Genomic prediction ability for feed efficiency traits using different models and pseudo-phenotypes under several validation strategies in Nelore cattle

Brunes, L. C.; Baldi, Fernando; Lopes, Fernando Brito; Narciso, Marcelo Gonçalves; Lôbo, Raysildo Barbosa; Espigolan, Rafael; Costa, M. F. O. e; Magnabosco, C. de U.

doi:10.1016/j.animal.2020.100085

Cited by 12 publications

(7 citation statements)

References 43 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.…”

Section: Resultssupporting

confidence: 84%

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: Resultssupporting

confidence: 84%

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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“…The heritability, genetic, and phenotypic correlations estimates obtained for the studied traits are presented in Table 2. The heritability estimates for RFI (0.23) and DMI (0.31) were moderate and similar to those reported for beef cattle with estimates ranging from 0.17 to 0.28 (RFI) and from 0.23 to 0.40 (DMI) (Barwick et al, 2009; Benfica et al, 2020; Brunes, Baldi, Lopes, Lobo, et al, 2021; Brunes, Baldi, Lopes, Narciso, et al, 2021; de Moraes et al, 2017; de Oliveira, 2019; Grion et al, 2014; Herd & Arthur, 2009; Koch et al, 1963; Robinson & Oddy, 2004; Santana et al, 2014; Silva et al, 2016). In this regard, the heritability estimates obtained herein pointed out that genetic improvement for RFI and DMI can be achieved through selection.…”

Section: Resultssupporting

confidence: 73%

“…Additionally, the direct genetic response per generation to improve RFI (−0.047 kg of DM/day) was lower than the correlated genetic response selecting for DMI (−0.089 kg of DM/day). According to Brunes, Baldi, Lopes, Narciso, et al, 2021; Brunes, Baldi, Lopes, Lobo, et al, 2021, a high selection response for RFI using DMI as selection criterion is expected due to the moderate to high genetic correlation estimate between DMI and RFI and the high estimated heritability for DMI than RFI.…”

Section: Resultsmentioning

confidence: 99%

“…RFI can be defined as the difference between the observed feed intake recorded over a period and the expected feed intake based on the animal's growth rate and maintenance requirement, that is, measured as dry matter intake (DMI) corrected for liveweight and growth rate (Koch et al, 1963). RFI is the most preferred indicator of feed efficiency in beef cattle as the selection to improve growth (Berry & Crowley, 2012, 2013; Brunes, Baldi, Lopes, Lobo, et al, 2021; Brunes, Baldi, Lopes, Narciso, et al, 2021; Crowley et al, 2010; Herd & Arthur, 2009), reproductive (Berry & Crowley, 2013), and carcass‐related traits (Berry & Crowley, 2013; Brunes, Baldi, Lopes, Lobo, et al, 2021; Brunes, Baldi, Lopes, Narciso, et al, 2021; Herd & Arthur, 2009; Torres‐Vázquez et al, 2018) would not affect RFI.…”

Section: Introductionmentioning

confidence: 99%

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Estimates of genetic and phenotypic parameters for feeding behaviour and feed efficiency‐related traits in Nelore cattle

Kava

Peripolli

Brunes

et al. 2023

J Animal Breeding Genetics

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The objective of this study was to obtain (co)variance components, heritability, and genetic and phenotypic correlation estimates for feed efficiency and feed behaviour-related indicator traits. Further, it aimed to predict the direct and correlated responses for feed efficiency traits when selection was applied for feeding behaviour-related traits in Nelore cattle. Phenotypic records (n = 4840) from 125 feed efficiency tests (RFI: Residual feed intake and DMI: Dry matter intake)

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“…Sci. 2021, 8, 329 2 of 18 (0.52 ± 0.04), and feed conversion ratio (0.46 ± 0.05) [6]. The conventional selection scheme for resistance has not been effective against mastitis due to the large number of candidate genes or QTLs related to non-immune factors that influence mastitis and the factors that make resistance to mastitis measurement difficult [4,7,8].…”

Section: Introductionmentioning

confidence: 99%

Genetic Diversity and Population Structure for Resistance and Susceptibility to Mastitis in Braunvieh Cattle

Trujano-Chavez

Sánchez-Ramos

Pérez‐Rodríguez

et al. 2021

Veterinary Sciences

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Mastitis is a disease that causes significant economic losses, since resistance to mastitis is a difficult trait to be improved due to its multifactorial occurrence. Therefore, our objective was to characterize a Mexican Braunvieh cattle population for genetic resistance and susceptibility to mastitis. We used 66 SNP markers for 45 candidate genes in 150 animals. The average heterozygosity was 0.445 ± 0.076, a value higher than those reported for some European breeds. The inbreeding coefficient was slightly negative for resistance to subclinical (−0.058 ± 0.055) and clinical (−0.034 ± 0.076) mastitis, possibly due to low selection for the immunological candidate genes that influence these traits. The genotypic profiles for the candidate loci per K-means group were obtained, as well as the group distribution through the graphics of the principal component analysis. The genotypic profiles showed high genetic diversity among groups. Resistance to clinical mastitis had the lowest presence of the heterozygous genotypes. Although the percentage of highly inbred animals (>50%) is up to 13.3%, there are highly heterozygous groups in terms of the studied traits, a favorable indicator of the presence of genetic diversity. The results of this study constitute evidence of the genetic potential of the Mexican Braunvieh population to improve mastitis-related traits.

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Genomic prediction ability for feed efficiency traits using different models and pseudo-phenotypes under several validation strategies in Nelore cattle

Cited by 12 publications

References 43 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

Estimates of genetic and phenotypic parameters for feeding behaviour and feed efficiency‐related traits in Nelore cattle

Genetic Diversity and Population Structure for Resistance and Susceptibility to Mastitis in Braunvieh Cattle

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