Bioscience Evidence 2024, Vol.14, No.5, 195-205 http://bioscipublisher.com/index.php/be 199 4.2 Quantitative trait locus (QTL) mapping QQTL mapping is a powerful tool used to identify genomic regions associated with important agronomic traits in wheat. By analyzing the genetic composition and phenotypic characteristics of wheat populations, researchers can pinpoint specific loci that contribute to heterosis and yield potential. Recent studies have identified stable QTLs for yield-related traits such as thousand kernel weight (TKW), plant height (PH), and spike length (SL) across multiple environments, providing valuable targets for breeding programs (Guan et al., 2018; Ma et al., 2023; Rathan et al., 2023). The integration of QTL mapping with other genomic tools, such as meta-QTL analysis, has further refined the identification of key genomic regions, enhancing the precision of marker-assisted selection and facilitating the development of high-yielding hybrid wheat varieties (Figure 2) (Shariatipour et al., 2021; Li et al., 2023). Figure 2 KEGG pathway enrichment analysis of all genes in the MQTL region (Adapted from Li et al., 2023) Image caption: The x-axis represents the enrichment factor, and the y-axis shows the pathway names. The different colored circles represent p-values, with colors ranging from magenta to blue, indicating decreasing p-values and increasing enrichment levels. The size of the circles represents the number of genes enriched in each pathway (Adapted from Li et al., 2023) Li et al. (2023) analyzed the enrichment of genes extracted from the MQTL (meta-QTL) regions in the top 20 KEGG pathways. The enrichment analysis revealed significant enrichment of genes involved in key metabolic pathways such as starch and sucrose metabolism, protein export, and the TCA cycle, suggesting that these pathways may play crucial roles in controlling growth, yield, and stress resistance in crops like wheat. The enrichment of genes related to carbohydrate, lipid metabolism, and antioxidant metabolism also highlights the importance of these metabolic pathways in crop adaptation to environmental stresses. This figure provides insights into the functions of genes in complex traits, offering guidance for molecular breeding. 4.3 Utilization of genomic selection (GS) Genomic Selection (GS) leverages genome-wide marker data to predict the performance of wheat hybrids, streamlining the breeding process by selecting the best candidates for further development. Unlike traditional breeding methods, which rely on phenotypic selection, GS uses statistical models to estimate the genetic potential of individuals based on their genomic profiles. This approach has been shown to significantly reduce the time and cost associated with developing new wheat hybrids while maintaining or even improving genetic gains (Paux et al., 2012; Raj and Nadarajah, 2022). The application of GS in wheat breeding has been facilitated by advances in next-generation sequencing technologies and the availability of comprehensive genomic resources, enabling breeders to make more informed decisions and accelerate the development of superior hybrid wheat varieties (Gao et al., 2015).
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