Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials

Dias, Kaio Olímpio das Graças; Gezan, Salvador A.; Guimarães, C. T.; Nazarian, Alireza; Silva, Luciano da Costa e; Parentoni, S. N.; Guimarães, P. E. de O.; Anoni, Carina de Oliveira; Pádua, José Maria Villela; Pinto, M. de O.; Noda, R. W.; Ribeiro, Cléberson; Magalhães, J. V. de; Garcia, Antônio Augusto Franco; Souza, João Cândido de; Guimarães, L. J. M.; Pastina, Maria Marta

doi:10.1038/s41437-018-0053-6

Cited by 92 publications

(107 citation statements)

References 61 publications

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“…Our findings are also supported by goodness-of-fit values since dominance models are more parsimonious than the additive model. Accordingly, other studies have shown similar empirical results [Resende et al, 2017, Dias et al, 2018. Despite the importance of dominance effect in grain yield, additivity explained a large portion of variance in grain moisture, suggesting that both traits have different genetic architectures.…”

Section: Discussionsupporting

confidence: 68%

“…Progress in hybrid breeding can be greatly accelerated by the incorporation of genomic predictions into breeding schemes [Technow et al, 2014, Acosta-Pech et al, 2017, Dias et al, 2018, Werner et al, 2018. To this end, breeders have to face important issues regarding its implementation, including the impact of accounting for non-additive effects and dealing with G×E interaction.…”

Section: Discussionmentioning

confidence: 99%

“…This source of genetic variation was neglected for different reasons, including the lack of informative pedigrees, computational complexities related to estimation of dominance effects [Vitezica et al, 2013], the thought that most genetic variance is additive [Hill, 2010] or can be captured with additive parameterizations [Huang and Mackay, 2016], and the fact that even when non-additive effects are included in the models they are not easily partitioned from additive effects [Muñoz et al, 2014]. Nonetheless, recent inclusion of non-additive effects have demonstrated increased prediction accuracies in some traits in animal and plant breeding [Technow et al, 2014, de Almeida Filho et al, 2016, dos Santos et al, 2016, Resende et al, 2017, Dias et al, 2018 In addition to the source of genetic variability controlling a trait of interest, a second relevant issue to plant breeders is how to manage the challenges of genotype-by-environment (G×E) interaction. G×E interactions are expressed as changes in the relative performance of genotypes across environments, which can affect the genotype ranking.…”

mentioning

confidence: 99%

“…After the referred study, new methods addressing G×E interaction were proposed and investigated in several crops, including maize [Burgueño et al, 2012, Cuevas et al, 2016a, Lopez-Cruz et al, 2015, Dias et al, 2018, Fristche-Neto et al, 2018, e Souza et al, 2017, Ferrão et al, 2017. One particular modelintroduced by Lopez-Cruz et al [2015] and improved by Cuevas et al [2016a] and Cuevas et al [2016b] -explicitly models the interactions of each marker with the environment (M×E interaction).…”

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confidence: 99%

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Integration of Dominance and Marker×Environment Interactions into Maize Genomic Prediction Models

Ferrão

Marinho

Muñoz

et al. 2018

Preprint

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Hybrid breeding programs are driven by the potential to explore the heterosis phenomenon in traits with non-additive inheritance. Traditionally, progress has been achieved by crossing lines from different heterotic groups and measuring phenotypic performance of hybrids in multiple environment trials. With the reduction in genotyping prices, genomic selection has become a reality for phenotype prediction and a promising tool to predict hybrid performances. However, its prediction ability is directly associated with models that represent the trait and breeding scheme under investigation. Herein, we assess modelling approaches where dominance effects and multi-environment statistical are considered for genomic selection in maize hybrid. To this end, we evaluated the predictive ability of grain yield and grain moisture collected over three production cycles in different locations. Hybrid genotypes were inferred in silico based on their parental inbred lines using single-nucleotide polymorphism markers obtained via a 500k SNP chip. We considered the importance to decomposes additive and dominance marker effects into components that are constant across environments and deviations that are group-specific. Prediction within and across environments were tested. The incorporation of dominance effect increased the predictive ability for grain production by up to 30% in some scenarios. Contrastingly, additive models yielded better results for grain moisture. For multi-environment modelling, the inclusion of interaction effects increased the predictive ability overall. More generally, we demonstrate that including dominance and genotype by environment interactions resulted in gains in accuracy and hence could be considered for genomic selection implementation in maize breeding programs.Nearly a century ago, G. H. Schull proposed the term heterosis to describe the higher performance of "crossbred" individuals when compared with corresponding inbred or "pure-bred" genotypes [Shull, 1948]. Since his pioneer studies, the development of hybrid varieties has been an integral part of many plant breeding programs resulting in significant gains in global grain production. The clearest example of success has been reported in maize (Zea mays L.), which hybrid varieties are now widely adopted, replacing open-pollinated populations. Among the advantages, the better yield and greater uniformity are central features that favored its rapid acceptance by companies and producers [Crow, 1998].Hybrid vigor, in maize, is traditionally obtained by crossing inbred lines from genetically distinct pools, the so-called heterotic groups. Depending on the stage of the breeding program, selected hybrids can be

