Chengxiu Wu# (吴成秀), Zedong Geng# (耿泽栋), Weikun Li (李为坤), Junli Ye (叶军立), Xiaoyuan Hao (郝小媛), Jieting Xu (许洁婷), Minliang Jin (金敏亮), Xiaoyu Wu (吴晓昱), Yuanhao Du (杜沅昊), Yunyu Chen (陈韵宇), Cheng Ma (马骋), Yu Gao (高豫), Yuyue Chen (陈昱月), Tianjin Xie (谢田晋), Songtao Gui (桂松涛), Yuanyuan Chen (陈园园), Jingyun Luo (罗靓赟), Yupeng Liu (刘玉鹏), Wenyu Yang (杨文宇), Jianbing Yan (严建兵)*, Wanneng Yang* (杨万能), Yingjie Xiao* (肖英杰)

Genome Biology, Published: 29 January 2026

Abstract

Background

Phenotypic diversity arises from the process of development and is shaped by genomic variation in plants. However, the genetic basis of growth dynamics remains poorly understood in maize.

Results

Here, we analyze 679 maize inbred lines derived from a synthetic CUBIC population with approximately 2.8 million SNPs, leveraging high-throughput phenotyping to capture 1,002,240 RGB images across 18 growth stages. We quantify 67 image-based traits (i-traits), revealing distinct dynamic patterns throughout development. Genome-wide association studies identify 857 quantitative trait loci (QTLs) influencing growth variation, with 88.6% classified as period-specific dynamic QTLs exhibiting modest effects, and 11.4% as conservative QTLs with sustained effects. Notably, 1.5% of cryptic pleiotropic QTLs spanning different growth stages suggest genetic relocations during development. These QTLs enhance heritability estimates for mature traits by an average of 6.2%. We further characterize the novel function of key genes linked with these QTLs, including BRD1 with the pleiotropic effects on plant height and perimeter of convex hull and ZmGalOx1 with the broad-spectrum regulation of plant architecture. Developmental rewiring of epistatic networks shapes maize growth, underscoring the vitality of temporal genetic regulation. Trajectory modeling of i-traits across periods decodes the growth variation patterns, supporting the ontogenic hypothesis driven predictive breeding strategies.

Conclusion

The findings elucidate the genetic architecture underlying growth dynamics from a spatial–temporal perspective, offering novel insights for maize improvement.

论文链接：https://link.springer.com/article/10.1186/s13059-026-03957-8