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

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.

scholarly journals The effect of calpastatin polymorphism and its interaction with RYR1 genotypes on carcass and meat quality of crossbred pigs

2008 ◽  
Vol 19 (4) ◽  
pp. 294 ◽  
Author(s):  
A. RYBARCZYK ◽  
M. KMIEC ◽  
R. SZARUGA
Keyword(s):  
Meat Quality ◽  
Receptor Gene ◽  
Carcass Traits ◽  
Carcass Quality ◽  
Quality Traits ◽  
The Relationship ◽  
Crossbred Pigs ◽  
Cast Gene

The aim of the study was to establish the relationship between a calpastatin gene (CAST) polymorphism, the ryanodine receptor gene (RYR1) polymorphism and carcass/meat quality traits in crossbred pigs. No significant differences in the analyzed pigs were found between genotypes CC and CT at the locus RYR1 and CD and DD at the locus CAST/MspI in terms of carcass and meat quality. However, a significant association of the CAST/ApaLI polymorphism with carcass quality and meat marbling were observed. The carcasses of AB pigs had significantly higher carcass percentage of lean meat, thinner backfat and thicker muscle, as well as lower meat marbling, as compared with the BB pigs. Furthermore, interactions CAST/MspI × RYR1 and CAST/ApaLI × RYR1 were found significant in relation to all the studied carcass traits. The results presented here imply that the CAST gene recognized with ApaLI may be considered as important in terms of the way it affects porcine carcass quality traits. Moreover, the research has revealed a relationship between CAST and RYR1 genotypes as regards formation of carcass traits in pigs. Follow-up studies, however, should be carried out on larger populations representing all possible CAST genotypes.;

scholarly journals Fattening performance and carcass traits of implanted and supplemented grassfed bulls

2021 ◽  
Vol XXXI (2) ◽  
pp. 53-60
Author(s):  
Nelson Huerta-Leidenz ◽  
Nancy Jerez-Timaure ◽  
Susmira Godoy ◽  
Carlos Rodríguez-Matos ◽  
Omar Araujo-Febres
Keyword(s):  
Fixed Effects ◽  
Random Effect ◽  
Carcass Traits ◽  
Carcass Characteristics ◽  
Growth Trait ◽  
Daily Weight Gain ◽  
Carcass Quality ◽  
Fattening Performance ◽  
Mineral Supplementation ◽  
Beneficial Impact

Ninety-nine uncastrated males were randomly distributed into four grazing groups to examine variation in growth and carcass traits, due to the implant regime [Implantation of 72 miligrams (mg) of Ralgro® at day (d) 0 followed by its reimplantation at d 90 versus implantation of Revalor® at d 0 followed by 72 mg of Ralgro® at d 90)], and suplementation type [mineral supplementation (MS) versus strategic supplementation (SS)]. With a 2 x 2 factorial arrangement, the analysis of variance included the treatments and their interaction (implant regimen x supplementation) as fixed effects, and the breed type as a random effect. The interaction was not significant; neither did the implant regimen on any growth trait (P > 0.05). Compared to MS, the SS group had a greater daily weight gain (779 vs. 541 grams; P < 0.001), required a shorter (38.3 d lesser) time of fattening to reach the end point (198.3 versus 236.6 d; P < 0.001) with a heavier liveweight (498. 2 vs. 474. 4 kilograms; P = 0.02) at an earlier age (29.4 vs. 30.­8 months; P < 0.001), with a higher carcass dressing percentage (59.13 vs 57.62 %; P = 0.03) and younger carcass bone maturity (P < 0.001). With the exception of thoracic depth, carcass traits did not vary with the implant regimen (P > 0.05). The use of aggressive implant regimens to improve growth or carcass characteristics of grazing bulls is not justified. SS is a feasible practice to improve fattening performance of grazing bulls but no beneficial impact on their carcass quality was expected.

scholarly journals Detection of Genomic Regions with Pleiotropic Effects for Growth and Carcass Quality Traits in the Rubia Gallega Cattle Breed

