Genomic prediction and training set optimization in a structured Mediterranean oat population

Rio, Simon; Gallego-Sánchez, Luis M.; Montilla-Bascón, Gracia; Canales, Francisco José; Sánchez, Julio Isidro; Prats, Elena

doi:10.1007/s00122-021-03916-w

Cited by 16 publications

(21 citation statements)

References 85 publications

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“…The prediction abilities for different traits in the two gene pools were related to the genetic correlation between the germplasm in each subgene pool. Similarly, in a genomic prediction study of four traits (heading, height, biomass, and yield) in oats, it has been found that the multi-group training sets will obtain higher predictive ability (0.32–0.87) than across-group scenarios (−0.55–0.27) [ 28 ].…”

Section: Discussionmentioning

confidence: 99%

“…In the oat population, the prediction abilities of the testing sets with different training set organizations showed significant differences. For oat yield [ 28 ], selection based on population structure could increase the predictive ability of the training set to the testing set when compared with random selection. Yield prediction was improved by 0.35 and 0.03 in two predicted subpopulations of oats.…”

Section: Discussionmentioning

confidence: 99%

“…In particular, the common bean is a species with a strong population structure. In existing studies, population genetic structure has been considered in the GP of several types of crops with well-defined genetic population distribution and less domestication, such as wheat [ 25 , 26 ], rice [ 25 ], maize [ 27 ], and oats [ 28 ], to improve the predictive power. However, studies about modeling the distribution of common bean germplasm resources are limited.…”

Section: Introductionmentioning

confidence: 99%

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Development of a Model for Genomic Prediction of Multiple Traits in Common Bean Germplasm, Based on Population Structure

Shao

Hao

Wang

et al. 2022

Plants

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Due to insufficient identification and in-depth investigation of existing common bean germplasm resources, it is difficult for breeders to utilize these valuable genetic resources. This situation limits the breeding and industrial development of the common bean (Phaseolus vulgaris L.) in China. Genomic prediction (GP) is a breeding method that uses whole-genome molecular markers to calculate the genomic estimated breeding value (GEBV) of candidate materials and select breeding materials. This study aimed to use genomic prediction to evaluate 15 traits in a collection of 628 common bean lines (including 484 landraces and 144 breeding lines) to determine a common bean GP model. The GP model constructed by landraces showed a moderate to high predictive ability (ranging from 0.59–0.88). Using all landraces as a training set, the predictive ability of the GP model for most traits was higher than that using the landraces from each of two subgene pools, respectively. Randomly selecting breeding lines as additional training sets together with landrace training sets to predict the remaining breeding lines resulted in a higher predictive ability based on principal components analysis. This study constructed a widely applicable GP model of the common bean based on the population structure, and encouraged the development of GP models to quickly aggregate excellent traits and accelerate utilization of germplasm resources.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development of a Model for Genomic Prediction of Multiple Traits in Common Bean Germplasm, Based on Population Structure

Shao

Hao

Wang

et al. 2022

Plants

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“…Moreover, Gonçalves et al (2021) , reported that the Bayes B method was up to approximately two times more accurate than PLS in predicting fiber and sucrose con-tent in sugarcane stems. According to several studies ( Kainer et al, 2018 ; Thistlethwaite et al, 2019 ; Rio et al, 2021 ), the Bayesian methods, such as Bayes B, can improve the predictive ability in genome -based evaluations. For instance, Kainer et al (2018) evaluated the ability of different genomic prediction models of eight traits related to foliar terpene yield in Eucalyptus polybractea , using three different marker densities.…”

Section: Discussionmentioning

confidence: 99%

Heritable Variation of Foliar Spectral Reflectance Enhances Genomic Prediction of Hydrogen Cyanide in a Genetically Structured Population of Eucalyptus

Ballesta¹,

Ahmar²,

Lobos

et al. 2022

Front. Plant Sci.

