Genome-wide association study for meat tenderness in beef cattle identifies patterns of the genetic contribution in different post-mortem stages

Fonseca, Pablo Augusto de Souza; Caldwell, Tim; Mandell, I. B.; Wood, Katharine M; Cánovas, Ángela

doi:10.1016/j.meatsci.2022.108733

Cited by 5 publications

(3 citation statements)

References 108 publications

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“…The N < < P situation applies to all traits, but for WBSF this problem is more pronounced. In addition, WBSF may have had some genomic regions of higher effect on total genetic variance, as demonstrated in GWAS studies (Braz et al, 2019;Fonseca et al, 2022).…”

Section: Predictive Ability Using Mbmentioning

confidence: 93%

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 Ability Using Mbmentioning

confidence: 93%

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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“…Since GWASs are ideal for discovering genes that encode complex traits and their mechanisms [ 5 ], they have been widely conducted in recent years to identify relationships between genomic variants and economically significant traits in dairy cattle populations. As a result, a large number of candidate genes and quantitative genomic areas were detected to be associated with economically important traits, including milk-related traits, fertility, growth, and meat and carcass quality [ 3 , 4 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. GWASs conducted in various cattle breeds utilized different SNP chip platforms and were focused on the identification of potential candidate genes and genomic regions responsible for milk production traits including milk yield (MY), fat yield (FY), and protein yield (PY).…”

Section: Introductionmentioning

confidence: 99%

Genome-Wide Analysis of Milk Production Traits and Selection Signatures in Serbian Holstein-Friesian Cattle

Ristanic,

Zorc,

Glavinic

et al. 2024

Animals

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To improve the genomic evaluation of milk-related traits in Holstein-Friesian (HF) cattle it is essential to identify the associated candidate genes. Novel SNP-based analyses, such as the genetic mapping of inherited diseases, GWAS, and genomic selection, have led to a new era of research. The aim of this study was to analyze the association of each individual SNP in Serbian HF cattle with milk production traits and inbreeding levels. The SNP 60 K chip Axiom Bovine BovMDv3 was deployed for the genotyping of 334 HF cows. The obtained genomic results, together with the collected phenotypic data, were used for a GWAS. Moreover, the identification of ROH segments was performed and served for inbreeding coefficient evaluation and ROH island detection. Using a GWAS, a polymorphism, rs110619097 (located in the intron of the CTNNA3 gene), was detected to be significantly (p < 0.01) associated with the milk protein concentration in the first lactation (adjusted to 305 days). The average genomic inbreeding value (FROH) was 0.079. ROH islands were discovered in proximity to genes associated with milk production traits and genomic regions under selection pressure for other economically important traits of dairy cattle. The findings of this pilot study provide useful information for a better understanding of the genetic architecture of milk production traits in Serbian HF dairy cows and can be used to improve lactation performances in Serbian HF cattle breeding programs.

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“…One of the most powerful tools for analyzing such associations is the genome-wide association study (GWAS). In beef, a large number of GWAS analyses have been reported for different traits such as longitudinal, carcass, fatty acid profiles, meat tenderness and quality, and growth traits [20][21][22][23][24][25][26]. However, GWAS studies on reproductive traits in beef cows are still scarce [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Genetic Components of (Co)variance and Preliminary Genome-Wide Association Study for Reproductive Efficiency in Retinta Beef Cattle

Jiménez¹,

Morales

Menéndez-Buxadera

et al. 2023

Animals

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In this study, we analyzed the variation of reproductive efficiency, estimated as the deviation between the optimal and real parity number of females at each stage of the cow’s life, in 12,554 cows belonging to the Retinta Spanish cattle breed, using classical repeatability and random regression models. The results of the analyses using repeatability model and the random regression model suggest that reproductive efficiency is not homogeneous throughout the cow’s life. The h2 estimate for this model was 0.30, while for the random regression model it increased across the parities, from 0.24 at the first calving to 0.51 at calving number 9. Additionally, we performed a preliminary genome-wide association study for this trait in a population of 252 Retinta cows genotyped using the Axiom Bovine Genotyping v3 Array. The results showed 5 SNPs significantly associated with reproductive efficiency, located in two genomic regions (BTA4 and BTA28). The functional analysis revealed the presence of 5 candidate genes located within these regions, which were previously involved in different aspects related to fertility in cattle and mice models. This new information could give us a better understanding of the genetic architecture of reproductive traits in this species, as well as allow us to accurately select more fertile cows.

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Genome-wide association study for meat tenderness in beef cattle identifies patterns of the genetic contribution in different post-mortem stages

Cited by 5 publications

References 108 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

Genome-Wide Analysis of Milk Production Traits and Selection Signatures in Serbian Holstein-Friesian Cattle

Estimation of the Genetic Components of (Co)variance and Preliminary Genome-Wide Association Study for Reproductive Efficiency in Retinta Beef Cattle

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