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

Authors

Ye Bi, Virginia Polytechnic Institute and State University
Rafael Massahiro Yassue, Virginia Polytechnic Institute and State University, University of S ao Paulo
Puneet Paul, University of Nebraska-Lincoln
Balpreet Kaur Dhatt, University of Nebraska-Lincoln
Jaspreet Sandhu, University of Nebraska-Lincoln
Phuc Thi Do, Vietnam National University, University of Nebraska-Lincoln
Harkamal Walia, University of Nebraska-LincolnFollow
Toshihiro Obata, University of Nebraska-LincolnFollow
Gota Morota, Virginia Polytechnic Institute and State University

Document Type

Article

Date of this Version

2-27-2023

Citation

G3, 2023, 13(5), jkad052. https://doi.org/10.1093/g3journal/jkad052

Comments

Open access.

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 or extreme gradient boosting. 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 HNT 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 SNPs, metabolites collected from grains offer rich information to perform predictive analyses, including classification modeling of HNT responses and regression modeling of grain-size-related phenotypes in rice.