Reshaping epistatic network modules enhances wheat yield potential

上位性 产量(工程) 农学 计算机科学 数学 生物 遗传学 物理 基因 热力学
作者
Mou Yin,Haiming Han,Weihua Liu,Lingrang Kong,Xingpu Li,Wanquan Ji,Jizhong Wu,Guangbing Deng,Haibin Dong,Jinpeng Zhang,Shenghui Zhou,Zhimeng Zhang,Yuanzheng Hou,Haojie Wang,Caihong Zhao,Xuanzhao Li,Qiaoling Luo,Xiaomin Guo,Xinming Yang,Hong‐Qing Ling
标识
DOI:10.1101/2025.04.01.646715
摘要

Abstract Increasing wheat yield is a key approach to ensuring global food security and an enduring focus in crop breeding. Here, we assembled a panel of 3,030 wheat lines to dissect the genetic mechanisms of yield improvement. We conducted large-scale field trials across five ecological environments over two consecutive years to evaluate yield performance and constructed a haplotype atlas using the Wheat 660K genotype array. 234 quantitative trait loci (QTLs) and 522 haplotype blocks (HBs) were identified for 14 traits through genome-wide association studies (GWAS), including 10 QTLs and 35 HBs associated with grain yield. Genomic analysis revealed that wheat yield improvement is primarily driven by changes in the epistatic network rather than the introduction of new haplotype segments. SNPs or HBs explain 53.3%-62.9% of the phenotypic variation in yield, whereas epistasis explains 70.4%, highlighting the role of epistasis in yield improvement. Moreover, yield improvement is significantly correlated with the accumulation of favorable epistatic modules. Pedigree-based analysis revealed that the modules remained relatively stable under the pressure of breeding selection, while new favorable modules were also created during hybrid breeding, highlighting that crop design breeding should be based on genetic modules. This study provides insights into genetic mechanism of wheat yield improvement and guidance for designing future wheat in the era of artificial intelligence.
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