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.

Genotype by environment interactions in lamb weight and carcass composition traits

2002 ◽  
Vol 75 (1) ◽  
pp. 3-14 ◽  
Author(s):  
N. Maniatis ◽  
G. E. Pollott
Keyword(s):  
Variance Components ◽  
Mixed Model ◽  
Genotype By Environment Interaction ◽  
Carcass Composition ◽  
Phenotypic Variance ◽  
Environment Interaction ◽  
Additive Effects ◽  
Environmental Variance ◽  
Genotype By Environment Interactions ◽  
Genotype By Environment

AbstractThe systematic use of the same genotype in several different environments provides information that can be used to estimate genotype by environment interaction (G ✕ E) variances and parameters. Data from the UK Suffolk Sire Referencing Scheme Ltd were used to investigate a range of sire and dam by environment interactions in lamb weight (at 8 weeks and scanning) and body composition traits (muscle and fat depth). These interactions were calculated in a DFREML mixed model containing direct additive, maternal additive, maternal environmental random variance components and the covariance between direct and maternal additive effects. Sire interactions with year, flock and flock-year and dam effects within and between litters were investigated. The addition of all G ✕ E (co)variance components resulted in an improved fit of the model for all traits. Sire interactions accounted for between 2 and 3% of the phenotypic variance in all traits, usually at the expense of both additive effects. Maternal litter environmental variance components ranged from 10% (fat depth and muscle depth) to 20% (8-week weight) of phenotypic variance. Most of this variation was found in the residual component of variance when the term was omitted from the model. When fitting sire G✕ E components in a model the covariance between direct and maternal additive genetic effects, as a proportion of phenotypic variance, was reduced to a low level (from –0·36 to –0·08 for 8-week weight). Genotype by environment interactions form a significant source of variation in lamb growth and composition traits and reduce the high negative correlation between additive effects found previously in these traits.

scholarly journals Association between lactoferrin single nucleotide polymorphisms and milk production traits in Polish Holstein cattle

2014 ◽  
Vol 57 (1) ◽  
pp. 1-12
Author(s):  
Adrianna Pawlik ◽  
Grazyna Sender ◽  
Magdalena Sobczynska ◽  
Agnieszka Korwin-Kossakowska ◽  
Jolanta Oprzadek ◽  
...  
Keyword(s):  
Genotype By Environment Interaction ◽  
Bovine Lactoferrin ◽  
Environment Interaction ◽  
Production Traits ◽  
Breeding Values ◽  
Genotype By Environment ◽  
Lactoferrin Gene ◽  
Milk Performance ◽  
Estimated Breeding Values ◽  
The Impact

Abstract. Bovine lactoferrin exhibits strong potential for further applications as a mastitis resistance marker. Since selection for mastitis resistance should not interfere with dairy performance, we investigated the association between bovine lactoferrin gene polymorphism and production traits in Polish Holsteins. The associations between four SNPs, localized in the 5’-flanking region and in exons 4 and 9 of the lactoferrin gene, and dairy performance were examined. SNPs were associated with almost all test-day milk performance traits. Significant associations were found between lactoferrin genotypes and the estimated breeding values for those traits. To find out whether the discrepancies between the lactoferrin gene SNP’s influence on phenotype (test-day milk performance) and on estimated breeding values originate from the impact of other factors, we explored the genotype by environment interaction. Substantial impacts of SCC, lactation stage and parity were found. This paper suggests that the genotype by environment interaction may significantly change associations between genes and traits. It is important to include similar analyses to the studies on disease markers before using them in the selection.

Genetic evaluation including intermediate omics features

Genetics ◽  
2021 ◽  
Author(s):  
Ole F Christensen ◽  
Vinzent Börner ◽  
Luis Varona ◽  
Andres Legarra
Keyword(s):  
Evaluation System ◽  
Mixed Model ◽  
Simulated Data ◽  
Genetic Evaluation ◽  
Omics Data ◽  
Estimation Of Parameters ◽  
Data Set ◽  
Breeding Values ◽  
Mixed Model Equation ◽  
Estimated Breeding Values

Abstract In animal and plant breeding and genetics there has been an increasing interest in intermediate omics traits, such as metabolomics and transcriptomics, that mediate the effect of genetics on the phenotype of interest. For inclusion of such intermediate traits into a genetic evaluation system, there is a need for a statistical model that integrates phenotypes, genotypes, pedigree and omics traits, and a need for associated computational methods that provide estimated breeding values. In this paper, a joint model for phenotypes and omics data is presented, and a formula for the breeding values on individuals is derived. For complete omics data, three equivalent methods for best linear unbiased prediction of breeding values are presented. In all three cases, this requires solving two mixed model equation systems. Estimation of parameters using restricted maximum likelihood is also presented. For incomplete omics data, extensions of two of these methods are presented, where in both cases the extension consists of extending an omics related similarity matrix to incorporate individuals without omics data. The methods are illustrated using a simulated data set.

