A large-scale QTL mapping on 10 plant architecture traits across 10 RIL populations reveals the complex genetic basis of plant architecture in maize.

Summary: Circular RNAs (circRNAs) are covalently closed RNA molecules. Recent studies have shown that circRNAs can arise from the transcripts of transposons. Given the prevalence of transposons in the maize genome and dramatic genomic variation driven by transposons, we hypothesize that transposons in maize may be involved in the formation of circRNAs and further modulate phenotypic variation. We performed circRNA‐Seq on B73 seedling leaves and uncovered 2804 high‐confidence maize circRNAs, which show distinct genomic features. Comprehensive analyses demonstrated that sequences related to LINE1‐like elements (LLEs) and their Reverse Complementary Pairs (LLERCPs) are significantly enriched in the flanking regions of circRNAs. Interestingly, as the number of LLERCPs increase, the accumulation of circRNAs varies, whereas that of linear transcripts decreases. Furthermore, genes with LLERCP‐mediated circRNAs are enriched among loci that are associated with phenotypic variation. These results suggest that circRNAs are likely to be involved in the modulation of phenotypic variation by LLERCPs. Further, we showed that the presence/absence variation of LLERCPs was associated with expression variation of circRNA‐circ1690 and was related to ear height, potentially through the interplay between circRNAs and functional linear transcripts. Our first study of maize circRNAs uncovers a potential new way for transposons to modulate transcriptomic and phenotypic variations.

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.