Determining stability of accuracy of genomic estimated breeding values in future generations in commercial pig populations

Abstract Genomic information has a limited dimensionality (Me) related to the effective population size. Under the additive model, the persistence of genomic accuracies over generations should be high when the nongenomic information (pedigree and phenotypes) is equivalent to Me animals with high accuracy. The objective of this study was to evaluate the decay in accuracy over time and to compare the magnitude of decay with varying quantities of data, and with traits of low and moderate heritability. The dataset included 161,897 phenotypic records for a growth trait (GT) and 27,669 phenotypic records for a fitness trait related to prolificacy (FT) in a population with dimensionality around 5,000. The pedigree included 404,979 animals from 2008 to 2020, of which 55,118 were genotyped. Two single-trait models were used with all ancestral data and sliding subsets of 3-, 2-, and 1- generation intervals. Single-step GBLUP (ssGBLUP) was used to compute genomic estimated breeding values (GEBV). Estimated accuracies were calculated by the linear regression (LR) method. The validation population consisted of single generations succeeding the training population and continued forward for all generations available. The average accuracy for the first generation after training with all ancestral data was 0.69 and 0.46 for GT and FT, respectively. The average decay in accuracy from the first generation after training to generation 9 was -0.13, and -0.19 for GT and FT, respectively. The persistence of accuracy improves with more data. Old data has a limited impact on predictions for young animals for a trait with a large amount of information but a bigger impact for a trait with less information.

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Prediction Accuracy of Pedigree and Genomic Estimated Breeding Values over Generations in Layer Chickens

10.31274/ans_air-180814-1181 ◽

2014 ◽

Author(s):

Ziqing Weng ◽

Anna Wolc ◽

Rohan L Fernando ◽

Jack C. M. Dekkers ◽

Dorian J. Garrick

Keyword(s):

Prediction Accuracy ◽

Breeding Values ◽

Layer Chickens ◽

Genomic Estimated Breeding Values ◽

Estimated Breeding Values

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Genomic Predictions for Fillet Yield and Firmness in Rainbow Trout Using Reduced-density SNP Panels

10.21203/rs.3.rs-36925/v2 ◽

2020 ◽

Author(s):

Rafet Al-Tobasei ◽

Ali R. Ali ◽

Andre L. S. Garcia ◽

Daniela Lourenco ◽

Tim Leeds ◽

...

Keyword(s):

Rainbow Trout ◽

Predictive Ability ◽

Study Data ◽

Single Step ◽

Breeding Values ◽

Snp Chip ◽

Estimated Breeding Values ◽

Snp Panels ◽

Fillet Yield ◽

Genomic Predictions

Abstract BackgroundOne of the most important goals for the rainbow trout aquaculture industry is to improve fillet yield and fillet quality. Previously, we showed that a 50K transcribed-SNP chip can be used to detect quantitative trait loci (QTL) associated with fillet yield and fillet firmness. In this study, data from 1,568 fish genotyped for the 50K transcribed-SNP chip and ~774 fish phenotyped for fillet yield and fillet firmness were used in a single-step genomic BLUP (ssGBLUP) model to compute the genomic estimated breeding values (GEBV). In addition, pedigree-based best linear unbiased prediction (PBLUP) was used to calculate traditional, family-based estimated breeding values (EBV). ResultsThe genomic predictions outperformed the traditional EBV by 35% for fillet yield and 42% for fillet firmness. The predictive ability for fillet yield and fillet firmness was 0.19 - 0.20 with PBLUP, and 0.27 with ssGBLUP. Additionally, reducing SNP panel densities indicated that using 500 – 800 SNPs in genomic predictions still provides predictive abilities higher than PBLUP. ConclusionThese results suggest that genomic evaluation is a feasible strategy to identify and select fish with superior genetic merit within rainbow trout families, even with low-density SNP panels.

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Genomic Predictions for Muscle Yield and Fillet Firmness in Rainbow Trout using Reduced-Density SNP Panels

10.21203/rs.3.rs-36925/v1 ◽

2020 ◽

Author(s):

Rafet Al-Tobasei ◽

Ali R. Ali ◽

Andre L. S. Garcia ◽

Daniela Lourenco ◽

Tim Leeds ◽

...

