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Hyperspectral imaging technology combined with genome-wide association study rapidly identifies more genes related to rice quality

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2018 ASABE Annual International Meeting  1800489.(doi:10.13031/aim.201800489)
Authors:   Dawei Sun, Haiyan Cen, Haiyong Weng, Liang Wan, Alwaseela Abdalla Mohamed Hassan, Ahmed Islam El-Manawy, Yueming Zhu, Haowei Fu, Qingyao Shu, Fei Liu, Yong He
Keywords:   GWAS, Hyperspectral imaging, phenotyping

Abstract. The fast development of plant genomics due to the next generation sequencing (NGS) technology has caused the progress of plant phenomics. In this study, three categories of phenotypic traits were acquired including 26 agronomic traits, 4 biochemical traits and 7 optical traits with 4 HSI traits extracted from HSI images that showed high correlation with rice quality traits that are difficult to measure, such as protein content R2=0.7193. A GWAS analysis of all the traits was applied to identify SNP markers related to rice quality. Results showed optical traits could identify more SNP markers and locate more related bioprocess pathways, which could provide a more comprehensive insight to the bioprocess that is associated with the rice quality. This study combined hyperspectral imaging and GWAS analysis to dissect complex traits of rice seeds, which could accurately quantify and qualify rice seeds and provide a new means to bridge the gap between genotyping and phenotyping.

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