Genome-Wide Association Studies and Comparison of Models and Cross-Validation Strategies for Genomic Prediction of Quality Traits in Advanced Winter Wheat Breeding Lines

Kristensen, Peter Skov; Jahoor, A.; Andersen, Jeppe Reitan; Cericola, Fabio; Orabi, Jihad; Janss, Luc; Jensen, Just

doi:10.3389/fpls.2018.00069

Cited by 69 publications

(83 citation statements)

References 44 publications

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“…With both methods (RR and TP), we observed high prediction accuracies ranging from 0.4 to 0.6 (Arruda et al., ; Duhnen et al., ; Kristensen et al., ; Leplat, Jensen, & Madsen, ). While similar accuracies have been reported by other studies for complex traits, it is important to note that the current study utilized a diversity panel with considerable population stratification and the prediction models did not account for population structure.…”

Section: Discussionmentioning

confidence: 92%

Utilizing random regression models for genomic prediction of a longitudinal trait derived from high‐throughput phenotyping

2018

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The accessibility of high‐throughput phenotyping platforms in both the greenhouse and field, as well as the relatively low cost of unmanned aerial vehicles, has provided researchers with an effective means to characterize large populations throughout the growing season. These longitudinal phenotypes can provide important insight into plant development and responses to the environment. Despite the growing use of these new phenotyping approaches in plant breeding, the use of genomic prediction models for longitudinal phenotypes is limited in major crop species. The objective of this study was to demonstrate the utility of random regression ( RR ) models using Legendre polynomials for genomic prediction of shoot growth trajectories in rice ( Oryza sativa ). An estimate of shoot biomass, projected shoot area ( PSA ), was recorded over a period of 20 days for a panel of 357 diverse rice accessions using an image‐based greenhouse phenotyping platform. A RR that included a fixed second‐order Legendre polynomial, a random second‐order Legendre polynomial for the additive genetic effect, a first‐order Legendre polynomial for the environmental effect, and heterogeneous residual variances was used to model PSA trajectories. The utility of the RR model over a single time point ( TP ) approach, where PSA is fit at each time point independently, is shown through four prediction scenarios. In the first scenario, the RR and TP approaches were used to predict PSA for a set of lines lacking phenotypic data. The RR approach showed a 11.6% increase in prediction accuracy over the TP approach. Much of this improvement could be attributed to the greater additive genetic variance captured by the RR approach. The remaining scenarios focused forecasting future phenotypes using a subset of early time points for known lines with phenotypic data, as well new lines lacking phenotypic data. In all cases, PSA could be predicted with high accuracy ( r : 0.79 to 0.89 and 0.55 to 0.58 for known and unknown lines, respectively). This study provides the first application of RR models for genomic prediction of a longitudinal trait in rice and demonstrates that RR models can be effectively used to improve the accuracy of genomic prediction for complex traits compared to a TP approach.

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Section: Discussionmentioning

confidence: 92%

Utilizing random regression models for genomic prediction of a longitudinal trait derived from high‐throughput phenotyping

2018

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“…Grain or flour samples of each line were phenotyped for the quality traits TKW, grain protein content, Zeleny sedimentation, and falling number ( Supplementary Table S1) as described in [16]. Briefly, TKW was determined by weighing a small seed sample and counting the number of seeds using image analysis, grain protein content was determined by nearinfrared spectroscopy, and Zeleny sedimentation and falling number were determined following the international standard methods (ISO 5529 and ISO 3093, respectively).…”

Section: Phenotypingmentioning

confidence: 99%

Multi-Trait and Trait-Assisted Genomic Prediction of Winter Wheat Quality Traits Using Advanced Lines from Four Breeding Cycles

Kristensen¹,

Jahoor²,

Andersen³

et al. 2019

Crop Breed Genet Genom.

