Dataset Information

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

ABSTRACT: Recent 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.

SUBMITTER: Momen M

PROVIDER: S-EPMC6778811 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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

Momen Mehdi M Campbell Malachy T MT Walia Harkamal H Morota Gota G

G3 (Bethesda, Md.) 20191007 10

Recent 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 optima ...[more]

PMID: 31427454

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Project description:Background and aimsPhenotypic plasticity can affect the geographical distribution of taxa and greatly impact the productivity of crops across contrasting and variable environments. The main objectives of this study were to identify genotype-phenotype associations in key biomass and phenology traits and the strength of phenotypic plasticity of these traits in a short-rotation coppice willow population across multiple years and contrasting environments to facilitate marker-assisted selection for these traits.MethodsA hybrid Salix viminalis ?×?( S. viminalis × Salix schwerinii ) population with 463 individuals was clonally propagated and planted in three common garden experiments comprising one climatic contrast between Sweden and Italy and one water availability contrast in Italy. Several key phenotypic traits were measured and phenotypic plasticity was estimated as the trait value difference between experiments. Quantitative trait locus (QTL) mapping analyses were conducted using a dense linkage map and phenotypic effects of S. schwerinii haplotypes derived from detected QTL were assessed.Key resultsAcross the climatic contrast, clone predictor correlations for biomass traits were low and few common biomass QTL were detected. This indicates that the genetic regulation of biomass traits was sensitive to environmental variation. Biomass QTL were, however, frequently shared across years and across the water availability contrast. Phenology QTL were generally shared between all experiments. Substantial phenotypic plasticity was found among the hybrid offspring, that to a large extent had a genetic origin. Individuals carrying influential S. schwerinii haplotypes generally performed well in Sweden but less well in Italy in terms of biomass production.ConclusionsThe results indicate that specific genetic elements of S. schwerinii are more suited to Swedish conditions than to those of Italy. Therefore, selection should preferably be conducted separately for such environments in order to maximize biomass production in admixed S. viminalis × S. schwerinii populations.

Dataset Information

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

Publications

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

Similar Datasets

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