Advancing winter wheat breeding using high throughput genotypic and phenotypic tools

Abstract

Plant breeding has been used for over 10,000 years to adapt crops like wheat to different environments. Continuous breeding efforts have led to high-yielding and resilient wheat varieties, with Canada being the 6th largest producer in 2024-25. However, challenges like leaf rust, caused by Puccinia triticina Erikss., still impact global wheat production. Rapid pathogen evolution requires ongoing identification and deployment of novel resistance sources. Accurate phenotyping of key traits like disease resistance, winter survival, and plant height is a major bottleneck in wheat breeding. Advances in high-throughput (HTP) genotyping and phenotyping offer new opportunities to enhance genetic gain. This study integrated genetic mapping and UAV-based HTP approaches to evaluate leaf rust resistance and key agronomic traits in winter wheat. A doubled-haploid (DH) population (n = 130) developed from the cross W538/Emerson was evaluated for leaf rust resistance at the seedling and adult plant stages. Genotyping was performed using a 25K Infinium SNP array, and linkage and QTL analyses mapped resistance genes. Seedling-stage resistance was associated with a locus on chromosome 1B, while adult plant resistance was governed by multiple QTL, including QLr.umb-1B, QLr.umb-2A, QLr.umb-3B, and QLr.umb-4D. Lines carrying multiple resistance QTL exhibited enhanced leaf rust resistance, highlighting the importance of QTL stacking for durable resistance. UAV-based HTP methods were evaluated for assessing spring stand and plant height in winter wheat breeding nurseries. Manual ratings were compared with RGB and multispectral-based UAV metrics, including relative plant pixel area and vegetation indices like NDVI and EPVI. NDVI was the most robust method for spring stand assessment, with four times higher heritability than manual ratings. Manual measurements were more accurate than UAV-based methods for plant height, but SfM and LiDAR had comparable performance. This study highlights the complementary value of genetic mapping and UAV-based HTP in wheat breeding, emphasizing multi-QTL resistance for leaf rust and the potential of UAVs to improve phenotyping efficiency for key agronomic traits.

Plant phenotyping relevance

UAV画像・マルチスペクトル・SfM・LiDARによる春季スタンドと草丈の測定を、手動評価と比較・検証しており、植物表現型取得法が研究の実質的な構成要素である。

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