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Genetically correlated traits can be used for improving predictive abilities of genomic predictions including several traits in multi-trait models. Here, the wheat quality traits thousand-kernel weight, grain protein content, Zeleny sedimentation, and falling number were phenotyped in 1152 advanced winter wheat lines from four cycles of a commercial breeding program. Multi-trait and trait-assisted genomic prediction models including two or four traits were studied and compared with single-trait models. In the trait-assisted genomic predictions, breeding values of the trait of interest were predicted for lines that had been phenotyped for additional traits. Predictive abilities of single-trait models ranged from 0.5 for thousand-kernel weight to 0.65 for falling number based on 10-fold cross-validations. Predictive abilities were in most cases not significantly different between single-and multi-trait models, when no phenotypic data was included for lines in the validation set. However, predictive abilities for grain protein content increased when using trait-assisted models, where the phenotypic data for Zeleny sedimentation or falling number were available for the lines in the validation set. The trait-assisted models also resulted in increased predictive abilities for Zeleny sedimentation, when phenotypic data for grain protein content was included. The latter situation could be relevant for breeding programs for improving wheat quality.

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“…With both methods (RR and TP), we observed high prediction accuracies ranging from 0.4 to 0.6 (Arruda et al, 2015;Duhnen et al, 2017;Kristensen et al, 2018;Leplat, Jensen, & Madsen, 2016).…”

Section: Advantages Of Rr Over Univariate Genomic Predictionmentioning

confidence: 95%

Utilizing random regression models for genomic prediction of a longitudinal trait derived from high-throughput phenotyping

Campbell

Walia

Morota

2018

Preprint

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The accessibility of high-throughput phenotyping platforms in both the greenhouse and field, as well as the relatively low cost of unmanned aerial vehicles, has provided researchers with an effective means to characterize large populations throughout the growing season. These longitudinal phenotypes can provide important insight into plant development and responses to the environment. Despite the growing use of these new phenotyping approaches in plant breeding, the use of genomic prediction models for longitudinal phenotypes is limited in major crop species. The objective of this study was to demonstrate the utility of random regression (RR) models using Legendre polynomials for genomic prediction of shoot growth trajectories in rice (Oryza sativa). An estimate of shoot biomass, projected shoot area (PSA), was recorded over a period of 20 days for a panel of 357 diverse rice accessions using an image-based greenhouse phenotyping platform. A RR that included a fixed second-order Legendre polynomial, a random second-order Legendre polynomial for the additive genetic effect, a first-order Legendre polynomial for the environmental effect, and heterogeneous residual variances was used to model PSA trajectories. The utility of the RR model over a single time point (TP)approach, where PSA is fit at each time point independently, is shown through four prediction scenarios. In the first scenario, the RR and TP approaches were used to predict PSA for a set of lines lacking phenotypic data. The RR approach showed a 11.6% increase in prediction accuracy over the TP approach. Much of this improvement could be attributed to the greater additive genetic variance captured by the RR approach. The remaining scenarios focused forecasting future phenotypes using a subset of early time points for known lines with phenotypic data, as well new lines lacking phenotypic data.In all cases, PSA could be predicted with high accuracy (r: 0.79 to 0.89 and 0.55 to 0.58 for known and unknown lines, respectively). This study provides the first application of RR models for genomic prediction of a longitudinal trait in rice and demonstrates that RR models can be effectively used to improve the accuracy of genomic prediction for complex traits compared to a TP approach. K E Y W O R D Sgenetics, genomic prediction, high-throughput phenotyping, phenomics This manuscript has been released as a preprint on bioRxiv (https://doi.

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Genome-Wide Association Studies and Comparison of Models and Cross-Validation Strategies for Genomic Prediction of Quality Traits in Advanced Winter Wheat Breeding Lines

Cited by 69 publications

References 44 publications

Utilizing random regression models for genomic prediction of a longitudinal trait derived from high‐throughput phenotyping

Utilizing random regression models for genomic prediction of a longitudinal trait derived from high‐throughput phenotyping

Multi-Trait and Trait-Assisted Genomic Prediction of Winter Wheat Quality Traits Using Advanced Lines from Four Breeding Cycles

Utilizing random regression models for genomic prediction of a longitudinal trait derived from high-throughput phenotyping

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