scholarly journals Applying the Metafounders Approach for Genomic Evaluation in a Multibreed Beef Cattle Population

2020 ◽  
Vol 11 ◽  
Author(s):  
Vinícius Silva Junqueira ◽  
Paulo Sávio Lopes ◽  
Daniela Lourenco ◽  
Fabyano Fonseca e Silva ◽  
Fernando Flores Cardoso
Keyword(s):  
Beef Cattle ◽  
Variance Components ◽  
Genetic Relationships ◽  
A Priori ◽  
Predictive Ability ◽  
Single Step ◽  
Pedigree Information ◽  
Phenotypic Variance ◽  
Genomic Information ◽  
The Impact

Pedigree information is incomplete by nature and commonly not well-established because many of the genetic ties are not known a priori or can be wrong. The genomic era brought new opportunities to assess relationships between individuals. However, when pedigree and genomic information are used simultaneously, which is the case of single-step genomic BLUP (ssGBLUP), defining the genetic base is still a challenge. One alternative to overcome this challenge is to use metafounders, which are pseudo-individuals that describe the genetic relationship between the base population individuals. The purpose of this study was to evaluate the impact of metafounders on the estimation of breeding values for tick resistance under ssGBLUP for a multibreed population composed by Hereford, Braford, and Zebu animals. Three different scenarios were studied: pedigree-based model (BLUP), ssGBLUP, and ssGBLUP with metafounders (ssGBLUPm). In ssGBLUPm, a total of four different metafounders based on breed of origin (i.e., Hereford, Braford, Zebu, and unknown) were included for the animals with missing parents. The relationship coefficient between metafounders was in average 0.54 (ranging from 0.34 to 0.96) suggesting an overlap between ancestor populations. The estimates of metafounder relationships indicate that Hereford and Zebu breeds have a possible common ancestral relationship. Inbreeding coefficients calculated following the metafounder approach had less negative values, suggesting that ancestral populations were large enough and that gametes inherited from the historical population were not identical. Variance components were estimated based on ssGBLUPm, ssGBLUP, and BLUP, but the values from ssGBLUPm were scaled to provide a fair comparison with estimates from the other two models. In general, additive, residual, and phenotypic variance components in the Hereford population were smaller than in Braford across different models. The addition of genomic information increased heritability for Hereford, possibly because of improved genetic relationships. As expected, genomic models had greater predictive ability, with an additional gain for ssGBLUPm over ssGBLUP. The increase in predictive ability was greater for Herefords. Our results show the potential of using metafounders to increase accuracy of GEBV, and therefore, the rate of genetic gain in beef cattle populations with partial levels of missing pedigree information.

scholarly journals Multi-Trait Single-Step GBLUP Improves Accuracy of Genomic Prediction for Carcass Traits Using Yearling Weight and Ultrasound Traits in Hanwoo

2021 ◽  
Vol 12 ◽  
Author(s):  
Hossein Mehrban ◽  
Masoumeh Naserkheil ◽  
Deukhwan Lee ◽  
Noelia Ibáñez-Escriche
Keyword(s):  
Beef Cattle ◽  
Carcass Traits ◽  
Single Step ◽  
Birth Date ◽  
Linear Regression Method ◽  
Genomic Information ◽  
Muscle Area ◽  
Eye Muscle ◽  
Correlated Traits ◽  
The Impact

