Triticeae Genomics and Genetics, 2024, Vol.15, No.3, 137-151 http://cropscipublisher.com/index.php/tgg 142 4.3 Genome-wide association studies (GWAS) GWAS has been a powerful tool in identifying genetic loci associated with various traits. A high-resolution GWAS identified 153 QTLs for disease resistance and cold tolerance, with high prediction accuracies for genomic selection models (Pang et al., 2021). Another study utilized GWAS to map QTLs for kernel-related traits, identifying five major and stable QTLs across multiple environments (Ren et al., 2021). These studies underscore the utility of GWAS in fine mapping and candidate gene identification, contributing significantly to wheat breeding programs. 4.4 Integration with genomic selection The integration of high-density genetic maps with genomic selection (GS) has led to the development of predictive breeding models. For example, genomic prediction models based on GWAS data showed high prediction accuracies for resistance to leaf rust, yellow rust, powdery mildew, and cold damage (Pang et al., 2021). The successful application of GS in wheat has led to the rapid development of varieties with enhanced yield, disease resistance, and stress tolerance, demonstrating the transformative impact of high-density genetic mapping on crop improvement. These models are valuable for enhancing the efficiency of breeding programs by enabling the selection of superior genotypes based on their genetic potential. High-density genetic mapping has revolutionized wheat genetics by providing high-resolution maps, facilitating QTL mapping for key traits, enabling GWAS, and integrating with genomic selection to develop predictive breeding models. These advancements have significantly contributed to the improvement of wheat varieties with enhanced yield, disease resistance, and stress tolerance. 5 Case Studies 5.1 Notable high-density genetic maps in wheat 5.1.1 Case study 1 In a study on Fusarium head blight (FHB) resistance in tetraploid wheat, Sari et al. (2018) used high-density genetic mapping to identify quantitative trait loci (QTL) associated with resistance. The study utilized the 90K Infinium iSelect chip to genotype two doubled haploid populations and conducted phenotypic evaluations in multiple field FHB nurseries. The results showed genotype-by-environment interactions for the expression of FHB resistance QTL, indicating their significant application in breeding programs. Notably, the FHB resistance QTL on chromosomes 1A and 5A exhibited more stable expression across multiple environments, making them suitable candidates for breeding disease-resistant varieties. Additionally, the study found a negative correlation between FHB resistance and traits such as plant height and maturity, with taller plants and later-maturing varieties showing stronger resistance to FHB. These findings provide important genetic resources and markers for breeding FHB-resistant wheat, facilitating the pyramiding of resistance genes and marker-assisted backcrossing programs. 5.1.2 Case study 2 Saini et al. (2021) analyzed the consensus genomic regions associated with multiple disease resistance in wheat and identified candidate genes (CGs) related to multiple disease resistance through meta-QTL (MQTL) analysis. The study examined 493 QTLs from 58 studies, projecting 291 of these QTLs onto a consensus genetic map, resulting in the identification of 63 MQTLs (Figure 2). Among these, 60 MQTLs were anchored to the wheat reference physical map, and 38 were validated through genome-wide association studies (GWAS). Additionally, the study identified 874 CGs, with 194 genes showing differential expression in five transcriptome studies (Saini et al., 2021). The results indicate that these genes are valuable for fine mapping of multi-disease resistance genes and marker-assisted breeding. This research provides a theoretical basis for the identification and breeding of wheat with multiple disease resistance. 5.2 Disease resistance mapping 5.2.1 Rust resistance High-density genetic maps play a crucial role in identifying and characterizing resistance genes and quantitative trait loci (QTL) for these diseases, thereby facilitating the development of resistant varieties. A comprehensive
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