Dataset Information

Using hyperspectral analysis as a potential high throughput phenotyping tool in GWAS for protein content of rice quality.

ABSTRACT:

Background

The advances of hyperspectral technology provide a new analytic means to decrease the gap of phenomics and genomics caused by the fast development of plant genomics with the next generation sequencing technology. Through hyperspectral technology, it is possible to phenotype the biochemical attributes of rice seeds and use the data for GWAS.

Results

The results of correlation analysis indicated that Normalized Difference Spectral Index (NDSI) had high correlation with protein content (PC) with R_NDSI ²?=?0.68. Based on GWAS analysis using all the traits, NDSI was able to identify the same SNP loci as rice protein content that was measured by traditional methods. In total, hyperspectral trait NDSI identified all the 43 genes that were identified by biochemical trait PC. NDSI identified 1 extra SNP marker on chromosome 1, which annotated extra 22 genes that were not identified by PC. Kegg annotation results showed that traits NDSI annotated 3 pathways that are exactly the same as PC. The cysteine and methionine metabolic pathway identified by both NDSI and PC was reported important for biosynthesis and metabolism of some of amino acids/protein in rice seeds.

Conclusion

This study combined hyperspectral technology and GWAS analysis to dissect PC of rice seeds, which was high throughput and proven to be able to apply to GWAS as a new phenotyping tool. It provided a new means to phenotype one of the important biochemical traits for the determination of rice quality that could be used for genetic studies.

SUBMITTER: Sun D

PROVIDER: S-EPMC6532189 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Using hyperspectral analysis as a potential high throughput phenotyping tool in GWAS for protein content of rice quality.

Sun Dawei D Cen Haiyan H Weng Haiyong H Wan Liang L Abdalla Alwaseela A El-Manawy Ahmed Islam AI Zhu Yueming Y Zhao Nan N Fu Haowei H Tang Juan J Li Xiaolong X Zheng Hongkun H Shu Qingyao Q Liu Fei F He Yong Y

Plant methods 20190523

<h4>Background</h4>The advances of hyperspectral technology provide a new analytic means to decrease the gap of phenomics and genomics caused by the fast development of plant genomics with the next generation sequencing technology. Through hyperspectral technology, it is possible to phenotype the biochemical attributes of rice seeds and use the data for GWAS.<h4>Results</h4>The results of correlation analysis indicated that Normalized Difference Spectral Index (NDSI) had high correlation with pr ...[more]

PMID: 31139243

Similar Datasets

Project description:BackgroundRice is a major staple food crop for more than half the world's population. As the global population is expected to reach 9.7 billion by 2050, increasing the production of high-quality rice is needed to meet the anticipated increased demand. However, global environmental changes, especially increasing temperatures, can affect grain yield and quality. Heat stress is one of the major causes of an increased proportion of chalkiness in rice, which compromises quality and reduces the market value. Researchers have identified 140 quantitative trait loci linked to chalkiness mapped across 12 chromosomes of the rice genome. However, the available genetic information acquired by employing advances in genetics has not been adequately exploited due to a lack of a reliable, rapid and high-throughput phenotyping tool to capture chalkiness. To derive extensive benefit from the genetic progress achieved, tools that facilitate high-throughput phenotyping of rice chalkiness are needed.ResultsWe use a fully automated approach based on convolutional neural networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM) to detect chalkiness in rice grain images. Specifically, we train a CNN model to distinguish between chalky and non-chalky grains and subsequently use Grad-CAM to identify the area of a grain that is indicative of the chalky class. The area identified by the Grad-CAM approach takes the form of a smooth heatmap that can be used to quantify the degree of chalkiness. Experimental results on both polished and unpolished rice grains using standard instance classification and segmentation metrics have shown that Grad-CAM can accurately identify chalky grains and detect the chalkiness area.ConclusionsWe have successfully demonstrated the application of a Grad-CAM based tool to accurately capture high night temperature induced chalkiness in rice. The models trained will be made publicly available. They are easy-to-use, scalable and can be readily incorporated into ongoing rice breeding programs, without rice researchers requiring computer science or machine learning expertise.

Dataset Information

Using hyperspectral analysis as a potential high throughput phenotyping tool in GWAS for protein content of rice quality.

Background

Results

Conclusion

Publications

Using hyperspectral analysis as a potential high throughput phenotyping tool in GWAS for protein content of rice quality.

Similar Datasets

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