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

ABSTRACT: 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 genome-level understanding of the impact of higher night temperature stress on the weight of individual grains is essential for future development of more resilient rice. We investigated the utility of metabolites obtained from grains to classify high night temperature (HNT) conditions of genotypes, and metabolites and single-nucleotide polymorphisms (SNPs) 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 HNT conditions with high accuracy using random forest

SUBMITTER: Bi Y 

PROVIDER: S-EPMC10151405 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature