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

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dc.contributor.authorBi, Yeen
dc.contributor.authorYassue, Rafael Massahiroen
dc.contributor.authorPaul, Puneeten
dc.contributor.authorDhatt, Balpreet Kauren
dc.contributor.authorSandhu, Jaspreeten
dc.contributor.authorDo, Phuc Thien
dc.contributor.authorWalia, Harkamalen
dc.contributor.authorObata, Toshihiroen
dc.contributor.authorMorota, Gotaen
dc.date.accessioned2023-10-16T18:56:19Zen
dc.date.available2023-10-16T18:56:19Zen
dc.date.issued2023-05en
dc.description.abstractThe 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.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1093/g3journal/jkad052en
dc.identifier.issn2160-1836en
dc.identifier.issue5en
dc.identifier.pmid36881928en
dc.identifier.urihttp://hdl.handle.net/10919/116482en
dc.identifier.volume13en
dc.language.isoenen
dc.publisherOxford University Pressen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectbinary classificationen
dc.subjectgenomic predictionen
dc.subjectgrain sizeen
dc.subjecthigh night temperatureen
dc.subjectmetabolic predictionen
dc.subjectriceen
dc.titleEvaluating metabolic and genomic data for predicting grain traits under high night temperature stress in riceen
dc.title.serialG3-Genes Genomes Geneticsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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