Modeling Genotype × Environment Interaction Using Additive Genetic Covariances of Relatives for Predicting Breeding Values of Wheat Genotypes

Crossa, J.; Burgueño, Juan; Cornelius, P. L.; McLaren, Graham; Trethowan, Richard; Krishnamachari, Anitha

doi:10.2135/cropsci2005.11-0427

Cited by 138 publications

(189 citation statements)

References 31 publications

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“…Animal breeders have used this model for predicting breeding values either in a mixed model (best linear unbiased prediction, BLUP) (Henderson 1984) or in a Bayesian framework (Gianola and Fernando 1986). More recently, plant breeders have incorporated pedigree information into linear mixed models for predicting breeding values (Crossa et al 2006Oakey et al 2006;Burgueño et al 2007;Piepho et al 2007).…”

Section: P Edigree-based Prediction Of Genetic Valuesmentioning

confidence: 99%

Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers

et al. 2010

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The availability of dense molecular markers has made possible the use of genomic selection (GS) for plant breeding. However, the evaluation of models for GS in real plant populations is very limited. This article evaluates the performance of parametric and semiparametric models for GS using wheat (Triticum aestivum L.) and maize (Zea mays) data in which different traits were measured in several environmental conditions. The findings, based on extensive cross-validations, indicate that models including marker information had higher predictive ability than pedigree-based models. In the wheat data set, and relative to a pedigree model, gains in predictive ability due to inclusion of markers ranged from 7.7 to 35.7%. Correlation between observed and predictive values in the maize data set achieved values up to 0.79. Estimates of marker effects were different across environmental conditions, indicating that genotype 3 environment interaction is an important component of genetic variability. These results indicate that GS in plant breeding can be an effective strategy for selecting among lines whose phenotypes have yet to be observed.

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Section: P Edigree-based Prediction Of Genetic Valuesmentioning

confidence: 99%

Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers

et al. 2010

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“…Such an approach does not shed light on the underlying genetic architecture of G´E. Examples of methods that deal with G´E implicitly without modeling M´E include the family of linear-by-linear models of Cornelius et al (1996) as well as more modern methods such as the multivariate pedigree-or marker-based models, where G´E is modeled using structured or unstructured covariance functions (e.g., Piepho, 1997Piepho, , 1998Smith et al, 2005;Crossa et al, 2006;Burgueño et al, 2007Burgueño et al, , 2012El-Soda et al, 2014). When genomic data are available, G´E can be modeled explicitly by means of M´E when marker effects can vary among environments or groups of environments and by recognizing that these effects may be correlated.…”

Section: Introductionmentioning

confidence: 99%

“…Standard multienvironment mixed-model approaches (e.g., Piepho, 1997Piepho, , 1998Smith et al, 2005;Crossa et al, 2006;Burgueño et al, 2007Burgueño et al, , 2012 rely on Gaussian assumptions; when applied to genomic data, these approaches induce shrinkage (i.e., reduce marker effects) but not variable (marker) selection. One advantage of the M´E model is that it can be used with prior information that induces shrinkage as well as priors that produce variable selection.…”

Section: Introductionmentioning

confidence: 99%

Extending the Marker × Environment Interaction Model for Genomic‐Enabled Prediction and Genome‐Wide Association Analysis in Durum Wheat

