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

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.

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36 Effect of Blending and Tuning Relationship Matrices in Single-step Genomic BLUP

Journal of Animal Science ◽

10.1093/jas/skab235.032 ◽

2021 ◽

Vol 99 (Supplement_3) ◽

pp. 19-20

Author(s):

Taylor M McWhorter ◽

Andre Garcia ◽

Matias Bermann ◽

Andres Legarra ◽

Ignacio Aguilar ◽

...

Keyword(s):

Single Step ◽

Fixation Index ◽

Genomic Information ◽

Software Suite ◽

Genetic Base ◽

Actual Difference ◽

The Impact ◽

Genomic Predictions

Abstract Single-step GBLUP (ssGBLUP) relies on the combination of genomic (G) and pedigree relationships for all (A) and genotyped animals (A22). The procedure implemented in the BLUPF90 software suite first involves combining a small percentage of A22 into G (blending) to avoid singularity problems, then an adjustment to account for the fact the genetic base in G and A22 is different (tuning). However, blending before tuning may not reflect the actual difference between pedigree and genomic base because the blended matrix already contains a portion of A22. The objective of this study was to evaluate the impact of tuning before blending on predictivity, bias, and inflation of GEBV, indirect predictions (IP), and SNP effects from ssGBLUP using American Angus and US Holstein data. We used four different scenarios to obtain genomic predictions: BlendFirst_TunedG2, TuneFirst_TunedG2, BlendFirst_TunedG4, and TuneFirst_TunedG4. TunedG2 adjusts mean diagonals and off-diagonals of G to be similar to the ones in A22, whereas TunedG4 adjusts based on the fixation index. Over 6 million growth records were available for Angus and 5.9 million udder depth records for Holsteins. Genomic information was available on 51,478 Angus and 105,116 Holstein animals. Predictivity and reliability were obtained for 19,056 and 1,711 validation Angus and Holsteins, respectively. We observed the same predictivity and reliability for GEBV or IP in all four scenarios, ranging from 0.47 to 0.60 for Angus and was 0.67 for Holsteins. Slightly less bias was observed when tuning was done before blending. Correlation of SNP effects between scenarios was > 0.99. Refined tuning before blending had no impact on GEBV and marginally reduced the bias. This option will be implemented in the BLUPF90 software suite.

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Comparison of genomic predictions for lowly heritable traits using multi-step and single-step genomic best linear unbiased predictor in Holstein cattle

Journal of Dairy Science ◽

10.3168/jds.2017-14193 ◽

2018 ◽

Vol 101 (9) ◽

pp. 8076-8086 ◽

Cited By ~ 7

Author(s):

A.R. Guarini ◽

D.A.L. Lourenco ◽

L.F. Brito ◽

M. Sargolzaei ◽

C.F. Baes ◽

...

Keyword(s):

Single Step ◽

Holstein Cattle ◽

Best Linear Unbiased Predictor ◽

Best Linear Unbiased ◽

Genomic Predictions

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Sharing reference data and including cows in the reference population improve genomic predictions in Danish Jersey

animal ◽

10.1017/s1751731115001792 ◽

2016 ◽

Vol 10 (6) ◽

pp. 1067-1075 ◽

Cited By ~ 19

Author(s):

G. Su ◽

P. Ma ◽

U.S. Nielsen ◽

G.P. Aamand ◽

G. Wiggans ◽

...

Keyword(s):

Reference Data ◽

Reference Population ◽

Genomic Predictions

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A common reference population from four European Holstein populations increases reliability of genomic predictions

Genetics Selection Evolution ◽

10.1186/1297-9686-43-43 ◽

2011 ◽

Vol 43 (1) ◽

Cited By ~ 133

Author(s):

Mogens S Lund ◽

Adrianus PW de Roos ◽

Alfred G de Vries ◽

Tom Druet ◽

Vincent Ducrocq ◽

...

