Improving production efficiency in the presence of genotype by environment interactions in pig genomic selection breeding programmes

2016 ◽  
Vol 134 (2) ◽  
pp. 119-128 ◽  
Author(s):  
K.G. Nirea ◽  
T.H.E. Meuwissen
Keyword(s):  
Genomic Selection ◽  
Production Efficiency ◽  
Genotype By Environment Interactions ◽  
Genotype By Environment ◽  
Breeding Programmes ◽  
Selection Breeding

Analysing genetic variation in farm animals.

2021 ◽  
pp. 178-192
Author(s):  
Geoff Simm ◽  
Geoff Pollott ◽  
Raphael Mrode ◽  
Ross Houston ◽  
Karen Marshall
Keyword(s):  
Genetic Variation ◽  
Molecular Genetics ◽  
Snp Markers ◽  
Farm Animals ◽  
Genotype By Environment Interactions ◽  
Genotype By Environment ◽  
Breeding Programmes

Abstract In this chapter, topics focused on how to quantify the extent to which genes affect measured traits and how to use this information in breeding programmes. Highlights include: estimating heritability; estimating non-additive parameters, correlations, and genotype by environment interactions, molecular genetics and trait variations; and calculating inbreeding using SNP markers.

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 Genetic benefits of genomic selection breeding programmes considering foreign sire contributions

2019 ◽  
Vol 51 (1) ◽  
Author(s):  
Daragh Matthews ◽  
John F. Kearney ◽  
Andrew R. Cromie ◽  
Fiona S. Hely ◽  
Peter R. Amer
Keyword(s):  
Genomic Selection ◽  
Genetic Benefits ◽  
Breeding Programmes ◽  
Selection Breeding

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.

Strategy of whole genomic selection breeding and its application prospect in aquaculture

2013 ◽  
Vol 18 (4) ◽  
pp. 936-943
Author(s):  
Yang YU ◽  
Xiaojun ZHANG ◽  
Fuhua LI ◽  
Jianhai XIANG
Keyword(s):  
Genomic Selection ◽  
Selection Breeding

scholarly journals The heritable landscape of near-infrared and Raman spectroscopic measurements to improve lipid content in Atlantic salmon fillets

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Gareth F. Difford ◽  
Siri S. Horn ◽  
Katinka R. Dankel ◽  
Bente Ruyter ◽  
Binyam S. Dagnachew ◽  
...  
Keyword(s):  
Atlantic Salmon ◽  
Lipid Content ◽  
Production Efficiency ◽  
Near Infrared ◽  
Reference Method ◽  
Partial Least Square ◽  
Least Square ◽  
Genetic Management ◽  
Genetic Progress ◽  
Breeding Programmes

Abstract Background Product quality and production efficiency of Atlantic salmon are, to a large extent, influenced by the deposition and depletion of lipid reserves. Fillet lipid content is a heritable trait and is unfavourably correlated with growth, thus genetic management of fillet lipid content is needed for sustained genetic progress in these two traits. The laboratory-based reference method for recording fillet lipid content is highly accurate and precise but, at the same time, expensive, time-consuming, and destructive. Here, we test the use of rapid and cheaper vibrational spectroscopy methods, namely near-infrared (NIR) and Raman spectroscopy both as individual phenotypes and phenotypic predictors of lipid content in Atlantic salmon. Results Remarkably, 827 of the 1500 individual Raman variables (i.e. Raman shifts) of the Raman spectrum were significantly heritable (heritability (h2) ranging from 0.15 to 0.65). Similarly, 407 of the 2696 NIR spectral landscape variables (i.e. wavelengths) were significantly heritable (h2 = 0.27–0.40). Both Raman and NIR spectral landscapes had significantly heritable regions, which are also informative in spectroscopic predictions of lipid content. Partial least square predicted lipid content using Raman and NIR spectra were highly concordant and highly genetically correlated with the lipid content values ($${r}_{\text{g}}$$ r g = 0.91–0.98) obtained with the reference method using Lin’s concordance correlation coefficient (CCC = 0.63–0.90), and were significantly heritable ($${h}^{2}$$ h 2 = 0.52–0.67). Conclusions Both NIR and Raman spectral landscapes show substantial additive genetic variation and are highly genetically correlated with the reference method. These findings lay down the foundation for rapid spectroscopic measurement of lipid content in salmonid breeding programmes.

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