Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations

Here, the authors introduce a metabolome-wide association study that combines a genome-wide association study of cerebrospinal fluid metabolites with publicly available genome-wide association study summary statistics of neurological and psychiatric conditions to identify 19 significant CSF metabolite-phenotype associations. The study of metabolomics and disease has enabled the discovery of new risk factors, diagnostic markers, and drug targets. For neurological and psychiatric phenotypes, the cerebrospinal fluid (CSF) is of particular importance. However, the CSF metabolome is difficult to study on a large scale due to the relative complexity of the procedure needed to collect the fluid. Here, we present a metabolome-wide association study (MWAS), which uses genetic and metabolomic data to impute metabolites into large samples with genome-wide association summary statistics. We conduct a metabolome-wide, genome-wide association analysis with 338 CSF metabolites, identifying 16 genotype-metabolite associations (metabolite quantitative trait loci, or mQTLs). We then build prediction models for all available CSF metabolites and test for associations with 27 neurological and psychiatric phenotypes, identifying 19 significant CSF metabolite-phenotype associations. Our results demonstrate the feasibility of MWAS to study omic data in scarce sample types.

Automated summary

This study introduces a metabolome-wide association study that identifies 19 significant CSF metabolite-phenotype associations by combining a genome-wide association study of cerebrospinal fluid metabolites with publicly available genome-wide association study summary statistics of neurological and psychiatric conditions. The feasibility of studying omic data in scarce sample types is demonstrated through the MWAS approach used in the research.