Genome-enabled prediction of meat and carcass traits using Bayesian regression, single-step genomic best linear unbiased prediction and blending methods in Nelore cattle

Lopes, Fernando Brito; Baldi, Fernando; Passafaro, Tiago Luciano; Brunes, L. C.; Costa, M. F. O. e; Eifert, E. C.; Narciso, Marcelo Gonçalves; Rosa, Guilherme J. M.; Lôbo, Raysildo Barbosa; Magnabosco, C. de U.

doi:10.1016/j.animal.2020.100006

Cited by 11 publications

(8 citation statements)

References 42 publications

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“…Although the Bayesian methods differ in a priori assumptions about marker effects, their predictive ability was very similar and small differences were detected for any of the traits (Figure 2). The similarity between the different models was reported by other authors (Brunes et al, 2020;Lopes et al, 2021;Resende et al, 2012), and could be attributed to the polygenic nature of the evaluated traits, where a large number of markers explain the genetic additive variance. The exception was observed for WBSF, where the predictive ability was higher using the Bayes B model (0.47), and lower using the Bayes Cπ model (0.39).…”

Section: Predictive Abilitysupporting

confidence: 84%

“…Single-trait analyses were performed using the restricted maximum likelihood method with the REMLF90 software (Misztal et al, 2019). In routine Nellore cattle genomic evaluations, the WBSF is evaluated in a single trait model (Lobo et al, 2021), since the genetic correlation with other traits such as REA and BF is low (Lopes et al, 2021). The multiple-trait model was tested in previous study (Lopes et al, 2021), and was observed that there was not much gain in prediction accuracy in the genomic analyses performed using the multi-trait, over the single-trait, approach for REA and WBSF.…”

Section: Estimation Of Genetic Parametersmentioning

confidence: 99%

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Genomic prediction for meat and carcass traits in Nellore cattle using a Markov blanket algorithm

Lopes

Baldi

Brunes

et al. 2022

J Animal Breeding Genetics

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This study was carried out to evaluate the advantage of preselecting SNP markers using Markov blanket algorithm regarding the accuracy of genomic prediction for carcass and meat quality traits in Nellore cattle. This study considered 3675, 3680, 3660 and 524 records of rib eye area (REA), back fat thickness (BF), rump fat (RF), and Warner–Bratzler shear force (WBSF), respectively, from the Nellore Brazil Breeding Program. The animals have been genotyped using low‐density SNP panel (30 k), and subsequently imputed for arrays with 777 k SNPs. Four Bayesian specifications of genomic regression models, namely Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression methods were compared in terms of prediction accuracy using a five folds cross‐validation. Prediction accuracy for REA, BF and RF was all similar using the Bayesian Alphabet models, ranging from 0.75 to 0.95. For WBSF, the predictive ability was higher using Bayes B (0.47) than other methods (0.39 to 0.42). Although the prediction accuracies using Markov blanket of SNP markers were lower than those using all SNPs, for WBSF the relative gain was lower than 13%. With a subset of informative SNPs markers, identified using Markov blanket, probably, is possible to capture a large proportion of the genetic variance for WBSF. The development of low‐density and customized arrays using Markov blanket might be cost‐effective to perform a genomic selection for this trait, increasing the number of evaluated animals, improving the management decisions based on genomic information and applying genomic selection on a large scale.

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Section: Predictive Abilitysupporting

confidence: 84%

Section: Estimation Of Genetic Parametersmentioning

confidence: 99%

Genomic prediction for meat and carcass traits in Nellore cattle using a Markov blanket algorithm

Lopes

Baldi

Brunes

et al. 2022

J Animal Breeding Genetics

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“…It is well-established that DNA-based inheritance enables the transmission of selected phenotypes across generations either without changes in the DNA sequence through epigenetic inheritance [31] or through functional mutations involving changes in only one base pair (single nucleotide polymorphisms-SNP). Through next-generation sequencing, SNP are valuable for detecting genetic variability and genomic prediction in sheep breeding programs [32], developing breed-specific DNA markers for breed identification [33,34], animal productivity [35], parentage assignment [36,37], forensics [38], and prediction of meat quality traits [39][40][41].…”

Section: Discussionmentioning

confidence: 99%

Next Generation Sequencing of Single Nucleotide Polymorphic DNA-Markers in Selecting for Intramuscular Fat, Fat Melting Point, Omega-3 Long-Chain Polyunsaturated Fatty Acids and Meat Eating Quality in Tattykeel Australian White MARGRA Lamb

