Case Study
Integrating Global Untargeted Metabolomics and GWAS to Characterize Genotype-Metabolite Associations Impacting Health and Disease
This study shows how integrating global untargeted metabolomics and genome-wide association studies (GWAS) can yield new insights into genotype-phenotype associations in disease.
The findings, obtained from a large cohort of over 14,000 individuals, is the first large-scale analysis of its kind, laying the foundation for future studies characterizing the complex genomic-metabolite relationships driving health and disease.
The findings, obtained from a large cohort of over 14,000 individuals, is the first large-scale analysis of its kind, laying the foundation for future studies characterizing the complex genomic-metabolite relationships driving health and disease.
The Challenge: Assessing the Genetic Determinants of Unique Human Metabolomes in High Throughput
Analysing metabolites helps to characterize the state of biological physiology at the time of sample collection, which is impacted by a mix of environmental and genetic factors. The presence and abundance of metabolites, known as the metabolome, can be linked to diverse diseases through genetic alterations and biochemical mechanisms.1 Resolving the relationships between a person’s genome and metabolome depends on accurately characterizing, annotating, and quantifying thousands of metabolites from human clinical specimens.2 Past efforts have featured large-scale genetic studies that link a defined set of chemically characterized and annotated metabolites such as lipoprotein lipids and fatty acids.3 However, a similarly large effort for untargeted metabolomics—where all metabolites are identified irrespective of type—would further enhance efforts to advance our understanding of the onset, activity, and progression of various diseases by establishing links between genotype and phenotype.
Metabolon Insight: Using Untargeted Metabolomics to Identify Correlations Between Metabolites and Genetic mutations
To address this knowledge gap, researchers from across Europe used Metabolon’s Global Discovery Panel to profile the plasma of 14,296 individuals with European ancestry from a UK and a European cohort.4 Using the Global Discovery Panel allowed researchers to generate a large untargeted metabolomics dataset that was integrated with genotyping data from microarrays. The researchers then determined possible causal relationships between genetic variation and metabolite abundances using GWAS and conditional analyses. With thousands of metabolites covered in the panel, the researchers were strongly equipped to characterize the genetic influences underlying a person’s metabolome.
The Solution: Using GWAS and Conditional Analysis to Identify Genetically Influenced Metabotypes (GIMs)
Combining global untargeted metabolomics with GWAS yielded 1847 associations between 646 metabolites and 330 genomic regions. Conditional analysis on this dataset identified 2599 conditionally independent variant associations, allowing the researchers to group metabolites influenced by at least one shared genetic signal into 423 genetically influenced metabotypes (GIMs), which functionally link metabolites to an enzyme, transporter, or metabolic regulator encoded by a gene or variant. One such metabotype linked genetic variation at the SRD5A2 gene with the abundance of eight steroid metabolites from steroid hormone biosynthesis. Lower levels of steroid metabolites, such as epiandrosterone sulfate, can indicate lowered SRD5A2 activity, which is associated with male-pattern baldness and benign prostatic hyperplasia.5
The Outcome: Integrating metabolomics, GWAS, and Conditional Analysis to Further Uncover the Human Genomic Landscape
The Global Discovery Panel helped stratify metabolomic and GWAS data into diverse metabolite-gene associations. This study represents the first large-scale correlation using high-throughput untargeted metabolomics characterization. This work provides a path to better resolve the relationships between a person’s genome and the metabolites that play a role in health and disease.
References
1. Shah SH, Newgard CB. Integrated Metabolomics and Genomics: Systems Approaches to Biomarkers and Mechanisms of Cardiovascular Disease. Circ Cardiovasc Genet. 2015;8(2):410-419. doi:10.1161/CIRCGENETICS.114.000223
2. Wishart DS, Guo A, Oler E, et al. HMDB 5.0: the Human Metabolome Database for 2022. Nucleic Acids Research. 2022;50(D1):D622-D631. doi:10.1093/nar/gkab1062
3. Nag A, Dhindsa RS, Middleton L, et al. Effects of protein-coding variants on blood metabolite measurements and clinical biomarkers in the UK Biobank. The American Journal of Human Genetics. 2023;110(3):487-498. doi:10.1016/j.ajhg.2023.02.002
4. Surendran P, Stewart ID, Au Yeung VPW, et al. Rare and common genetic determinants of metabolic individuality and their effects on human health. Nat Med. 2022;28(11):2321-2332. doi:10.1038/s41591-022-02046-0
5. G. Lewis J, George PM, Elder PA. Plasma androsterone/epiandrosterone sulfates as markers of 5α-reductase activity: Effect of finasteride in normal men. Steroids. 1997;62(8):632-635. doi:10.1016/S0039-128X(97)00048-2
