[O41] Genetic Improvement of Crop Yield

[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.