Case Study

Integrating Metabolomics and GWAS to Identify Rare Variant-metabolite Associations

Metabolite GWAS associations generated with help from the Metabolon Discovery Panel provide deeper insights into rare genetic variants and mechanisms underlying disease.

Here, researchers leveraged the increased frequency of rare alleles in the Finnish population compared to non-Finnish Europeans (NFE) to identify rare genetic variants that contribute to alterations in disease-relevant metabolites. Combining global untargeted metabolomics with genome-wide association studies (GWAS), they revealed 303 novel gene-metabolite associations, many of which were enriched in the Finnish population. Further investigations into these associations uncovered many causal variant-metabolite links with implications for diseases including Alzheimer’s disease, Huntington’s disease, and schizophrenia.

Here, researchers leveraged the increased frequency of rare alleles in the Finnish population compared to non-Finnish Europeans (NFE) to identify rare genetic variants that contribute to alterations in disease-relevant metabolites. Combining global untargeted metabolomics with genome-wide association studies (GWAS), they revealed 303 novel gene-metabolite associations, many of which were enriched in the Finnish population. Further investigations into these associations uncovered many causal variant-metabolite links with implications for diseases including Alzheimer’s disease, Huntington’s disease, and schizophrenia.

Integrating metabolomics and GWAS to identify rare variant-metabolite associations

The Challenge: Impact of Rare Variants

While GWAS have been instrumental in understanding genetic polymorphisms underlying diseases, they often lack insights into disease-causing mechanisms. Metabolomics, on the other hand, is a comprehensive tool for identifying and quantifying thousands of metabolites, offering systems-level data on mechanisms and targetable molecules for treatment. Understanding that many metabolites are highly heritable, studies have begun to integrate metabolomic data with GWAS, with several reports revealing common variants between genes and metabolites. However, the impact of rare variants on heritable plasma metabolites and the role of variant-metabolite associations in disease is less understood.

Metabolon Insight: Genotype-metabolite Associations

To understand the impact of rare variants on metabolites, researchers obtained data from middle-aged and older men from northeast Finland, a unique population of individuals with increased rare allele frequency.1 Using the Metabolon Discovery Panel, they assayed 1544 plasma metabolites and integrated the results with GWAS to identify genotype-metabolite associations. Further analyses, including fine mapping, knowledge-based approaches, and Bayesian colocalization, were employed to identify causal variants and gain insights into disease mechanisms.

The Solution: Identification of Causal Variants and Links to Disease

The integration of the Metabolon Discovery Panel and GWAS revealed 2030 independent metabolite-variant pairs, 303 of which were novel associations. Interestingly, more than one-third of these novel signals were rare variants or enriched in the Finnish population, highlighting the advantage of integrating metabolite and disease genetic associations in this specific population.

Fine-mapping or knowledge-based approaches identified many gene-metabolite associations linked with disease. For instance, a novel association between the HDAC6 missense variant, p.Arg832His, and the metabolite, N6-acetyllysine, was uncovered, suggesting that HDAC6 may be a strong candidate as a causal variant regulating N6-acetyllysine levels—a significant finding considering that other studies have reported elevated HDAC6 and N6-acetyllysine levels in Alzheimer’s disease.

The current study also uncovered additional causal variants involving genes and metabolites implicated in schizophrenia, Huntington’s disease, and human bile acid metabolism. Further analyses identified several other genes that are common between metabolite levels and diseases, such as a link between campesterol and gallstones and a relationship between DBH, vanillymandelate, and hypertension.

Finally, these results also provided important insights into physiological mechanisms. Notably, SLC23A3 was nominated as a causal gene for regulating 19 metabolites across various biochemical classes, suggesting a broad role for this gene in mediating metabolite function.

The Outcome: Genotype-Metabolite Associations Provide a Deeper Look into Disease Pathology

A comprehensive understanding of the relationships between genetics and metabolites is crucial for unraveling the pathways and mechanisms underlying disease biology. Through the integration of metabolomics and GWAS, researchers here identified many rare variant-metabolite associations and provided important insights into gene-metabolite mechanisms and their role in mediating disease biology. Ultimately, their work may contribute to a deeper understanding of the genetic and metabolic interplay present in various disease states.

References

1. 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. Published 2022 Mar 28. doi:10.1038/s41467-022-29143-5

References

1. Zgoda-Pols, J.R., et al., Metabolomics analysis reveals elevation of 3-indoxyl sulfate in plasma and brain during chemically-induced acute kidney injury in mice: investigation of nicotinic acid receptor agonists. Toxicol Appl Pharmacol, 2011. 255(1): p. 48-56.

2. Bryant, J.A., et al., The impact of an oral purified microbiome therapeutic on the gastrointestinal microbiome. Nat Med, 2026. 32(1): p. 186-196

3. McGovern, B .H., et al., SER-109, an Investigational Microbiome Drugto Reduce Recurrence After Clostridioides difficile Infection: Lessons Learned From a Phase 2 Trial. Clin Infect Dis, 2021. 72(12): p. 2132-2140.

