scholarly journals Genomic Selection Improves Response to Selection in Resilience by Exploiting Genotype by Environment Interactions

2016 ◽  
Vol 7 ◽  
Author(s):  
Han A. Mulder
Keyword(s):  
Genomic Selection ◽  
Response To Selection ◽  
Genotype By Environment Interactions ◽  
Genotype By Environment

scholarly journals Accuracies of univariate and multivariate genomic prediction models in African Cassava

2017 ◽  
Author(s):  
Uche Godfrey Okeke ◽  
Deniz Akdemir ◽  
Ismail Rabbi ◽  
Peter Kulakow ◽  
Jean-Luc Jannink
Keyword(s):  
Genomic Selection ◽  
Mixed Model ◽  
Genetic Evaluation ◽  
Environment Interaction ◽  
Genotype By Environment Interactions ◽  
Breeding Values ◽  
Genotype By Environment ◽  
Step Model ◽  
One Step ◽  
Estimated Breeding Values

List of abbreviationsGSGenomic SelectionBLUPBest Linear Unbiased PredictionEBVsEstimated Breeding ValuesEGVsEstimated genetic ValuesGEBVsGenomic Estimated Breeding ValuesSNPsSingle Nucleotide polymorphismsGxEGenotype-by-environment interactionsGxEGenotype-by-environment interactionsGxGGene-by-gene interactionsGxGxEGene-by-gene-by-environment interactionsuTUnivariate single environment one-step modeluEUnivariate multi environment one-step modelMTMulti-trait single environment one-step modelMEMultivariate single trait multi environment modelAbstractBackgroundGenomic selection (GS) promises to accelerate genetic gain in plant breeding programs especially for long cycle crops like cassava. To practically implement GS in cassava breeding, it is useful to evaluate different GS models and to develop suitable models for an optimized breeding pipeline.MethodsWe compared prediction accuracies from a single-trait (uT) and a multi-trait (MT) mixed model for single environment genetic evaluation (Scenario 1) while for multi-environment evaluation accounting for genotype-by-environment interaction (Scenario 2) we compared accuracies from a univariate (uE) and a multivariate (ME) multi-environment mixed model. We used sixteen years of data for six target cassava traits for these analyses. All models for Scenario 1 and Scenario 2 were based on the one-step approach. A 5-fold cross validation scheme with 10-repeat cycles were used to assess model prediction accuracies.ResultsIn Scenario 1, the MT models had higher prediction accuracies than the uT models for most traits and locations analyzed amounting to 32 percent better prediction accuracy on average. However for Scenario 2, we observed that the ME model had on average (across all locations and traits) 12 percent better predictive power than the uE model.ConclusionWe recommend the use of multivariate mixed models (MT and ME) for cassava genetic evaluation. These models may be useful for other plant species.

scholarly journals Reciprocal Recurrent Genomic Selection Is Impacted by Genotype-by-Environment Interactions

2021 ◽  
Vol 12 ◽  
Author(s):  
Maximilian Rembe ◽  
Jochen Christoph Reif ◽  
Erhard Ebmeyer ◽  
Patrick Thorwarth ◽  
Viktor Korzun ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Wheat Breeding ◽  
Breeding Strategy ◽  
Hybrid Breeding ◽  
Phenotypic Data ◽  
Predictive Equations ◽  
Genotype By Environment Interactions ◽  
Genotype By Environment ◽  
Selection Gain ◽  
Genome Wide

Reciprocal recurrent genomic selection is a breeding strategy aimed at improving the hybrid performance of two base populations. It promises to significantly advance hybrid breeding in wheat. Against this backdrop, the main objective of this study was to empirically investigate the potential and limitations of reciprocal recurrent genomic selection. Genome-wide predictive equations were developed using genomic and phenotypic data from a comprehensive population of 1,604 single crosses between 120 female and 15 male wheat lines. Twenty superior female lines were selected for initiation of the reciprocal recurrent genomic selection program. Focusing on the female pool, one cycle was performed with genomic selection steps at the F2 (60 out of 629 plants) and the F5 stage (49 out of 382 plants). Selection gain for grain yield was evaluated at six locations. Analyses of the phenotypic data showed pronounced genotype-by-environment interactions with two environments that formed an outgroup compared to the environments used for the genome-wide prediction equations. Removing these two environments for further analysis resulted in a selection gain of 1.0 dt ha−1 compared to the hybrids of the original 20 parental lines. This underscores the potential of reciprocal recurrent genomic selection to promote hybrid wheat breeding, but also highlights the need to develop robust genome-wide predictive equations.

Genotype by environment interactions and combining ability for strawberry families grown in diverse environments

Euphytica ◽  
2017 ◽  
Vol 213 (5) ◽  
Author(s):  
Megan M. Mathey ◽  
Sonali Mookerjee ◽  
Lise L. Mahoney ◽  
Kazim Gündüz ◽  
Umesh Rosyara ◽  
...  
Keyword(s):  
Combining Ability ◽  
Genotype By Environment Interactions ◽  
Genotype By Environment

Genetic Parameters and Genotype-by-Environment Interactions in Regional Progeny Tests of Pinus taeda L. in the Southern USA

2020 ◽  
Author(s):  
Edwin Lauer ◽  
Andrew Sims ◽  
Steven McKeand ◽  
Fikret Isik
Keyword(s):  
Genetic Variation ◽  
Pinus Taeda ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Breeding Program ◽  
Model Fit ◽  
Genotype By Environment Interactions ◽  
Genotype By Environment ◽  
Progeny Tests ◽  
Pinus Taeda L

Abstract Genetic parameters were estimated using a five-series multienvironment trial of Pinus taeda L. in the southern USA. There were 324 half-sib families planted in five test series across 37 locations. A set of six variance/covariance matrices for the genotype-by-environment (G × E) effect for tree height and diameter were compared on the basis of model fit. In single-series analysis, extended factor analytical models provided generally superior model fit to simpler models for both traits; however, in the combined-series analysis, diameter was optimally modeled using simpler variance/covariance structures. A three-way compound term for modeling G × E interactions among and within series yielded substantial improvements in terms of model fit and standard errors of predictions. Heritability of family means ranged between 0.63 and 0.90 for both height and diameter. Average additive genetic correlations among sites were 0.70 and 0.61 for height and diameter, respectively, suggesting the presence of some G × E interaction. Pairs of sites with the lowest additive genetic correlations were located at opposite ends of the latitude range. Latent factor regression revealed a small number of parents with large factor scores that changed ranks significantly between southern and northern environments. Study Implications Multienvironmental progeny tests of loblolly pine (Pinus taeda L.) were established over 10 years in the southern United States to understand the genetic variation for the traits of economic importance. There was substantial genetic variation between open-pollinated families, suggesting that family selection would be efficient in the breeding program. Genotype-by-environment interactions were negligible among sites in the deployment region but became larger between sites at the extremes of the distribution. The data from these trials are invaluable in informing the breeding program about the genetic merit of selection candidates and their potential interaction with the environment. These results can be used to guide deployment decisions in the southern USA, helping landowners match germplasm with geography to achieve optimal financial returns and conservation outcomes.

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