A Bayesian Genomic Multi-output Regressor Stacking Model for Predicting Multi-trait Multi-environment Plant Breeding Data

Montesinos‐López, Osval A.; Montesinos‐López, Abelardo; Crossa, J.; Cuevas, Jaime; Montesinos-López, J. Cricelio; Gutiérrez, Zitlalli Salas; Lillemo, Morten; Juliana, Philomin; Singh, Ravi P.

doi:10.1534/g3.119.400336

Cited by 28 publications

(47 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2018) and Montesinos‐López et al. (2019c, 2019d), in which a small gain in terms of prediction performance is obtained with regard to univariate deep learning prediction.…”

Section: Discussionmentioning

confidence: 97%

“…For this reason, the BMTME generalizes conventional multi-trait analysis (which takes into account the correlation between traits) to also take into account the correlation between environments. In the context of deep learning, there are applications of multi-trait deep learning for genomic selection such as those published by Montesinos-López et al (2018) and Montesinos-López et al (2019c, 2019d, in which a small gain in terms of prediction performance is obtained with regard to univariate deep learning prediction.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Genome‐based prediction of multiple wheat quality traits in multiple years

et al. 2020

Self Cite

View full text Add to dashboard Cite

Wheat quality improvement is an important objective in all wheat breeding programs. However, due to the cost, time and quantity of seed required, wheat quality is typically analyzed only in the last stages of the breeding cycle on a limited number of samples. The use of genomic prediction could greatly help to select for wheat quality more efficiently by reducing the cost and time required for this analysis. Here were evaluated the prediction performances of 13 wheat quality traits under two multi-trait models (Bayesian multi-trait multi-environment [BMTME] and multi-trait ridge regression [MTR]) using five data sets of wheat lines evaluated in the field during two consecutive years. Lines in the second year (testing) were predicted using the quality information obtained in the first year (training). For most quality traits were found moderate to high prediction accuracies, suggesting that the use of genomic selection could be feasible. The best predictions were obtained with the BMTME model in all traits and the worst with the MTR model. The best predictions with the BMTME model under the mean arctangent absolute percentage error (MAAPE) were for test weight across the five data sets, whereas the worst predictions were for the alveograph trait

show abstract

“…(2018) and Montesinos‐López et al. (2019c, 2019d), in which a small gain in terms of prediction performance is obtained with regard to univariate deep learning prediction.…”

Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Genome‐based prediction of multiple wheat quality traits in multiple years

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, stacking a large number of base learners requires a level-2 model to perform the multicollinearity data analysis in the model [78]. Thus, ridge regression, least absolute shrinkage, selection operator (LASSO), and elastic net regression (ENET) can be used as level-2 models for collinearity analysis [78][79][80][81]. The grain yield predictions made by single models under full and limited irrigation treatments were similar across the growth stages, indicating the adaptability of UAV data for grain yield prediction.…”

Section: Discussionmentioning

confidence: 99%

Assessment of Ensemble Learning to Predict Wheat Grain Yield Based on UAV-Multispectral Reflectance

Fei

Hassan

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

Grain yield is increasingly affected by climate factors such as drought and heat. To develop resilient and high-yielding cultivars, high-throughput phenotyping (HTP) techniques are essential for precise decisions in wheat breeding. The ability of unmanned aerial vehicle (UAV)-based multispectral imaging and ensemble learning methods to increase the accuracy of grain yield prediction in practical breeding work is evaluated in this study. For this, 211 winter wheat genotypes were planted under full and limited irrigation treatments, and multispectral data were collected at heading, flowering, early grain filling (EGF), and mid-grain filling (MGF) stages. Twenty multispectral vegetation indices (VIs) were estimated, and VIs with heritability greater than 0.5 were selected to evaluate the models across the growth stages under both irrigation treatments. A framework for ensemble learning was developed by combining multiple base models such as random forest (RF), support vector machine (SVM), Gaussian process (GP), and ridge regression (RR). The R2 values between VIs and grain yield between for individual base models were ranged from 0.468 to 0.580 and 0.537 to 0.598 for grain yield prediction in full and limited irrigation treatments across growth stages, respectively. The prediction results of ensemble models were ranged from 0.491 to 0.616 and 0.560 to 0.616 under full and limited irrigation treatments respectively, and were higher than that of the corresponding base learners. Moreover, the grain yield prediction results were observed high at mid grain filling stage under both full (R2 = 0.625) and limited (R2 = 0.628) irrigation treatments through ensemble learning based stacking of four base learners. Further improvements in ensemble learning models can accelerate the use of UAV-based multispectral data for accurate predictions of complex traits like grain yield in wheat.

