Molecular Plant Breeding 2024, Vol.15, No.1, 8-14 http://genbreedpublisher.com/index.php/mpb 11 in evaluating multiple metabolic and quality characteristics through cross validation, and estimated the impact of different factors on its accuracy. The results indicated that the accuracy of predicting phenotype values is closely related to the heritability of traits. The size of the training population increases the accuracy of predictions. The optimal conditions were to use 122 varieties and 5 995 single nucleotide polymorphism (SNP) markers (Duangjit et al., 2016). Obviously, by integrating genetic and genomic data, the accuracy of predicting tomato fruit quality traits can be significantly improved. In citrus breeding, the study “Genome Wide Selection in Citrus Breeding” (Genetics and Molecular Research, 2016) evaluated the efficiency of genome-wide selection (GWS) in citrus populations and compared it with phenotypic selection. Research has shown that GWS can accurately predict phenotypes and shorten selection cycles. This indicated that genome selection is useful in citrus breeding as it can predict phenotypes early and accurately (Gois et al., 2016). Similarly, in maize breeding, the study “Genome Wide Association Study for Drought, Aflatoxin Resistance, and Important Agronomic Traits of Maize Hybrids in the Sub-Tropics” (The Plos One, 2015) used a diversity panel consisting of 346 maize inbred lines from temperate, subtropical, and tropical regions for genome-wide association analysis. The study found 10 quantitative trait variations related to important agronomic traits such as grain yield, plant and spike height, and flowering time. These findings demonstrated the potential of genomic association studies in identifying major variations that affect quantity and complex traits such as yield under drought conditions (Farfan et al., 2015). Similarly, in rice breeding, the study “Genomic Selection and Association Mapping in Rice (Oryza sativa): Effect of Trait Genetic Architecture, Training Population Composition, Marker Number and Statistical Model on Accuracy of Rice Genomic Selection in Elite, Tropical Rice Breeding Lines” (Plos Genetics, 2015) evaluated the effectiveness of genomic selection (GS) in rice breeding. Research has found that genome prediction models outperform predictions based solely on lineage records for all three traits, including grain yield and flowering time. The study also suggested that using a subset of markers every 0.2 cM for genome selection is sufficient in these rice breeding materials (Spindel et al., 2015). 4.2 Improving plant varieties based on genotype selection Another important application of Breeding 3.0 is the improvement of plant varieties based on genotype selection. By utilizing genomic information and genotype selection technologies, Breeding 3.0 can more accurately select plant individuals with target traits, accelerating the breeding process. For example, in wheat breeding, the study “Genomic Selection for Yield and Yyield-related Traits in Durum Wheat” (Molecular Breeding, 2018) evaluated the potential of single trait (ST) and multi trait (MT) genomic prediction models for yield and quality traits in durum wheat. The study used a breeding population (BP) of 170 varieties and advanced breeding lines, as well as 154 double haploid (DH) lines. Both populations underwent Infinium iSelect 90K SNP chip genotyping and multiple trait phenotypes. The study applied six ST-GS models and three MT prediction methods to predict yield, protein content, gluten index, and dough characteristics. The accuracy of ST prediction varies between 0.5 and 0.8 for different traits and models. Except for BayesA and BayesB better predicting gluten index, toughness, and strength in DH populations, the prediction accuracy of most traits in both populations was comparable (Haile et al., 2018). This study indicated that genotype selection can significantly improve the selection efficiency of yield and related traits in durum wheat. In cassava breeding, the study “Genome-wide association and prediction reveals the genetic architecture of cassava mosaic disease resistance and prospects for rapid genetic improvement” (The Plant Geneme, 2015) was the first genome-wide association mapping study conducted on 6128 African cassava breeding lines, aimed at identifying genes related to cassava mosaic disease resistance. Research has found that a region on chromosome 8 is the main resistance region, but 13 small effect regions have also been identified. In addition, the study also evaluated the accuracy of genomic selection for CMD resistance (Wolfe et al., 2015). 4.3 Contribution of genome-wide selection to quantitative trait analysis The genome-wide selection technology in Breeding 3.0 plays an important role in analyzing quantitative traits. Through genome-wide selection, we can accurately analyze the genetic basis of quantitative traits and identify key genes related to these traits. For example, in rice breeding, the study “Genome-wide association mapping for yield and other agronomic traits in an elite breeding population of tropical rice (Oryza sativa)” (PLoS One, 2015) successfully identified 52 QTLs for 11 agronomic traits by using genome-wide association analysis technology, including large effect QTLs for flowering time and grain length/grain width/grain length-width ratio. The study
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