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Section: Discussionsupporting

confidence: 68%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Integration of Dominance and Marker×Environment Interactions into Maize Genomic Prediction Models

Ferrão

Marinho

Muñoz

et al. 2018

Preprint

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“…In terms of GS, Dias et al. () demonstrated joint modelling of additive and dominance effects for a maize MET data set. Their approach also accommodated non‐genetic sources of variation and employed a factor analytic model for both sources of VEI following Oakey et al.…”

Section: Introductionmentioning

confidence: 99%

Genomic selection in multi‐environment plant breeding trials using a factor analytic linear mixed model

Tolhurst

Mathews

Smith

et al. 2019

J Animal Breeding Genetics

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Genomic selection (GS) is a statistical and breeding methodology designed to improve genetic gain. It has proven to be successful in animal breeding; however, key points of difference have not been fully considered in the transfer of GS from animal to plant breeding. In plant breeding, individuals (varieties) are typically evaluated across a number of locations in multiple years (environments) in formally designed comparative experiments, called multi‐environment trials (METs). The design structure of individual trials can be complex and needs to be modelled appropriately. Another key feature of MET data sets is the presence of variety by environment interaction (VEI), that is the differential response of varieties to a change in environment. In this paper, a single‐step factor analytic linear mixed model is developed for plant breeding MET data sets that incorporates molecular marker data, appropriately accommodates non‐genetic sources of variation within trials and models VEI. A recently developed set of selection tools, which are natural derivatives of factor analytic models, are used to facilitate GS for a motivating data set from an Australian plant breeding company. The power and versatility of these tools is demonstrated for the variety by environment and marker by environment effects.

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Exploring genotype × environment interaction in sweet sorghum under tropical environments

et al. 2021

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Understanding the genotype‐by‐environment interactions (GEI) is crucial to release sweet sorghum [Sorghum bicolor (L.) Moench] cultivars with stable and high agronomic performance under tropical environments. Therefore, linear mixed models could be used to face this challenge by leveraging the biological process of GEI into cultivar recommendation. The goals of this study were: (a) to explore GEI patterns under tropical conditions to select stable and high‐yielding genotypes for energy‐use in the bioethanol industry; and (b) to evaluate the advantages of linear mixed models taking into account simultaneously the genetic and the residual correlations across environments and the genomic relationship between genotypes. The breeding dataset was comprised of 41 genotypes evaluated for Tonnes of brix per hectare (TBH) in late‐stage trials over 32 tropical environments. The models incorporating simultaneously the genomic relationship matrix of genotypes and the genetic and the residual correlations across environments showed the lowest values of Akaike Information Criterion (AIC) compared to the standard phenotypic models, and also increased the expected genetic gains of TBH across years. Based on the best models selected by AIC, the genotypes 38 (CMSXS5006), 6 (BRS511), 5 (CMSXS633), 9 (CMSXS637), and 12 (BRS506) exhibited the highest productivity in TBH. Particularly, the genotype 9 (CMSXS637) showed broad stability and high productivity under tropical environments, besides the desirable traits for bioethanol production. These results highlight the importance of modeling the genomic relationship between genotypes and the genetic and the residual correlations across environments to increase the breeding efficiency of sweet sorghum under tropical environments.

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Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials

Cited by 92 publications

References 61 publications

Integration of Dominance and Marker×Environment Interactions into Maize Genomic Prediction Models

Integration of Dominance and Marker×Environment Interactions into Maize Genomic Prediction Models

Genomic selection in multi‐environment plant breeding trials using a factor analytic linear mixed model

Exploring genotype × environment interaction in sweet sorghum under tropical environments

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