Evaluating metabolic and genomic data for predicting grain traits under high night temperature stress in rice

The asymmetric increase in average nighttime temperatures relative to increase in average daytime temperatures due to climate change is decreasing grain yield and quality in rice. Therefore, a better understanding of the impact of higher night temperature on single grain at whole genome level is essential for future development of more resilient rice. We investigated the utility of metabolites obtained from grains to classify high night temperature conditions of genotypes, and metabolites and single nucleotide polymorphisms to predict grain length, width, and perimeter phenotypes using a rice diversity panel. We found that the metabolic profiles of rice genotypes alone could be used to classify control and high night temperature conditions with high accuracy using random forest or extreme gradient boosting. The best linear unbiased prediction and BayesC showed greater metabolic prediction performance than machine learning models for grain-size phenotypes. Metabolic prediction was most effective for grain width, resulting in the highest prediction performance. Genomic prediction performed better than metabolic prediction. Integrating metabolites and genomics simultaneously in a prediction model slightly improved prediction performance. We did not observe a difference in prediction between the control and high night temperature conditions. Several metabolites were identified as auxiliary phenotypes that could be used to enhance the multi-trait genomic prediction of grain-size phenotypes. Our results showed that, in addition to single nucleotide polymorphisms, metabolites collected from grains offer rich information to perform predictive analyses, including classification modeling of high night temperature responses and regression modeling of grain size-related phenotypes in rice.