A comprehensive comparison between single- and two-step GBLUP methods in a simulated beef cattle population

2018 ◽  
Vol 98 (3) ◽  
pp. 565-575 ◽  
Author(s):  
Mario L. Piccoli ◽  
Luiz F. Brito ◽  
José Braccini ◽  
Fernanda V. Brito ◽  
Fernando F. Cardoso ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Production Systems ◽  
Simulated Data ◽  
Single Step ◽  
Breeding Values ◽  
True Breeding ◽  
Blending Method ◽  
Estimated Breeding Values ◽  
Genetic Evaluations ◽  
Genomic Predictions

The statistical methods used in the genetic evaluations are a key component of the process and can be best compared by using simulated data. The latter is especially true in grazing beef cattle production systems, where the number of proven bulls with highly reliable estimated breeding values is limited to allow for a trustworthy validation of genomic predictions. Therefore, we simulated data for 4980 beef cattle aiming to compare single-step genomic best linear unbiased prediction (ssGBLUP), which simultaneously incorporates pedigree, phenotypic, and genomic data into genomic evaluations, and two-step GBLUP (tsGBLUP) procedures and genomic estimated breeding values (GEBVs) blending methods. The greatest increases in GEBV accuracies compared with the parents’ average estimated breeding values (EBVPA) were 0.364 and 0.341 for ssGBLUP and tsGBLUP, respectively. Direct genomic value and GEBV accuracies when using ssGBLUP and tsGBLUP procedures were similar, except for the GEBV accuracies using Hayes’ blending method in tsGBLUP. There was no significant or slight bias in genomic predictions from ssGBLUP or tsGBLUP (using VanRaden’s blending method), indicating that these predictions are on the same scale compared with the true breeding values. Overall, genetic evaluations including genomic information resulted in gains in accuracy >100% compared with the EBVPA. In addition, there were no significant differences between the selected animals (10% males and 50% females) by using ssGBLUP or tsGBLUP.

scholarly journals Selective genotyping and phenotypic data inclusion strategies of crossbred progeny for combined crossbred and purebred selection in swine breeding

2021 ◽  
Author(s):  
Garrett M See ◽  
Benny E Mote ◽  
Matthew L Spangler
Keyword(s):  
Genetic Gain ◽  
Production Systems ◽  
Snp Markers ◽  
Selective Genotyping ◽  
Phenotypic Data ◽  
Breeding Values ◽  
True Breeding ◽  
Estimated Breeding Values ◽  
Highly Correlated ◽  
Genetic Evaluations

Abstract Inclusion of crossbred (CB) data into traditionally purebred (PB) genetic evaluations has been shown to increase the response in CB performance. Currently it is unrealistic to collect data on all CB animals in swine production systems, thus, a subset of CB animals must be selected to contribute genomic/phenotypic information. The aim of this study was to evaluate selective genotyping strategies in a simulated 3-way swine crossbreeding scheme. The swine crossbreeding scheme was simulated and produced 3-way CB animals for 6 generations with three distinct purebred breeds each with 25 and 175 mating males and females, respectively. F1 crosses (400 mating females) produced 4,000 terminal CB progeny which were subjected to selective genotyping. The genome consisted of 18 chromosomes with 1,800 QTL and 72k SNP markers. Selection was performed using estimated breeding values (EBV) for CB performance. It was assumed that both PB and CB performance was moderately heritable (h2=0.4). Several scenarios altering the genetic correlation between PB and CB performance (rpc=0.1, 0.3, 0.5, 0.7 or 0.9) were considered. CB animals were chosen based on phenotypes to select 200, 400 or 800 CB animals to genotype per generation. Selection strategies included: 1) Random: random selection, 2) Top: highest phenotype, 3) Bottom: lowest phenotype, 4) Extreme: half highest and half lowest phenotypes, and 5) Middle: average phenotype. Each selective genotyping strategy, except for Random, was considered by selecting animals in half-sib (HS) or full-sib (FS) families. The number of PB animals with genotypes and phenotypes each generation was fixed at 1680. Each unique genotyping strategy and rpc scenario was replicated 10 times. Selection of CB animals based on the Extreme strategy resulted in the highest (P<0.05) rates of genetic gain in CB performance (ΔG) when rpc<0.9. For highly correlated traits (rpc=0.9) selective genotyping did not impact (P>0.05) ΔG. No differences (P>0.05) were observed in ΔG between Top, Bottom or Middle when rpc>0.1. Higher correlations between true breeding values (TBV) and EBV were observed using Extreme when rpc<0.9. In general, family sampling method did not impact ΔG or the correlation between TBV and EBV. Overall, the Extreme genotyping strategy produced the greatest genetic gain and the highest correlations between TBV and EBV, suggesting that two tailed sampling of CB animals is the most informative when CB performance is the selection goal.

