Triticeae Genomics and Genetics, 2024, Vol.15, No.2, 88-99 http://cropscipublisher.com/index.php/lgg 90 and R/qtl have been developed to handle large datasets and perform complex analyses, enhancing the efficiency and precision of QTL mapping studies (Su et al., 2018; Yang et al., 2019). 2.2.3 Multi-parental cross designs Multi-parental cross designs, such as nested association mapping (NAM) and multi-parent advanced generation inter-cross (MAGIC) populations, have emerged as powerful tools for QTL mapping. These designs incorporate genetic diversity from multiple parents, creating populations with a higher level of recombination and genetic variation. This allows for the dissection of complex traits and the identification of novel QTLs that are not detectable in traditional bi-parental populations. Studies using these designs have successfully identified QTLs for various traits, including yield, disease resistance, and quality traits in wheat (Hu et al., 2020; Li et al., 2020). The increased recombination in these populations enhances the resolution of QTL mapping, facilitating the identification of candidate genes and the understanding of genetic mechanisms underlying important traits. For example, a study on a double haploid population derived from a cross between two wheat lines identified nine QTLs for thousand-grain weight, with some QTLs showing strong and stable effects across multiple environments (Liu et al., 2020). These multi-parental designs facilitate the identification of novel QTLs and the dissection of complex genetic interactions. The integration of high-resolution genetic maps, advanced statistical tools, and multi-parental cross designs has revolutionized QTL mapping in wheat. These methodological advances have not only improved the precision and reliability of QTL identification but also provided valuable insights into the genetic mechanisms underlying important agronomic traits. 3 Success Stories in Wheat QTL Mapping 3.1 QTL mapping for grain yield Quantitative Trait Loci (QTL) mapping has significantly advanced our understanding of the genetic basis of grain yield in wheat. Numerous studies have successfully mapped QTLs that contribute to yield and its components, such as thousand kernel weight (TKW), grain number per spike, and grain weight. Shariatipour et al. (2021) by meta-analysis identified 735 QTLs for grain yield (GY) and related traits, consolidating them into 100 meta-QTLs (MQTLs) with reduced confidence intervals, thus enhancing the precision of QTL mapping. Yang et al. (2019) evaluated 266 recombinant inbred lines derived from two wheat lines, Zhongmai 871 and Zhongmai 895, across various environments. Key traits analyzed included thousand grain weight (TGW), grain length (GL), grain width (GW), grain number per spike (GNS), and grain filling rate (GFR). The researchers used a high-density SNP array to perform initial QTL mapping, identifying four major genetic regions on chromosomes 1AL, 2BS, 3AL, and 5B that significantly influence TGW-related traits (Figure 1). This was followed by using Kompetitive Allele Specific PCR (KASP) markers to validate these QTLs in a larger set of lines, confirming their influence across multiple environments and explaining a significant proportion of the phenotypic variance. These findings suggest that the identified QTLs, especially those on chromosomes 1AL and 5B, could be critical targets for marker-assisted selection in wheat breeding programs aimed at improving yield and other agronomically important traits. Additionally, a genome-wide QTL mapping study by Dhakal et al. (2021) in wheat identified 51 QTLs for grain yield and agronomic traits across 28 diverse environments, highlighting the robustness of these QTLs in different conditions. 3.2 QTL Mapping for grain quality Grain quality traits, such as grain protein content (GPC), grain hardness (GH), and starch pasting properties, are crucial for wheat's end-use quality. Li et al. (2020) used Specific-Locus Amplified Fragment Sequencing (SLAF-seq) and Bulked Segregant RNA Sequencing (BSR-Seq) techniques to construct a high-density genetic map of wheat. Based on this map, they identified quantitative trait loci (QTL) associated with wheat quality traits (Figure 2). In 193 recombinant inbred lines derived from two wheat varieties, Chuanmai 42 and Chuanmai 39, the researchers identified 30 QTLs that could explain up to 47.99% of the phenotypic variation. The traits involved included falling number (FN), grain protein content (GPC), grain hardness (GH), and starch pasting characteristics. Notably, a major QTL for GH on chromosome 5D was found to be closely linked to the pina-D1 and pinb-D1
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