Triticeae Genomics and Genetics, 2024, Vol.15, No.4, 206-220 http://cropscipublisher.com/index.php/tgg 213 Furthermore, the rapid introgression platform established for transferring the genetic variations of Aegilops tauschii to elite wheats has enriched the wheat germplasm pool. This platform has generated synthetic octoploid wheat pools, which have shown great potential for wheat breeding, as confirmed by laboratory and field analyses. The integration of these diverse genetic resources into elite wheat germplasm has significantly contributed to the development of high-yielding, stress-resistant wheat varieties, thereby enhancing global wheat productivity and food security (Mujeeb-Kazi et al., 2013). 6 Methodologies for Characterizing and Utilizing Synthetic Wheat 6.1 Marker-assisted selection (MAS) and genomic selection Marker-assisted selection (MAS) and genomic selection (GS) are pivotal methodologies in modern wheat breeding, particularly for leveraging genetic diversity from synthetic wheat. MAS involves the use of molecular markers to assist in the selection of desirable traits, which can significantly enhance the efficiency and precision of breeding programs. Various types of molecular markers, such as single nucleotide polymorphisms (SNPs), have been effectively utilized in plant breeding (He et al., 2014). The advent of next-generation sequencing (NGS) technologies has further revolutionized MAS by enabling high-throughput genotyping and the discovery of new markers through techniques like genotyping-by-sequencing (GBS) (He et al., 2014). MAS has been successfully applied to improve disease resistance in wheat, with notable examples including the transfer of resistance genes such as Lr34 and Yr36 for rust resistance, and Fhb1 for Fusarium head blight resistance (Miedaner and Korzun, 2012). However, MAS is often limited by the small effects of individual quantitative trait loci (QTL) and the complexity of polygenic traits (Gupta et al., 2010; Miedaner and Korzun, 2012). To address these limitations, genomic selection (GS) has emerged as a more comprehensive approach. GS uses genome-wide marker data to predict the genetic value of selection candidates, capturing both small and large effect QTLs (Heffner et al., 2011). Studies have shown that GS can achieve higher prediction accuracies than conventional MAS, making it a promising tool for improving complex traits in wheat (Heffner et al., 2011; Arruda et al., 2016). 6.2 Advanced backcross QTL analysis Advanced backcross QTL (AB-QTL) analysis is a powerful method for identifying and utilizing beneficial alleles from wild relatives of wheat. This approach involves backcrossing a wild relative with an elite cultivar and then using molecular markers to identify QTLs associated with desirable traits. For instance, a study involving a cross between the German winter wheat variety 'Prinz' and the synthetic wheat line W-7984 identified 40 putative QTLs for yield and yield components (Huang et al., 2003). Despite the overall inferior agronomic performance of the synthetic wheat, 60% of the identified QTLs from W-7984 had positive effects on agronomic traits, demonstrating the potential of AB-QTL analysis to transfer favorable alleles from wild relatives into elite wheat varieties (Huang et al., 2003). The AB-QTL strategy has been particularly effective in improving complex traits such as yield, where multiple QTLs contribute to the overall performance. By integrating molecular breeding techniques with traditional backcrossing, breeders can more efficiently incorporate beneficial alleles from synthetic wheat into commercial cultivars, thereby enhancing genetic diversity and improving crop performance (Huang et al., 2003). 6.3 Use of high-throughput phenotyping High-throughput phenotyping (HTP) is an essential tool for characterizing and utilizing synthetic wheat in breeding programs. HTP involves the use of advanced imaging and sensor technologies to rapidly and accurately measure a wide range of phenotypic traits in large populations. This approach allows for the collection of extensive phenotypic data, which can be integrated with genotypic information to identify and select superior genotypes (Gupta et al., 2010; He et al., 2014). The integration of HTP with MAS and GS can significantly enhance the efficiency of breeding programs. For example, HTP can be used to measure traits such as plant height, biomass, and disease resistance, which are then correlated with molecular marker data to identify QTLs and predict genetic values (Gupta et al., 2010; He et al.,
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