Keyword(s):

Rainbow Trout ◽

Predictive Ability ◽

Study Data ◽

Single Step ◽

Breeding Values ◽

Snp Chip ◽

Estimated Breeding Values ◽

Snp Panels ◽

Family Based ◽

Genomic Predictions

Abstract Background One of the most important goals for the rainbow trout aquaculture industry is to improve muscle yield and fillet quality. Previously, we showed that a 50K transcribed-SNP chip can be used to detect quantitative trait loci (QTL) associated with muscle yield and fillet firmness. In this study, data from 1,568 fish genotyped for the 50K transcribed-SNP chip and ~774 fish phenotyped for muscle yield and fillet firmness were used in a single-step genomic BLUP (ssGBLUP) model to compute the genomic estimated breeding values (GEBV). In addition, pedigree-based best linear unbiased prediction (PBLUP) was used to calculate traditional, family-based estimated breeding values (EBV). Results The genomic predictions outperformed the traditional EBV by 35% for muscle yield and 42% for fillet firmness. The predictive ability for muscle yield and fillet firmness was 0.19 - 0.20 with PBLUP, and 0.27 with ssGBLUP. Additionally, reducing SNP panel densities indicated that using 500 – 800 SNPs in genomic predictions still provides predictive abilities higher than PBLUP. Conclusion These results suggest that genomic evaluation is a feasible strategy to identify and select fish with superior genetic merit within rainbow trout families, even with low-density SNP panels.

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61 The impact of selective phenotyping and genotyping over generations in beef cattle

Journal of Animal Science ◽

10.1093/jas/skz122.068 ◽

2019 ◽

Vol 97 (Supplement_2) ◽

pp. 37-39

Author(s):

Andrea Plotzki Reis ◽

Rodrigo Fagundes da Costa ◽

Fabyano Fonseca e Silva ◽

Fernando Flores Cardoso ◽

Matthew L Spangler

Keyword(s):

Beef Cattle ◽

Prediction Accuracy ◽

Cost Benefit ◽

Single Step ◽

Breeding Values ◽

Additional Information ◽

Economic Framework ◽

Estimated Breeding Values ◽

The Cost ◽

The Impact

Abstract The aim of this study was to investigate selective phenotyping to maintain adequate prediction accuracy. A simulation was conducted, with 10 replicates, using QMSim to mimic the structure and size of a Braford population. A population with 50 generations, 500 animals per generation, was created with phenotyping and genotyping beginning in generation 11. The scenarios investigated were: 1) Randomly phenotype and genotype 10, 25, 50, 75, and 100% of individuals each generation and; 2) Randomly phenotype and genotype 10, 25, 50, 75, and 100% of individuals in every-other generation. Estimated breeding values (EBV) were obtained using single-step GBLUP and accuracy was determined as the correlation between true BV from simulation and those estimated from the blupf90 family of programs. For scenarios where phenotyping and genotyping occurred every generation, EBV accuracies in generation 11 and 50 ranged from 0.32 to 0.32, 0.42 to 0.43, 0.49 to 0.51, 0.53 to 0.56 and 0.57 to 0.59 when 10, 25, 50, 75, and 100% of animals were chosen, respectively. The highest accuracies were 0.40 and 0.50 in generation 38 for scenarios 10 and 25%; 0.56, 0.61 and 0.64 in generation 40 for scenarios 50, 75 and 100%, respectively. When animals were selected every-other generation, EBV accuracy in generation 11 and 50 ranged from 0.24 to 0.26, 0.36 to 0.36, 0.43 to 0.42, 0.48 to 0.44 and 0.53 to 0.48 for 10, 25, 50, 75 and 100% of selected animals, respectively. The highest accuracies were in generation 23 for scenario 10% (0.31), in generation 37 for scenarios 25 (0.43), 50 (0.50) and 75% (0.55) and in generation 39 for 100% (0.59). Although increasing the density of phenotyped and genotyped animals increased prediction accuracy, some gains were marginal. These differences in accuracy must be contemplated in an economic framework to determine the cost-benefit of additional information.