There has been a growing interest in the genetic improvement of carcass traits as an important and primary breeding goal in the beef cattle industry over the last few decades. The use of correlated traits and molecular information can aid in obtaining more accurate estimates of breeding values. This study aimed to assess the improvement in the accuracy of genetic predictions for carcass traits by using ultrasound measurements and yearling weight along with genomic information in Hanwoo beef cattle by comparing four evaluation models using the estimators of the recently developed linear regression method. We compared the performance of single-trait pedigree best linear unbiased prediction [ST-BLUP and single-step genomic (ST-ssGBLUP)], as well as multi-trait (MT-BLUP and MT-ssGBLUP) models for the studied traits at birth and yearling date of steers. The data comprised of 15,796 phenotypic records for yearling weight and ultrasound traits as well as 5,622 records for carcass traits (backfat thickness, carcass weight, eye muscle area, and marbling score), resulting in 43,949 single-nucleotide polymorphisms from 4,284 steers and 2,332 bulls. Our results indicated that averaged across all traits, the accuracy of ssGBLUP models (0.52) was higher than that of pedigree-based BLUP (0.34), regardless of the use of single- or multi-trait models. On average, the accuracy of prediction can be further improved by implementing yearling weight and ultrasound data in the MT-ssGBLUP model (0.56) for the corresponding carcass traits compared to the ST-ssGBLUP model (0.49). Moreover, this study has shown the impact of genomic information and correlated traits on predictions at the yearling date (0.61) using MT-ssGBLUP models, which was advantageous compared to predictions at birth date (0.51) in terms of accuracy. Thus, using genomic information and high genetically correlated traits in the multi-trait model is a promising approach for practical genomic selection in Hanwoo cattle, especially for traits that are difficult to measure.

scholarly journals Grid search approach to discriminate between old and recent inbreeding using phenotypic, pedigree and genomic information

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Pattarapol Sumreddee ◽  
El Hamidi Hay ◽  
Sajjad Toghiani ◽  
Andrew Roberts ◽  
Samuel E. Aggrey ◽  
...  
Keyword(s):  
Common Ancestor ◽  
Growth Traits ◽  
Average Daily Gain ◽  
Recent Common Ancestor ◽  
Pedigree Information ◽  
Grid Search ◽  
Genomic Information ◽  
The Impact ◽  
Daily Gain ◽  
Harmful Effects

Abstract Background Although inbreeding caused by the mating of animals related through a recent common ancestor is expected to have more harmful effects on phenotypes than ancient inbreeding (old inbreeding), estimating these effects requires a clear definition of recent (new) and ancient (old) inbreeding. Several methods have been proposed to classify inbreeding using pedigree and genomic data. Unfortunately, these methods are largely based on heuristic criteria such as the number of generations from a common ancestor or length of runs of homozygosity (ROH) segments. To mitigate these deficiencies, this study aimed to develop a method to classify pedigree and genomic inbreeding into recent and ancient classes based on a grid search algorithm driven by the assumption that new inbreeding tends to have a more pronounced detrimental effect on traits. The proposed method was tested using a cattle population characterized by a deep pedigree. Results Effects of recent and ancient inbreeding were assessed on four growth traits (birth, weaning and yearling weights and average daily gain). Thresholds to classify inbreeding into recent and ancient classes were trait-specific and varied across traits and sources of information. Using pedigree information, inbreeding generated in the last 10 to 11 generations was considered as recent. When genomic information (ROH) was used, thresholds ranged between four to seven generations, indicating, in part, the ability of ROH segments to characterize the harmful effects of inbreeding in shorter periods of time. Nevertheless, using the proposed classification method, the discrimination between new and old inbreeding was less robust when ROH segments were used compared to pedigree. Using several model comparison criteria, the proposed approach was generally better than existing methods. Recent inbreeding appeared to be more harmful across the growth traits analyzed. However, both new and old inbreeding were found to be associated with decreased yearling weight and average daily gain. Conclusions The proposed method provided a more objective quantitative approach for the classification of inbreeding. The proposed method detected a clear divergence in the effects of old and recent inbreeding using pedigree data and it was superior to existing methods for all analyzed traits. Using ROH data, the discrimination between old and recent inbreeding was less clear and the proposed method was superior to existing approaches for two out of the four analyzed traits. Deleterious effects of recent inbreeding were detected sooner (fewer generations) using genomic information than pedigree. Difference in the results using genomic and pedigree information could be due to the dissimilarity in the number of generations to a common ancestor. Additionally, the uncertainty associated with the identification of ROH segments and associated inbreeding could have an effect on the results. Potential biases in the estimation of inbreeding effects may occur when new and old inbreeding are discriminated based on arbitrary thresholds. To minimize the impact of inbreeding, mating designs should take the different inbreeding origins into consideration.

