Triticeae Genomics and Genetics, 2024, Vol.15, No.2, 88-99 http://cropscipublisher.com/index.php/lgg 96 5.1.2 Genetic background effects The genetic background of the mapping population also poses a significant challenge in QTL mapping. The genetic background refers to the overall genetic makeup of an organism, which can modulate the effects of individual QTLs. QTLs identified in one genetic background may not have the same effect in another due to differences in genetic interactions. This phenomenon complicates the transfer of QTLs across different wheat varieties and can limit the applicability of QTLs identified in specific mapping populations (Xu et al., 2023). The effects of QTLs can be masked or modified by the genetic background, leading to difficulties in detecting and validating QTLs. For example, in a study mapping QTLs for salt stress tolerance, it was found that the B and D genomes of wheat contributed more significantly to salinity tolerance, indicating that the genetic background plays a crucial role in the expression of these traits (Ilyas et al., 2019). Additionally, the co-localization of QTLs for different traits, such as grain yield and micronutrient content, further complicates the identification of specific QTLs due to the pleiotropic effects of certain genomic regions (Shariatipour et al., 2021). 5.2 Strategies for overcoming challenges To address these challenges, researchers have developed several strategies that enhance the reliability and applicability of QTL mapping in wheat. 5.2.1 Integrative approaches To overcome the challenges posed by environmental interactions and genetic background effects, integrative approaches that combine multiple mapping techniques and data sources are essential. For instance, the use of meta-QTL analysis allows for the consolidation of QTLs from multiple studies, leading to the identification of more stable and reliable QTLs (Shariatipour et al., 2021). This approach reduces the confidence interval of QTLs and helps in pinpointing candidate genes with greater accuracy. Additionally, combining QTL mapping with transcriptomic data, such as bulked segregant RNA-seq (BSR-Seq), can provide insights into the underlying genetic mechanisms and validate the effects of identified QTLs (Li et al., 2020). 5.2.2 Use of high-density maps The use of high-density genetic maps significantly enhances the resolution and accuracy of QTL mapping. High-density maps constructed using advanced genotyping technologies, such as SNP arrays and specific-locus amplified fragment sequencing (SLAF-seq), enable the identification of QTLs with higher precision (Li et al., 2020; Ren et al., 2021). For example, a high-density genetic map with 11,583 markers was used to map QTLs for kernel-related traits, resulting in the identification of stable QTLs across multiple environments (Ren et al., 2021). Similarly, the use of a Wheat50K SNP array-derived map facilitated the mapping of QTLs for plant height and grain traits, highlighting the importance of high-density maps in QTL studies (Lv et al., 2021). While challenges such as environmental interactions and genetic background effects complicate QTL mapping, strategies like integrative approaches and the use of high-density maps provide effective solutions. These advancements have enhanced the reliability and applicability of QTL mapping, contributing to the ongoing improvement of wheat varieties. 6 Future Directions in QTL Mapping 6.1 Integration with genomic selection The integration of QTL mapping with genomic selection (GS) represents a promising future direction for enhancing wheat breeding programs. Genomic selection leverages genome-wide markers to predict the genetic value of individuals, thereby accelerating the breeding cycle. By combining QTL mapping with GS, breeders can more accurately identify and select for desirable traits. For instance, the high-density genetic maps and identified QTLs for traits such as grain protein content, grain hardness, and starch pasting properties (Li et al., 2020) can be incorporated into GS models to improve the precision of selection. Additionally, the meta-QTLs identified through comparative genomics (Shariatipour et al., 2021) provide stable targets that can be integrated into GS frameworks, enhancing the reliability of trait prediction across different environments.
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