49 Predicting Breeding Values of Purebred Pigs for Crossbred Performance Using Crossbred Phenotypes and Genotypes

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 23-23
Author(s):  
Natalia Leite ◽  
Ching-Yi Chen ◽  
Willian O Herring ◽  
Shogo Tsuruta ◽  
Daniela Lourenco
Keyword(s):  
Quality Control ◽  
Animal Model ◽  
Average Daily Gain ◽  
Snp Markers ◽  
Single Step ◽  
Low Density ◽  
Genomic Information ◽  
Breeding Values ◽  
Partial Data ◽  
Daily Gain

Abstract Phenotyping a large number of crossbred progeny for the evaluation of purebred animals can be expensive. As genotyping with low-density panels is becoming cheaper, we aimed to evaluate the tradeoff between having different percentages of genotypes and phenotypes for crossbred progeny of candidate boars. We used the linear regression (LR) method to investigate changes in accuracy, bias, and inflation of breeding values for crossbred traits in purebred boars. A total of 304,582 purebred and 147,474 crossbred animals were phenotyped for average daily gain (ADG) and backfat thickness (BF), out of which 46,691 purebred and 13,117 crossbred animals were genotyped. Genomic information consisted of imputed genotypes for 40,247 SNP markers after quality control. A four-trait animal model under single-step GBLUP was used that included phenotypes recorded in purebred and crossbred animals as correlated traits. The LR statistics were calculated based on breeding values of young purebred sires from complete and partial data. The first complete data included genotypes for purebreds and phenotypes for purebreds and crossbreds, whereas the second included also genotypes for crossbreds. The partial data included phenotypes on 50% or none of the progeny of validation sires, with or without genotypes for crossbred animals. When 50% of the progeny has phenotypes, adding genotypes for crossbred progeny marginally increased accuracy of ADG (0.77 vs 0.78) for 47 boars with more than 150 progeny with phenotypes. No increase was observed for BF. A small increase in bias and inflation by adding crossbred genotypes was observed for ADG but not for BF. When no phenotypes were available for crossbred progeny, accuracy for both traits was lower but improved with crossbred genotypes for ADG (0.61 vs 0.64) for boars with more than 150 progeny. The tradeoff between phenotypes and genotypes should be further investigated in larger datasets with more validation boars.

scholarly journals 209 Genomic selection for multiple maternal and growth traits in large white pigs using Single-Step GBLUP

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 42-42
Author(s):  
Breno Fragomeni ◽  
Zulma Vitezica ◽  
Justine Liu ◽  
Yijian Huang ◽  
Kent Gray ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Growth Traits ◽  
Computing Time ◽  
Average Daily Gain ◽  
Genetic Correlations ◽  
Single Step ◽  
Genomic Information ◽  
Genomic Evaluation ◽  
Large White ◽  
Daily Gain

Abstract The objective of this study was to implement a multi-trait genomic evaluation for maternal and growth traits in a swine population. Phenotypes for preweaning mortality, litter size, weaning weight, and average daily gain were available for 282K Large White pigs. The pedigree included 314k individuals, of which 35,731 were genotyped for 45K SNPs. Variance components were estimated in a multi-trait animal model without genomic information by AIREMLF90. Genomic breeding values were estimated using the genomic information by single-step GBLUP. The algorithm for proven and young (APY) was used to reduce computing time. Genetic correlation between proportion and the total number of preweaning deaths was 0.95. A strong, positive genetic correlation was also observed between weaning weight and average daily gain (r = 0.94). Conversely, the genetic correlations between mortality and growth traits were negative, with an average of -0.7. To avoid computations by expensive threshold models, preweaning mortality was transformed from a binary trait to two linear dam traits: proportion and a total number of piglets dead before weaning. Because of the high genetic correlations within groups of traits, inclusion of only one growth and one mortality trait in the model decreases computing time and allows for the inclusion of other traits. Reduction in computing time for the evaluation using APY was up to 20x, and no differences in EPD ranking were observed. The algorithm for proven and young improves the efficiency of genomic evaluation in swine without harming the quality of predictions. For this population, a binary trait of mortality can be replaced by a linear trait of the dam, resulting in a similar ranking for the selection candidates.

scholarly journals Short-term selection for yearling weight in a small-experimental Iranian Afshari sheep flock

2009 ◽  
Vol 89 (3) ◽  
pp. 301-307 ◽  
Author(s):  
Farhad Ghafouri-Kesbi ◽  
Moradpasha Eskandarinasab ◽  
Ahmad Hassanabadi
Keyword(s):  
Animal Model ◽  
Average Daily Gain ◽  
Genetic Correlations ◽  
Live Weight ◽  
Breeding Program ◽  
Sheep Flock ◽  
Breeding Values ◽  
Generation Interval ◽  
Selection For ◽  
Daily Gain