scholarly journals Prediction of breeding values for group-recorded traits including genomic information and an individually recorded correlated trait

Heredity ◽  
2020 ◽  
Vol 126 (1) ◽  
pp. 206-217
Author(s):  
Xiang Ma ◽  
Ole F. Christensen ◽  
Hongding Gao ◽  
Ruihua Huang ◽  
Bjarne Nielsen ◽  
...  
Keyword(s):  
Egg Production ◽  
Quantitative Traits ◽  
Group Structure ◽  
Genetic Evaluation ◽  
Genomic Information ◽  
Bivariate Model ◽  
Breeding Values ◽  
Lower Accuracy ◽  
Small Group Size ◽  
Estimated Breeding Values

AbstractRecords on groups of individuals could be valuable for predicting breeding values when a trait is difficult or costly to measure on single individuals, such as feed intake and egg production. Adding genomic information has shown improvement in the accuracy of genetic evaluation of quantitative traits with individual records. Here, we investigated the value of genomic information for traits with group records. Besides, we investigated the improvement in accuracy of genetic evaluation for group-recorded traits when including information on a correlated trait with individual records. The study was based on a simulated pig population, including three scenarios of group structure and size. The results showed that both the genomic information and a correlated trait increased the accuracy of estimated breeding values (EBVs) for traits with group records. The accuracies of EBV obtained from group records with a size 24 were much lower than those with a size 12. Random assignment of animals to pens led to lower accuracy due to the weaker relationship between individuals within each group. It suggests that group records are valuable for genetic evaluation of a trait that is difficult to record on individuals, and the accuracy of genetic evaluation can be considerably increased using genomic information. Moreover, the genetic evaluation for a trait with group records can be greatly improved using a bivariate model, including correlated traits that are recorded individually. For efficient use of group records in genetic evaluation, relatively small group size and close relationships between individuals within one group are recommended.

Practical aspects of a genetic evaluation system using parentage assigned from genetic markers

2005 ◽  
Vol 45 (8) ◽  
pp. 935 ◽  
Author(s):  
K. G. Dodds ◽  
J. A. Sise ◽  
M. L. Tate
Keyword(s):  
Genetic Markers ◽  
Evaluation System ◽  
Fixed Effects ◽  
Genetic Evaluation ◽  
Diagnostic Systems ◽  
Breeding Values ◽  
Mating Group ◽  
Animal Management ◽  
The Mean ◽  
Estimated Breeding Values

Animal breeding values can be calculated when genetic markers have been used to help determine the parentage of some of the animals, but their parentage has been incompletely determined. The pedigree sampling method is 1 computing strategy for calculating these breeding values. This paper describes and discusses methods for dealing with a number of practical issues that arise when implementing such a system for industry use. In particular, diagnostic systems for detecting inadequacies or possible errors in the genotyping systems and the recording of animal management are developed. Also, characteristics of the best assigned pedigrees are calculated according to mating group and used to check for sires missing from these groups. The correlation between breeding values estimated from a single sampled pedigree (using parentage probabilities) and those estimated as the mean from many sampled pedigrees gives a diagnostic to indicate which estimated breeding values are more influenced by uncertainties in relationships. For the analysis of survival traits, a method to enumerate and assign likely parentage to dead offspring which have not been DNA sampled and genotyped is described. When embryo transfer technology is used, the genetic dam needs to be included as a possible dam when considering parentage. If some fixed effects which depend on the parent are missing, these can be sampled similarly to parentage, and this may improve the evaluation if certain assumptions are met. A method to provide a likely list of parents, the ‘fitted pedigree’, which is based on the most likely parents, but modified to reduce the occurrence of unlikely family sets (e.g. very large litters) is also presented. The use of these methods will enhance the practical application of DNA parenting when used in conjunction with genetic evaluation.

scholarly journals Training Population Optimization for Genomic Selection in Miscanthus

