have significant impact on genetic associations, revealing direct and indirect associations on glucose and phenylalanine. 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 for the first time the metabolic associations of an understudied phenotype, 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 acetoacetate and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases.

Exploring metabolic changes in gout – Insights from a genetic study

“…Metabolic association profiles of gout risk-increasing alleles. Metabolic effects are extracted from the metabolomics GWAS 30 , except for uric acid (Urate) and the inflammatory marker c-reactive protein (CRP), which were extracted from the MRBase-website 26,27 (respective data ids: ukb-d-30880_irnt and ukb-d-30710_irnt). The effect sizes are scaled between -1 and 1 by dividing the original effect sizes by the largest absolute effect estimate of each variant.…”

“…Missing data are marked with a (-) symbol. The SLC25A11 variant was associated with gout in a subgroup of men from FinnGen, but its metabolic associations are based on the whole sample of the metabolomics GWAS 30 . The meanings of metabolite abbreviations are listed in Table S3.…”

An atlas of genetic scores to predict multi-omic traits

“…The residuals were then inverse-normal rank-transformed, which were finally used to perform GWAS of these traits and their genetic-score development. Details of QC and GWAS for these traits have been described previously 58 .…”

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 .…”