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

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.

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 Changes in genomic predictions when new information is added

2021 ◽  
Vol 99 (2) ◽  
Author(s):  
Jorge Hidalgo ◽  
Daniela Lourenco ◽  
Shogo Tsuruta ◽  
Yutaka Masuda ◽  
Stephen Miller ◽  
...  
Keyword(s):  
Single Step ◽  
Pedigree Information ◽  
Weaning Weight ◽  
Breeding Values ◽  
Core Group ◽  
Absolute Change ◽  
Angus Cattle ◽  
Average Maximum ◽  
Estimated Breeding Values ◽  
Genetic Evaluations

Abstract The stability of genomic evaluations depends on the amount of data and population parameters. When the dataset is large enough to estimate the value of nearly all independent chromosome segments (~10K in American Angus cattle), the accuracy and persistency of breeding values will be high. The objective of this study was to investigate changes in estimated breeding values (EBV) and genomic EBV (GEBV) across monthly evaluations for 1 yr in a large genotyped population of beef cattle. The American Angus data used included 8.2 million records for birth weight, 8.9 for weaning weight, and 4.4 for postweaning gain. A total of 10.1 million animals born until December 2017 had pedigree information, and 484,074 were genotyped. A truncated dataset included animals born until December 2016. To mimic a scenario with monthly evaluations, 2017 data were added 1 mo at a time to estimate EBV using best linear unbiased prediction (BLUP) and GEBV using single-step genomic BLUP with the algorithm for proven and young (APY) with core group fixed for 1 yr or updated monthly. Predictions from monthly evaluations in 2017 were contrasted with the predictions of the evaluation in December 2016 or the previous month for all genotyped animals born until December 2016 with or without their own phenotypes or progeny phenotypes. Changes in EBV and GEBV were similar across traits, and only results for weaning weight are presented. Correlations between evaluations from December 2016 and the 12 consecutive evaluations were ≥0.97 for EBV and ≥0.99 for GEBV. Average absolute changes for EBV were about two times smaller than for GEBV, except for animals with new progeny phenotypes (≤0.12 and ≤0.11 additive genetic SD [SDa] for EBV and GEBV). The maximum absolute changes for EBV (≤2.95 SDa) were greater than for GEBV (≤1.59 SDa). The average(maximum) absolute GEBV changes for young animals from December 2016 to January and December 2017 ranged from 0.05(0.25) to 0.10(0.53) SDa. Corresponding ranges for animals with new progeny phenotypes were from 0.05(0.88) to 0.11(1.59) SDa for GEBV changes. The average absolute change in EBV(GEBV) from December 2016 to December 2017 for sires with ≤50 progeny phenotypes was 0.26(0.14) and for sires with >50 progeny phenotypes was 0.25(0.16) SDa. Updating the core group in APY without adding data created an average absolute change of 0.07 SDa in GEBV. Genomic evaluations in large genotyped populations are as stable and persistent as the traditional genetic evaluations, with less extreme changes.

scholarly journals Integration of genomic information into beef cattle and sheep genetic evaluations in Australia

2012 ◽  
Vol 52 (3) ◽  
pp. 126 ◽  
Author(s):  
Andrew A. Swan ◽  
David J. Johnston ◽  
Daniel J. Brown ◽  
Bruce Tier ◽  
Hans-U. Graser
Keyword(s):  
Beef Cattle ◽  
Covariance Matrices ◽  
Relationship Matrix ◽  
Genomic Information ◽  
Genetic Covariance ◽  
Breeding Values ◽  
Snp Panels ◽  
Genetic Evaluations ◽  
Genomic Predictions ◽  
Cattle And Sheep

