Whole-Genome Selection in Livestock

Singh, Birbal; Mal, Gorakh; Gautam, Sanjeev K.; Mukesh, Manishi

doi:10.1007/978-3-030-21309-1_31

Cited by 5 publications

(4 citation statements)

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“…GS has made a substantial contribution to the modern breeding process, as compared with traditional methods; the main advantages of this method include improved estimation accuracy of breeding value (BV; Sun et al 2014 ; Weller et al 2017 ), increased genetic progress, and reduced breeding costs ( Miglior et al 2017 ; Wiggans et al 2017 ). With the successive publication of various livestock genome sequences and the continuous upgrade of commercial SNP microarrays, different types and densities of microarrays have been adopted in the GP of different livestock ( Singh et al 2019 ). Accuracy and cost are generally the most critical factors in GP, compared with low-density SNP microarrays, the high-density SNP microarrays could accommodate more SNP sites that may lead to higher coverage of the genotype data ( Di et al 2005 ).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Bayesian alphabet and GBLUP based on different marker density for genomic prediction in Alpine Merino sheep

Zhu

Guo

Yuan

et al. 2021

G3 Genes|Genomes|Genetics

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The marker density, the heritability level of trait and the statistical models adopted are critical to the accuracy of genomic prediction (GP) or selection (GS). If the potential of GP is to be fully utilized to optimize the effect of breeding and selection, in addition to incorporating the above factors into simulated data for analysis, it is essential to incorporate these factors into real data for understanding their impact on GP accuracy, more clearly and intuitively. Herein, we studied the genomic prediction of six wool traits of sheep by two different models, including Bayesian Alphabet (BayesA, BayesB, BayesC π and Bayesian LASSO) and genomic best linear unbiased prediction (GBLUP). We adopted 5-fold cross-validation to perform the accuracy evaluation based on the genotyping data of Alpine Merino sheep (n = 821). The main aim was to study the influence and interaction of different models and marker densities on GP accuracy. The GP accuracy of the six traits was found to be between 0.28 and 0.60, as demonstrated by the cross-validation results. We showed that the accuracy of GP could be improved by increasing the marker density, which is closely related to the model adopted and the heritability level of the trait. Moreover, based on two different marker densities, it was derived that the prediction effect of GBLUP model for traits with low heritability was better; while with the increase of heritability level, the advantage of Bayesian Alphabet would be more obvious, therefore, different models of GP are appropriate in different traits. These findings indicated the significance of applying appropriate models for GP which would assist in further exploring the optimization of GP.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Bayesian alphabet and GBLUP based on different marker density for genomic prediction in Alpine Merino sheep

Zhu

Guo

Yuan

et al. 2021

G3 Genes|Genomes|Genetics

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“…Genomic selection (GS) presents high accuracy in predicting genomic breeding values, accelerating the process of genetic improvement (SINGH et al, 2019;LIU et al, 2019). Statistical methodologies generally used for genomic prediction, such as RR-BLUP, G-BLUP, Bayes A, and Bayes B (MEUWISSEN et al, 2001), are based on errors and; consequently, phenotypic values normality assumptions.…”

Section: Introductionmentioning

confidence: 99%

Genome-enabled prediction through quantile random forest for complex traits

Valadares,

Nascimento,

Celeri

et al. 2023

Cienc. Rural

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Quantile Random Forest (QRF) is a non-parametric methodology that combines the advantages of Random Forest (RF) and Quantile Regression (QR). Specifically, this approach can explore non-linear functions, determining the probability distribution of a response variable and extracting information from different quantiles instead of just predicting the mean. This evaluated the performance of the QRF in the genomic prediction for complex traits (epistasis and dominance). In addition, compare the accuracies obtained with those derived from the G-BLUP. The simulation created an F2 population with 1,000 individuals and genotyped for 4,010 SNP markers. Besides, twelve traits were simulated from a model considering additive and non-additive effects, QTL (Quantitative trait loci) numbers ranging from eight to 120, and heritability of 0.3, 0.5, or 0.8. For training and validation, the 5-fold cross-validation approach was used. For each fold, the accuracies of all the proposed models were calculated: QRF in five different quantiles and three G-BLUP models (additive effect, additive and epistatic effects, additive and dominant effects). Finally, the predictive performance of these methodologies was compared. In all scenarios, the QRF accuracies were equal to or greater than the methodologies evaluated and proved to be an alternative tool to predict genetic values in complex traits.

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“…GS has made a substantial contribution to the modern breeding process, as compared to traditional methods; the main advantages of this method include improved estimation accuracy of breeding value (BVs) [17,18], increased genetic progress, and reduced breeding costs [19,20]. With the successive publish of various livestock genome sequences and the continuous upgrade of commercial SNP microarrays, different types and densities of microarrays have been adopted in the GP of different livestock [21]. Accuracy and cost are generally the most critical factors in GP, compared to low-density SNP microarrays, the high-density SNP microarrays could accommodate more SNP sites that may lead to higher coverage of the genotype data [22].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of GBLUP and Bayes-Alphabet Based on Different Marker Density For Genomic Prediction in Alpine Merino Sheep

Zhu¹,

Guo²,

Yuan³

et al. 2021

Preprint

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BackgroundThe marker density, the heritability level of trait and the statistical models adopted are critical to the accuracy of genomic prediction (GP) or genomic selection (GS). The studies on the impact of the above factors on accuracy of GP are usually focused on the comparison and discussion of simulated datasets. If the potential of GS is to be fully utilized to optimize the effect of breeding and selection, it is essential to incorporate these factors into real data for understanding their impact on GP accuracy, more clearly and intuitively. Herein, we studied the genomic prediction of six wool traits of sheep by two different models, including genomic best linear unbiased prediction (GBLUP), and Bayes-Alphabet. We adopted 5-fold cross-validation to perform the accuracy evaluation based on the genotyping data of Alpine Merino sheep (n=821). ResultsThe GP accuracy of the six traits was found to be between 0.28 and 0.60, as demonstrated by the cross-validation results. We showed that the accuracy of GP could be improved by increasing the marker density, which is closely related to the model adopted and the heritability level of the trait. Moreover, based on two different marker densities, it was derived that the prediction effect of GBLUP model for traits with low heritability was better (GBLUP has the highest accuracy of 28.57% higher than Bayes-Alphabet); while with the increase of heritability level, the advantage of Bayes-Alphabet would be more obvious, therefore, different models of GP are appropriate in different traits. ConclusionThis is the first study of optimization of GP has been applied to the domesticated Alpine Merino sheep populations. The main aim was to study the influence and interaction of different models and marker densities on GP accuracy. These findings indicated the significance of applying appropriate models for GP which would assist in further exploring the optimization of GP.

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Whole-Genome Selection in Livestock

Cited by 5 publications

References 46 publications

Evaluation of Bayesian alphabet and GBLUP based on different marker density for genomic prediction in Alpine Merino sheep

Evaluation of Bayesian alphabet and GBLUP based on different marker density for genomic prediction in Alpine Merino sheep

Genome-enabled prediction through quantile random forest for complex traits

Evaluation of GBLUP and Bayes-Alphabet Based on Different Marker Density For Genomic Prediction in Alpine Merino Sheep

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