Animals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1682
Author(s):  
Maria Martinez-Castillero ◽  
Carlos Then ◽  
Juan Altarriba ◽  
Houssemeddine Srihi ◽  
David López-Carbonell ◽  
...  
Keyword(s):  
Association Studies ◽  
Cattle Breed ◽  
Snp Markers ◽  
Single Step ◽  
Pleiotropic Effects ◽  
Carcass Quality ◽  
Genome Wide Association Studies ◽  
Quality Traits ◽  
Nucleotide Polymorphisms ◽  
Genomic Regions

The breeding scheme in the Rubia Gallega cattle population is based upon traits measured in farms and slaughterhouses. In recent years, genomic evaluation has been implemented by using a ssGBLUP (single-step Genomic Best Linear Unbiased Prediction). This procedure can reparameterized to perform ssGWAS (single-step Genome Wide Association Studies) by backsolving the SNP (single nucleotide polymorphisms) effects. Therefore, the objective of this study was to identify genomic regions associated with the genetic variability in growth and carcass quality traits. We implemented a ssGBLUP by using a database that included records for Birth Weight (BW-327,350 records-), Weaning Weight (WW-83,818-), Cold Carcass Weight (CCW-91,621-), Fatness (FAT-91,475-) and Conformation (CON-91,609-). The pedigree included 464,373 individuals, 2449 of which were genotyped. After a process of filtering, we ended up using 43,211 SNP markers. We used the GBLUP and SNPBLUP model equivalences to obtain the effects of the SNPs and then calculated the percentage of variance explained by the regions of the genome between 1 Mb. We identified 7 regions of the genome for CCW; 8 regions for BW, WW, FAT and 9 regions for CON, which explained the percentage of variance above 0.5%. Furthermore, a number of the genome regions had pleiotropic effects, located at: BTA1 (131–132 Mb), BTA2 (1–11 Mb), BTA3 (32–33 Mb), BTA6 (36–38 Mb), BTA16 (24–26 Mb), and BTA 21 (56–57 Mb). These regions contain, amongst others, the following candidate genes: NCK1, MSTN, KCNA3, LCORL, NCAPG, and RIN3.

scholarly journals 214 Correlation among host gut microbiome and their relationship with meat quality and carcass composition traits of swine

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 44-44
Author(s):  
Piush Khanal ◽  
Christian Maltecca ◽  
Clint Schwab ◽  
Justin Fix ◽  
Francesco Tiezzi
Keyword(s):  
Meat Quality ◽  
Gut Microbiome ◽  
Fixed Effects ◽  
Average Daily Gain ◽  
Carcass Traits ◽  
Alpha Diversity ◽  
Carcass Composition ◽  
Additive Genetic Effect ◽  
Quality Traits ◽  
Meat Quality Traits

Abstract Study on correlation among host gut microbiome and their relationship with meat quality and carcass composition traits remains limited. The objectives of this study were 1) to estimate the microbial correlation between meat quality and carcass traits; and 2) to estimate the genetic correlation between microbial alpha diversity, and meat quality and carcass traits in commercial swine population. Data were collected from Duroc sired three-way cross individuals (n = 1,123) genotyped with 60K SNP chips. Fecal 16S microbial sequences for all individuals were obtained at three different stages: weaning (WEAN: 18.64 ± 1.09 days); week 15 (W_15: 118.2 ± 1.18 days); and off test (OT: 196.4 ± 7.80 days). Alpha diversity was measured at each stage [WEAN (alpha_w), W_15 (alpha_15) and OT (alpha_off)] using the Shannon index, which was computed as: ∑ ni=1piln(pi) where pi was the proportional abundance of ith operational taxonomic unit. Microbial correlations were estimated using multi-trait model, which included fixed effects of dam line, contemporary group and sex, as well as random effects of pen, additive genetic and microbiome. Bivariate analyses were conducted between different traits and alpha_w, alpha_15 and alpha_off with the same fixed effects and random pen and additive genetic effect. Analyses were conducted in ASREML v.4. Microbial correlations ranged from -0.93 ± 0.11 between firmness and slice shear force to 0.97 ± 0.02 between carcass average daily gain (CADG) and loin weight. For meat quality traits, correlations were weak, except for alpha_15 with Minolta a* (-0.45±0.19). Alpha_15 showed weak correlations except with CADG (-0.43±0.19). All correlations between alpha_ot and growth, carcass and meat quality traits were weak. These results may establish a newer approach of genetic evaluation process by utilizing gut microbiome information.

