Application of linear mixed models for multiple harvest/site trial analyses in perennial plant breeding

Chaves, Saulo Fabrício da Silva; Evangelista, Jeniffer Santana Pinto Coelho; Alves, Rodrigo Silva; Ferreira, Filipe Manoel; Dias, Luiz A. S.; Alves, R. N. B.; Dias, Kaio O. G.; Bhering, Leonardo Lopes

doi:10.1007/s11295-022-01576-5

Cited by 10 publications

(17 citation statements)

References 72 publications

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“…A concept analogous to stability called “persistence” is used by breeders of perennial forages and refers to the ability to survive and keep producing dry matter for long periods [ 23 , 24 ]. In perennial fruit plants, this concept can be readjusted as the ability to maintain a high fruit yield for several years [ 2 ]. Therefore, persistence is analogous to an ecological stability of the genotypes ( P i ) based on the distance between each genotype in relation to the ideotype.…”

Section: Methodsmentioning

confidence: 99%

“…This differential performance over time can be a reflex of the genotypes-by-harvests interaction (GHI). The GHI in perennial species refers to the variation in gene expression and, consequently, the phenotypic traits of a plant due to the different environmental conditions and agricultural practices that occur in each planting cycle [ 2 ]. Therefore, in perennial fruit breeding, repeated measures on the same plant over time are important, which increases costs and the duration of breeding cycle [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

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Recommendation of Tahiti acid lime cultivars through Bayesian probability models

Malikouski,

Ferreira,

Chaves

et al. 2024

PLoS ONE

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Probabilistic models enhance breeding, especially for the Tahiti acid lime, a fruit essential to fresh markets and industry. These models identify superior and persistent individuals using probability theory, providing a measure of uncertainty that can aid the recommendation. The objective of our study was to evaluate the use of a Bayesian probabilistic model for the recommendation of superior and persistent genotypes of Tahiti acid lime evaluated in 12 harvests. Leveraging the Monte Carlo Hamiltonian sampling algorithm, we calculated the probability of superior performance (superior genotypic value), and the probability of superior stability (reduced variance of the genotype-by-harvests interaction) of each genotype. The probability of superior stability was compared to a measure of persistence estimated from genotypic values predicted using a frequentist model. Our results demonstrated the applicability and advantages of the Bayesian probabilistic model, yielding similar parameters to those of the frequentist model, while providing further information about the probabilities associated with genotype performance and stability. Genotypes G15, G4, G18, and G11 emerged as the most superior in performance, whereas G24, G7, G13, and G3 were identified as the most stable. This study highlights the usefulness of Bayesian probabilistic models in the fruit trees cultivars recommendation.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recommendation of Tahiti acid lime cultivars through Bayesian probability models

Malikouski,

Ferreira,

Chaves

et al. 2024

PLoS ONE

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“…The mixed model approach provides a flexible framework in which genetic and/or statistical imbalances may be modeled appropriately (Cullis et al, 2020;Kelly et al, 2007;Melo et al, 2020). Chaves et al (2022) evaluated different (co)variance structures for multiharvest and multi-site trial analyses using two sets of perennial species. For Eucalyptus spp.…”

Section: Core Ideasmentioning

confidence: 99%

“…Chaves et al. (2022) evaluated different (co)variance structures for multi‐harvest and multi‐site trial analyses using two sets of perennial species. For Eucalyptus spp.…”

Section: Introductionmentioning

confidence: 99%

Analysis of advanced generation multistage field trials data in autogamous plant breeding: An evaluation in common Bean

et al. 2023

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The performance of inbred lines in advanced endogamous generation is commonly evaluated in successive generations of testing and selection, which we defined as "multistage field trials" (MSFT). MSFT data routinely exhibit heterogeneity of (co)variances at several levels due to genetic and/or statistical imbalance. Nowadays, mixed models have been widely used to deal with unbalanced data. However, few studies on common bean have addressed the use of a mixed model approach with modeling of (co)variance structures for random effects in MSFT. Furthermore, factor analysis and genotype-ideotype distance (FAI-BLUP) selection index was originally proposed using best linear unbiased predictions from individual analysis. In this regard, we aimed to study the implications of modeling (co)variance structures for random effects in the estimation of genetic parameters and evaluate the accuracy and efficiency of inbred line selection by the modeled FAI-BLUP approach. A total of five trials were evaluated from 2018 to 2020. The results revealed that the unstructured covariance matrix fitted better for grain yield, whereas the matrix with uniform correlation and heterogeneity of variances fitted better for grain aspect and plant architecture. The modeled FAI-BLUP approach increased the values of selection accuracy and selection efficiency. Our results suggest that modeling the different structures of (co)variances and selecting the best-performing genotypes by modeled FAI-BLUP approach should be used in common bean assays involving unbalanced data.

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“…Linear mixed models (LMM) are routinely used to estimate breeding values or genotypic values in tree breeding (Gezan et al, 2017;Chaves et al, 2022). The LMM was extended to include IGE as a random effect for animal or plant breeding (Muir, 2005).…”

Section: Introductionmentioning

confidence: 99%

A data-driven approach for enhancing forest productivity by accounting for indirect genetic effects

Ferreira

Chaves

Bhering

et al. 2023

Preprint

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Maintaining the past decades current genetic gains for tree species is a challenging task for foresters and tree breeders due to biotic and abiotic factors. Planting a mixture of genotypes or clonal composites can be an alternative to increase the phytosanitary security and yield of forest plantations. These clonal composites are more complex than monocultures due to inter-genotypic competition and indirect genetic effects that can affect the total heritable variation. This study aims to understand how indirect genetic effects can impact the response to selection and how the stand composition can be used to explore these effects and enhance forest yield. We used a clonally trial of Eucalyptus urophylla x Eucalyptus grandis hybrids in a randomized complete block design with 24 replications, containing a single tree per plot evaluated for mean annual increment at 3 and 6 years. We focus on partitioning the genetic variation of trees into direct and indirect genetic effects based on competition intensity factors. We identified clones as aggressive, homeostatic, and sensitive based on the magnitude of indirect genetic effects. By accounting for indirect genetic effects, for mean annual increment, the total heritability decreased 39 and 44% for 3 and 6 years, respectively. We proposed a workflow that uses the direct and indirect genetic effect to predict the mean value of clonal composite combinations and to select the one with highest yield. Our methodology accounted for spatial variability and interplot competition that can contribute to the total heritable variance and response to selection in forest trials. Based on the models evaluated, the clones are easily classified according to their deviation from the indirect genetic effects mean. Also, we extract useful information to predict different clonal composites compositions, their expected average performance, and define the best recommended combination to be planted in large scale.

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Application of linear mixed models for multiple harvest/site trial analyses in perennial plant breeding

Cited by 10 publications

References 72 publications

Recommendation of Tahiti acid lime cultivars through Bayesian probability models

Recommendation of Tahiti acid lime cultivars through Bayesian probability models

Analysis of advanced generation multistage field trials data in autogamous plant breeding: An evaluation in common Bean

A data-driven approach for enhancing forest productivity by accounting for indirect genetic effects

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