The 10th Asian Crop Science Association Conference

講演情報

Oral sessions

Crop Genetics and Physiology » O41: Genetic Improvement of Crop Yield

[O41] Genetic Improvement of Crop Yield

2021年9月9日(木) 09:45 〜 11:45 Room 4 (Oral) (Crop Genetics and Physiology)

Chair: Taichiro Ookawa (Tokyo University of Agriculture and Technology, Japan )
Chair: Hiroshi Fukayama (Kobe University, Japan)
Chair: Masahiro Kishii (International Maize and Wheat Improvement Center, Mexico)
Chair: Shunsuke Adachi (Tokyo University of Agriculture and Technology, Japan )

10:25 〜 10:40

[O41-03] Predictive Modeling of Leaf Photosynthetic Rate in Field-Grown Rice Using Transcriptome Dataset

*Nominated for Presentation Awards

Sotaro Honda1, Satoshi Ohkubo2, Makoto Kashima3, Nan Su San2, Anothai Nakkasame2, Hiroki Saito4, Taiichiro Ookawa2, Atsushi J. Nagano5, Shunsuke Adachi6 (1.Graduate School of Agricultural and Life Sciences, the University of Tokyo, Japan, 2.Graduate School of Agriculture, Tokyo University of Agriculture and Technology, Japan, 3.College of Science and Engineering, Aoyama Gakuin University, Japan, 4.Japan International Research Center for Agricultural Sciences, Japan, 5.Faculty of Agriculture, Ryukoku University, Japan, 6.College of Agricuture, Ibaraki University, Japan)

Leaf photosynthetic rate is affected by several environmental factors including irradiance and temperature, as well as by genetic factors and plant age. Despite several models estimating photosynthetic rates under various environments have been released, a model considering genetic factors and plant age besides environmental factors has remained to be developed. Transcriptome containing over 23,000 gene expression data per sample appears to represent the physiological condition of a field-grown plant influenced by all these factors. Therefore, it may provide sufficient explanatory variables for a statistical model. To test this hypothesis, we obtained leaf CO2 assimilation rates over 13,000 data points from 80 inbred rice lines derived from cvs. Koshihikari and Takanari across their growth periods. We also prepared the transcriptome profile corresponding to each photosynthesis data point from another predictive model estimating transcriptome from genotypic data, meteorological data and scaled age (Kashima et al. 2020). Taken together, we developed a novel predictive regression model with LASSO (Tibshirani 1996). This model predicted the photosynthetic dynamics of rice lines which were different from the model training lines. Our results suggest that the statistical modeling using transcriptome is a promising approach to predict photosynthetic dynamics of a certain plant under unexperienced field environment such as future climate change conditions. This approach would be also applied to the other agronomic traits which should be improved for stable food supply.