Maize Genomics and Genetics 2024, Vol.15, No.4, 204-217 http://cropscipublisher.com/index.php/mgg 206 researchers to explore genetic diversity, understand evolutionary processes, and develop improved maize varieties for sustainable agriculture. Figure 1 Timeline of sequencing technologies, major genomes and maize genomics (Adopted from Andorf et al., 2019) Image caption: The figure shows three timelines. The first timeline list the release dates of major sequencing technologies focusing on the early first-generation technologies (1950–1990) and the next-generation sequencing technologies (2000s). The second timeline shows the release dates of four major genomes (yeast, arabidopsis, human and rice) and the first reported pan-genome (bacteria). The third timeline shows the release dates of maize genomes and major genotype datasets (Adopted from Andorf et al., 2019) 3 Genomic Selection and Marker-Assisted Breeding 3.1 Principles of genomic selection in maize breeding Genomic selection (GS) is a revolutionary approach in plant breeding that leverages genome-wide marker data to predict the breeding values of individuals. Unlike traditional marker-assisted selection (MAS), which focuses on a few markers associated with specific traits, GS uses all available marker data to estimate the genetic potential of breeding candidates. This comprehensive approach allows for the selection of superior genotypes with greater accuracy and efficiency, thereby accelerating the breeding cycle and enhancing genetic gains (Crossa et al., 2017; Rice and Lipka, 2021; Budhlakoti et al., 2022). The principle of GS is rooted in the use of genomic-estimated breeding values (GEBVs), which are derived from statistical models that incorporate genome-wide marker information. These models predict the performance of untested genotypes based on their genetic makeup, enabling breeders to make informed decisions without extensive phenotypic evaluations. This is particularly advantageous for complex traits controlled by multiple genes with small effects, where traditional MAS falls short (Jannink et al., 2010; Budhlakoti et al., 2022). GS has shown significant promise in maize breeding, where it has been applied to develop superior inbreds and hybrids. The integration of high-throughput phenotypic, genotypic, and other -omic data has further refined GS models, allowing for the encapsulation of non-additive genetic effects, genotype-by-environment interactions, and multiple levels of the biological hierarchy. These advancements have led to more accurate predictions of breeding values and, consequently, more efficient breeding programs (Crossa et al., 2017; Rice and Lipka, 2021).
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