ArticleScientific reports2017
An integrated hyperspectral imaging and genome-wide association analysis platform provides spectral and genetic insights into the natural variation in rice.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed, 55 citations in OpenAlex.
- Unraveling plant phenotype to genotype associations with daily hyperspectral traits inPlant phenomics (Washington, D.C.) · 2026Article
- Hyperspectral-based classification of individual wheat plants into fine-scale reproductive stages.Plant methods · 2025Article
- Needle- and Canopy-Level Genetic Variation in Scots Pine (Evolutionary applications · 2025Article
- Integrative phenomics, metabolomics and genomics analysis provides new insights for deciphering the genetic basis of metabolism in polished rice.Genome biology · 2025Article
- Integrative multi-omics analysis reveals genetic and heterotic contributions to male fertility and yield in potato.Nature communications · 2024Article
- Genomic regions influencing the hyperspectral phenome of deoxynivalenol infected wheat.Scientific reports · 2024Article
- Maize and heat stress: Physiological, genetic, and molecular insights.The plant genome · 2024Review
- An Overview of High-Throughput Crop Phenotyping: Platform, Image Analysis, Data Mining, and Data Management.Methods in molecular biology (Clifton, N.J.) · 2024Review
- Handheld snapshot multi-spectral camera at tens-of-megapixel resolution.Nature communications · 2023Article
- Experimental error analysis of biomechanical phenotyping for stalk lodging resistance in maize.Scientific reports · 2023Article
- Article
- Advanced high-throughput plant phenotyping techniques for genome-wide association studies: A review.Journal of advanced research · 2022Review
- Challenges for a Massive Implementation of Phenomics in Plant Breeding Programs.Methods in molecular biology (Clifton, N.J.) · 2022Article
- OMICs, Epigenetics, and Genome Editing Techniques for Food and Nutritional Security.Plants (Basel, Switzerland) · 2021Review
- Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges.Plant communications · 2021Review
- Decoding Plant-Environment Interactions That Influence Crop Agronomic Traits.Plant & cell physiology · 2020Review
- NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Study the Residues of Different Concentrations of Omethoate on Wheat Grain Surface.Sensors (Basel, Switzerland) · 2019Article
- Advancing Bromegrass Breeding Through Imaging Phenotyping and Genomic Selection: A Review.Frontiers in plant science · 2019Review
- Crop Phenomics: Current Status and Perspectives.Frontiers in plant science · 2019Review
- Using hyperspectral analysis as a potential high throughput phenotyping tool in GWAS for protein content of rice quality.Plant methods · 2019Article
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With progress of genetic sequencing technology, plant genomics has experienced rapid development and subsequently triggered the progress of plant phenomics. In this study, a high-throughput hyperspectral imaging system (HHIS) was developed to obtain 1,540 hyperspectral indices at whole-plant level during tillering, heading, and ripening stages. These indices were used to quantify traditional agronomic traits and to explore genetic variation. We performed genome-wide association study (GWAS) of these indices and traditional agronomic traits in a global rice collection of 529 accessions. With the genome-level suggestive P-value threshold, 989 loci were identified. Of the 1,540 indices, we detected 502 significant indices (designated as hyper-traits) that exhibited phenotypic and genetic relationship with traditional agronomic traits and had high heritability. Many hyper-trait-associated loci could not be detected using traditional agronomic traits. For example, we identified a candidate gene controlling chlorophyll content (Chl). This gene, which was not identified based on Chl, was significantly associated with a chlorophyll-related hyper-trait in GWAS and was demonstrated to control Chl. Moreover, our study demonstrates that red edge (680-760 nm) is vital for rice research for phenotypic and genetic insights. Thus, combination of HHIS and GWAS provides a novel platform for dissection of complex traits and for crop breeding.
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