生物
数量性状位点
遗传建筑学
全基因组关联研究
特质
关联映射
遗传关联
遗传学
候选基因
基因组
选择(遗传算法)
表型
人口
基因
基因型
单核苷酸多态性
人口学
计算机科学
人工智能
社会学
程序设计语言
作者
Jie Gao,Xin Hu,Chunyan Gao,Guang Chen,Hui Feng,Zhen Jia,Peimin Zhao,Haiyang Yu,Huaiwen Li,Zedong Geng,Jingbo Fu,Jun Zhang,Yikeng Cheng,Bo Yang,Zhanghan Pang,Daoquan Xiang,Jizeng Jia,Handong Su,Hailiang Mao,Caixia Lan
摘要
Summary Dissecting the genetic basis of complex traits such as dynamic growth and yield potential is a major challenge in crops. Monitoring the growth throughout growing season in a large wheat population to uncover the temporal genetic controls for plant growth and yield‐related traits has so far not been explored. In this study, a diverse wheat panel composed of 288 lines was monitored by a non‐invasive and high‐throughput phenotyping platform to collect growth traits from seedling to grain filling stage and their relationship with yield‐related traits was further explored. Whole genome re‐sequencing of the panel provided 12.64 million markers for a high‐resolution genome‐wide association analysis using 190 image‐based traits and 17 agronomic traits. A total of 8327 marker‐trait associations were detected and clustered into 1605 quantitative trait loci (QTLs) including a number of known genes or QTLs. We identified 277 pleiotropic QTLs controlling multiple traits at different growth stages which revealed temporal dynamics of QTLs action on plant development and yield production in wheat. A candidate gene related to plant growth that was detected by image traits was further validated. Particularly, our study demonstrated that the yield‐related traits are largely predictable using models developed based on i‐traits and provide possibility for high‐throughput early selection, thus to accelerate breeding process. Our study explored the genetic architecture of growth and yield‐related traits by combining high‐throughput phenotyping and genotyping, which further unravelled the complex and stage‐specific contributions of genetic loci to optimize growth and yield in wheat.
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