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Level-biases in estimated breeding values due to the use of different SNP panels over time in ssGBLUP

Genetics Selection Evolution ◽

10.1186/s12711-019-0517-z ◽

2019 ◽

Vol 51 (1) ◽

Cited By ~ 1

Author(s):

Øyvind Nordbø ◽

Arne B. Gjuvsland ◽

Leiv Sigbjørn Eikje ◽

Theo Meuwissen

Keyword(s):

Value Added ◽

Single Step ◽

Fine Tuning ◽

Genomic Relationship Matrix ◽

Relationship Matrix ◽

Optimal Selection ◽

Breeding Values ◽

Estimated Breeding Values ◽

Snp Panels ◽

Genomic Predictions

Abstract Background The main aim of single-step genomic predictions was to facilitate optimal selection in populations consisting of both genotyped and non-genotyped individuals. However, in spite of intensive research, biases still occur, which make it difficult to perform optimal selection across groups of animals. The objective of this study was to investigate whether incomplete genotype datasets with errors could be a potential source of level-bias between genotyped and non-genotyped animals and between animals genotyped on different single nucleotide polymorphism (SNP) panels in single-step genomic predictions. Results Incomplete and erroneous genotypes of young animals caused biases in breeding values between groups of animals. Systematic noise or missing data for less than 1% of the SNPs in the genotype data had substantial effects on the differences in breeding values between genotyped and non-genotyped animals, and between animals genotyped on different chips. The breeding values of young genotyped individuals were biased upward, and the magnitude was up to 0.8 genetic standard deviations, compared with breeding values of non-genotyped individuals. Similarly, the magnitude of a small value added to the diagonal of the genomic relationship matrix affected the level of average breeding values between groups of genotyped and non-genotyped animals. Cross-validation accuracies and regression coefficients were not sensitive to these factors. Conclusions Because, historically, different SNP chips have been used for genotyping different parts of a population, fine-tuning of imputation within and across SNP chips and handling of missing genotypes are crucial for reducing bias. Although all the SNPs used for estimating breeding values are present on the chip used for genotyping young animals, incompleteness and some genotype errors might lead to level-biases in breeding values.

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Genomic predictions for fillet yield and firmness in rainbow trout using reduced-density SNP panels

BMC Genomics ◽

10.1186/s12864-021-07404-9 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Rafet Al-Tobasei ◽

Ali Ali ◽

Andre L. S. Garcia ◽

Daniela Lourenco ◽

Tim Leeds ◽

...

Keyword(s):

Rainbow Trout ◽

Predictive Ability ◽

Study Data ◽

Single Step ◽

Breeding Values ◽

Snp Chip ◽

Estimated Breeding Values ◽

Snp Panels ◽

Fillet Yield ◽

Genomic Predictions

Abstract Background One of the most important goals for the rainbow trout aquaculture industry is to improve fillet yield and fillet quality. Previously, we showed that a 50 K transcribed-SNP chip can be used to detect quantitative trait loci (QTL) associated with fillet yield and fillet firmness. In this study, data from 1568 fish genotyped for the 50 K transcribed-SNP chip and ~ 774 fish phenotyped for fillet yield and fillet firmness were used in a single-step genomic BLUP (ssGBLUP) model to compute the genomic estimated breeding values (GEBV). In addition, pedigree-based best linear unbiased prediction (PBLUP) was used to calculate traditional, family-based estimated breeding values (EBV). Results The genomic predictions outperformed the traditional EBV by 35% for fillet yield and 42% for fillet firmness. The predictive ability for fillet yield and fillet firmness was 0.19–0.20 with PBLUP, and 0.27 with ssGBLUP. Additionally, reducing SNP panel densities indicated that using 500–800 SNPs in genomic predictions still provides predictive abilities higher than PBLUP. Conclusion These results suggest that genomic evaluation is a feasible strategy to identify and select fish with superior genetic merit within rainbow trout families, even with low-density SNP panels.

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