Molecular Plant Breeding 2024, Vol.15, No.1, 8-14 http://genbreedpublisher.com/index.php/mpb 12 also found haplotypes that can be used to select dwarf, early flowering, and high-yield plants (Begum et al., 2015). Similarly, in apple breeding, the study “Genome-wide Association Mapping of Flowering and Ripening Periods in Apple” (Front. Plant Sci., 2017) conducted a large-scale genome-wide association study (GWAS) on these phenotypic characteristics by using association panels from 1 168 different apple genotypes across Europe. The study identified key SNPs that affect flowering and maturation stages, and explored candidate genes for these genomic regions (Urrestarazu et al., 2017). Breeding 3.0 has improved the accuracy and efficiency of breeding by integrating genetic and genomic data, improving plant varieties based on genotype selection, and making important contributions to quantitative trait analysis through genome-wide selection. These examples of applications fully demonstrate the potential and practical importance of Breeding 3.0 in plant breeding. 5 Challenges and Future Prospects 5.1 Technical and methodological challenges faced by Breeding 3.0 Although Breeding 3.0 has brought many innovations, it still faces some technical and methodological challenges. Firstly, Breeding 3.0 requires large-scale genetic and genomic data, which may be a challenge for resource limited breeding projects. Obtaining and analyzing large-scale genetic and genomic data requires high costs and complex technologies, which may limit the application of Breeding 3.0 in some regions and crops. Breeding 3.0 requires advanced computing and information processing systems to interpret and utilize a large amount of genetic and genomic data. Processing and interpreting such a large amount of data requires highly specialized skills and powerful computing power, which may limit the application of Breeding 3.0 in some breeding projects. Breeding 3.0 also needs to overcome ethical and legal issues related to genetic and genomic data. For example, privacy and intellectual property issues may pose challenges to data sharing and collaboration. In addition, for applications involving emerging technologies such as gene editing, relevant regulations and ethical guidelines need to be established to ensure their safety and sustainability. 5.2 Future directions and prospects of breeding 3.0 development The development prospects of Breeding 3.0 are broad, and there are many future directions to explore. Firstly, with the advancement of technology and the reduction of costs, Breeding 3.0 will be more widely applied to various crops and regions. This will help improve the adaptability, yield and quality of crops, and meet the growing global food demand. Breeding 3.0 will further integrate multiple genetic and genomic data, including phenotype, genome sequence, transcriptome data, etc., to gain a more comprehensive understanding and utilization of the genetic potential of crops. This will help discover more genes related to agronomic traits and improve the accuracy of predicting breeding values. Breeding 3.0 will continue to promote the development of gene editing and genome modification technologies. With the maturity and promotion of gene editing technology, we will be able to more accurately modify crop genomes and create new varieties with greater agronomic value. 5.3 The introduction of the concept of Breeding 4.0 Breeding 4.0 represents a new level in the field of breeding, which involves synthesizing any known allele genome into an ideal combination through the ability of the whole genome. We are currently at the forefront of Breeding 4.0, which can purposefully combine functional genetic variations faster and better than ever before. The development of this breeding level benefits from significant technological advancements in genetics and information systems. For example, the cost of genome resequencing research can now be lower than that of repeated yield trials, and genome editing is expected to enable parallel and precise modifications of hundreds (possibly hundreds) of positions per generation. High throughput phenotyping can measure numerous traits with unprecedented spatiotemporal resolution, and machine learning methods make the processing and interpretation of agronomic data far beyond human capabilities.
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