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Predicting Longitudinal Traits Derived from High-Throughput Phenomics in Contrasting Environments Using Genomic Legendre Polynomials and B-Splines

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dc.contributor.authorMomen, Mehdien
dc.contributor.authorCampbell, Malachy T.en
dc.contributor.authorWalia, Harkamalen
dc.contributor.authorMorota, Gotaen
dc.contributor.departmentAnimal and Poultry Sciencesen
dc.date.accessioned2019-12-20T18:33:28Zen
dc.date.available2019-12-20T18:33:28Zen
dc.date.issued2019-10en
dc.description.abstractRecent advancements in phenomics coupled with increased output from sequencing technologies can create the platform needed to rapidly increase abiotic stress tolerance of crops, which increasingly face productivity challenges due to climate change. In particular, high-throughput phenotyping (HTP) enables researchers to generate large-scale data with temporal resolution. Recently, a random regression model (RRM) was used to model a longitudinal rice projected shoot area (PSA) dataset in an optimal growth environment. However, the utility of RRM is still unknown for phenotypic trajectories obtained from stress environments. Here, we sought to apply RRM to forecast the rice PSA in control and water-limited conditions under various longitudinal cross-validation scenarios. To this end, genomic Legendre polynomials and B-spline basis functions were used to capture PSA trajectories. Prediction accuracy declined slightly for the water-limited plants compared to control plants. Overall, RRM delivered reasonable prediction performance and yielded better prediction than the baseline multi-trait model. The difference between the results obtained using Legendre polynomials and that using B-splines was small; however, the former yielded a higher prediction accuracy. Prediction accuracy for forecasting the last five time points was highest when the entire trajectory from earlier growth stages was used to train the basis functions. Our results suggested that it was possible to decrease phenotyping frequency by only phenotyping every other day in order to reduce costs while minimizing the loss of prediction accuracy. This is the first study showing that RRM could be used to model changes in growth over time under abiotic stress conditions.en
dc.description.notesThis work was supported by the National Science Foundation under Grant Number 1736192 to HW and GM, and Virginia Polytechnic Institute and State University startup funds to GM. MTC and HW designed and conducted the experiments. MM analyzed the data. MM and GM conceived the idea and wrote the manuscript. MTC and HW discussed results and revised the manuscript. GM supervised and directed the study. All authors read and approved the manuscript.en
dc.description.sponsorshipNational Science FoundationNational Science Foundation (NSF) [1736192]; Virginia Polytechnic Institute and State University startup fundsen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1534/g3.119.400346en
dc.identifier.eissn2160-1836en
dc.identifier.issue10en
dc.identifier.pmid31427454en
dc.identifier.urihttp://hdl.handle.net/10919/96168en
dc.identifier.volume9en
dc.language.isoenen
dc.publisherGenetics Society of Americaen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectgenomic predictionen
dc.subjectphenomicsen
dc.subjectlongitudinal modelingen
dc.subjectrandom regressionen
dc.subjecttime seriesen
dc.subjectGenPreden
dc.subjectShared Data Resourcesen
dc.titlePredicting Longitudinal Traits Derived from High-Throughput Phenomics in Contrasting Environments Using Genomic Legendre Polynomials and B-Splinesen
dc.title.serialG3-Genes Genomes Geneticsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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