Multiomics
Genomics Research
Learn how metabolomics can reveal new insights from the genome
Used By
Featured Genomics Resources
Metabolomics in Genomic Research
Genomics research aims to understand how our genetic information determines traits, health, and disease. Genomics uses advanced technologies and analysis to sequence and compare genomes to identify genetic variations and investigate how genes contribute to biological processes and conditions. However, our genes are not the only factors that influence our phenotype, it is also important to consider lifestyle, environment, and many other potential unknowns that could contribute to complex interactions happening within biological systems.
Combining genomics and metabolomics technologies offers a powerful approach for understanding complex biological systems and disease mechanisms. Genomics provides insights into genetic variations and potential disease risk factors, while metabolomics reveals the dynamic changes in metabolic pathways and their products. Integrating these technologies allows researchers to link genetic information with metabolic profiles, uncovering how genetic variations influence metabolic processes and contribute to disease. This synergy enhances the ability to identify biomarkers, understand disease mechanisms, and develop personalized treatments. By correlating genomic data with metabolite levels, scientists can gain a more comprehensive view of health and disease, leading to improved diagnostics and therapeutic strategies.
Unravel Genetic Mysteries with Deep Phenotyping Through Metabolic Profiling
Genomics and metabolomics together offer a more detailed and nuanced understanding of disease by integrating genetic information with metabolic insights. Genomics provides data on genetic variations that may predispose individuals to diseases or influence disease progression. Metabolomics complements this by analyzing the metabolic profile of an organism, revealing how these genetic factors impact metabolic pathways and lead to changes in metabolite levels.
Uncovering Tumor Biology
Lung adenocarcinoma (LUAD) is a subtype of non-small cell lung cancer and the most common form of lung malignancy. LUAD is closely associated with tobacco smoking, yet it is also a frequent cancer type in non-smokers. This malignancy has a poor prognosis, with only a 19% five-year survival rate in the U.S. Despite advances in targeted therapies. In oncology, tumor phenotyping and multiomics can help identify therapeutic strategies for different tumor subtypes. Beyond just identifying driver and passenger mutations, one multiomics study of 110 lung adenocarcinoma samples combined genomics with proteomics and metabolomics to understand the consequences of genomic variations. For example, one key finding was that mutations in RB1, a tumor suppressor gene, increased CDK4 protein levels, which may contribute to RB1-mutant lung cancer’s resistance to CDK4/6 inhibitors. The findings underscore the potential for tailoring therapeutic approaches based on detailed multiomic profiles, which could pave the way for more personalized and effective treatments.
Gillette MA, Satpathy S, Cao S, et al. Proteogenomic characterization reveals therapeutic vulnerabilities in lung adenocarcinoma. Cell. 2020;182(1):200-225.e35. doi:10.1016/j.cell.2020.06.013
Biomarker Discovery
Cardiovascular disease (CVD) remains one of the leading causes of mortality worldwide. Understanding the genetic and metabolic underpinnings of CVD is crucial for developing effective prevention, diagnosis, and treatment strategies. Integrating genomics into a multiomics workflow enhances the discovery of disease risk biomarkers by linking genomic variations to the metabolome. A study of 1,391 plasma metabolites and GWAS in 6,136 Finnish males identified 277 causative genes and 303 novel associations, revealing how multiple genomic loci impact the same metabolite. The study found causal links between the metabolome and genome, such as an intronic variant of the ABCG8 protein associated with reduced campesterol levels and gallstone risk, and a SERPINA1 variant linked to N-acetylglucosaminylasparagine and liver disease. This work underscores the potential to improve disease prediction and personalize medical treatment based on a deeper understanding of genetic influences on metabolism. Expanding these findings could lead to breakthroughs in managing and preventing metabolic and cardiovascular diseases.
