A1219
Title: Quantitative genetic analysis of metabolites in rice
Authors: Gota Morota - Virginia Polytechnic Institute and State University (United States) [presenting]
Abstract: The asymmetric increase in average nighttime temperatures relative to the increase in average daytime temperatures due to climate change is decreasing grain yield and quality in rice. Therefore, a better genome-level understanding of the impact of higher night temperature stress on the weight of individual grains is essential for the future development of more resilient rice. The utility metabolites and single-nucleotide polymorphisms (SNPs) were investigated to predict grain length, width, and perimeter phenotypes using a rice diversity panel. Best linear unbiased prediction and BayesC showed greater metabolic prediction performance than machine learning models for grain-size phenotypes. The 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. A difference in prediction between the control and HNT conditions is not observed. Several metabolites were identified as auxiliary phenotypes that could be used to enhance the multi-trait genomic prediction of grain-size phenotypes. The results showed that, in addition to SNPs, metabolites collected from grains offer rich information to perform regression modelling of grain-size-related phenotypes in rice.
