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.