In a recent study published in Nature Medicine, researchers systematically investigated the genetic makeup of nearly 20,000 women and men in terms of >900 metabolites.
Study: rare and common genetic determinants of metabolic individuality and their effects on human health. Image credit: PopTika/Shutterstock
Metabolites circulating in the human body reflect human physiology and the chemical uniqueness of an individual. Human metabolism is dysregulated in various diseases and is affected by multiple dietary, genetic, drug-associated, and disease-associated factors. A wide range of high-throughput biomedical technologies are available to assess the genetic factors that affect human physiology; however, co-regulation data for different metabolites are limited.
About the study
In the present study, the 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 metabolites influenced by a shared genetic signal ≥1.0. We analyzed samples from two cohort studies in the United Kingdom (UK): INTERVAL and EPIC-Norfolk . Metabolites were measured by liquid chromatography and mass spectrometry and classified as related to lipids, amino acids, xenobiotics, nucleotides, peptides, carbohydrates, cofactors and vitamins, and energy metabolism.
Compounds with undetermined chemical identities were referred to as unannotated compounds. A multivariable linear regression model was performed for analysis. Metabolomic measurements were made between 2015 and 2017 for the EPIC-Norfolk samples. Metabolite levels were assessed in two sets of about 6,000 samples each. The team validated regional sentinel-metabolite variant associations using meta-analysis of discovery set and validation set data.
Among 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’) to UK Biobank participants using weighted genetic scores and estimated their associations with 1,457 disease terms compiled (‘fecodes’). A genome-wide association analysis (GWAS) was performed for each metabolite separately for the samples. In addition, conditional analysis, colocalization analysis, and enrichment analysis were performed for genes causing IEMs (inborn errors of metabolism).
Allelic heterogeneity was assessed and genetic co-regulation of different metabolites was evaluated. The team also performed phenotypic analyzes of genetic variants associated with metabolites and determined metabolic associations across the phenomenon. Results were technically validated using whole-exome sequencing (WES) data from 3,924 INTERVAL study samples.
The most likely causal genes were determined and the novelty of the variant association was assessed by comparing the findings with those of two previously conducted studies. Based on the identified genetic associations and the hand-selected scientific literature, high-confidence causal genes regulating metabolites were challenged and their clinical relevance assessed in more than 1,400 phenotypes.
results
Convergence of the phenotypic and metabolic presentations of rare IEM-causing genes with genetic variants of genes identified in the general population was observed. In total, 423 GIMs were identified, including mainly ≤15 genetic variants and ≤89 metabolites. For 62% (n = 264) of GIM, a gene was assigned out of 253 probable causal genes based on extensive data mining. GIMs such as steroid 5α-reductase 2 (SRD5A2) and dihydropyrimidine dehydrogenase (DPYD) showed important clinical implications.
Higher SRD5A2 activity was associated with an increased risk 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 were observed between several androgen metabolites and male pattern baldness, with rs112881196 as the causal variant. The fatty acid desaturase (FAD) S1/S2 locus was associated with the most annotated metabolites.
The average phenotypic variance explained by conditionally independent variants was 5.2%, the highest for the amino acid and energy classes. Lower levels of SRD5A inhibitor were associated with more significant risks of depression, with rs62142080 as the likely causal variant. The rs72977723 variant involved uracil breakdown, while rs184097503 and rs28933981 increased thyroxine transport capacities. GIMs were observed that captured multiple gene functions, such as those of SLC7A2 transporters (Slc7a2 solute transporter family 7) associated with arginine or lysine levels.
An 8.0-fold enrichment of IEM-causing genes was observed with IEM variants mapping to genes causing disorders related to mitochondria, amino acids, and fatty acids. Lower levels of vanillylmandelate were associated with a lower risk of hypertension, with rs6271 as the causal variant. Causative genes for coronary artery disease were also identified [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 was found to be protective against the development of benign neoplasms in the colon.
Overall, the study findings highlighted the genetic determinants of human metabolite variations and could guide future metabolome-wide association assessments.