show abstract

“…Genotypic information is available in the form of the Genomic Relationship Matrix

(obtained from centered and standardized marker data). Both phenotypic and genomic data were previously used in Montesinos-López et al (2019) and can be found in https://data.cimmyt.org/data set.xhtml? persistentId=hdl : 11529/10548141.…”

Section: Methodsmentioning

confidence: 99%

“…Multiple trait selection is a concern addressed by animal and plant breeding in the past ( Smith 1936 ; Hazel 1943 ; Henderson and Quass 1976 ) and also in the era of GS ( Sun et al 2017 ; Montesinos-López et al 2019 ; Neyhart et al 2019 ; Lenz et al 2020 ). Multi-trait selection models are promising because they have the potential to increase the accuracy of GEBV (given that they use information about genetically correlated traits), especially in the presence of low heritability traits ( Jia and Jannink 2012 ; Guo et al 2014 ; Ward et al 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Application of multi-trait Bayesian decision theory for parental genomic selection

Villar-Hernández

Pérez-Elizalde

Martini

et al. 2021

G3 Genes|Genomes|Genetics

Self Cite

View full text Add to dashboard Cite

In all breeding programs, the decision about which individuals to select and intermate to form the next selection cycle is crucial. The improvement of genetic stocks requires considering multiple traits simultaneously, given that economic value and net genetic merits depend on many traits; therefore, with the advance of computational and statistical tools and genomic selection (GS), researchers are focusing on multi-trait selection. Selection of the best individuals is difficult, especially in traits that are antagonistically correlated, where improvement in one trait might imply a reduction in other(s). There are approaches that facilitate multi-trait selection, and recently a Bayesian decision theory (BDT) has been proposed. Parental selection using BDT has the potential to be effective in multi-trait selection given that it summarizes all relevant quantitative genetic concepts such as heritability, response to selection and the structure of dependence between traits (correlation). In this study, we applied BDT to provide a treatment for the complexity of multi-trait parental selection using three multivariate loss functions (LF), Kullback-Leibler (KL), Energy Score, and Multivariate Asymmetric Loss (MALF), to select the best performing parents for the next breeding cycle in two extensive real wheat data sets. Results show that the high ranking lines in genomic estimated breeding value (GEBV) for certain traits did not always have low values for PEL. For both data sets, the KL loss function gave similar importance to all traits including grain yield. In contrast, the Energy Score and MALF gave better performance in 3 of 4 traits that were different than grain yield. The BDT approach should help breeders to decide based not only on the GEBV per se of the parent to be selected, but also on the level of uncertainty according to the Bayesian paradigm.

show abstract

A Bayesian Genomic Multi-output Regressor Stacking Model for Predicting Multi-trait Multi-environment Plant Breeding Data

Cited by 28 publications

References 30 publications

Genome‐based prediction of multiple wheat quality traits in multiple years

Genome‐based prediction of multiple wheat quality traits in multiple years

Assessment of Ensemble Learning to Predict Wheat Grain Yield Based on UAV-Multispectral Reflectance

Application of multi-trait Bayesian decision theory for parental genomic selection

Contact Info

Product

Resources

About