Bridging Genomic Insights to Agricultural Practice: GWAS Applications in Mining Nutrient Utilization Efficiency Genes for Crop Improvement
文献类型： 外文期刊
作者： Liang, Yu Lei 1 ; Yan, Peng Shuai 2 ; Li, Wen Xue 3 ; Ding, Wo Na 4 ; Li, Gui Xin 1 ; Xu, Ji Ming 1 ; Ding, Zhong Jie 1 ; Zheng, Shao Jian 1 ;
作者机构： 1.Zhejiang Univ, Coll Life Sci, State Key Lab Plant Environm Resilience, Hangzhou, Peoples R China
2.Henan Agr Univ, State Key Lab High Efficiency Prod Wheat Maize Dou, Zhengzhou, Peoples R China
3.Chinese Acad Agr Sci, Inst Crop Sci, State Key Lab Crop Gene Resources & Breeding, Natl Engn Lab Crop Mol Breeding, Beijing, Peoples R China
4.Ningbo Univ, Coll Sci & Technol, Ningbo Key Lab Agr Germplasm Resources Min & Envir, Ningbo, Peoples R China
关键词：
crop genetics; GWAS; maize (
期刊名称：PLANT CELL AND ENVIRONMENT （ 影响因子：6.3； 五年影响因子：7.7 ）
ISSN： 0140-7791
年卷期： 2025 年
页码：
收录情况： SCI
摘要： In the context of global food security challenges and unsustainable agricultural practices, improving crop nutrient use efficiency (NUE) has become pivotal for achieving yield stability and environmental sustainability. Traditional breeding approaches face limitations in dissecting complex traits like NUE due to low genetic resolution and restricted allelic diversity. This review highlights the transformative role of genome-wide association studies (GWAS) in bridging genomic insights with agricultural innovation. By leveraging high-throughput sequencing, advanced statistical models, and diverse germplasm resources, GWAS enables precise identification of genetic loci governing nitrogen (N), phosphorus (P), potassium (K), and micronutrient utilization efficiency. We summarize breakthroughs in identification of critical genes (e.g., OsTCP19, ZmNLP3.2, GmPHF1, ZmNAC78) and their regulatory roles in nitrogen-responsive tillering mechanisms, potassium-sodium interaction networks, phosphorus starvation adaptation pathways, or micronutrient accumulation in grains. Furthermore, we discuss the integration of GWAS with multi-omics technologies, epigenetics, and machine learning to overcome challenges such as false positives, genetic heterogeneity, and genotype-environment interactions. These advancements provide a robust framework for developing nutrient-efficient crops through precision breeding, ultimately contributing to sustainable intensification of agriculture by optimizing resource use and minimizing ecological footprints.
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