Molecular Plant Breeding 2024, Vol.15, No.3, 100-111 http://genbreedpublisher.com/index.php/mpb 107 Figure 5 Cross-designs of a 4-way MAGIC population where the founders are A, B, C, and D (Adopted from Arrones et al., 2020) Image caption: (A) “funnel” design; (B) “diallel” design; and (C) achievement of homozygous individuals by doubled haploids (DH) production, or by several rounds of selfing following the single-seed descent (SSD) method (Adopted from Arrones et al., 2020) 5.2 Statistical models and computational tools employed in genetic studies involving MAGIC populations The complexity of MAGIC populations requires sophisticated statistical models and computational tools for effective QTL mapping. A random-model approach has been developed to address the challenges posed by multiple founder alleles and the control of genetic background information (Wei and Xu, 2015). In this approach, the founder effects of each locus are treated as random effects following a normal distribution with locus-specific variance, and a polygenic effect is fitted to the model to control the genetic background (Wei and Xu, 2015). This method contrasts with fixed-model approaches and involves scanning the entire genome one locus at a time using likelihood-ratio test statistics, which has been shown to increase statistical power and reduce type I error compared to other methods like composite interval mapping (CIM) and multiparent whole-genome average interval mapping (MPWGAIM) (Wei and Xu, 2015). Specific software development has also played a crucial role in facilitating the genetic analysis of MAGIC populations. These tools are designed to handle the complex genetic constitutions and to assist in the selection of recombinant elite breeding material (Arrones et al., 2020). The continuous growth in the number of MAGIC populations across different crops underscores the need for and effectiveness of these computational resources in plant breeding and genetic analysis (Arrones et al., 2020). In summary, the methodological approaches to analyzing and exploiting MAGIC populations involve a combination of strategic cross-designs, advanced genotyping techniques, and the application of robust statistical models and computational tools. These methodologies enable the effective dissection of complex traits and the selection of superior breeding material, promising significant contributions to the future of plant breeding. 6 Challenges and Limitations 6.1 Technical and practical challenges in developing and utilizing MAGIC populations Developing and utilizing MAGIC populations present several technical and practical challenges. Firstly, the creation of MAGIC populations involves crossing multiple founder lines, which requires careful selection and an understanding of the genetic background and compatibility of each parent. This process can be technically demanding, as it involves multiple generations of crossing and selection to ensure a thorough mixing of the parental genomes.
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