Triticeae Genomics and Genetics, 2024, Vol.15, No.4, 185-195 http://cropscipublisher.com/index.php/tgg 186 This study also seeks to explore the potential of marker-assisted selection (MAS) in accelerating the breeding process by utilizing identified QTLs for targeted trait improvement. 2 Methodological Advances in QTL Analysis 2.1 Traditional QTL mapping techniques Traditional QTL mapping techniques have laid the foundation for understanding the genetic basis of quantitative traits. These methods typically involve the use of biparental populations, where two parent lines are crossed to produce a mapping population. The phenotypic data of the progeny, along with their genotypic data, are analyzed to detect associations between markers and traits. Early methods, such as single-marker analysis and interval mapping, were based on simple statistical models like analysis of variance (ANOVA) and regression analysis (Doerge, 2002; Singh and Singh, 2015). Single QTL mapping methods detect one QTL at a time, which can be limiting when dealing with complex traits controlled by multiple loci. To address this, multiple QTL mapping (MQM) was developed, combining multiple regression analysis with interval mapping to include all significant QTLs in the genetic model (Singh and Singh, 2005). Composite interval mapping (CIM) further improved the precision of QTL detection by incorporating background genetic variation into the model, thus increasing the power to detect QTLs and estimate their effects more accurately (Jiang and Zeng, 1995; Singh and Singh, 2015). 2.2 Modern QTL mapping approaches Modern QTL mapping approaches have evolved to address the limitations of traditional methods, particularly in terms of resolution and the ability to handle complex traits. One significant advancement is the use of multiparental populations, such as recombinant inbred lines (RILs) derived from multiple parents. These populations provide a broader genetic base and higher mapping resolution compared to biparental populations. For instance, a study on durum wheat utilized a RIL population derived from four elite cultivars, resulting in the identification of 16 QTLs across different environments and detection methods (Figure 1) (Milner et al., 2016). Figure 1 Proportion of 338 RILs assigned to each of the four founders across the NCCR linkage map (Adapted from Milner et al., 2016) Image caption: The chart shows that the probabilities of the founders vary across different chromosome regions, indicating that each founder contributes differently to different chromosomes. For example, Claudio and RasconTarro show significant probability fluctuations across multiple chromosomes, while Colosseo and Neodur have relatively stable probabilities. Overall, the probabilities for all founders range between 0.15 and 0.35. These probability curves help to understand each founder's genetic contribution across the genome, which is significant for breeding and genetic analysis (Adapted from Milner et al., 2016)
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