In a recent study published in natural medicineresearchers systematically studied the genetic makeup of nearly 20,000 women and men in terms of >900 metabolites.
Metabolites circulating in the human body reflect human physiology and the chemical uniqueness of an individual. Human metabolism is dysregulated in several diseases and is affected by multiple dietary, genetic, drug and disease-associated factors. A wide range of high-throughput biomedical technologies are available to enable the evaluation of genetic factors affecting human physiology; however, co-regulatory data for different metabolites are limited.
About the study
In the present study, researchers investigated the genetic determinants of variations in human physiology using untargeted metabolomic data.
The team analyzed the genetic architecture of 913 metabolites among more than 14,000 individuals. Data were used to define genetically influenced metabotypes (GIMs) or groups of metabolite influenced by ≥1.0 shared genetic signal. Samples from two UK-based cohort studies: INTERVAL and EPIC-Norfolk, were analyzed. Metabolites were measured by liquid chromatography and mass spectrometry and classified as related to lipid, amino acid, xenobiotic, nucleotide, peptide, carbohydrate, cofactor, and vitamin and energy metabolism.
Compounds with undetermined chemical identities were called unannotated compounds. Multivariate linear regression modeling was performed for the analysis. Metabolomics measurements were performed between 2015 and 2017 for the EPIC-Norfolk samples. Metabolites levels were assessed in two runs of approximately 6,000 samples each. The team validated regional sentinel variant-metabolite associations by meta-analyzing data from the discovery set and the validation set.
Of the EPIC-Norfolk study participants, 5,698 and 5,841 individuals were classified into the validation and discovery sets, respectively. Genotyping and imputation analyzes were performed in which the team imputed genetically predicted metabolite levels (“metabolite scores”) in UK Biobank participants using weighted genetic scores and estimated their associations with 1,457 aggregated disease terms (“phenodes”). Genome-wide association analysis (GWAS) was performed for each metabolite separately for the samples. In addition, conditional analysis, colocalization analysis and enrichment analysis for genes responsible for IEMs (inborn errors of metabolism) were performed.
Allelic heterogeneity was assessed and genetic co-regulation of different metabolites was assessed. The team also performed phenotypic analysis of metabolite-associated genetic variants, and phenome-wide metabolic associations were determined. The results were technically validated using whole exome sequence (WES) data from 3,924 samples from the INTERVAL study.
The most likely causative genes were determined and the novelty of the variant association was assessed by comparing the results with those of two previously conducted studies. Based on identified genetic associations and hand-curated scientific literature, high-confidence causative genes regulating metabolites were challenged and their clinical relevance was assessed on over 1,400 phenotypes.
Convergence of phenotypic and metabolic presentations of rare genes responsible for IEM has been observed with genetic variants of genes identified in the general population. A total of 423 GIMs were identified, including mainly ≤15 genetic variants and ≤89 metabolites. For 62% (n=264) GIM, one out of 253 probable causative genes was assigned based on extensive data mining. GIMs such as steroid 5α-reductase 2 (SRD5A2) and dihydropyrimidine dehydrogenase (DPYD) have shown important clinical implications.
Higher SRD5A2 activity was associated with higher risks of male pattern baldness. Genetic associations were consistent with lower SRD5A2 activity and lower levels of androsterone, epiandrosterone, 3α-androstanediol, and 3β-androstanediol conjugates. Shared genetic signals have been observed between various androgenic metabolites and male pattern baldness, with rs112881196 as the causative variant. The fatty acid desaturase (FAD) S1/S2 locus was associated with the most annotated metabolites.
The mean phenotypic variance explained by conditionally independent variants was 5.2%, highest for amino acid and energy classes. Lower levels of SRD5A inhibitors were associated with greater depression risks, with rs62142080 being the likely causative variant. The rs72977723 variant involved uracil degradation, while rs184097503 and rs28933981 increased thyroxine transport capacities. GIMs capturing several gene functions, such as those of SLC7A2 transporters (Slc7a2 solute carrier family 7) associated with arginine or lysine levels, have been observed.
An 8.0-fold enrichment of IEM-causing genes was observed with IEM variants mapped to genes causing mitochondrial, amino acid, and fatty acid-related disorders. Lower vanillylmandelate levels were associated with lower hypertension risks, with rs6271 as the causative variant. Causative genes have also been identified for coronary heart disease [PCSK9 (Proprotein convertase subtilisin/kexin type 9), SORT1 (Sortiliin 1) and LDLR (low-density lipoprotein receptor)]macular degeneration [LIPC (hepatic lipase) and apolipoprotein E (APOE)/apolipoprotein C (APOC) 1,2,4]Crohn’s disease [GCKR (glucokinase regulator) and FADS2] and chronic kidney disease [GATM (Glycine amidinotransferase)].
Association between metabolites and diseases, such as urate levels with gout [odds ratio (OR) of 2.2]bile acids with cholelithiasis (OR of 0.6 for glycohyocholate) and complex lipids with hypercholesterolemia [OR of 1.8 for 1-dihomo-linoleoyl-GPC (20:2)] were observed. Plasma homoarginine was found to play a key role in the pathology of chronic kidney disease and 3-methylglutarylcarnitine protected against the development of benign neoplasms in the colon.
Overall, the study results shed light on the genetic determinants of human metabolite variations and may guide future metabolome-wide association assessments.
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