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Front Plant Sci. 2018 Dec 20;9:1880. doi: 10.3389/fpls.2018.01880. eCollection 2018.

Selection of Transcripts Affecting Initial Growth Rate of Rice Backcrossed Inbred Lines Using RNA Sequencing Data.

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Central Region Agricultural Research Center, National Agriculture and Food Research Organization, Joetsu, Japan.
Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan.
Agriculture and Biotechnology Business Division, Toyota Motor Corporation, Miyoshi, Japan.
Kyushu Okinawa Agricultural Research Center, National Agriculture and Food Research Organization, Fukuoka, Japan.
T-Frontier Division, Toyota Motor Corporation, Toyota, Japan.
Faculty of Agriculture, Ryukoku University, Otsu, Japan.


Seedling growth is an important factor for direct seeding of rice. However, the genetic and transcriptomic factors involved in this process are largely unknown. In this study, transcripts affecting shoot weight were identified in rice (Oryza sativa L.) using RNA sequencing (RNA-Seq) data from 20 backcrossed inbred lines (BILs) and their parental cultivars. The selection frequency of the genes for the regression model was determined using repeated analysis of random subsets of the transcriptome. The qLTG3-1gene, controlling low-temperature germinability, and short grain 1 gene (SG1), known to decrease organ elongation, showed high frequency. The quantitative trait loci (QTLs) analysis performed for BILs revealed that qLTG3-1 was included in the QTLs for shoot weight but SG1 was not. No nucleotide polymorphisms were found in the coding region of SG1 in either of the parental cultivars. Quantitative real-time PCR showed that SG1 expression was negatively correlated with shoot weight for all 104 BILs analyzed in this study. Expression QTL (eQTLs) analysis showed an eQTL for SG1 expression located in the same region as the QTL for shoot weight. However, no eQTLs were detected on the same chromosome as SG1, suggesting that nucleotide polymorphisms around the gene do not affect its expression in analyzed growth stage. Overall, these results indicate that RNA-Seq is a useful tool for identifying transcripts that can be related to seedling growth rate.


LASSO; Oryza sativa; QTL; RNA-Seq; SG1; eQTL; qLTG3-1; transcriptome

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