Triticeae Genomics and Genetics, 2024, Vol.15, No.3, 137-151 http://cropscipublisher.com/index.php/tgg 141 TILLING, and Genome-Wide Association Studies (GWAS), providing methods for identifying genes/markers related to agronomic traits (Gardiner et al., 2019). The development of NGS technology has made the development of ultra-high-density genetic linkage maps possible, thereby enabling the localization of candidate sites in the genome. Efficient genotyping and sequencing analyses have reduced the cumbersome steps of traditional fine mapping, improving the efficiency of trait analysis and fine mapping (Jaganathan et al., 2020). 3.4 Bioinformatics tools and software 3.4.1 Data analysis pipelines Data analysis pipelines are essential for processing and analyzing the large volumes of data generated by high-throughput genotyping platforms. Data analysis pipelines, such as the Genome Analysis Toolkit (GATK) and Tassel, streamline the process of converting raw genotype data into usable formats. These pipelines include tools for quality control, alignment, variant detection, and annotation. They enable researchers to efficiently analyze genetic data and identify significant associations between markers and traits, ensuring accuracy and consistency in data analysis (Guo et al., 2020; Sun et al., 2020). 3.4.2 Mapping algorithms Mapping algorithms, such as JoinMap, MapMaker, and R/qtl, are used to construct genetic maps from marker data and identify QTL. These algorithms analyze genetic data to determine the order and distance of markers on chromosomes. Advanced algorithms also integrate multi-parent and multi-trait data, enhancing the resolution and accuracy of genetic maps. They also identify genomic regions associated with specific traits, providing valuable information for marker-assisted selection and breeding programs (Guo et al., 2020; Jadon et al., 2023). 4 Achievements in High-Density Genetic Mapping 4.1 High-resolution maps High-resolution genetic maps have been pivotal in advancing wheat genetics. The development of these maps involves the integration of high-density single nucleotide polymorphism (SNP) arrays and genotypingby-sequencing (GBS) techniques. For instance, a high-density genetic map containing 10 739 loci was constructed using recombinant inbred lines (RILs) derived from a cross of 'Tainong 18×Linmai 6' (Guo et al., 2020). Similarly, another study developed a high-density genetic linkage map with 6 312 SNP and SSR markers to identify QTLs controlling kernel size and weight (Su et al., 2018). These maps have significantly enhanced the precision of QTL mapping, facilitating the identification of stable QTLs across multiple environments (Ren et al., 2021). 4.2 QTL mapping and trait discovery 4.2.1 Yield and agronomic traits QTL mapping has been instrumental in identifying genetic loci associated with yield and other agronomic traits. For example, a study mapped QTLs for kernel length, width, and thousand kernel weight, identifying stable QTLs in multiple environments (Su et al., 2018). Another research identified 85 QTLs for traits such as grain yield, plant height, and spike length under water deficit conditions, highlighting the importance of these loci in breeding programs aimed at improving yield under stress conditions (Sisi et al., 2022). 4.2.2 Disease resistance High-density genetic mapping has also facilitated the discovery of QTLs related to disease resistance. A high-resolution genome-wide association study (GWAS) identified 153 QTLs for resistance to leaf rust, yellow rust, and powdery mildew, with several QTLs delimited to ≤ 1.0 Mb intervals (Pang et al., 2021). This fine mapping is crucial for the identification of candidate genes and the development of disease-resistant wheat varieties. 4.2.3 Abiotic stress tolerance Mapping QTLs for abiotic stress tolerance has provided insights into the genetic basis of traits such as salt tolerance. For instance, a study mapped 90 stable QTLs for 15 agronomic traits under salt stress, with several QTLs validated in natural populations (Luo et al., 2020). These findings are essential for breeding wheat varieties that can withstand harsh environmental conditions.
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