Triticeae Genomics and Genetics, 2024, Vol.15, No.4, 185-195 http://cropscipublisher.com/index.php/tgg 192 of applying QTL information in practical breeding and genetics. Addressing these issues will require continued advancements in statistical methods, computational tools, and experimental approaches, as well as collaborative efforts across disciplines and institutions. 6 Future Directions inTriticeae QTL Research and Breeding 6.1 Advancements in genomic technologies The field of quantitative trait loci (QTL) analysis in Triticeae has significantly benefited from advancements in genomic technologies. Traditional methods, such as analysis of variance, have evolved to incorporate high-resolution genetic maps and multiple markers, enhancing the power to study and locate multiple interacting QTLs (Doerge, 2002). The introduction of whole-genome resequencing techniques, such as QTL-seq, has revolutionized the rapid identification of QTLs. This method involves sequencing DNA from two bulked populations with extreme trait values, allowing for the efficient mapping of QTLs in a variety of plant species, including rice and potentially Triticeae (Takagi et al., 2013). Moreover, Bayesian mapping methods have been developed to handle incomplete inbred line cross data, providing probabilistic measures to estimate the number and location of QTLs on chromosomes (Sillanpää and Arjas, 1998). These advancements not only improve the precision of QTL mapping but also facilitate the identification of candidate genes associated with important agronomic traits. For instance, comparative genomic analysis has identified stable QTLs for grain yield, quality traits, and micronutrient contents in wheat, highlighting the potential for these technologies to enhance breeding programs (Shariatipour et al., 2021). 6.2 Integration of multi-omics data The integration of multi-omics data, including genomics, transcriptomics, proteomics, and metabolomics, represents a promising direction for QTL research in Triticeae. Expression QTL (eQTL) analysis, which treats transcripts as individual phenotypes, has emerged as a powerful tool to understand the molecular basis of quantitative traits (Kliebenstein, 2009). This approach can elucidate the ecological and evolutionary significance of transcript variation and accelerate the identification of genes underlying complex traits. Additionally, multi-trait analysis models that consider the correlated structure of multiple traits can improve the statistical power and precision of QTL mapping. These models allow for the testing of hypotheses related to genetic correlations between traits, pleiotropy, and QTL by environment interactions, which are crucial for understanding the genetic architecture of complex traits (Jiang and Zeng, 1995). The integration of multi-omics data can provide a comprehensive view of the genetic and molecular networks that control important agronomic traits, facilitating the development of more resilient and high-yielding Triticeae varieties. 6.3 Climate change adaptation Climate change poses significant challenges to agriculture, necessitating the development of crop varieties that can withstand abiotic stresses such as drought, heat, and salinity. QTL mapping for stress tolerance traits is critical for breeding Triticeae varieties that can adapt to changing environmental conditions. For example, QTLs associated with salt stress tolerance have been identified in wheat, with specific loci linked to traits such as relative water content, membrane stability, and chlorophyll content (Ilyas et al., 2019). These findings highlight the potential for marker-assisted selection to develop salinity-tolerant wheat varieties. Furthermore, the use of multiparental cross populations in QTL mapping can enhance the genetic basis and mapping resolution for stress tolerance traits. This approach has been successfully applied in durum wheat to identify QTLs for agronomic traits across different environments, demonstrating the prevalence of environment-specific QTLs with small effects (Milner et al., 2016). By leveraging the genetic diversity and adaptability of Triticeae species, breeders can develop varieties that are better equipped to cope with the impacts of climate change. In conclusion, the future of QTL research in Triticeae lies in the continued advancement of genomic technologies, the integration of multi-omics data, and the focus on climate change adaptation. These directions will not only enhance our understanding of the genetic basis of complex traits but also drive the development of resilient and high-yielding Triticeae varieties, ensuring food security in the face of global environmental challenges.
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