A selection experiment was initiated in 2000 in an Afshari sheep flock at the department of animal breeding and genetics of the University of Zanjan, Iran. The aim was to evaluate the response of Afshari sheep to selection for yearling live weight. Here, we evaluate the results of this breeding program obtained between 2000 and 2005. Traits studied were birth weight (BW), weaning weight (WW), yearling weight (YW), average daily gain from birth to weaning (WWDG) and average daily gain from weaning to yearling age (YWDG). Mixed model methodology based on a multi-trait animal model was employed to estimate (co)variance components and corresponding genetic parameters. Estimates of breeding values were obtained by the best linear unbiased prediction (BLUP) method. Generation intervals on the four genetic pathways were estimated as the average age of parents at the birth of their progeny kept for reproduction. The heritability estimates were 0.34, 0.27, 0.14, 0.20 and 0.01 for BW, WW, YW, WWDG and YWDG, respectively. Estimates of genetic correlations among traits studied were positive, and ranged from low (0.07, YW/WWDG) to high (0.76, YW/YWDG). Genetic improvements over the experiment based on estimated breeding values were 0.104, 0.824, 1.247, 0.005 and ≈0.00 kg for BW, WW, YW, WWDG and YWDG, respectively. Annual genetic gain for YW was relatively high, 0.311 kg yr-1, which demonstrated the effectiveness of the implemented breeding program. Correlated responses in BW, WW, WWDG and YWDG were 0.021, 0.167, 0.001 and ≈0.00 kg yr-1, respectively. Estimates of heritabilities and observed genetic trends indicated that selective breeding can lead to significant genetic improvement in Afshari sheep. The average generation interval was estimated to be 3.35 yr. The shorter generation interval was observed on the sire side compared with the dam side (3.30 yr vs. 3.78 yr), indicating faster generation turnover for sires than for dams. Key words: Sheep, animal model, genetic trend, generation interval, heritability

scholarly journals Genomic Prediction of Average Daily Gain, Back-Fat Thickness, and Loin Muscle Depth Using Different Genomic Tools in Canadian Swine Populations

2021 ◽  
Vol 12 ◽  
Author(s):  
Siavash Salek Ardestani ◽  
Mohsen Jafarikia ◽  
Mehdi Sargolzaei ◽  
Brian Sullivan ◽  
Younes Miar
Keyword(s):  
Genomic Prediction ◽  
Performance Test ◽  
Average Daily Gain ◽  
Accurate Method ◽  
Single Step ◽  
Prediction Methods ◽  
Genetic Progress ◽  
Breeding Values ◽  
Fat Thickness ◽  
Daily Gain

Improvement of prediction accuracy of estimated breeding values (EBVs) can lead to increased profitability for swine breeding companies. This study was performed to compare the accuracy of different popular genomic prediction methods and traditional best linear unbiased prediction (BLUP) for future performance of back-fat thickness (BFT), average daily gain (ADG), and loin muscle depth (LMD) in Canadian Duroc, Landrace, and Yorkshire swine breeds. In this study, 17,019 pigs were genotyped using Illumina 60K and Affymetrix 50K panels. After quality control and imputation steps, a total of 41,304, 48,580, and 49,102 single-nucleotide polymorphisms remained for Duroc (n = 6,649), Landrace (n = 5,362), and Yorkshire (n = 5,008) breeds, respectively. The breeding values of animals in the validation groups (n = 392–774) were predicted before performance test using BLUP, BayesC, BayesCπ, genomic BLUP (GBLUP), and single-step GBLUP (ssGBLUP) methods. The prediction accuracies were obtained using the correlation between the predicted breeding values and their deregressed EBVs (dEBVs) after performance test. The genomic prediction methods showed higher prediction accuracies than traditional BLUP for all scenarios. Although the accuracies of genomic prediction methods were not significantly (P > 0.05) different, ssGBLUP was the most accurate method for Duroc-ADG, Duroc-LMD, Landrace-BFT, Landrace-ADG, and Yorkshire-BFT scenarios, and BayesCπ was the most accurate method for Duroc-BFT, Landrace-LMD, and Yorkshire-ADG scenarios. Furthermore, BayesCπ method was the least biased method for Duroc-LMD, Landrace-BFT, Landrace-ADG, Yorkshire-BFT, and Yorkshire-ADG scenarios. Our findings can be beneficial for accelerating the genetic progress of BFT, ADG, and LMD in Canadian swine populations by selecting more accurate and unbiased genomic prediction methods.