Yin X, Chan LS, Bose D, et al. Genome-wide association studies of metabolites in Finnish men identify disease-relevant loci. Nat Commun. 2022;13(1):1644. doi:10.1038/s41467-022-29143-5
Metabolite Gene Interactions
Multiomics workflows incorporating genomics can uncover relationships between metabolites and genome variants. This study analyzed 1,666 circulating metabolites in 11,840 adults of diverse ancestries, validating 761 known variant-metabolite pairs and identifying 1,975 new associations. Seventy-nine novel pairs were replicated, with 73 conserved across ancestries. Notably, three pairs were linked to the X chromosome, and 13 metabolite levels were associated with risks of conditions like type 2 diabetes and macular degeneration. The study also revealed that rare variants have a significantly stronger effect on metabolite levels than common variants. This study underscores the importance of incorporating diverse genetic backgrounds in research, as it identifies novel genetic associations that may be missed in populations of single ancestry. By revealing new connections between genetic variants, metabolite levels, and disease outcomes, the research paves the way for precision medicine approaches tailored to multi-ethnic populations.
Feofanova EV, Brown MR, Alkis T, et al. Whole-genome sequencing analysis of human metabolome in multi-ethnic populations. Nat Commun. 2023;14(1):3111. doi:10.1038/s41467-023-38800-2
Metabolomics Applications for Genomics Research
- EFunctional Annotation of Genes
- EBiomarker Discovery
- EGene-Environment Interactions
- EValidation of Genetic Findings
- EPathway Analysis
- EStudying Complex Traits
- EUnderstanding Disease Mechanism
- EPrecision Medicine
- EMetabolic Phenotyping
“We observed that this integrated approach [with metabolomics and genomics] significantly improved the prioritization of the genes containing the disease-causing variant when compared with the two approaches individually..”
Bongaerts M, Bonte R, Demirdas S, et al
Integration of metabolomics with genomics: Metabolic gene prioritization using metabolomics data and genomic variant (CADD) scores. Molecular Genetics and Metabolism. 2022;136(3):199-218. doi:https://doi.org/10.1016/j.ymgme.2022.05.002
A Cross-platform Approach Identifies Genetic Regulators of Human Metabolism and Health
Blood metabolite levels are highly heritable, and studies often examine genome-metabolite associations using a single metabolomic platform, which limits their scope. By integrating data across multiple metabolomic platforms, researchers have enhanced statistical power, providing a more comprehensive understanding of gene-metabolite associations, human physiology, and disease.
A cross-platform dataset was created using metabolites from various platforms, including Metabolon’s Global Discovery Panel, allowing for genome-wide meta-analyses on 174 metabolites. This approach identified 499 gene-metabolite associations across 144 loci, revealing that genetic effects on metabolite levels are more significant than previously thought. It also demonstrated that combining measurements from different platforms is viable and enhances analysis power.
Figure 1. Metabolite associated single nucleotide polymorphisms.
Notably, the study identified a variant in the GLP2R receptor (rs17681684) linked to type 2 diabetes (T2D) phenotypes and elevated plasma citrulline, providing insights into T2D pathophysiology. Additionally, a strong link was found between serine levels and reduced risk of macular telangiectasia type 2. These findings highlight the potential of using blood metabolite levels for disease treatment through supplementation or pharmacological interventions and underscore the clinical value of integrating metabolome data across platforms.
Lotta, LA, Pietzner, M, Stewart, ID, et al. A cross-platform approach identifies genetic regulators of human metabolism and health. Nat Genet 2021;(53):54-64.
Genomics Publications and Citations
Metabolon has contributed extensively to multiomics publications.
Multiomics Knowledge Base
Dive deeper by viewing our case studies and webinars. Learn more about how Metabolon furthers multiomics research.
Contact Us
Talk with an expert
Request a quote for our services, get more information on sample types and handling procedures, request a letter of support, or submit a question about how metabolomics can advance your research.
Corporate Headquarters
617 Davis Drive, Suite 100
Morrisville, NC 27560