scholarly journals Development of genomic predictions for Angus cattle in Brazil incorporating genotypes from related American sires

2022 ◽  
Author(s):  
Gabriel Soares Campos ◽  
Fernando Flores Cardoso ◽  
Claudia Cristina Gulias Gomes ◽  
Robert Domingues ◽  
Luciana Correia de Almeida Regitano ◽  
...  
Keyword(s):  
Reference Population ◽  
Single Step ◽  
Linear Regression Method ◽  
Effective Population ◽  
Genetic Progress ◽  
Angus Cattle ◽  
Improvement Programs ◽  
Snp Panels ◽  
Estimate Variance ◽  
Genomic Predictions

Abstract Genomic prediction has become the new standard for genetic improvement programs, and currently, there is a desire to implement this technology for the evaluation of Angus cattle in Brazil. Thus, the main objective of this study was to assess the feasibility of evaluating young Brazilian Angus (BA) bulls and heifers for 12 routinely recorded traits using single-step genomic BLUP (ssGBLUP) with and without genotypes from American Angus (AA) sires. The second objective was to obtain estimates of effective population size (Ne) and linkage disequilibrium (LD) in the Brazilian Angus population. The dataset contained phenotypic information for up to 277,661 animals belonging to the Promebo® breeding program, pedigree for 362,900, of which 1,386 were genotyped for 50k, 77k, and 150k SNP panels. After imputation and quality control, 61,666 SNP were available for the analyses. In addition, genotypes from 332 American Angus (AA) sires widely used in Brazil were retrieved from the AA Association database to be used for genomic predictions. Bivariate animal models were used to estimate variance components, traditional EBV, and genomic EBV (GEBV). Validation was carried out with the linear regression method (LR) using young-genotyped animals born between 2013 and 2015 without phenotypes in the reduced dataset and with records in the complete dataset. Validation animals were further split into progeny of BA and AA sires to evaluate if their progenies would benefit by including genotypes from AA sires. The Ne was 254 based on pedigree and 197 based on LD, and the average LD (±SD) and distance between adjacent SNPs across all chromosomes was 0.27 (±0.27) and 40743.68 bp, respectively. Prediction accuracies with ssGBLUP outperformed BLUP for all traits, improving accuracies by, on average, 16% for BA young bulls and heifers. The GEBV prediction accuracies ranged from 0.37 (total maternal for weaning weight and tick count) to 0.54 (yearling precocity) across all traits, and dispersion (LR coefficients) fluctuated between 0.92 and 1.06. Inclusion of genotyped sires from the AA improved GEBV accuracies by 2%, on average, compared to using only the BA reference population. Our study indicated that genomic information could help to improve GEBV accuracies and hence genetic progress in the Brazilian Angus population. The inclusion of genotypes from American Angus sires heavily used in Brazil just marginally increased the GEBV accuracies for selection candidates.

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

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.

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

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.

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

2019 ◽  
Vol 51 (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.

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

BMC Genomics ◽  
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.

scholarly journals 335 Genomic predictions with a multi-breed genomic relationship matrix

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 49-50
Author(s):  
Yvette Steyn ◽  
Daniela Lourenco ◽  
Ignacy Misztal
Keyword(s):  
Prediction Accuracy ◽  
Negative Impact ◽  
Reference Population ◽  
Single Step ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Genomic Relationship ◽  
Effective Population ◽  
Specific Allele ◽  
Missing Genotypes

Abstract Multi-breed evaluations have the advantage of increasing the size of the reference population for genomic evaluations and are quite simple; however, combining breeds usually have a negative impact on prediction accuracy. The aim of this study was to evaluate the use of a multi-breed genomic relationship matrix (G), where SNP for each breed are non-shared. The multi-breed G is set assuming known genotypes for one breed and missing genotypes for the remaining breeds. This setup may avoid spurious IBS relationships between breeds and considers breed-specific allele frequencies. This scenario was contrasted to multi-breed evaluations where all SNP are shared, i.e., the same SNP, and to single-breed evaluations. Different SNP densities, namely 9k and 45k, and different effective population sizes (Ne) were tested. Five breeds mimicking recent beef cattle populations that diverged from the same historical population were simulated using different selection criteria. It was assumed that QTL effects were the same over all breeds. For the recent population, generations 1 to 9 had approximately half of the animals genotyped, whereas all 1200 animals were genotyped in generation 10. Genotyped animals in generation 10 were set as validation; therefore, each breed had a validation set. Analysis were performed using single-step GBLUP (ssGBLUP). Prediction accuracy was calculated as correlation between true (T) and genomic estimated (GE) BV. Accuracies of GEBV were lower for the larger Ne and low SNP density. All three scenarios using 45K resulted in similar accuracies, suggesting that the marker density is high enough to account for relationships and linkage disequilibrium with QTL. A shared multi-breed evaluation using 9K resulted in a decrease of accuracy of 0.08 for a smaller Ne and 0.11 for a larger Ne. This loss was mostly avoided when markers were treated as non-shared within the same genomic relationship matrix.

scholarly journals Implications of SNP weighting on single-step genomic predictions for different reference population sizes