et al. 2016

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2193RESEARCH G enetic ´ environment interaction (G´E) affects trait heritability and the relative rankings of phenotypes across environments; this introduces challenges when making breeding decisions. The effects of G´E on heritability may be due to scale effects such as changes in the size of quantitative trait loci (QTL) effects across environments or to differential genetic effects on environmental variance. However, G´E can also modulate QTL effects, thus introducing changes in the relative rank of genotypes across environments (Dickerson, 1962;Cockerham, 1963). Falconer (1952) suggested modeling performance in two environments as two correlated traits; this allows modeling both scale effects and rerankings. Extending the Marker ´ Environment Interaction Model for Genomic-Enabled Prediction and Genome-Wide Association Analysis in Durum WheatJosé Crossa,* Gustavo de los Campos, Marco Maccaferri, Roberto Tuberosa, J. Burgueño, and Paulino Pérez-Rodríguez* ABSTRACTThe marker ´ environment interaction (M´E) genomic model can be used to generate predictions for untested individuals and identify genomic regions in which effects are stable across environments and others that show environmental specificity. The objectives of this study were (i) to extend the M´E model using priors that produce shrinkage and variable selection such as Bayesian ridge regression (BRR) and BayesB (BB), respectively, and (ii) to evaluate the genomic prediction accuracy of M´E, single-environment, and across-environment models using a multiparental durum wheat (Triticum turgidum L. spp. duram) population characterized for grain yield (GY), grain volume weight (GVW), 1000-kernel weight (GWT), and heading date (HD) in four environments. Breeding value predictions were generated for two prediction problems: cross-validation problem 1 (CV1) and cross-validation problem 2 (CV2). In general, results showed that the M´E model performed better than the single-environment and acrossenvironment models, in terms of minimizing the model residual variance, for both CV1 and CV2. The improved data-fitting gain over the other models was more evident for GWT and HD (up to twofold differences) than to GY and GVW, which showed more complex genetic bases and smaller single-marker effects. Considering the Bayesian models used, BB showed better overall prediction accuracy than BRR. As proofof-concept for the M´E model, the major controllers of HD-Ppd and FT on chromosomes 2A, 2B, and 7A-showed stable effects across environments as well as environment-specific effects. For GY, besides the regions on chromosomes 2B and 7A, additional chromosome regions with large marker effects were detected in all chromosome groups.

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“…Further study is necessary to determine the likelihood of such a situation. Other authors have demonstrated superior model fit, as measured by information criteria, using GRM with FA structures rather than compound symmetric structures (Crossa et al, 2006;Oakey et al, 2007). This suggests that these authors analyzed data sets with complex relationship patterns similar to the Toeplitz simulations in this study.…”

Section: Resultsmentioning

confidence: 76%

“…More factors allow for greater flexibility, but may reduce model parsimony. Multiple researchers have demonstrated that a factor analytic structure can be combined with pedigree information to improve model fit, as measured by information criteria (Crossa et al, 2006;Oakey et al, 2007;Kelly et al, 2009;Beeck et al, 2010). These researchers have analyzed a limited number of real MET data sets; a simulation study could determine if the FA model with a GRM is the most effective model for a much wider range of MET.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Linear Mixed Models for Multiple Environment Plant Breeding Trials

Walker¹,

Pita²,

Campbell³

2011

Conference on Applied Statistics in Agriculture

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Evaluations of multiple environment trials (MET) often reveal substantial genotype by environment interactions, and the effects of genotypes within environments are often estimated using cell means, i.e. the simple mean of the observations of each genotype in each environment. However, these estimates are inaccurate, especially for unreplicated or partially replicated trials, so alternative methods of analysis are necessary. One possible approach utilizes information, often from pedigree data, about relationships among the tested genotypes through the use of a genetic relationship matrix (GRM). Predictive accuracy may also be improved by the use of factor analytic (FA) structures for environmental covariances. In this study, data were simulated to resemble results from a range of MET. These simulated data sets covered a range of scenarios with varying numbers of environments and genotypes, environmental relationship patterns, field trial designs, and magnitudes of experimental error. The simulated data were used to evaluate 20 mixed models, ten of which included GRMs and ten which did not. The models included ten structures for environmental covariances including structures with no environmental correlation, structures with constant correlation among environments, and six FA structures. These models were compared to each other and to cell means and Additive Main effects and Multiplicative Interaction (AMMI) methods in terms of successful convergence and predictive accuracy. For most of the scenarios, models which included a GRM and a compound symmetric, constant variance structure produced the most accurate estimates. Models with GRM and FA structures were more accurate only when used to evaluate scenarios simulated with Toeplitz patterns of relationships and more than 25 genotypes or five environments. Unfortunately, the improved accuracy with the FA structures in these scenarios came at the cost of reduced convergence rates, so FA structures may not be reliable enough for some uses.

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Modeling Genotype × Environment Interaction Using Additive Genetic Covariances of Relatives for Predicting Breeding Values of Wheat Genotypes

Cited by 138 publications

References 31 publications

Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers

Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers

Extending the Marker × Environment Interaction Model for Genomic‐Enabled Prediction and Genome‐Wide Association Analysis in Durum Wheat

Comparison of Linear Mixed Models for Multiple Environment Plant Breeding Trials

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