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

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.

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

Canadian Journal of Animal Science ◽

10.1139/cjas-2017-0176 ◽

2018 ◽

Vol 98 (3) ◽

pp. 565-575 ◽

Cited By ~ 5

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.

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Use of molecular technologies for the advancement of animal breeding: genomic selection in dairy cattle populations in Australia, Ireland and New Zealand

Animal Production Science ◽

10.1071/an12304 ◽

2013 ◽

Vol 53 (9) ◽

pp. 869 ◽

Cited By ~ 24

Author(s):

Richard J. Spelman ◽

Ben J. Hayes ◽

Donagh P. Berry

Keyword(s):

New Zealand ◽

Dairy Cattle ◽

Evaluation System ◽

Test Results ◽

Genomic Information ◽

Breeding Programs ◽

Breeding Values ◽

Genetic Evaluations ◽

Initial Results ◽

Genomic Predictions

The New Zealand, Australian and Irish dairy industries have used genomic information to enhance their genetic evaluations over the last 2–4 years. The improvement in the accuracy obtained from including genomic information on thousands of animals in the national evaluation system has revolutionised the dairy breeding programs in the three countries. The genomically enhanced breeding values (GEBV) of young bulls are more reliable than breeding values based on parent average, thus allowing the young bulls to be reliably selected and used in the national herd. Traditionally, the use of young bulls was limited and bulls were not used extensively until they were 5 years old when the more reliable progeny test results became available. Using young sires, as opposed to progeny-tested sires, in the breeding program dramatically reduces the generation interval, thereby facilitating an increase in the rate of genetic gain by 40–50%. Young sires have been marketed on their GEBV in the three countries over the last 2–4 years. Initial results show that the genomic estimates were overestimated in both New Zealand and Ireland. Adjustments have since been introduced into their respective national evaluations to reduce the bias. A bias adjustment has been included in the Australian evaluation since it began; however, official genomic evaluations have not been in place as long as in New Zealand and Ireland, so there has been less opportunity to validate if the correction accounts for all bias. Sequencing of the dairy cattle population has commenced in an effort to further improve the genomic predictions and also to detect causative mutations that underlie traits of economic performance.

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Comparison of models for missing pedigree in single-step genomic prediction

Journal of Animal Science ◽

10.1093/jas/skab019 ◽

2021 ◽

Vol 99 (2) ◽

Author(s):

Yutaka Masuda ◽

Shogo Tsuruta ◽

Matias Bermann ◽

Heather L Bradford ◽

Ignacy Misztal

Keyword(s):

Density Function ◽

Single Step ◽

Joint Density ◽

Pedigree Information ◽

Relationship Matrix ◽

Genomic Information ◽

Breeding Values ◽

Joint Density Function ◽

Information Updates ◽

Genetic Trends

Abstract Pedigree information is often missing for some animals in a breeding program. Unknown-parent groups (UPGs) are assigned to the missing parents to avoid biased genetic evaluations. Although the use of UPGs is well established for the pedigree model, it is unclear how UPGs are integrated into the inverse of the unified relationship matrix (H-inverse) required for single-step genomic best linear unbiased prediction. A generalization of the UPG model is the metafounder (MF) model. The objectives of this study were to derive 3 H-inverses and to compare genetic trends among models with UPG and MF H-inverses using a simulated purebred population. All inverses were derived using the joint density function of the random breeding values and genetic groups. The breeding values of genotyped animals (u2) were assumed to be adjusted for UPG effects (g) using matrix Q2 as u2∗=u2+Q2g before incorporating genomic information. The Quaas–Pollak-transformed (QP) H-inverse was derived using a joint density function of u2∗ and g updated with genomic information and assuming nonzero cov(u2∗,g′). The modified QP (altered) H-inverse also assumes that the genomic information updates u2∗ and g, but cov(u2∗,g′)=0. The UPG-encapsulated (EUPG) H-inverse assumed genomic information updates the distribution of u2∗. The EUPG H-inverse had the same structure as the MF H-inverse. Fifty percent of the genotyped females in the simulation had a missing dam, and missing parents were replaced with UPGs by generation. The simulation study indicated that u2∗ and g in models using the QP and altered H-inverses may be inseparable leading to potential biases in genetic trends. Models using the EUPG and MF H-inverses showed no genetic trend biases. These 2 H-inverses yielded the same genomic EBV (GEBV). The predictive ability and inflation of GEBVs from young genotyped animals were nearly identical among models using the QP, altered, EUPG, and MF H-inverses. Although the choice of H-inverse in real applications with enough data may not result in biased genetic trends, the EUPG and MF H-inverses are to be preferred because of theoretical justification and possibility to reduce biases.

