Rice Genomics and Genetics 2024, Vol.15, No.2, 48-57 http://cropscipublisher.com/index.php/rgg 54 precisely modify specific sites in the rice genome to directly create new varieties with excellent agronomic traits. For multiple genes that affect complex traits, the agronomic traits of rice can be comprehensively controlled through a multi-gene simultaneous editing strategy to achieve comprehensive improvements in yield, quality and stress resistance. Conduct field trials and quality evaluation of rice materials improved through molecular marker-assisted selection or gene editing to ensure that the selected new varieties have excellent agronomic traits and adaptability. Evaluate the performance of new varieties in different ecological environments to ensure their broad adaptability and stable yield performance. The use of GWAS for rice genetic improvement is a systematic process, involving the entire chain from gene discovery to variety improvement. With the rapid development of genomics, bioinformatics, and molecular biotechnology, this strategy provides unprecedented accuracy and efficiency for rice breeding, and is expected to bring more high-yielding, high-quality, and stress-resistant rice in the near future. 5.2 The role of GWAS in breeding material selection, molecular marker-assisted selection and predictive breeding GWAS play a crucial role in modern plant breeding, especially in the selection of breeding materials, marker-assisted selection (MAS), and predictive breeding. By analyzing the correlation between genotype and phenotype, GWAS can efficiently identify genes and genetic markers related to important agronomic traits, providing scientific basis for precise breeding decisions. GWAS can help breeders identify individuals carrying favorable alleles in a wide range of germplasm resources. By analyzing the association between genetic diversity and specific traits in different rice varieties or wild species, GWAS reveals the genetic basis of trait formation and provides a basis for the optimization of germplasm resources. Genetic markers associated with superior agronomic traits identified through GWAS can be used as tools for selecting high-performance breeding materials. These markers help breeders identify potential excellent varieties at the initial screening stage, thereby shortening the breeding cycle and improving breeding efficiency. Genetic markers identified by GWAS provide powerful tools for MAS. Breeders can use these markers to directly select for specific traits, especially those for which phenotypic identification is difficult or costly, such as disease resistance, stress tolerance, etc. During the background selection (background purification) process, MAS can ensure that genomic regions other than target traits can quickly return to the state of the superior parent, thereby accelerating the breeding process and reducing undesirable cumulative linkage effects. The genotype-phenotype association information provided by GWAS can be used to build prediction models that can predict the trait performance of unphenotyped individuals. Predictive breeding uses this information to evaluate the performance of potential breeding materials without field testing, significantly saving time and resources. By integrating GWAS results and other genetic information, predictive breeding can optimize mating design and selection strategies and improve breeding efficiency. For example, by predicting the phenotypic performance of different genome combinations, breeders can selectively select the most promising hybrid combinations, thereby improving the success rate of breeding and the performance of innovative varieties. The applications of GWAS in breeding material selection, MAS and predictive breeding complement each other and jointly promote the precision and efficiency of plant breeding. The genetic markers and genes identified through GWAS not only accelerate the screening and optimization process of breeding materials, but also provide reliable molecular tools for MAS and make predictive breeding possible, which greatly enhances the accuracy and predictability of breeding. With the genome sequencing Ming et al. (2023) explored the role of cis-regulatory variation in rice domestication and breeding and its impact on panicle shape traits by analyzing young panicle (1~2 mm) transcriptome data of 275 representative rice varieties. The researchers analyzed the transcriptome data of 275 representative rice varieties at the young panicle (1~2 mm) stage of branch stem and spikelet primordium differentiation. They conducted transcriptome breadth
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