Pewan

Otto

Huerlimann

et al. 2021

Foods

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Meat quality data can only be obtained after slaughter when selection decisions about the live animal are already too late. Carcass estimated breeding values present major precision problems due to low accuracy, and by the time an informed decision on the genetic merit for meat quality is made, the animal is already dead. We report for the first time, a targeted next-generation sequencing (NGS) of single nucleotide polymorphisms (SNP) of lipid metabolism genes in Tattykeel Australian White (TAW) sheep of the MARGRA lamb brand, utilizing an innovative and minimally invasive muscle biopsy sampling technique for directly quantifying the genetic worth of live lambs for health-beneficial omega-3 long-chain polyunsaturated fatty acids (n-3 LC-PUFA), intramuscular fat (IMF), and fat melting point (FMP). NGS of stearoyl-CoA desaturase (SCD), fatty acid binding protein-4 (FABP4), and fatty acid synthase (FASN) genes identified functional SNP with unique DNA marker signatures for TAW genetics. The SCD g.23881050T>C locus was significantly associated with IMF, C22:6n-3, and C22:5n-3; FASN g.12323864A>G locus with FMP, C18:3n-3, C18:1n-9, C18:0, C16:0, MUFA, and FABP4 g.62829478A>T locus with IMF. These add new knowledge, precision, and reliability in directly making early and informed decisions on live sheep selection and breeding for health-beneficial n-3 LC-PUFA, FMP, IMF and superior meat-eating quality at the farmgate level. The findings provide evidence that significant associations exist between SNP of lipid metabolism genes and n-3 LC-PUFA, IMF, and FMP, thus underpinning potential marker-assisted selection for meat-eating quality traits in TAW lambs.

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“…The prediction accuracy for REA with the default ssGBLUP was 41% higher than that with the BLUP model. Lopes et al (2021) compared the predictive ability for REA in Nelore cattle using different models and reported a higher (0.42) predictive ability using the ssGBLUP. Silva et al (2021), comparing the genomic prediction accuracy for REA in Nelore cattle using different prediction models, also obtained higher predictive ability for the ssGBLUP (0.62) than the BLUP model (0.24).…”

Section: Resultsmentioning

confidence: 99%

Weighted genomic prediction for growth and carcass‐related traits in Nelore cattle

et al. 2023

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This study aimed to assess the impact of differential weighting in genomic regions harboring candidate causal loci on the genomic prediction accuracy and dispersion for growth and carcass‐related traits in Nelore cattle. The dataset contained 168 793 phenotypic records for adjusted weight at 450 days of age (W450), 83 624 for rib eye area (REA), 24 480 for marbling (MAR) and 82 981 for subcutaneous backfat thickness (BFT) and rump fat thickness (RFT). The pedigree harbored information from 244 254 animals born between 1977 and 2016, including 6283 sires and 50 742 dams. Animals (n = 7769) were genotyped with the low‐density panel (Clarifide® Nelore 3.0), and the genotypes were imputed to a panel containing 735 044 markers. A linear animal model was applied to estimate the genetic parameters and to perform the weighted single‐step genome‐wide association study (WssGWAS). A total of seven models for genomic prediction were evaluated combining the SNP weights obtained in the iterations of the WssGWAS and the candidate QTL. The heritability estimated for W450 (0.35) was moderate, and for carcass‐related traits, the estimates were moderate for REA (0.27), MAR (0.28) and RFT (0.28), and low for BFT (0.18). The prediction accuracy for W450 incorporating reported QTL previously described in the literature along with different SNPs weights was like those described for the default ssGBLUP model. The use of the ssGWAS to weight the SNP effects displayed limited advantages for the REA prediction accuracy. Comparing the ssGBLUP with the BLUP model, a meaningful improvement in the prediction accuracy from 0.09 to 0.63 (700%) was observed for MAR. The highest prediction accuracy was obtained for BFT and RFT in all evaluated models. The application of information obtained from the WssGWAS is an alternative to reduce the genomic prediction dispersion for growth and carcass‐related traits, except for MAR. Furthermore, the results obtained herein pointed out that is possible to improve the prediction accuracy and reduce the genomic prediction dispersion for growth and carcass‐related traits in young animals.

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Genome-enabled prediction of meat and carcass traits using Bayesian regression, single-step genomic best linear unbiased prediction and blending methods in Nelore cattle

Cited by 11 publications

References 42 publications

Genomic prediction for meat and carcass traits in Nellore cattle using a Markov blanket algorithm

Genomic prediction for meat and carcass traits in Nellore cattle using a Markov blanket algorithm

Next Generation Sequencing of Single Nucleotide Polymorphic DNA-Markers in Selecting for Intramuscular Fat, Fat Melting Point, Omega-3 Long-Chain Polyunsaturated Fatty Acids and Meat Eating Quality in Tattykeel Australian White MARGRA Lamb

Weighted genomic prediction for growth and carcass‐related traits in Nelore cattle

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