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Plants produce a wide diversity of specialized metabolites, which fulfill a wide range of biological functions, helping plants to interact with biotic and abiotic factors. In this study, an integrated approach based on high-throughput plant phenotyping, genome-wide haplotypes, and pedigree information was performed to examine the extent of heritable variation of foliar spectral reflectance and to predict the leaf hydrogen cyanide content in a genetically structured population of a cyanogenic eucalyptus (Eucalyptus cladocalyx F. Muell). In addition, the heritable variation (based on pedigree and genomic data) of more of 100 common spectral reflectance indices was examined. The first profile of heritable variation along the spectral reflectance curve indicated the highest estimate of genomic heritability (hg2=0.41) within the visible region of the spectrum, suggesting that several physiological and biological responses of trees to environmental stimuli (ex., light) are under moderate genetic control. The spectral reflectance index with the highest genomic-based heritability was leaf rust disease severity index 1 (hg2=0.58), followed by the anthocyanin reflectance index and the Browning reflectance index (hg2=0.54). Among the Bayesian prediction models based on spectral reflectance data, Bayes B had a better goodness of fit than the Bayes-C and Bayesian ridge regression models (in terms of the deviance information criterion). All models that included spectral reflectance data outperformed conventional genomic prediction models in their predictive ability and goodness-of-fit measures. Finally, we confirmed the proposed hypothesis that high-throughput phenotyping indirectly capture endophenotypic variants related to specialized metabolites (defense chemistry), and therefore, generally more accurate predictions can be made integrating phenomics and genomics.

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“…Therefore, the topic of multi-environmental prediction models and integration of ECs has gradually developed over the past decade in the plant breeding community (Crossa et al, 2022). In contrast to optimizing the composition of the training set (genotypes), optimizing the environmental information to be used for training the models has received less attention (Isidro et al, 2015;Rio et al, 2021).…”

Section: Impact Of Training Setmentioning

confidence: 99%

Multi-environment genomic selection in rice elite breeding lines

Nguyễn

Morantte

Lopena

et al. 2022

Preprint

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Background: Assessing the performance of elite lines in target environments is essential for breeding programs to select the most relevant genotypes. One of the main complexities in this task resides in accounting for the genotype by environment interactions. Genomic prediction models that integrate information from multi-environment trials and environmental covariates can be efficient tools in this context. The objective of this study was to assess the predictive ability of different genomic prediction models to optimize the use of multi-environment information. We used 111 elite breeding lines representing the diversity of the International Rice Research Institute (IRRI) breeding program for irrigated ecosystems. The lines were evaluated for three traits (days to flowering, plant height, and grain yield) in 15 environments in Asia and Africa and genotyped with 882 SNP markers. We evaluated the efficiency of genomic prediction to predict untested environments using seven multi-environment models and three cross-validation scenarios. Results: The elite lines were found to belong to the indica group and more specifically the indica-1B subgroup which gathered improved material originating from the Green Revolution. Phenotypic correlations between environments were high for days to flowering and plant height (33% and 54% of pairwise correlation greater than 0.5 ) but low for grain yield (lower than 0.2 in most cases). Clustering analyses based on environmental covariates separated Asia’s and Africa's environments into different clusters or subclusters. The predictive abilities ranged from 0.06 to 0.79 for days to flowering, 0.25 to 0.88 for plant height, and -0.29 to 0.62 for grain yield. We found that models integrating genotype-by-environment interaction effects did not perform significantly better than models integrating only main effects (genotypes and environment or environmental covariates). The different cross-validation scenarios showed that, in most cases, the use of all available environments gave better results than a subset. Conclusion: Multi-environment genomic prediction models with main effects were sufficient for accurate phenotypic prediction of elite lines in targeted environments. The recommendation for the breeders is to use simple multi-environment models with all available information for routine application in breeding programs.

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Genomic prediction and training set optimization in a structured Mediterranean oat population

Cited by 16 publications

References 85 publications

Development of a Model for Genomic Prediction of Multiple Traits in Common Bean Germplasm, Based on Population Structure

Development of a Model for Genomic Prediction of Multiple Traits in Common Bean Germplasm, Based on Population Structure

Heritable Variation of Foliar Spectral Reflectance Enhances Genomic Prediction of Hydrogen Cyanide in a Genetically Structured Population of Eucalyptus

Multi-environment genomic selection in rice elite breeding lines

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