2017 ◽  
Vol 134 (6) ◽  
pp. 463-471 ◽  
Author(s):  
D. A. L. Lourenco ◽  
B. O. Fragomeni ◽  
H. L. Bradford ◽  
I. R. Menezes ◽  
J. B. S. Ferraz ◽  
...  
Keyword(s):  
Reference Population ◽  
Single Step ◽  
Population Sizes ◽  
Genomic Predictions

scholarly journals Genomic Predictions For Fillet Yield And Firmness In Rainbow Trout Using Reduced-Density SNP Panels

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 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 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). 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.

scholarly journals Validation of single‐step GBLUP genomic predictions from threshold models using the linear regression method: An application in chicken mortality

2020 ◽  
Vol 138 (1) ◽  
pp. 4-13
Author(s):  
Matias Bermann ◽  
Andres Legarra ◽  
Mary Kate Hollifield ◽  
Yutaka Masuda ◽  
Daniela Lourenco ◽  
...  
Keyword(s):  
Linear Regression ◽  
Single Step ◽  
Regression Method ◽  
Linear Regression Method ◽  
Threshold Models ◽  
Genomic Predictions

scholarly journals PSVIII-38 Genomic prediction for tick resistance in Angus cattle

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 263-263
Author(s):  
Gabriel Campos ◽  
Fernando F Cardoso ◽  
Claudia Cristina Gulias-Gomes ◽  
Robert Domingues ◽  
Luciana Regitano ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Predictive Ability ◽  
Relative Increase ◽  
Single Step ◽  
Angus Cattle ◽  
Increase In Accuracy ◽  
Estimate Variance ◽  
Tick Counts ◽  
Reduced Data ◽  
Tick Resistance

Abstract The main objective of this study was to evaluate the feasibility of single-step genomic BLUP (ssGBLUP) for genetic evaluation of tick resistance in Angus cattle in Brazil. Additionally, we investigated population parameters, namely effect population size (Ne) and inbreeding (F) based on pedigree (PED) and genomic (GEN) information. Half-body tick counts were recorded up to three times in the same animal, totaling 2291 records. To normalize the distribution, records were log-transformed prior to the analysis. From 7073 animals in the pedigree, 1299 were genotyped with 3 different SNP chips of density 50k, 77k, and 150k. After imputation and quality control, 61,066 SNP remained. A repeatability animal model was used to estimate variance components, traditional EBV, and genomic EBV (GEBV). Validation was carried out for young genotyped animals, with no phenotypes in the reduced data but at least one record in the complete data, using two different approaches: 1) predictive ability as the correlation between phenotypes adjusted for fixed effects and (G)EBV; 2) a method based on linear regressions that is called LR, which uses correlations between (G)EBV in the complete and reduced data as a measure of consistency between subsequent evaluations. Heritability for tick counts was 0.18 ± 0.03. Based on PED and GEN, Ne was 254 and 199, whereas F was 0.016 and 0.003, respectively. Predictive ability for tick counts was 0.11 for EBV and 0.14 for GEBV, which is considered low. Conversely, when LR validation was used, the relative increase in accuracy by adding extra phenotypic information was 0.49 for EBV and 0.61 for GEBV. Even though tick counts has low heritability, our study indicates that genomic selection can help to improve prediction accuracy and, therefore, to increase tick resistance in this Angus population.

scholarly journals Trends in genetic diversity and the effect of inbreeding in American Angus cattle under genomic selection

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Emmanuel A. Lozada-Soto ◽  
Christian Maltecca ◽  
Duc Lu ◽  
Stephen Miller ◽  
John B. Cole ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Beef Cattle ◽  
Genomic Selection ◽  
Inbreeding Depression ◽  
Growth Traits ◽  
Effective Population ◽  
Angus Cattle ◽  
Genomic Inbreeding ◽  
Chromosome Segments ◽  
The Impact

Abstract Background While the adoption of genomic evaluations in livestock has increased genetic gain rates, its effects on genetic diversity and accumulation of inbreeding have raised concerns in cattle populations. Increased inbreeding may affect fitness and decrease the mean performance for economically important traits, such as fertility and growth in beef cattle, with the age of inbreeding having a possible effect on the magnitude of inbreeding depression. The purpose of this study was to determine changes in genetic diversity as a result of the implementation of genomic selection in Angus cattle and quantify potential inbreeding depression effects of total pedigree and genomic inbreeding, and also to investigate the impact of recent and ancient inbreeding. Results We found that the yearly rate of inbreeding accumulation remained similar in sires and decreased significantly in dams since the implementation of genomic selection. Other measures such as effective population size and the effective number of chromosome segments show little evidence of a detrimental effect of using genomic selection strategies on the genetic diversity of beef cattle. We also quantified pedigree and genomic inbreeding depression for fertility and growth. While inbreeding did not affect fertility, an increase in pedigree or genomic inbreeding was associated with decreased birth weight, weaning weight, and post-weaning gain in both sexes. We also measured the impact of the age of inbreeding and found that recent inbreeding had a larger depressive effect on growth than ancient inbreeding. Conclusions In this study, we sought to quantify and understand the possible consequences of genomic selection on the genetic diversity of American Angus cattle. In both sires and dams, we found that, generally, genomic selection resulted in decreased rates of pedigree and genomic inbreeding accumulation and increased or sustained effective population sizes and number of independently segregating chromosome segments. We also found significant depressive effects of inbreeding accumulation on economically important growth traits, particularly with genomic and recent inbreeding.

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