Triticeae Genomics and Genetics, 2024, Vol.15, No.2, 88-99 http://cropscipublisher.com/index.php/lgg 89 high-density genetic maps and meta-QTL analysis, and how these technologies enhance the precision and reliability of QTL identification. Additionally, common challenges in QTL mapping, such as environmental interactions and genetic complexity, will be identified, and strategies to overcome these challenges will be discussed. The study will also address future research directions and potential applications of QTL mapping in wheat breeding programs, with a particular emphasis on integrating QTL data with genomic selection and other modern breeding techniques. By providing these valuable insights, the study aims to assist researchers and breeders in developing high-yielding, high-quality, and stress-resistant wheat varieties. 2 Methodological Advances in QTL Mapping 2.1 Traditional approaches Traditional QTL mapping approaches in wheat have primarily relied on bi-parental populations, such as recombinant inbred lines (RILs) and doubled haploids (DHs). These methods typically use molecular markers like restriction fragment length polymorphisms (RFLPs), amplified fragment length polymorphisms (AFLPs), and simple sequence repeats (SSRs) to construct genetic maps and identify QTLs associated with phenotypic traits. While effective in identifying major QTLs, these traditional methods have limitations, including low resolution due to limited recombination events and a narrow genetic base, which restricts the detection of QTLs with small effects and the exploration of genetic diversity (Guo et al., 2020). For instance, early studies utilized recombinant inbred lines (RILs) and SSR markers to map QTLs for various traits under specific environmental conditions, but the low marker density restricted the precision of QTL localization (Ilyas et al., 2019). Despite these limitations, traditional approaches laid the groundwork for understanding the genetic basis of complex traits in wheat and highlighted the importance of developing more refined mapping techniques. 2.2 Modern techniques and tools Recent advances in molecular biology, genomics, and bioinformatics have significantly enhanced QTL mapping methodologies. These modern techniques offer higher resolution, greater accuracy, and the ability to explore complex traits influenced by multiple genetic and environmental factors. 2.2.1 High-resolution genetic maps The development of high-density genetic maps using single nucleotide polymorphism (SNP) arrays and genotyping-by-sequencing (GBS) has revolutionized QTL mapping. These maps provide a more detailed representation of the wheat genome, enabling the precise localization of QTLs and the identification of closely linked markers for marker-assisted selection (MAS). High-density SNP arrays, such as the Wheat55K and Wheat660K, have been widely used to construct genetic maps with thousands of markers distributed across all wheat chromosomes (Liu et al., 2018; Ren et al., 2021). These high-resolution maps have significantly improved the power to detect QTLs and have facilitated the fine-mapping of important agronomic traits. Techniques such as specific-locus amplified fragment sequencing (SLAF-seq) and single nucleotide polymorphism (SNP) arrays have enabled the construction of high-density maps with thousands of markers. For example, a study utilizing the Wheat55K SNP array constructed a genetic map with 11 583 markers, facilitating the identification of stable QTLs for kernel-related traits across multiple environments (Ren et al., 2021). Similarly, SLAF-seq was employed to create a high-density map with 193 RILs, leading to the identification of 30 QTLs for quality traits such as grain protein content and grain hardness (Li et al., 2021). These high-resolution maps allow for more precise localization of QTLs and the identification of candidate genes. 2.2.2 Statistical and computational tools Advancements in statistical and computational tools have also played a crucial role in modern QTL mapping. Meta-QTL analysis, for instance, consolidates QTL data from multiple studies to identify stable and reliable QTLs. A meta-analysis of 735 QTLs from 27 mapping populations condensed these into 100 meta-QTLs, significantly reducing the confidence intervals and improving the reliability of QTL identification (Shariatipour et al., 2021). The use of advanced statistical methods such as inclusive composite interval mapping (ICIM) and multi-environment trial (MET) analysis has enabled the detection of QTLs with greater accuracy and consistency across different environments (Ren et al., 2021). Additionally, software tools like QTL Cartographer, MapQTL,
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