Genomic information has the potential to change the way beef cattle and sheep are selected and to substantially increase genetic gains. Ideally, genomic data will be used in combination with pedigree and phenotypic data to increase the accuracy of estimated breeding values (EBVs) and selection indexes. The first example of this in Australia was the integration of four markers for tenderness into beef cattle breeding values. Subsequently, the availability of high-density single nucleotide polymorphism (SNP) panels has made selection using genomic information possible, while at the same time creating significant challenges for genetic evaluation with regard to both data management and statistical modelling. Reference populations have been established in both the beef cattle and sheep industries, in which an extensive range of phenotypes have been collected and animals genotyped mainly using 50K SNP panels. From this information, genomic predictions of breeding value have been developed, albeit with varying levels of accuracy. These predictions have been incorporated into routine genetic evaluations using three approaches and trial results are now available to breeders. In the first, genomic predictions have been included in genetic evaluation models as additional traits. The challenges with this method have been the construction of consistent genetic covariance matrices, and a significant increase in computing time. The second approach has been to use a selection index procedure to blend genomic predictions with existing EBVs. This method has been shown to produce very similar results, and has the advantage of being simple to implement and fast to operate, although consistent genetic covariance matrices are still required. Third, in sheep a single-step analysis combining a genomic relationship matrix with a standard pedigree-based relationship matrix has been used to estimate breeding values for carcass and eating-quality traits. It is likely that this procedure or one similar will be incorporated into routine evaluations in the near future. While significant progress has been made in implementing methods of integrating genomic information in both beef and sheep evaluations in Australia, the major challenges for the future will be to continue to collect the phenotypes needed to derive accurate genomic predictions, and in managing much larger volumes of genomic data as the number of animals genotyped and the density of markers increase.

PSXII-9 Modeling genetic differences of combined broiler chicken populations in single-step GBLUP

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 254-254
Author(s):  
Matias Bermann ◽  
Daniela Lourenco ◽  
Vivian Breen ◽  
Rachel Hawken ◽  
Fernando Brito Lopes ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Broiler Chicken ◽  
Predictive Ability ◽  
Single Step ◽  
Fixed Effect ◽  
Model Specification ◽  
Breeding Values ◽  
Chicken Population ◽  
Estimated Breeding Values ◽  
Genomic Predictions

Abstract The objectives of this study were to model the inclusion of a group of external birds into a local broiler chicken population for the purpose of genomic evaluations and evaluating the behavior of two accuracy estimators under different model specifications. The pedigree was composed by 242,413 birds and genotypes were available for 107,216 birds. A five-trait model that included one growth, two yield, and two efficiency traits was used for the analyses. The strategies to model the introduction of external birds were to include a fixed effect representing the origin of parents and to use UPG or metafounders. Genomic estimated breeding values (GEBV) were obtained with single-step GBLUP (ssGBLUP) using the Algorithm for Proven and Young (APY). Bias, dispersion, and accuracy of GEBV for the validation birds, i.e., from the most recent generation, were computed. The bias and dispersion were estimated with the LR-method, whereas accuracy was estimated by the LR-method and predictive ability. Models with fixed UPG and estimated inbreeding or random UPG resulted in similar GEBV. The inclusion of an extra fixed effect in the model made the GEBV unbiased and reduced the inflation, while models without such an effect were significantly biased. Genomic predictions with metafounders were slightly biased and inflated due to the unbalanced number of observations assigned to each metafounder. When combining local and external populations, the greatest accuracy and smallest bias can be obtained by adding an extra fixed effect to account for the origin of parents plus UPG with estimated inbreeding or random UPG. To estimate the accuracy, the LR-method is more consistent among models, whereas predictive ability greatly depends on the model specification, that is, on the fixed effects included in the model. When changing model specification, the largest variation for the LR-method was 20%, while for predictive ability was 110%.

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.

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