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.

scholarly journals The Effect of Integrating Genomic Information into Genetic Evaluations of Chinese Merino Sheep

Animals ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 569
Author(s):  
Chen Wei ◽  
Hanpeng Luo ◽  
Bingru Zhao ◽  
Kechuan Tian ◽  
Xixia Huang ◽  
...  
Keyword(s):  
Rank Correlation ◽  
Best Linear Unbiased Prediction ◽  
Single Step ◽  
Breeding Value ◽  
Genomic Information ◽  
Linear Unbiased Prediction ◽  
Breeding Values ◽  
Merino Sheep ◽  
Best Linear Unbiased ◽  
Unbiased Prediction

Genomic evaluations are a method for improving the accuracy of breeding value estimation. This study aimed to compare estimates of genetic parameters and the accuracy of breeding values for wool traits in Merino sheep between pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP) using Bayesian inference. Data were collected from 28,391 yearlings of Chinese Merino sheep (classified in 1992–2018) at the Xinjiang Gonaisi Fine Wool Sheep-Breeding Farm, China. Subjectively-assessed wool traits, namely, spinning count (SC), crimp definition (CRIM), oil (OIL), and body size (BS), and objectively-measured traits, namely, fleece length (FL), greasy fleece weight (GFW), mean fiber diameter (MFD), crimp number (CN), and body weight pre-shearing (BWPS), were analyzed. The estimates of heritability for wool traits were low to moderate. The largest h2 values were observed for FL (0.277) and MFD (0.290) with ssGBLUP. The heritabilities estimated for wool traits with ssGBLUP were slightly higher than those obtained with PBLUP. The accuracies of breeding values were low to moderate, ranging from 0.362 to 0.573 for the whole population and from 0.318 to 0.676 for the genotyped subpopulation. The correlation between the estimated breeding values (EBVs) and genomic EBVs (GEBVs) ranged from 0.717 to 0.862 for the whole population, and the relative increase in accuracy when comparing EBVs with GEBVs ranged from 0.372% to 7.486% for these traits. However, in the genotyped population, the rank correlation between the estimates obtained with PBLUP and ssGBLUP was reduced to 0.525 to 0.769, with increases in average accuracy of 3.016% to 11.736% for the GEBVs in relation to the EBVs. Thus, genomic information could allow us to more accurately estimate the relationships between animals and improve estimates of heritability and the accuracy of breeding values by ssGBLUP.

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 Stability of estimated breeding values for average daily gain in Pannon White rabbits

2011 ◽  
Vol 56 (No. 8) ◽  
pp. 365-369 ◽  
Author(s):  
I. Nagy ◽  
J. Farkas ◽  
P. Gyovai ◽  
I. Radnai ◽  
Z. Szendrő
Keyword(s):  
Average Daily Gain ◽  
Rank Correlation ◽  
Correlation Coefficients ◽  
The Other ◽  
Breeding Value ◽  
Breeding Values ◽  
Spearman’S Rank Correlation ◽  
The Common ◽  
Estimated Breeding Values ◽  
Daily Gain

Stability of estimated breeding values for average daily gain (ADG) between 5 and 10 weeks of age was analysed for 47 242 Pannon White rabbits, reared in 7470 litters and born between 2000 and 2008. The dataset was divided into 5 successive 5-year periods: (1) 2000–2004, (2) 2001–2005, (3) 2002–2006, (4) 2003–2007, and (5) 2004–2008. Then, after selecting the appropriate part of the pedigree for these sub-datasets, genetic parameters and breeding values were estimated for ADG using REML and BLUP methods. In the applied models sex, year-month, animal and random litter effects were considered. Estimated heritabilities for all 5 periods from 1 to 5 were moderate and stable (0.28 ± 0.01, 0.28 ± 0.02, 0.29 ± 0.02, 0.27 ± 0.02, and 0.28 ± 0.02). Magnitudes of random litter effects were low and stable (0.14 ± 0.01, 0.15 ± 0.01, 0.15 ± 0.01, 0.16 ± 0.01, and 0.16 ± 0.01). After breeding value estimation the dataset of period 5 was merged pair-wise with the other periods 4, 3, 2 and 1 using an inner join. Thus only the common records of the datasets representing the periods 5-4, 5-3, 5-2, and 5-1 were included in the merged datasets. In these merged datasets each rabbit had two breeding values for ADG based on two different periods. Spearman's rank correlation coefficients were calculated between the breeding values based on the dataset of period 5 and the other periods. With the successive years the rank correlation coefficients decreased (0.989, 0.979, 0.965 and 0.924). The correlation coefficients between ranks remained moderately high, even when the proportion of the common rabbits in the merged datasets was low. However, a reasonable re-ranking occurred among the top animals. Rank correlations for the top 100 and 1000 animals varied from 0.41 to 0.55 and from 0.37 to 0.54, respectively, which could influence selection efficiency if the rolling base were used for genetic evaluation.

scholarly journals Advantage of including Genomic Information to Predict Breeding Values for Lactation Yields of Milk, Fat, and Protein or Somatic Cell Score in a New Zealand Dairy Goat Herd