4. Feuerstadt, P., et al., SER-109, an Oral Microbiome Therapy for Recurrent Clostridioides difficile Infection. N Engl J Med, 2022. 386(3): p. 220-229.

5. Hu, Z., et al., Targeted metabolomics reveals novel diagnostic biomarkers for colorectal cancer. Mol Oncol, 2025. 19(6): p. 1737-1750.

6. Butler, F.M., et al., Vegetarian Dietary Patterns and Diet-Related Metabolites Are Associated With Kidney Function in the Adventist Health Study-2 Cohort. J Ren Nutr, 2025.

7. Stanford, J., et al., Metabolomic Profiling and Diet Quality Scoring in a Randomized Crossover Trial of Healthy and Typical Dietary Patterns. Mol Nutr Food Res, 2025 . 69(23): p. e70271.

8. O’Connor, L.E., et al., Metabolomic Profiling of an Ultraprocessed Dietary Pattern in a Domiciled Randomized Controlled Crossover Feeding Trial. J Nutr, 2023. 153(8): p. 2181-2192.

9. Fritsch, D.A., et al., Microbiome function underpins the efficacy of a fiber-supplemented dietary intervention in dogs with chronic large bowel diarrhea. BMC Vet Res, 2022. 18(1): p. 245.

10. Leal, L.N., et al., Preweaning nutrient supply improves lactation productivity and reduces the risk of culling in Holstein cows. J Dairy Sci, 2025. 108(6): p. 5875-5888.

11. Ahsin, M., et al., Soil and pasture health underlie improved beef nutrient density determined by untargeted metabolomics in Southern US grass finished beef systems. NPJ Sci Food, 2025. 9(1): p. 151.

12. Yin, W., et al., Plasma lipid profiling across species for the identification of optimal animal models of human dyslipidemia. J Lipid Res, 2012. 53(1): p. 51-65.

13. Porter, F .D., et al., Cholesterol oxidation products are sensitive and specific blood-based biomarkers for Niemann-Pick C1 disease. Sci Transl Med, 2010. 2(56): p. 56ra81.

14. Needham, B .D., et al., Plasma and Fecal Metabolite Profiles in Autism Spectrum Disorder. Biol Psychiatry, 2021. 89(5): p. 451-462

15. Li, C., et al., Estradiol and mTORC2 cooperate to enhance prostaglandin biosynthesis and tumorigenesis in TSC2-deficient LAM cells. J Exp Med, 2014. 211(1): p. 15-28.

16. Green, P.G., et al., Metabolic flexibility and reverse remodelling of the failing human heart. Eur Heart J, 2025. 46(25): p. 2422-2433.

17. Maekawa, H., et al., SGLT2 inhibition protects kidney function by SAM-dependent epigenetic repression of inflammatory genes under metabolic stress. J Clin Invest, 2025. 135(19).

18. Wu, D., et al., Integrated screens reveal that guanine nucleotide depletion, which is irreversible via targeting IMPDH2, inhibits pancreatic cancer and potentiates KRAS inhibition. Gut, 2026.

19. Schwerdtfeger, L.A., et al., Gut microbiota and metabolites are linked to disease progression in multiple sclerosis. Cell Rep Med, 2025. 6(4): p. 102055.

20. Wu, H., et al., Microbiome-metabolome dynamics associated with impaired glucose control and responses to lifestyle changes. Nat Med, 2025. 31(7): p. 2222-2231.

21. Jacobs, J.P., et al., Cognitive behavioral therapy for irritable bowel syndrome induces bidirectional alterations in the brain-gut-microbiome axis associated with gastrointestinal symptom improvement. Microbiome, 2021. 9(1): p. 236.

22. Pietzner, M., et al., Plasma metabolites to profile pathways in noncommunicable disease multimorbidity. Nat Med, 2021. 27(3): p. 471-479.

23. Faquih, T.O., et al., Robust Metabolomic Age Prediction Based on a Wide Selection of Metabolites. J Gerontol A Biol Sci Med Sci, 2025. 80(3).

24. Scherer, N., et al., Coupling metabolomics and exome sequencing reveals graded effects of rare damaging heterozygous variants on gene function and human traits. Nat Genet, 2025. 57(1): p. 193-205.

25. Holmes, Z.C., et al., Untargeted metabolomic analysis of human milk from healthy mothers reveals drivers of metabolite variability. Sci Rep, 2024. 14(1): p. 20827.

26. Titz, B., et al., Implications of Ocular Confounding Factors for Aqueous Humor Proteomic and Metabolomic Analyses in Retinal Diseases. Transl Vis Sci Technol, 2024. 13(6): p. 17.

27. Bloom, S.M., et al., Cysteine dependence of Lactobacillus iners is a potential therapeutic target for vaginal microbiota modulation. Nat Microbiol, 2022. 7(3): p. 434-450.

28. Leimer, E.M., et al., Lipid profile of human synovial fluid following intra-articular ankle fracture. J Orthop Res, 2017. 35(3): p. 657-666.