数字农科院2.0

From germplasm diversity to genetic design: strategies for photoperiod adaptation in rice

文献类型： 外文期刊

作者： Zhou, Leina;Liu, Yinyin;Sandamal, Salinda;Zhang, Yuhang;Tennakoon, Asanka;Qian, Qian;Zheng, Xiaoming

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期刊名称： SEED BIOLOGY

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年卷期： 2025 年 4 卷

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收录情况： 无来源刊（2025版）

摘要： Rice (Oryza sativa L.) serves as a model for understanding how domestication and ecological adaptation together shape the global distribution of crops. Originating in tropical and subtropical regions characterized by short-day photoperiods and high temperatures, rice has undergone thousands of years of human selection and environmental shaping. These processes have enabled its cultivation across a broad range of latitudes, climates, and photoperiod conditions. A key driver of this ecological plasticity is the genetic reprogramming of heading date regulation. This review focuses on the genetic network underlying photoperiodic control of heading date (HD), with a focus on the evolutionary trajectories of major regulators, including Heading date 1 (Hd1), Grains Height Date 7 (Ghd7), Days to heading 8 (DTH8), Early heading date 1 (Ehd1) and Heading date 3a (Hd3a)/RICE FLOWERING LOCUST 1 (RFT1). The expansion of rice from tropical short-day to temperate long-day regions has been facilitated by loss-of-function mutations, expression divergence, and complex allelic combinations. Specific allele assemblies have also contributed to the divergence between indica and japonica subspecies, as well as to latitudinal adaptation. Despite the existence of over 780,000 germplasm accessions, a large portion of this germplasm diversity remains untapped. To bridge this gap, a conceptual framework for global adaptation that integrates multi-omics data, artificial intelligence (AI)-assisted modeling, and synthetic biology, is composed to reengineer heading date regulation. This review envisions a forward-looking breeding strategy that unites germplasm utilization, mechanistic understanding, and predictive design to develop rice varieties optimized for complex and variable agricultural environments.

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