scholarly journals 60 Re-ranking of estimated breeding values using different panel densities with ssGBLUP in broiler chickens

2019 ◽  
Vol 97 (Supplement_2) ◽  
pp. 36-37
Author(s):  
Mayara Salvian ◽  
Gerson Barreto Mourão ◽  
Gabriel Costa Monteiro Moreira ◽  
Mônica Corrêa Ledur ◽  
Luiz Lehmann Coutinho ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Broiler Chickens ◽  
Reference Population ◽  
Single Step ◽  
P Value ◽  
Call Rate ◽  
Breeding Values ◽  
Heritability Estimates ◽  
Estimated Breeding Values ◽  
The Impact

Abstract The aim of this study was to compare the rank of estimated breeding values (EBV) for organs (heart, liver, lungs and gizzard) and carcass (breast, thigh and drumstick) traits using pedigree-based BLUP (PBLUP) and single-step genomic BLUP (ssGBLUP) models. A total of 1,453 chickens (703 males and 750 females) from a paternal broiler (TT) reference population belonging to the Poultry Breeding Program from Embrapa Swine and Poultry were genotyped with the Axiom® Genome-Wide Chicken Genotyping Array (Affymetrix) 600K SNP panel. Samples with a call rate lower than 90% were removed. A SNP quality control was applied for removing SNP with call rate lower than 98%, MAF lower than 2% and significant deviations from HWE (p-value < 10–7) leaving 370,608 SNP for further analysis. Estimated breeding values were predicted using the blupf90 family of programs whereby a series of bi-variate animal models that included sex and hatching as fixed effects were fitted. Heritability estimates for carcass and organ traits obtained through PBLUP varied from low (0.16) for lungs to moderate (0.34 to 0.47) for heart, liver, gizzard, breast, thigh and drumstick. The genomic heritability estimates through ssGBLUP varied from low (0.14) for lungs to moderate (0.30 to .041) for all other traits. Five subsets (5, 10, 20, 40 and 80% of SNP) were randomly selected from the full SNP set to determine the impact, in terms of EBV rank, of using reduced subsets of SNP to inform relationships among individuals. Although the 5% subset of SNP consistently had the lowest correlation with the full set of SNP, all correlations were greater than 0.995. Results suggest that a relatively limited proportion of SNP could be used to reliably predict EBV via ssGBLUP in this population.

scholarly journals PSIII-13 Genetic parameters and the purebred–crossbred genetic correlation for growth, carcass, and meat quality traits in pigs

2019 ◽  
Vol 97 (Supplement_2) ◽  
pp. 164-165
Author(s):  
Hadi Esfandyari ◽  
Dinesh M Thekkoot ◽  
Robert A Kemp ◽  
Graham S Plastow ◽  
Jack C M Dekkers
Keyword(s):  
Meat Quality ◽  
Genetic Parameters ◽  
Carcass Traits ◽  
Genetic Correlations ◽  
National Research Council ◽  
Quality Traits ◽  
Meat Quality Traits ◽  
Heritability Estimates ◽  
Negative Estimate ◽  
Crossbred Pigs

