# Transcripts and genomic intervals associated with variation in metabolite abundance in maize leaves under field conditions

## TL;DR

This study explores how genetic and transcriptomic factors influence metabolite levels in maize under field conditions, identifying genomic regions and transcripts linked to metabolic variation.

## Contribution

The study is the first to use field-grown maize to link metabolite variation with genomic and transcriptomic data using GWAS, TWAS, and AI-based methods.

## Key findings

- Genome-wide association and random forest methods identified significant associations between genes and metabolite abundance.
- Transcriptome-wide association studies found only six significant transcript-metabolite associations.
- Genetic markers for metabolite variation sometimes overlapped with markers for non-metabolic traits.

## Abstract

Plants exhibit extensive environment-dependent intraspecific metabolic variation, which likely plays a role in determining variation in whole plant phenotypes. However, much of the work seeking to use natural variation to link genes and transcript’s impacts on plant metabolism has employed data from controlled environments. Here, we generated and analyzed data on the variation in the abundance of 26 metabolites across 660 maize inbred lines under field conditions. We employ these data and previously published transcript and whole plant phenotype data reported for the same field experiment to identify both genomic intervals (through genome-wide association studies (GWAS)) and transcripts (using both transcriptome-wide association studies (TWAS) and an explainable artificial intelligence (AI) approach based on random forest (RF)) associated with variation in metabolite abundance. Both genome-wide association and random forest-based methods identified substantial numbers of significant associations including genes with plausible links to the metabolites they are associated with. In contrast, the transcriptome-wide association identified only six significant associations. In three cases, genetic markers associated with metabolic variation in our study colocalized with markers linked to variation in non-metabolic traits scored in the same experiment. We speculate that the poor performance of transcriptome-wide association studies in identifying transcript-metabolite associations may reflect a high prevalence of non-linear interactions between transcripts and metabolites and/or a bias towards rare transcripts playing a large role in determining intraspecific metabolic variation.

The online version contains supplementary material available at 10.1186/s12864-025-11580-3.