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Beef cattle breeding in Australia with genomics: opportunities and needs

Animal Production Science ◽

10.1071/an11116 ◽

2012 ◽

Vol 52 (3) ◽

pp. 100 ◽

Cited By ~ 14

Author(s):

D. J. Johnston ◽

B. Tier ◽

H.-U. Graser

Keyword(s):

Beef Cattle ◽

Genomic Selection ◽

Genetic Gain ◽

Information Needs ◽

Genomic Information ◽

Female Reproduction ◽

Genetic Progress ◽

Cattle Breeding ◽

Phenotypic Data ◽

Genomic Predictions

Opportunities exist in beef cattle breeding to significantly increase the rates of genetic gain by increasing the accuracy of selection at earlier ages. Currently, selection of young beef bulls incorporates several economically important traits but estimated breeding values for these traits have a large range in accuracies. While there is potential to increase accuracy through increased levels of performance recording, several traits cannot be recorded on the young bull. Increasing the accuracy of these traits is where genomic selection can offer substantial improvements in current rates of genetic gain for beef. The immediate challenge for beef is to increase the genetic variation explained by the genomic predictions for those traits of high economic value that have low accuracies at the time of selection. Currently, the accuracies of genomic predictions are low in beef, compared with those in dairy cattle. This is likely to be due to the relatively low number of animals with genotypes and phenotypes that have been used in developing genomic prediction equations. Improving the accuracy of genomic predictions will require the collection of genotypes and phenotypes on many more animals, with even greater numbers needed for lowly heritable traits, such as female reproduction and other fitness traits. Further challenges exist in beef to have genomic predictions for the large number of important breeds and also for multi-breed populations. Results suggest that single-nucleotide polymorphism (SNP) chips that are denser than 50 000 SNPs in the current use will be required to achieve this goal. For genomic selection to contribute to genetic progress, the information needs to be correctly combined with traditional pedigree and performance data. Several methods have emerged for combining the two sources of data into current genetic evaluation systems; however, challenges exist for the beef industry to implement these effectively. Changes will also be needed to the structure of the breeding sector to allow optimal use of genomic information for the benefit of the industry. Genomic information will need to be cost effective and a major driver of this will be increasing the accuracy of the predictions, which requires the collection of much more phenotypic data than are currently available.

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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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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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Core-dependent changes in genomic predictions using the Algorithm for Proven and Young in single-step genomic best linear unbiased prediction

Journal of Animal Science ◽

10.1093/jas/skaa374 ◽

2020 ◽

Vol 98 (12) ◽

Author(s):

Ignacy Misztal ◽

Shogo Tsuruta ◽

Ivan Pocrnic ◽

Daniela Lourenco

Keyword(s):

