The power and democratization of high-throughput sequencing technologies have propelled the genomics era forward. Advances in this field have enabled scientists to sequence highly diverse human genomes at a low cost. These genomes, in turn, have contributed to explosive growth in studies that correlate genetic variants with disease prevalence and risk.
But these correlations are incomplete. A genetic variant associated with disease for one person may not exert nearly as much of a risk in another person.1 In fact, in most cases, an individual’s genotype is not the only determinant of their phenotype. Instead, a wide range of environmental variables shapes these phenotypes, as seen in studies of the composition of individual human gut microbiota.2
At the 2022 Annual Meeting held by the American Society of Human Genetics (ASHG), Dr. Kari Wong, Ph.D., Metabolon’s Associate Director of Scientific Strategy, presented several examples of how overlaying metabolomic profiling with genomic data addresses gaps in knowledge to understand the complex contribution of genetic and non-genetic factors to disease. She argued that metabolomics, the characterization of small molecules in a sample, complement genomics approaches to interrogate disease mechanisms. Integrating these two omics may provide insights into the mechanisms underlying genetic modulators of disease and produce novel disease management protocols.
In the talk, Dr. Wong provides two examples of genomics and metabolomics being used together to map gene variations to clinical outcomes. The first example features the correlation of levels of hexadecanedioate, a fatty acid, in the circulation to mutations in SLCO1B1. The combination of Mendilian randomization and pre-clinical models revealed the role of this gene-metabolite interaction with hypertension.3-7 In the second example, thousands of urine samples were analyzed to identify hundreds of novel metabolites whose abundances, ratios, and encoded genes correlated with reduced kidney function.8 With this data, researchers were able to link genetic variants, metabolic pathways, and clinical outcomes together. The metabolite data provided mechanistic information linking genes to disease status.
Through these case studies, we at Metabolon see metabolomics as the tool to procure the “Last Mile” solution. While genomics sheds light on disease risk, it often lacks the ability to inform on causal mechanisms for disease outcomes. As such, we believe that leveraging metabolomics technologies refines our understanding of the relationship between genetic variation and host health. Additionally, metabolomic profiles reflect the pathways implicated in polygenic diseases. Metabolomics can be used to monitor longitudinal changes in human phenotypes by measuring metabolite levels over time, and in turn the data can be harnessed to tease apart the dynamic nature of health and disease.
Finally, Metabolon has robust processes in place to generate high-quality data and insights for your metabolomics research. In our talk, you will learn how our platform will meet all your metabolomics needs. Our comprehensive, industry-defining metabolomics library will provide you with a robust approach to identify not only metabolites but also entire pathways in your data.
At this point, we hope you see the potential of metabolomics to link genetic variation with host phenotypes. Speak to a Metabolon representative today to learn how you can get a leg up in your metabolomics R&D, take the next step, and walk the last mile.
References
- Hirschhorn JN, Lohmueller K, Byrne E, Hirschhorn K. A comprehensive review of genetic association studies. Genet Med. 2002;4(2):45-61. doi:10.1097/00125817-200203000-00002
- Rothschild D, Weissbrod O, Barkan E, et al. Environment dominates over host genetics in shaping human gut microbiota. Nature. 2018;555(7695):210-215. doi:10.1038/nature25973
- Shin SY, Fauman EB, Petersen AK, et al. An atlas of genetic influences on human blood metabolites. Nat Genet. 2014;46(6):543-550. doi:10.1038/ng.2982
- SEARCH Collaborative Group, Link E, Parish S, et al. SLCO1B1 variants and statin-induced myopathy–a genomewide study. N Engl J Med. 2008;359(8):789-799. doi:10.1056/NEJMoa0801936;
- Menni C, Graham D, Kastenmüller G, et al. Metabolomic identification of a novel pathway of blood pressure regulation involving hexadecanedioate. Hypertension. 2015;66(2):422-429. doi:10.1161/HYPERTENSIONAHA.115.05544
- Yu B, Li AH, Metcalf GA, et al. Loss-of-function variants influence the human serum metabolome. Sci Adv. 2016;2(8):e1600800. Published 2016 Aug 31. doi:10.1126/sciadv.1600800
- Menni C, Metrustry SJ, Ehret G, et al. Molecular pathways associated with blood pressure and hexadecanedioate levels. PLoS One. 2017;12(4):e0175479. Published 2017 Apr 12. doi:10.1371/journal.pone.0175479
- Schlosser P, Li Y, Sekula P, et al. Genetic studies of urinary metabolites illuminate mechanisms of detoxification and excretion in humans. Nat Genet. 2020;52(2):167-176. doi:10.1038/s41588-019-0567-8
