Molecular Plant Breeding 2024, Vol.15, No.5, 233-246 http://genbreedpublisher.com/index.php/mpb 235 of HTP in identifying disease resistance loci and improving the selection of resistant genotypes, ultimately contributing to the development of more resilient and productive wheat varieties. 3 High-Throughput Phenotyping in Disease Resistance Trait Identification 3.1 Leveraging HTP for rapid identification of quantitative resistance loci (QTLs) The identification of QTLs associated with disease resistance is a critical step in breeding programs aimed at developing resistant wheat varieties. HTP technologies, such as unmanned aerial systems (UAS) and automated image analysis, have significantly accelerated this process. For instance, UAS-based phenotyping has been successfully applied to assess complex traits like lodging in wheat, demonstrating high correlations with visual estimates and broad-sense heritability (Singh et al., 2019). This approach allows for the rapid and accurate identification of QTLs, which can then be used in marker-assisted selection (MAS) to develop disease-resistant varieties (Zhu, 2024). In another study, automated image analysis was employed to measure quantitative resistance to Septoria tritici blotch (STB) in wheat. This method enabled the identification of small chromosome intervals containing candidate genes for STB resistance, highlighting the power of HTP in pinpointing specific genomic regions associated with disease resistance (Yates et al., 2019). Similarly, the use of 35K Axiom Array SNP genotyping assays in conjunction with HTP allowed for the identification of novel QTLs for stem rust resistance in wheat, further validating the effectiveness of HTP in QTL identification (Pradhan et al., 2023). 3.2 Successful examples of HTP in detecting resistance to common wheat diseases HTP has been instrumental in detecting resistance to several common wheat diseases. For example, Singh et al. (2019) demonstrated the use of precision phenotyping to identify novel loci for quantitative resistance to STB. Their study involved a replicated field experiment with 335 winter wheat cultivars, which were phenotyped using automated image analysis. This approach led to the identification of 26 chromosome intervals associated with STB resistance, some of which were novel and others that overlapped with previously known resistance intervals (Singh et al., 2019; Yates et al., 2019). Another successful application of HTP is in the detection of resistance to Fusarium head blight (FHB). A meta-analysis of QTLs associated with FHB resistance identified 209 QTLs across 21 chromosomes, providing valuable markers for marker-assisted breeding (Liu et al., 2009). Additionally, HTP has been used to identify QTLs conferring high-temperature adult-plant (HTAP) resistance to stripe rust in wheat. This study identified eight QTLs significantly associated with HTAP resistance, with major loci on chromosomes 2B and 4A explaining a substantial portion of the phenotypic variation (Chen et al., 2012). 3.3 Importance of integrating phenotypic data with genotypic information for enhanced selection efficiency The integration of phenotypic data with genotypic information is crucial for enhancing the efficiency of selection in breeding programs. HTP generates large volumes of phenotypic data that, when combined with genotypic information, can provide a comprehensive understanding of the genetic architecture of disease resistance traits. This integration allows for the identification of candidate genes and the development of more accurate genomic prediction models. For instance, the integration of HTP data with genome-wide association studies (GWAS) has been shown to improve the identification of QTLs for disease resistance. In the case of stem rust resistance, the combination of phenotypic data from HTP with SNP genotyping assays enabled the identification of 20 reliable QTLs, including novel genomic regions that can be used in breeding programs (Figure 1) (Pradhan et al., 2023). Similarly, the use of HTP in conjunction with statistical genomic methods has been proposed to enhance the genetic improvement of longitudinal traits in crops, providing insights into plant functioning and gene activation in response to environmental stimuli (Moreira et al., 2020). Moreover, the integration of phenotypic and genotypic data can facilitate the development of MAS strategies. For example, MAS has been successfully applied to transfer resistance genes and QTLs into elite breeding material, as
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