Animals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 24
Author(s):  
Megan Scholtens ◽  
Nicolas Lopez-Villalobos ◽  
Klaus Lehnert ◽  
Russell Snell ◽  
Dorian Garrick ◽  
...  
Keyword(s):  
New Zealand ◽  
Somatic Cell ◽  
Milk Fat ◽  
Single Step ◽  
Somatic Cell Score ◽  
Dairy Goat ◽  
Nucleotide Polymorphisms ◽  
Genomic Information ◽  
Breeding Values ◽  
Goat Population

Selection on genomic breeding values (GBVs) is now readily available for ranking candidates in improvement schemes. Our objective was to quantify benefits in terms of accuracy of prediction from including genomic information in the single-trait estimation of breeding values (BVs) for a New Zealand mixed breed dairy goat herd. The dataset comprised phenotypic and pedigree records of 839 does. The phenotypes comprised estimates of 305-day lactation yields of milk, fat, and protein and average somatic cell score from the 2016 production season. Only 388 of the goats were genotyped with a Caprine 50K SNP chip and 41,981 of the single nucleotide polymorphisms (SNPs) passed quality control. Pedigree-based best linear unbiased prediction (PBLUP) was used to obtain across-breed breeding values (EBVs), whereas a single-step BayesC model (ssBC) was used to estimate across-breed GBVs. The average prediction accuracies ranged from 0.20 to 0.22 for EBVs and 0.34 to 0.43 for GBVs. Accuracies of GBVs were up to 103% greater than EBVs. Breed effects were more reliably estimated in the ssBC model compared with the PBLUP model. The greatest benefit of genomic prediction was for individuals with no pedigree or phenotypic records. Including genomic information improved the prediction accuracy of BVs compared with the current pedigree-based BLUP method currently implemented in the New Zealand dairy goat population.

scholarly journals Single-step genomic evaluation with metafounders for feed conversion ratio and average daily gain in Danish Landrace and Yorkshire pigs

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Chuanke Fu ◽  
Tage Ostersen ◽  
Ole F. Christensen ◽  
Tao Xiang
Keyword(s):  
Average Daily Gain ◽  
Predictive Ability ◽  
Single Step ◽  
Feed Conversion ◽  
Genomic Evaluation ◽  
Bivariate Model ◽  
Bivariate Models ◽  
Yorkshire Pigs ◽  
Optimal Weighting ◽  
Daily Gain

Abstract Background The single-step genomic best linear unbiased prediction (SSGBLUP) method is a popular approach for genetic evaluation with high-density genotype data. To solve the problem that pedigree and genomic relationship matrices refer to different base populations, a single-step genomic method with metafounders (MF-SSGBLUP) was put forward. The aim of this study was to compare the predictive ability and bias of genomic evaluations obtained with MF-SSGBLUP and standard SSGBLUP. We examined feed conversion ratio (FCR) and average daily gain (ADG) in DanBred Landrace (LL) and Yorkshire (YY) pigs using both univariate and bivariate models, as well as the optimal weighting factors (ω), which represent the proportions of the genetic variance not captured by markers, for ADG and FCR in SSGBLUP and MF-SSGBLUP. Results In general, SSGBLUP and MF-SSGBLUP showed similar predictive abilities and bias of genomic estimated breeding values (GEBV). In the LL population, the predictive ability for ADG reached 0.36 using uni- or bi-variate SSGBLUP or MF-SSGBLUP, while the predictive ability for FCR was highest (0.20) for the bivariate model using MF-SSGBLUP, but differences between analyses were very small. In the YY population, predictive ability for ADG was similar for the four analyses (up to 0.35), while the predictive ability for FCR was highest (0.36) for the uni- and bi-variate MF-SSGBLUP analyses. SSGBLUP and MF-SSGBLUP exhibited nearly the same bias. In general, the bivariate models had lower bias than the univariate models. In the LL population, the optimal ω for ADG was ~ 0.2 in the univariate or bivariate models using SSGBLUP or MF-SSGBLUP, and the optimal ω for FCR was 0.70 and 0.55 for SSGBLUP and MF-SSGBLUP, respectively. In the YY population, the optimal ω ranged from 0.25 to 0. 35 for ADG across the four analyses and from 0.10 to 0.30 for FCR. Conclusions Our results indicate that MF-SSGBLUP performed slightly better than SSGBLUP for genomic evaluation. There was little difference in the optimal weighting factors (ω) between SSGBLUP and MF-SSGBLUP. Overall, the bivariate model using MF-SSGBLUP is recommended for single-step genomic evaluation of ADG and FCR in DanBred Landrace and Yorkshire pigs.

Sign in / Sign up

Export Citation Format

Share Document