Abstract Growth, meat quality, and carcass traits are of economic importance in swine breeding. Understanding their genetic basis in commercial crossbred pigs and purebred-crossbred genetic correlations are necessary for a successful breeding program. The objectives of this study were to 1) estimate genetic parameters for growth, meat quality and carcass traits in a purebred sire line and related commercial crossbred pigs and 2) estimate the corresponding genetic correlations between purebreds and crossbreds (rpc). We analyzed 115266, 10927 and 43057 purebred records for growth, meat quality (n = 4) and carcass traits (n = 7), respectively. For crossbreds, there were 2000 pigs with growth records, with 900 of them having meat quality and carcass data. A series of univariate and bivariate analyses were used to estimate genetic parameters and rpc. Growth showed moderate heritability (0.20 ± 0.10 to 0.25 ± 0.01) in both purebreds and crossbreds. Heritability estimates for meat quality traits ranged from 0.21 ± 0.03 to 0.42 ± 0.04 in purebreds and from 0.17 ± 0.14 to 0.47 ± 0.15 in crossbreds. Carcass traits had higher heritability estimates in purebreds compared to crossbreds, except for hot carcass weight (0.10 ± 0.02 vs. 0.24 ± 0.16). Genetic correlations among meat quality traits were variable in both populations, whereas genetic correlations among carcass traits were similar in purebreds and crossbreds. Estimates of rpc were high for growth (0.99 ± 0.5) and for meat quality traits (0.94 ± 0.39 to 0.99 ± 0.2), except for Minolta color (-0.48 ± 0.56). Carcass traits had moderate to high estimates of rpc (0.64 ± 0.4 to 0.92 ± 0.3). Carcass fat had a negative estimate of rpc (-0.1 ± 0.5). However, ultrasound fat as an indicator trait for carcass fat had a high positive estimate of rpc (0.88 ± 0.14). Our results indicate that selection in purebreds can be efficient to improve these traits in both purebreds and crossbreds but for some traits, genetic gain can be improved by applying combined crossbred and purebred selection. Funding provided by Genome Canada and the National Research Council.

scholarly journals 212 Genomic prediction of carcass average daily gain, fat and loin depth in three-way crossbred pigs including information collected on purebreds

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 40-40
Author(s):  
Francesco Tiezzi ◽  
Clint Schwab ◽  
Justin Fix ◽  
Christian Maltecca
Keyword(s):  
Prediction Accuracy ◽  
Average Daily Gain ◽  
Genetic Correlations ◽  
Predictive Ability ◽  
Predictive Performance ◽  
Breeding Value ◽  
Training Set ◽  
Basic Model ◽  
Crossbred Pigs ◽  
Daily Gain

Abstract The purpose of this study was to predict three-way crossbred performance for carcass traits using different crossbred/purebred reference populations. Carcass measures (average daily gain, back-fat and loin depths) were collected in 4,893 three-way-cross individuals (CB individuals, 1,252 being genotyped). Live measures of body weight and tissue deposition were collected on 3,050 purebred Duroc individuals (PB individuals, 941 being genotyped), paternal-half-sibs (PHS) of the CB individuals. Models’ predictive performance was tested via 4-fold cross-validation. The basic model included CB phenotypes from the training set without inclusion of genomic information (i.e. pedigree BLUP). We also sequentially included: 1) CB genotypes; 2) PB phenotypes and genotypes for the training families (PBt); 3) PB phenotypes and genotypes for the validation families (PBv). Variance components (heritabilities and genetic correlations between CB and PB traits) were not estimated but fixed at different values within a plausible interval, the combination of such parameters that gave the best predictive ability was considered for that model. Results reported pedigree prediction of CB traits to show about 0.25 accuracy (correlation between breeding value and adjusted phenotype) for the three traits. The inclusion of CB genotypes was beneficial, with an increase ranging from 25 to 50% (depending on the trait) compared to pedigree prediction. When PBt genotypes and phenotypes were included, prediction accuracy dropped to almost null accuracy. When PBv genotypes and phenotypes were included, predictive performance was better than models that included CB information only. Results suggest that PB information can improve selection accuracy for CB traits, with the condition PB are PHS of the CB in validation. Otherwise, inclusion of PB information from the training set can be detrimental. CB genotypes, on the other hand, always improve prediction accuracy. We can conclude that reference populations aimed at improving CB performance should include phenotypes and genotypes from these individuals.