Keyword(s):

Reference Population ◽

Common Reference ◽

Genomic Predictions

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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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Optimizing genomic reference populations to improve crossbred performance

Genetics Selection Evolution ◽

10.1186/s12711-020-00573-3 ◽

2020 ◽

Vol 52 (1) ◽

Author(s):

Yvonne C. J. Wientjes ◽

Piter Bijma ◽

Mario P. L. Calus

Keyword(s):

Genomic Prediction ◽

Genetic Relatedness ◽

Reference Population ◽

Breeding Goal ◽

Poultry Breeding ◽

Population Sizes ◽

Estimated Breeding Values ◽

Production Animals ◽

Chromosome Segments ◽

Different Levels

Abstract Background In pig and poultry breeding, the objective is to improve the performance of crossbred production animals, while selection takes place in the purebred parent lines. One way to achieve this is to use genomic prediction with a crossbred reference population. A crossbred reference population benefits from expressing the breeding goal trait but suffers from a lower genetic relatedness with the purebred selection candidates than a purebred reference population. Our aim was to investigate the benefit of using a crossbred reference population for genomic prediction of crossbred performance for: (1) different levels of relatedness between the crossbred reference population and purebred selection candidates, (2) different levels of the purebred-crossbred correlation, and (3) different reference population sizes. We simulated a crossbred breeding program with 0, 1 or 2 multiplication steps to generate the crossbreds, and compared the accuracy of genomic prediction of crossbred performance in one generation using either a purebred or a crossbred reference population. For each scenario, we investigated the empirical accuracy based on simulation and the predicted accuracy based on the estimated effective number of independent chromosome segments between the reference animals and selection candidates. Results When the purebred-crossbred correlation was 0.75, the accuracy was highest for a two-way crossbred reference population but similar for purebred and four-way crossbred reference populations, for all reference population sizes. When the purebred-crossbred correlation was 0.5, a purebred reference population always resulted in the lowest accuracy. Among the different crossbred reference populations, the accuracy was slightly lower when more multiplication steps were used to create the crossbreds. In general, the benefit of crossbred reference populations increased when the size of the reference population increased. All predicted accuracies overestimated their corresponding empirical accuracies, but the different scenarios were ranked accurately when the reference population was large. Conclusions The benefit of a crossbred reference population becomes larger when the crossbred population is more related to the purebred selection candidates, when the purebred-crossbred correlation is lower, and when the reference population is larger. The purebred-crossbred correlation and reference population size interact with each other with respect to their impact on the accuracy of genomic estimated breeding values.

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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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213 Genomic selection of carcass quality traits in crossbred pigs using a reference population

Journal of Animal Science ◽

10.1093/jas/skz258.082 ◽

2019 ◽

Vol 97 (Supplement_3) ◽

pp. 41-41

Author(s):

Matteo Bergamaschi ◽

Christian Maltecca ◽

Clint Schwab ◽

Justin Fix ◽

Francesco Tiezzi

Keyword(s):

Fixed Effects ◽

Carcass Traits ◽

Predictive Ability ◽

Reference Population ◽

Single Step ◽

Carcass Quality ◽

Quality Traits ◽

Single Gender ◽

Training Set ◽

Crossbred Pigs

Abstract The objective of this work was to evaluate the predictive ability of different models applied to carcass traits in crossbred pigs. The pigs were divided in 2 finishing flows: A=36,110 and B=95,041 animals, and were progeny of 386 sires (almost entirely genotyped with the 60k SNP chip). In flow A, individuals were housed into single-sire single-gender pens, and split-marketing on a pen basis was applied. In flow B, individuals were kept in standard commercial conditions and split-marketing on an individuals basis was applied. A dataset containing individual records of three carcass traits: back-fat (BF), loin depth (LD), and carcass daily gain (CACG) was used. Data from flow A were divided into training and validation sets on the basis of contemporary groups (8 in training and 1 in testing). Variance components and solutions were obtained using the BLUPF90 suite of programs. Models included fixed effects (dam line, sow parity, sex, cross fostering, and contemporary group) and random effects (additive genetic, batch, litter, and residual). Models tested were univariate vs multivariate and pedigree vs single-step. The addition of flow B records to the training set was evaluated, by including or excluding these records. Heritabilities were 0.68±0.023 for BF, 0.47±0.018 for LD, and 0.55±0.023 for CACG. CACG gain was correlated with BF (0.43±0.029) and LD (0.39±0.03). Low genetic correlation was found between BF and LD (0.17±0.034). Prediction accuracies were 0.39±0.05, 0.17±0.06, and 0.13±0.03 for BF, LD, and CACG respectively. The mean accuracy of BF, LD, and CG increased (~6%) when records from flow B were included in the training set, whereas the increase of accuracy between models (univariate vs multivariate) was not significant. The inclusion of sire genotypes did not improve prediction accuracy significantly. Based on these results, the prediction of carcass quality traits in crossbred pigs is possible.

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