The past decade has witnessed rapid developments in gene discovery, biological big data (BBD), artificial intelligence (AI)‐aided technologies, and molecular breeding. These advancements are expected to accelerate crop breeding under the pressure of increasing demands for food. Here, we first summarize current breeding methods and discuss the need for new ways to support breeding efforts. Then, we review how to combine BBD and AI technologies for genetic dissection, exploring functional genes, predicting regulatory elements and functional domains, and phenotypic prediction. Finally, we propose the concept of intelligent precision design breeding (IPDB) driven by AI technology and offer ideas about how to implement IPDB. We hope that IPDB will enhance the predictability, efficiency, and cost of crop breeding compared with current technologies. As an example of IPDB, we explore the possibilities offered by CropGPT, which combines biological techniques, bioinformatics, and breeding art from breeders, and presents an open, shareable, and cooperative breeding system. IPDB provides integrated services and communication platforms for biologists, bioinformatics experts, germplasm resource specialists, breeders, dealers, and farmers, and should be well suited for future breeding.

Functional cloning and manipulation of genes controlling various agronomic traits are important for boosting crop production. Although bulked segregant analysis (BSA) is an efficient method for functional cloning, its low throughput cannot satisfy the current need for crop breeding and food security. Here, we review the rationale and development of conventional BSA and discuss its strengths and drawbacks. We then propose next-generation BSA (NG-BSA) integrating multiple cutting-edge technologies, including high-throughput phenotyping, biological big data, and the use of machine learning. NG-BSA increases the resolution of genetic mapping and throughput for cloning quantitative trait genes (QTGs) and optimizes candidate gene selection while providing a means to elucidate the interaction network of QTGs. The ability of NG-BSA to efficiently batch-clone QTGs makes it an important tool for dissecting molecular mechanisms underlying various traits, as well as for the improvement of Breeding 4.0 strategy, especially in targeted improvement and population improvement of crops.

Drought stress presents a significant threat to the growth and development of maize. It is important to study the genes and mechanisms that contribute to drought tolerance. In this study, we identified ZmHDT103 (that encodes a histone deacetylase) by conducting a homologous sequence comparison and found that the expression of ZmHDT103 in maize seedlings is responsive to treatment with polyethylene glycol (PEG). We utilized CRISPR/Cas9 gene editing technology to generate three distinct knockout lines and obtained the ChinaMU mutant of the ZmHDT103 gene. Under drought conditions, the seedlings of ZmHDT103 mutants exhibited significantly lower water loss rate (WLR), relative electrolytic leakage (REL), hydrogen peroxide (H₂O₂) level, and malonaldehyde (MDA) level than those of their wild-type (WT) counterparts. Additionally, the seedlings of ZmHDT103 mutants exhibited significantly higher levels of abscisic acid (ABA), relative water content (RWC), peroxidase (POD), and proline (Pro) than those of the WT control. These findings indicate that ZmHDT103 acts as a negative regulator of drought tolerance in maize.

The popularity of genomic selection (GS) has increased owing to its prospects in commercial breeding. It is necessary to enhance GS to increase its efficiency. In this study, a maize BC₁F_3:4 population, consisting of 481 families, was evaluated for days to anthesis in four environments, and genotyped with DNA chips including 55,000 single nucleotide polymorphisms (SNPs). This population was used to investigate whether GS could be enhanced by borrowing information from the genetic basis and genotype-by-environment (G × E) interaction. The results showed that: 1) fitting the top four large-effect SNPs as fixed effects could increase prediction accuracy, including three minor-effect SNPs explaining less than 10% phenotypic variance; 2) the increase of prediction accuracy when fitting large-effect SNPs as fixed effects was related to the decrease of genetic variance; 3) generally, the GS model fitting large-effect SNPs as fixed effects and G × E component enhanced GS. Therefore, we propose fitting large-effect markers as fixed effects and G × E effect for crop breeding projects in order to obtain accurately predicted phenotypic data and conduct efficient selection of desired plants.