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

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.

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

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.

scholarly journals The impact of truncating data on the predictive ability for single-step genomic best linear unbiased prediction

2018 ◽  
Vol 135 (4) ◽  
pp. 251-262 ◽  
Author(s):  
Jeremy T. Howard ◽  
Tom A. Rathje ◽  
Caitlyn E. Bruns ◽  
Danielle F. Wilson-Wells ◽  
Stephen D. Kachman ◽  
...  
Keyword(s):  
Predictive Ability ◽  
Best Linear Unbiased Prediction ◽  
Single Step ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased ◽  
The Impact ◽  
Unbiased Prediction

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 Bias in estimates of variance components in populations undergoing genomic selection: a simulation study

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Hongding Gao ◽  
Per Madsen ◽  
Gert Pedersen Aamand ◽  
Jørn Rind Thomasen ◽  
Anders Christian Sørensen ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Variance Components ◽  
Genetic Variance ◽  
Phase 1 ◽  
Single Step ◽  
Residual Variance ◽  
Primary Concern ◽  
Conventional Breeding ◽  
Unbiased Estimates ◽  
The Impact

Abstract Background After the extensive implementation of genomic selection (GS), the choice of the statistical model and data used to estimate variance components (VCs) remains unclear. A primary concern is that VCs estimated from a traditional pedigree-based animal model (P-AM) will be biased due to ignoring the impact of GS. The objectives of this study were to examine the effects of GS on estimates of VC in the analysis of different sets of phenotypes and to investigate VC estimation using different methods. Data were simulated to resemble the Danish Jersey population. The simulation included three phases: (1) a historical phase; (2) 20 years of conventional breeding; and (3) 15 years of GS. The three scenarios based on different sets of phenotypes for VC estimation were as follows: (1) Pheno1: phenotypes from only the conventional phase (1–20 years); (2) Pheno1 + 2: phenotypes from both the conventional phase and GS phase (1–35 years); (3) Pheno2: phenotypes from only the GS phase (21–35 years). Single-step genomic BLUP (ssGBLUP), a single-step Bayesian regression model (ssBR), and P-AM were applied. Two base populations were defined: the first was the founder population referred to by the pedigree-based relationship (P-base); the second was the base population referred to by the current genotyped population (G-base). Results In general, both the ssGBLUP and ssBR models with all the phenotypic and genotypic information (Pheno1 + 2) yielded biased estimates of additive genetic variance compared to the P-base model. When the phenotypes from the conventional breeding phase were excluded (Pheno2), P-AM led to underestimation of the genetic variance of P-base. Compared to the VCs of G-base, when phenotypes from the conventional breeding phase (Pheno2) were ignored, the ssBR model yielded unbiased estimates of the total genetic variance and marker-based genetic variance, whereas the residual variance was overestimated. Conclusions The results show that neither of the single-step models (ssGBLUP and ssBR) can precisely estimate the VCs for populations undergoing GS. Overall, the best solution for obtaining unbiased estimates of VCs is to use P-AM with phenotypes from the conventional phase or phenotypes from both the conventional and GS phases.

scholarly journals Genomic Prediction in Local Breeds: The Rendena Cattle as a Case Study

Animals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1815
Author(s):  
Enrico Mancin ◽  
Beniamino Tuliozi ◽  
Cristina Sartori ◽  
Nadia Guzzo ◽  
Roberto Mantovani
Keyword(s):  
Average Daily Gain ◽  
Cattle Breed ◽  
Single Step ◽  
Genomic Information ◽  
Dispersion Parameters ◽  
Indigenous Cattle ◽  
North East ◽  
Estimated Breeding Values ◽  
The Impact