Prediction Accuracy ◽

Best Linear Unbiased Prediction ◽

Single Step ◽

Relationship Matrix ◽

Linear Unbiased Prediction ◽

Breeding Values ◽

Best Linear Unbiased ◽

The Impact ◽

Genomic Predictions ◽

Unbiased Prediction

Abstract Single-step genomic best linear unbiased prediction with the Algorithm for Proven and Young (APY) is a popular method for large-scale genomic evaluations. With the APY algorithm, animals are designated as core or noncore, and the computing resources to create the inverse of the genomic relationship matrix (GRM) are reduced by inverting only a portion of that matrix for core animals. However, using different core sets of the same size causes fluctuations in genomic estimated breeding values (GEBVs) up to one additive standard deviation without affecting prediction accuracy. About 2% of the variation in the GRM is noise. In the recursion formula for APY, the error term modeling the noise is different for every set of core animals, creating changes in breeding values. While average changes are small, and correlations between breeding values estimated with different core animals are close to 1.0, based on the normal distribution theory, outliers can be several times bigger than the average. Tests included commercial datasets from beef and dairy cattle and from pigs. Beyond a certain number of core animals, the prediction accuracy did not improve, but fluctuations decreased with more animals. Fluctuations were much smaller than the possible changes based on prediction error variance. GEBVs change over time even for animals with no new data as genomic relationships ties all the genotyped animals, causing reranking of top animals. In contrast, changes in nongenomic models without new data are small. Also, GEBV can change due to details in the model, such as redefinition of contemporary groups or unknown parent groups. In particular, increasing the fraction of blending of the GRM with a pedigree relationship matrix from 5% to 20% caused changes in GEBV up to 0.45 SD, with a correlation of GEBV > 0.99. Fluctuations in genomic predictions are part of genomic evaluation models and are also present without the APY algorithm when genomic evaluations are computed with updated data. The best approach to reduce the impact of fluctuations in genomic evaluations is to make selection decisions not on individual animals with limited individual accuracy but on groups of animals with high average accuracy.

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Metafounders May Reduce Bias in Composite Cattle Genomic Predictions

Frontiers in Genetics ◽

10.3389/fgene.2021.678587 ◽

2021 ◽

Vol 12 ◽

Author(s):

Sabrina Kluska ◽

Yutaka Masuda ◽

José Bento Sterman Ferraz ◽

Shogo Tsuruta ◽

Joanir Pereira Eler ◽

...

Keyword(s):

Beef Cattle ◽

Model Performance ◽

Single Step ◽

Breeding Value ◽

Relationship Matrix ◽

Nucleotide Polymorphisms ◽

Genomic Information ◽

Scrotal Circumference ◽

Pedigree Relationship ◽

The Stability

Metafounders are pseudo-individuals that act as proxies for animals in base populations. When metafounders are used, individuals from different breeds can be related through pedigree, improving the compatibility between genomic and pedigree relationships. The aim of this study was to investigate the use of metafounders and unknown parent groups (UPGs) for the genomic evaluation of a composite beef cattle population. Phenotypes were available for scrotal circumference at 14 months of age (SC14), post weaning gain (PWG), weaning weight (WW), and birth weight (BW). The pedigree included 680,551 animals, of which 1,899 were genotyped for or imputed to around 30,000 single-nucleotide polymorphisms (SNPs). Evaluations were performed based on pedigree (BLUP), pedigree with UPGs (BLUP_UPG), pedigree with metafounders (BLUP_MF), single-step genomic BLUP (ssGBLUP), ssGBLUP with UPGs for genomic and pedigree relationship matrices (ssGBLUP_UPG) or only for the pedigree relationship matrix (ssGBLUP_UPGA), and ssGBLUP with metafounders (ssGBLUP_MF). Each evaluation considered either four or 10 groups that were assigned based on breed of founders and intermediate crosses. To evaluate model performance, we used a validation method based on linear regression statistics to obtain accuracy, stability, dispersion, and bias of (genomic) estimated breeding value [(G)EBV]. Overall, relationships within and among metafounders were stronger in the scenario with 10 metafounders. Accuracy was greater for models with genomic information than for BLUP. Also, the stability of (G)EBVs was greater when genomic information was taken into account. Overall, pedigree-based methods showed lower inflation/deflation (regression coefficients close to 1.0) for SC14, WWM, and BWD traits. The level of inflation/deflation for genomic models was small and trait-dependent. Compared with regular ssGBLUP, ssGBLUP_MF4 displayed regression coefficient closer to one SC14, PWG, WWM, and BWD. Genomic models with metafounders seemed to be slightly more stable than models with UPGs based on higher similarity of results with different numbers of groups. Further, metafounders can help to reduce bias in genomic evaluations of composite beef cattle populations without reducing the stability of GEBVs.

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