Evidence map›Paper›PMID 42517969›Full record

ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2026

Hyperspectral phenotyping and GWAS identify novel QTLs for soybean photosynthetic rate.

Li Wang, Ning Fu, Jinyu Zhang, Ao Shen, Yuanmeng Yu, Yanhong Wang, Xiaoli Guan, Dan Zhang, Fang Huang, Xiaoyong Yang and 3 more

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Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

13 authors.

Li Wang *State Key Laboratory of High-Efficiency Production of Wheat-Maize Double Cropping, College of Agronomy, Henan Institute of Science and Technology, Xinxiang, 453003, China.ORCID https://orcid.org/0000-0001-8326-4199
Ning Fu *School of Computer Science and Technology, Henan Institute of Technology, Xinxiang, 453003, China.
Jinyu Zhang *State Key Laboratory of High-Efficiency Production of Wheat-Maize Double Cropping, College of Agronomy, Henan Institute of Science and Technology, Xinxiang, 453003, China.
Ao ShenState Key Laboratory of High-Efficiency Production of Wheat-Maize Double Cropping, College of Agronomy, Henan Institute of Science and Technology, Xinxiang, 453003, China.
Yuanmeng YuState Key Laboratory of High-Efficiency Production of Wheat-Maize Double Cropping, College of Agronomy, Henan Institute of Science and Technology, Xinxiang, 453003, China.
Yanhong WangState Key Laboratory of High-Efficiency Production of Wheat-Maize Double Cropping, College of Agronomy, Henan Institute of Science and Technology, Xinxiang, 453003, China.
Xiaoli GuanState Key Laboratory of High-Efficiency Production of Wheat-Maize Double Cropping, College of Agronomy, Henan Institute of Science and Technology, Xinxiang, 453003, China.
Dan ZhangCollaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou, 450046, China.
Fang HuangNational Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, China.
Xiaoyong YangBeijing ICAN Technology Co., Ltd., Beijing, 100000, China.
Deyue YuNational Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, China. dyyu@njau.edu.cn.
Zhongwen HuangState Key Laboratory of High-Efficiency Production of Wheat-Maize Double Cropping, College of Agronomy, Henan Institute of Science and Technology, Xinxiang, 453003, China. hzw@hist.edu.cn.
Yuming YangState Key Laboratory of High-Efficiency Production of Wheat-Maize Double Cropping, College of Agronomy, Henan Institute of Science and Technology, Xinxiang, 453003, China. yym@hist.edu.cn.

Funding

the Doctoral Research Startup Fund from the High-Level Talent Recruitment Program 103020224002/116the Doctoral Research Startup Fund from the High-Level Talent Recruitment Program 103020224002/119the National Natural Science Foundation of China 32201867the National Natural Science Foundation of China 32501932
6 · The paper itself

Abstract

Enhancing photosynthesis is an important approach to improve crop yields. Photosynthesis, as a key factor determining crop yield, is an important approach to increasing crop production and addressing global food security issues. Improving its efficiency is crucial in this regard. However, traditional photosynthetic phenotyping has long been a bottleneck in crop breeding due to time-consuming data collection. In this study, we simultaneously measured the spectral reflectance and the net photosynthetic rate (Pn) of soybean leaves to develop a high-precision model for estimating Pn based on hyperspectral data. By applying this model, we evaluated Pn in 219 soybean materials. A multi-environment genome-wide association study (GWAS) based on multi-environmental prediction Pn was carried out using the 3VmrMLM method, and 24 significant quantitative trait loci (QTLs) and four suggestive QTLs were identified. Among them, 24 QTLs overlapped with multiple previously reported QTL related to photosynthesis, chlorophyll content, quality, etc., or with genes related to key agronomic traits such as yield. Additionally, four new QTLs were discovered, and four candidate genes potentially associated with Pn were identified. Further, haplotype analysis identified their optimal haplotypes. This study presents a robust and nondestructive hyperspectral model for estimating the photosynthetic rate in soybeans, which is successfully applied to genetic analysis, yielding stable and biologically meaningful results. The approach offers an effective means to explore the genetic basis of photosynthesis and provides a solid theoretical foundation for large-scale, monitoring of soybean photosynthetic physiology.

Indexed as

Glycine maxPhotosynthesisQuantitative Trait LociChromosome MappingGenome-Wide Association StudyHaplotypesPhenotypePlant LeavesPolymorphism, Single Nucleotide

Identifiers

PMID42517969

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