Machine learning-based prediction of dynamic heterosis for plant height with pathway biomarkers in rice.

Abstract

Key message The metabolomic landscape of dynamic heterosis for plant height was displayed in rice. Heterosis-associated pathways across most developmental stages were developed into robust pathway biomarkers. The development of robust biomarkers enables accurate prediction of complex phenotypes, which contribute to the advancement of precision breeding. However, the dynamic nature of biomarkers is often underestimated, as their quantitative changes during development are directly connected to phenotypic transformations, influencing crop agronomic traits. Here, we performed comparative metabolomic analyses to investigate dynamic heterosis for plant height in rice, which is an agronomic trait varying throughout development and is a key determinant of yield heterosis. We found that the levels of pyruvaldehyde were predictive of height heterosis specifically at the seedling stage, while 4-hydroxycinnamic acid positively correlated with height heterosis across four developmental stages. We identified metabolic pathways associated with height heterosis and found that metabolomic changes during the elongation stage had a greater impact than those in other stages. Finally, 11 heterosis-associated pathways were developed into pathway biomarkers using random forest analysis, enabling the prediction of height heterosis in an independent population under different growth conditions. We elucidate the metabolomic landscape of dynamic height heterosis in rice through the identification of heterosis-associated analytes and pathways across stages. Our findings provide a strategy to develop robust biomarkers for heterosis of important agronomic traits by integrating metabolic pathways involved in heterosis across most developmental stages, contributing to the establishment of precision pairing in hybrid crop breeding.

Plant phenotyping relevance

植物高の異型接合優勢という明示的な植物形質を、代謝経路バイオマーカーとランダムフォレストで予測する手法を開発し、独立集団・異なる栽培条件で検証しており、単なる代謝測定ではなく形質予測法が中心である。

Code and data availability

植物フェノタイピング解析を再現する公開資産であることを、入力本文と直接リンクから確認できなかったため保留しました。

No evidence-backed public reproduction asset is currently recorded.