2020 ◽  
Vol 10 (7) ◽  
pp. 2465-2476
Author(s):  
Marcus O. Olatoye ◽  
Lindsay V. Clark ◽  
Nicholas R. Labonte ◽  
Hongxu Dong ◽  
Maria S. Dwiyanti ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Perennial Grass ◽  
Substantial Contribution ◽  
Lignocellulosic Ethanol ◽  
Training Population ◽  
Training Set ◽  
Breeding Values ◽  
Single Clone ◽  
Estimated Breeding Values ◽  
The Ideal

Miscanthus is a perennial grass with potential for lignocellulosic ethanol production. To ensure its utility for this purpose, breeding efforts should focus on increasing genetic diversity of the nothospecies Miscanthus × giganteus (M×g) beyond the single clone used in many programs. Germplasm from the corresponding parental species M. sinensis (Msi) and M. sacchariflorus (Msa) could theoretically be used as training sets for genomic prediction of M×g clones with optimal genomic estimated breeding values for biofuel traits. To this end, we first showed that subpopulation structure makes a substantial contribution to the genomic selection (GS) prediction accuracies within a 538-member diversity panel of predominately Msi individuals and a 598-member diversity panels of Msa individuals. We then assessed the ability of these two diversity panels to train GS models that predict breeding values in an interspecific diploid 216-member M×g F2 panel. Low and negative prediction accuracies were observed when various subsets of the two diversity panels were used to train these GS models. To overcome the drawback of having only one interspecific M×g F2 panel available, we also evaluated prediction accuracies for traits simulated in 50 simulated interspecific M×g F2 panels derived from different sets of Msi and diploid Msa parents. The results revealed that genetic architectures with common causal mutations across Msi and Msa yielded the highest prediction accuracies. Ultimately, these results suggest that the ideal training set should contain the same causal mutations segregating within interspecific M×g populations, and thus efforts should be undertaken to ensure that individuals in the training and validation sets are as closely related as possible.

scholarly journals Analysis of genotype by environment interactions in a maize mapping population

2021 ◽  
Author(s):  
Asher I Hudson ◽  
Sarah G Odell ◽  
Pierre Dubreuil ◽  
Marie-Helene Tixier ◽  
Sebastien Praud ◽  
...  
Keyword(s):  
Genome Wide Association Study ◽  
Genetic Correlations ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Crop Breeding ◽  
Genotype By Environment Interactions ◽  
Genotype By Environment ◽  
Genome Wide ◽  
A Genome ◽  
Genetic Covariances

Genotype by environment interactions are a significant challenge for crop breeding as well as being important for understanding the genetic basis of environmental adaptation. In this study, we analyzed genotype by environment interaction in a maize multi-parent advanced generation intercross population grown across five environments. We found that genotype by environment interactions contributed as much as genotypic effects to the variation in some agronomically important traits. In order to understand how genetic correlations between traits change across environments, we estimated the genetic variance-covariance matrix in each environment. Changes in genetic covariances between traits across environments were common, even among traits that show low genotype by environment variance. We also performed a genome-wide association study to identify markers associated with genotype by environment interactions but found only a small number of significantly associated markers, possibly due to the highly polygenic nature of genotype by environment interactions in this population.

scholarly journals Comparison of BLUP and Bayesian methods for different sizes of training population in genomic selection

2020 ◽  
Vol 44 (5) ◽  
pp. 994-1002
Author(s):  
Samet Hasan ABACI ◽  
Hasan ÖNDER
Keyword(s):  
Genomic Selection ◽  
Milk Yield ◽  
Predictive Ability ◽  
Breeding Value ◽  
Training Population ◽  
Breeding Values ◽  
Value Prediction ◽  
The Usa ◽  
Population Sizes ◽  
Estimated Breeding Values

This study aims to compare the accuracy of pedigree-based and genomic-based breeding value prediction for different training population sizes. In this study, Bayes (A, B, C, Cpi) and GBLUP methods for genomic selection and BLUP method for pedigree-based selection were used. Genomic and pedigree-based breeding values were estimated for partial milk yield (158 days) of Holstein cows (400 individuals) from a private enterprise in the USA. For this aim, populations were created for indirect breeding value estimates as training (322–360) and test (78–40) populations. In animals genotyped with a 54k SNP, the marker file was encoded as –10, 0, and 10 for AA, AB, and BB marker genotypes, respectively. Bayes and GBLUP methods were performed using GenSel 4.55 software. A total of 50,000 iterations were used, with the first 5000 excluded as the burn-in. Pedigree-based breeding values were estimated by REML using MTDFREML software employing an animal model. Correlations between partial milk yield and estimated breeding values were used to assess the predictive ability for methods. Bayes B method gave the highest accuracy for the indirect estimate of breeding value.

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