Triticeae Genomics and Genetics, 2024, Vol.15, No.2, 88-99 http://cropscipublisher.com/index.php/lgg 94 to root traits in hexaploid wheat. By crossing two wheat varieties, Spica and Maringa, which exhibit significantly different root morphologies, a recombinant inbred line (RIL) population was generated and evaluated under controlled conditions. Using the 90K SNP chip for genotyping these lines, an average of 7.5 QTLs per trait were identified. The detected QTLs were then compared with the Chinese Spring wheat reference genome to determine specific genes and genomic regions associated with root traits such as root dry weight and root diameter (Li et al. 2023). The use of IM allowed for the precise localization of QTLs associated with root architecture, which is crucial for nutrient and water absorption. 4.2.2 Composite interval mapping Composite interval mapping (CIM) is an advanced genetic mapping technique frequently used for the precise localization of quantitative trait loci (QTL). CIM combines the fundamental principles of interval mapping (IM) with multiple regression analysis, which allows it to effectively control for genetic background noise, thereby enhancing the accuracy and power of QTL detection. In the study by Li et al. (2020), composite interval mapping (CIM) was employed by utilizing a high-density genetic map in conjunction with bulked segregant RNA sequencing (BSR-Seq) to identify quantitative trait loci (QTL) related to wheat quality traits. The CIM method allows researchers to more precisely locate and validate QTL by leveraging the increased marker density. Through this approach, 30 QTLs were discovered, explaining up to 47.99% of the phenotypic variation for traits such as falling number (FN), grain protein content (GPC), grain hardness (GH), and starch pasting characteristics.This composite interval mapping technique, by narrowing confidence intervals and improving the precision of QTL detection, is of significant importance for the genetic improvement of wheat quality traits. It allows researchers to validate the stability and effects of QTL across multiple environments, thereby enabling more effective molecular marker-assisted selection in wheat breeding. 4.2.3 Multiple-trait composite interval mapping Multiple-trait Composite Interval Mapping (MCIM) extends CIM by simultaneously analyzing multiple traits, which can increase the power to detect QTLs and provide insights into the genetic correlations between traits. The study by Ilyas et al. (2020) focused on identifying quantitative trait loci (QTL) for physiological and biochemical traits of wheat under salt stress conditions (Figure 4). The research utilized a recombinant inbred line (RIL) population derived from a cross between the salt-tolerant variety Pasban90 and the salt-sensitive variety Frontana. The aim was to identify specific genomic regions associated with the plant's response to salinity, including traits such as relative water content, membrane stability index, chlorophyll content, and ion concentrations (sodium, potassium, chloride). In this study, the multi-trait composite interval mapping (MCIM) method was applied to locate QTLs for wheat physiological and biochemical traits under salt stress conditions. This method allows researchers to consider the genetic correlations and interactions among multiple traits simultaneously, leading to more precise identification of QTLs controlling these traits. Using a linkage map constructed with 202 polymorphic SSR markers, the study identified a total of 60 QTLs. The MCIM approach helped uncover both major and minor QTLs associated with wheat's response to salt stress. Applying this method is crucial for understanding how plants respond to environmental stress at the genomic level, particularly in identifying key genomic regions that enhance crop tolerance to salt stress. By using MCIM, the study not only mapped individual QTLs but also analyzed the genetic interactions between different QTLs, providing practical insights for future marker-assisted selection to breed salt-tolerant wheat varieties. This integrated multi-trait analysis approach offers a powerful tool for crop improvement, enhancing the environmental Adaptedability and yield of essential crops like wheat. These case studies and the application of advanced statistical methods underscore the progress and challenges in QTL mapping in wheat. The identification of QTLs through these approaches provides valuable insights for marker-assisted selection and the development of superior wheat cultivars.
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