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

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

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

scholarly journals PSVIII-38 Genomic prediction for tick resistance in Angus cattle

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 263-263
Author(s):  
Gabriel Campos ◽  
Fernando F Cardoso ◽  
Claudia Cristina Gulias-Gomes ◽  
Robert Domingues ◽  
Luciana Regitano ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Predictive Ability ◽  
Relative Increase ◽  
Single Step ◽  
Angus Cattle ◽  
Increase In Accuracy ◽  
Estimate Variance ◽  
Tick Counts ◽  
Reduced Data ◽  
Tick Resistance

Abstract The main objective of this study was to evaluate the feasibility of single-step genomic BLUP (ssGBLUP) for genetic evaluation of tick resistance in Angus cattle in Brazil. Additionally, we investigated population parameters, namely effect population size (Ne) and inbreeding (F) based on pedigree (PED) and genomic (GEN) information. Half-body tick counts were recorded up to three times in the same animal, totaling 2291 records. To normalize the distribution, records were log-transformed prior to the analysis. From 7073 animals in the pedigree, 1299 were genotyped with 3 different SNP chips of density 50k, 77k, and 150k. After imputation and quality control, 61,066 SNP remained. A repeatability animal model was used to estimate variance components, traditional EBV, and genomic EBV (GEBV). Validation was carried out for young genotyped animals, with no phenotypes in the reduced data but at least one record in the complete data, using two different approaches: 1) predictive ability as the correlation between phenotypes adjusted for fixed effects and (G)EBV; 2) a method based on linear regressions that is called LR, which uses correlations between (G)EBV in the complete and reduced data as a measure of consistency between subsequent evaluations. Heritability for tick counts was 0.18 ± 0.03. Based on PED and GEN, Ne was 254 and 199, whereas F was 0.016 and 0.003, respectively. Predictive ability for tick counts was 0.11 for EBV and 0.14 for GEBV, which is considered low. Conversely, when LR validation was used, the relative increase in accuracy by adding extra phenotypic information was 0.49 for EBV and 0.61 for GEBV. Even though tick counts has low heritability, our study indicates that genomic selection can help to improve prediction accuracy and, therefore, to increase tick resistance in this Angus population.

scholarly journals 208 Crossbred evaluations using single-step genomic BLUP and algorithm for proven and young with different sources of data

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 41-42 ◽  
Author(s):  
Ivan Pocrnic ◽  
Daniela Lourenco ◽  
Ching-Yi Chen ◽  
William O Herring ◽  
Ignacy Misztal
Keyword(s):  
Animal Model ◽  
Predictive Ability ◽  
Single Step ◽  
Eigenvalue Decomposition ◽  
Genomic Information ◽  
Training Set ◽  
Pure Lines ◽  
Chromosome Segments ◽  
Different Sources ◽  
Different Levels

Abstract Genomic selection is routinely applied to many purebred farm species but can be extended to predictions across purebreds as well as for crossbreds. This is useful for swine and poultry, for which selection in nucleus herds is typically performed on purebreds, whereas the commercial products are crossbreds. Single-step genomic BLUP (ssGBLUP) is a widely applied method that can use algorithm for proven and young (APY), that allows for greater computing efficiency by exploiting the theory of limited dimensionality of genomic information and chromosome segments (Me). This study investigates the predictivity as a proxy for accuracy across and within two purebred pig lines and their crosses, under the application of APY in ssGBLUP setup, and different levels of Me overlapping across populations. The data consisted of approximately 210k phenotypic records for two traits and more than 720k animals in pedigree. Genotypes for 43k SNP were available for 46k animals, from which 26k and 16k belong to purebreds, and 4k to crossbreds. The models included bivariate animal model with three lines evaluated as one joint line, and for each trait individually a three-trait animal model with each line treated as a separate trait. Both models provided the same predictivity across and within the lines. Using either of the pure lines data as a training set resulted in a similar predictivity for the crossbreeds. Across-line predictive ability was limited to less than half of the maximum predictivity for each line. For crossbreds, APY performed equivalently to direct inverse when the number of core animals was equal to the number of eigenvalues explaining 98–99% of the variance of G including all lines. Predictivity across the lines is achievable because of the shared Me between them. The number of those shared segments can be obtained via eigenvalue decomposition of genomic information available for each line.

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