Phenotype prediction in plants is improved by integrating large-scale transcriptomic datasets.

Abstract

Research on the dynamic expression of genes in plants is important for understanding different biological processes. We used the large amounts of transcriptomic data from various plant sample sources that are publicly available to investigate whether the expression levels of a subset of highly variable genes (HVGs) can be used to accurately identify the phenotypes of plants. Using maize ( Zea mays L.) as an example, we built machine learning (ML) models to predict phenotypes using a gene expression dataset of 21 612 bulk RNA sequencing samples. We showed that the ML models achieved excellent prediction accuracy using only the HVGs to identify different phenotypes, including tissue types, developmental stages, cultivars and stress conditions. By ML models, several important functional genes were found to be associated with different phenotypes. We performed a similar analysis in rice ( Orzya sativa L.) and found that the ML models could be generalized across species. However, the models trained from maize did not perform well in rice, probably because of the expression divergence of the conserved HVGs between the two species. Overall, our results provide an ML framework for phenotype prediction using gene expression profiles, which may contribute to precision management of crops in agricultural practices.

Plant phenotyping relevance

遺伝子発現データから植物の組織、発育段階、品種、ストレス状態を予測する機械学習フレームワークを開発しており、表現型推定手法が研究の中心である。

Code and data availability

The paper's maize/rice gene expression datasets are publicly deposited on FigShare, and the authors' analysis source code is available on GitHub with a Zenodo DOI archive. The underlying expression profiles were originally downloaded from the PlantExp database (maize taxonId=4577, rice taxonId=39947).