MtCro: multi-task deep learning framework improves multi-trait genomic prediction of crops.

Abstract

Genomic Selection (GS) predicts traits using genome-wide markers, speeding up genetic progress and enhancing breeding efficiency. Recent emphasis has been placed on deep learning models to enhance prediction accuracy. However, current deep learning models focus on learning specific phenotypes for the given task, overlooking the inter-correlations among different phenotypes. In response, we introduce MtCro, a multi-task learning approach that simultaneously captures diverse plant phenotypes within a shared parameter space. Extensive experiments reveal that MtCro outperforms mainstream models, including DNNGP and SoyDNGP, with performance gains of 1-9% on the Wheat2000 dataset, 1-8% on Wheat599, and 1-3% on Maize8652. Furthermore, comparative analysis shows a consistent 2-3% improvement in multi-phenotype predictions, emphasizing the impact of inter-phenotype correlations on accuracy. By leveraging multi-task learning, MtCro efficiently captures diverse plant phenotypes, enhancing both model training efficiency and prediction accuracy, ultimately accelerating the progress of plant genetic breeding. Our code is available on https://github.com/chaodian12/mtcro .

Plant phenotyping relevance

複数の作物表現型を予測するマルチタスク深層学習手法を開発し、複数データセットおよび既存モデルと比較検証しており、表現型推定手法が研究の中心である。

Code and data availability

The paper's authors explicitly state that the MtCro analysis code is publicly available on GitHub, matching an allowed URL. No separate phenotype dataset deposit by the authors is stated (Wheat2000/Wheat599 data were provided by DNNGP; Maize8652 is cited prior work), so only the authors' code qualifies as a paper-asset