Molecular Plant Breeding 2024, Vol.15, No.3, 100-111 http://genbreedpublisher.com/index.php/mpb 106 Figure 4 Construction of a tomato 8-way MAGIC population (Adopted from Pascual et al., 2015) Image caption: This figure illustrates the breeding scheme used to create a diverse tomato MAGIC population, combining large fruited (L1 Levovil, L2 Stupicke PR, L3 LA0147, L4 Ferum) and small fruited (C1 Cervil, C2 Criollo, C3 Plovdiv24A, C4 LA1420) founders. Initial crosses between S. lycopersicumandS. lycopersicumvar. cerasiforme lines produced F1 hybrids. Subsequent double crosses and controlled intercrossing of 240 plants led to the creation of 480 F1-like individuals, each incorporating parts of the eight founder genomes. These individuals were further propagated through three selfing generations to establish a collection of 397 MAGIC lines, characterized at the F4-like stage for this study (Adapted from Pascual et al., 2015) 5 Methodological Approaches 5.1 Overview of the techniques used to analyze and exploit MAGIC populations MAGIC populations represent a significant advancement in plant breeding and genetic analysis. Unlike traditional bi-parental populations, MAGIC populations are derived from multiple founder parents, which increases genetic diversity and improves the relevance of QTLs mapping to breeding programs (Wei and Xu, 2015; Huynh et al., 2018; Arrones et al., 2020). The development of MAGIC populations can be achieved through "funnel" or "diallel" cross-designs (Figure 5), ensuring a balanced representation of each parent's genome in the resulting RILs (Arrones et al., 2020). These populations are characterized by high levels of genetic recombination, absence of genetic structure, and substantial genetic and phenotypic diversity, making them a powerful tool for the dissection of complex traits (Arrones et al., 2020). The analysis of MAGIC populations involves genotyping and phenotyping the RILs to create a genetic mosaic that reflects the contribution of all founder alleles. This process is confirmed through techniques such as SNP genotyping, which helps in identifying the homozygosity and diversity of agronomic traits across different environments (Huynh et al., 2018). The genetic analysis of these populations allows for the identification of QTLs for various traits, which can lead to genetic gain and the discovery of genes responsible for important agronomic characteristics (Huynh et al., 2018).
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