An integrated hyperspectral imaging and genome-wide association analysis platform provides spectral and genetic insights into the natural variation in rice
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-plan...
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2017
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oai:doaj.org-article:bf18294cae7b41b1b75dedaa86963dfe2021-12-02T12:32:51ZAn integrated hyperspectral imaging and genome-wide association analysis platform provides spectral and genetic insights into the natural variation in rice10.1038/s41598-017-04668-82045-2322https://doaj.org/article/bf18294cae7b41b1b75dedaa86963dfe2017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-04668-8https://doaj.org/toc/2045-2322Abstract 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.Hui FengZilong GuoWanneng YangChenglong HuangGuoxing ChenWei FangXiong XiongHongyu ZhangGongwei WangLizhong XiongQian LiuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-10 (2017) |
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Medicine R Science Q Hui Feng Zilong Guo Wanneng Yang Chenglong Huang Guoxing Chen Wei Fang Xiong Xiong Hongyu Zhang Gongwei Wang Lizhong Xiong Qian Liu An integrated hyperspectral imaging and genome-wide association analysis platform provides spectral and genetic insights into the natural variation in rice |
| description |
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. |
| format |
article |
| author |
Hui Feng Zilong Guo Wanneng Yang Chenglong Huang Guoxing Chen Wei Fang Xiong Xiong Hongyu Zhang Gongwei Wang Lizhong Xiong Qian Liu |
| author_facet |
Hui Feng Zilong Guo Wanneng Yang Chenglong Huang Guoxing Chen Wei Fang Xiong Xiong Hongyu Zhang Gongwei Wang Lizhong Xiong Qian Liu |
| author_sort |
Hui Feng |
| title |
An integrated hyperspectral imaging and genome-wide association analysis platform provides spectral and genetic insights into the natural variation in rice |
| title_short |
An integrated hyperspectral imaging and genome-wide association analysis platform provides spectral and genetic insights into the natural variation in rice |
| title_full |
An integrated hyperspectral imaging and genome-wide association analysis platform provides spectral and genetic insights into the natural variation in rice |
| title_fullStr |
An integrated hyperspectral imaging and genome-wide association analysis platform provides spectral and genetic insights into the natural variation in rice |
| title_full_unstemmed |
An integrated hyperspectral imaging and genome-wide association analysis platform provides spectral and genetic insights into the natural variation in rice |
| title_sort |
integrated hyperspectral imaging and genome-wide association analysis platform provides spectral and genetic insights into the natural variation in rice |
| publisher |
Nature Portfolio |
| publishDate |
2017 |
| url |
https://doaj.org/article/bf18294cae7b41b1b75dedaa86963dfe |
| work_keys_str_mv |
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