Molecular Pathogens 2024, Vol.15, No.3, 106-118 http://microbescipublisher.com/index.php/mp 112 durable resistance to wheat diseases. Traditional breeding methods, such as direct hybridization, backcrossing, and selection, have been instrumental in introducing desirable traits, including disease resistance, into wheat varieties. However, these methods are often time-consuming and limited by the complexities of screening for multiple stress tolerance traits simultaneously (Mondal et al., 2016). Molecular breeding techniques, including marker-assisted selection (MAS), gene pyramiding, and genome editing, have significantly accelerated the breeding process. MAS, for instance, allows for the precise targeting of resistance genes at the seedling stage, thereby reducing the time and cost associated with phenotypic selection (Miedaner and Korzun, 2012). Gene pyramiding, which involves stacking multiple resistance genes, has been shown to enhance the durability and spectrum of disease resistance in wheat (Luo et al., 2021). The combination of these molecular techniques with conventional breeding methods has led to the development of wheat cultivars with enhanced resistance to a variety of pathogens, including rusts, blotch diseases, and powdery mildew (Hafeez et al., 2021). Despite these advancements, challenges remain. The continuous emergence of new pathogen races with novel virulence factors necessitates ongoing efforts to identify and deploy new resistance genes. The creation of a wheat resistance gene atlas, as proposed by some researchers, could provide a valuable resource for breeders to rapidly respond to these evolving threats (Hafeez et al., 2021). 5.2 High-throughput phenotyping High-throughput phenotyping (HTP) has emerged as a powerful tool in modern wheat breeding programs. HTP technologies enable the rapid and accurate assessment of phenotypic traits across large populations, thereby facilitating the identification of disease-resistant individuals (Mondal et al., 2016). These technologies include imaging systems, remote sensing, and automated data collection platforms, which can capture a wide range of phenotypic data, including disease symptoms, plant height, and biomass. The integration of HTP with molecular breeding techniques has the potential to significantly accelerate the breeding cycle. For example, HTP can be used to validate the effectiveness of resistance genes identified through MAS or genome-wide association studies (GWAS) (Jabran et al., 2023). This combined approach allows for the rapid screening of large breeding populations, thereby increasing the efficiency of selecting disease-resistant individuals. Moreover, HTP can be used to monitor the performance of resistance genes under different environmental conditions, providing valuable insights into their stability and effectiveness. This is particularly important given the impact of climate change on the prevalence and severity of wheat diseases (Mondal et al., 2016). 5.3 Bioinformatics and data analysis The advent of high-throughput sequencing technologies and the accumulation of large-scale genomic data have necessitated the development of advanced bioinformatics tools and data analysis methods. Bioinformatics plays a crucial role in the identification and characterization of resistance genes, as well as in the analysis of complex interactions between host plants and pathogens (Hafeez et al., 2021). One of the key applications of bioinformatics in wheat breeding is the identification of quantitative trait loci (QTL) associated with disease resistance. QTL mapping and GWAS have been used to identify numerous resistance loci, which can then be targeted in breeding programs using MAS (Jabran et al., 2023). Additionally, bioinformatics tools are essential for the analysis of transcriptomic and proteomic data, which can provide insights into the molecular mechanisms underlying disease resistance (Mapuranga et al., 2022). The integration of bioinformatics with molecular breeding techniques also facilitates the development of genomic selection (GS) models. GS involves the use of genome-wide markers to predict the breeding value of individuals, thereby enabling the selection of superior genotypes at an early stage. This approach has the potential to significantly enhance the efficiency and accuracy of breeding programs, particularly for complex traits such as disease resistance.
RkJQdWJsaXNoZXIy MjQ4ODYzNA==