The maintenance of local cattle breeds is key to selecting for efficient food production, landscape protection, and conservation of biodiversity and local cultural heritage. Rendena is an indigenous cattle breed from the alpine North-East of Italy, selected for dual purpose, but with lesser emphasis given to beef traits. In this situation, increasing accuracy for beef traits could prevent detrimental effects due to the antagonism with milk production. Our study assessed the impact of genomic information on estimated breeding values (EBVs) in Rendena performance-tested bulls. Traits considered were average daily gain, in vivo EUROP score, and in vivo estimate of dressing percentage. The final dataset contained 1691 individuals with phenotypes and 8372 animals in pedigree, 1743 of which were genotyped. Using the cross-validation method, three models were compared: (i) Pedigree-BLUP (PBLUP); (ii) single-step GBLUP (ssGBLUP), and (iii) weighted single-step GBLUP (WssGBLUP). Models including genomic information presented higher accuracy, especially WssGBLUP. However, the model with the best overall properties was the ssGBLUP, showing higher accuracy than PBLUP and optimal values of bias and dispersion parameters. Our study demonstrated that integrating phenotypes for beef traits with genomic data can be helpful to estimate EBVs, even in a small local breed.

scholarly journals Use of genomic information to exploit genotype-by-environment interactions for body weight of broiler chicken in bio-secure and production environments

2019 ◽  
Vol 51 (1) ◽  
Author(s):  
Thinh T. Chu ◽  
John W. M. Bastiaansen ◽  
Peer Berg ◽  
Hélène Romé ◽  
Danye Marois ◽  
...  
Keyword(s):  
Body Weight ◽  
Genetic Correlations ◽  
Single Step ◽  
Pedigree Information ◽  
Genomic Information ◽  
Genotype By Environment Interactions ◽  
Genotype By Environment ◽  
Increase In Accuracy ◽  
Close Relatives ◽  
Estimate Variance

Abstract Background The increase in accuracy of prediction by using genomic information has been well-documented. However, benefits of the use of genomic information and methodology for genetic evaluations are missing when genotype-by-environment interactions (G × E) exist between bio-secure breeding (B) environments and commercial production (C) environments. In this study, we explored (1) G × E interactions for broiler body weight (BW) at weeks 5 and 6, and (2) the benefits of using genomic information for prediction of BW traits when selection candidates were raised and tested in a B environment and close relatives were tested in a C environment. Methods A pedigree-based best linear unbiased prediction (BLUP) multivariate model was used to estimate variance components and predict breeding values (EBV) of BW traits at weeks 5 and 6 measured in B and C environments. A single-step genomic BLUP (ssGBLUP) model that combined pedigree and genomic information was used to predict EBV. Cross-validations were based on correlation, mean difference and regression slope statistics for EBV that were estimated from full and reduced datasets. These statistics are indicators of population accuracy, bias and dispersion of prediction for EBV of traits measured in B and C environments. Validation animals were genotyped and non-genotyped birds in the B environment only. Results Several indications of G × E interactions due to environmental differences were found for BW traits including significant re-ranking, heterogeneous variances and different heritabilities for BW measured in environments B and C. The genetic correlations between BW traits measured in environments B and C ranged from 0.48 to 0.54. The use of combined pedigree and genomic information increased population accuracy of EBV, and reduced bias of EBV prediction for genotyped birds compared to the use of pedigree information only. A slight increase in accuracy of EBV was also observed for non-genotyped birds, but the bias of EBV prediction increased for non-genotyped birds. Conclusions The G × E interaction was strong for BW traits of broilers measured in environments B and C. The use of combined pedigree and genomic information increased population accuracy of EBV substantially for genotyped birds in the B environment compared to the use of pedigree information only.

scholarly journals Metafounders May Reduce Bias in Composite Cattle Genomic Predictions

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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