Bioscience Evidence 2025, Vol.15, No.5, 249-259 http://bioscipublisher.com/index.php/be 252 4 Applications in Maize Breeding 4.1 Trait improvement examples Nowadays, corn breeding mostly utilizes big data and new biotechnologies. Researchers have made breakthroughs in multiple aspects of corn, including yield, drought resistance, disease resistance and nutritional quality, through techniques and methods such as genomic selection (GS), multi-omics data and machine learning models. Lorenzo et al. (2022) utilized the BREEDIT platform and CRISPR/Cas9 technology to simultaneously improve 48 growth-related genes in corn. The improved corn material has seen a 5% to 10% increase in leaf length and a 20% increase in leaf width. Molecular marker-assisted selection and genomic selection have also been widely applied in drought and disease resistance improvement, significantly enhancing the yield and stability of maize under adverse conditions (Gedil and Menkir, 2019; Liu and Qin, 2021; Prasanna et al., 2021; He et al., 2024). 4.2 Accelerated breeding cycles Big data and new technologies have significantly shortened the time required for corn breeding. Ploidy doubling (DH) and genome editing techniques (such as the IMGE system) can obtain homozygous superior lines within two generations, which is much faster than traditional methods (Nepolean et al., 2018; Wang et al., 2019; Prasanna et al., 2021). Meanwhile, high-throughput genotyping and phenotypic analysis, combined with automated data processing platforms (such as AutoGP), can quickly obtain and analyze genotype-phenotypic data, improving the selection efficiency. Furthermore, facilities such as "speed breeding" and digital greenhouses can enable corn to complete multiple generations of cycles in a year by controlling photoperiod and growth environment, accelerating the breeding and promotion of new varieties (Singh et al., 2020). 4.3 Digital platforms and breeding networks Digital platforms and global breeding networks have also made cooperation and innovation in corn breeding smoother. Platforms like AutoGP, which combine genotype extraction, phenotype extraction, GS models and multiple machine learning algorithms, can provide users with one-stop intelligent breeding tools and lower the threshold for using complex models (Wu et al., 2025). International maize improvement projects (such as IITA, CIMMYT) have promoted cross-institutional and cross-regional cooperation and variety sharing through digital data management, decision support systems and high-throughput phenotypic platforms (Gedil and Menkir, 2019; Prasanna et al., 2021; Liu et al., 2025). These platforms have not only improved the utilization rate of data, but also accelerated the adaptation and promotion of new varieties in different regions, promoting the modernization of global corn breeding. 5 Case Study: Big Data in Action for Maize Breeding 5.1 Background of the program With the development of high-throughput omics and automated phenotypic technologies, corn breeding has entered the stage of big data. Traditional methods have limited efficiency and accuracy when dealing with complex traits and multi-environment data. To better predict yield and improve traits, some international and regional breeding projects have begun to introduce multi-omics data, environmental information and artificial intelligence tools to make maize improvement smarter and more data-dependent (Beyene et al., 2021; Fritsche-neto et al., 2021; Sarzaeim et al., 2022; Zhang et al., 2023; Wu et al., 2024). 5.2 Implementation of big data analytics In 2024, Wu's team integrated multi-omics data from 156 maize recombinant inbred lines (including 2 496 SNP markers, 46 image traits across 16 growth stages, and 133 major metabolites), all collected through an automated phenotypic platform. The team used methods such as partial least squares, random forests, and Gaussian process regression to predict its yield, and compared different data types and feature screening methods. Research has confirmed that the use of multi-omics data combined with the random forest model can significantly improve the prediction accuracy (Wu et al., 2024). In 2021, Beyene et al. carried out international projects such as CIMMYT. In tropical corn breeding, they compared different genomic selection strategies by leveraging the genotype and phenotype data accumulated over many years and optimized the predictions through cross-validation.
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