Polymorphism of coupled indels in porcine TNNC2 alters its transcript splicing and is associated with meat quality traits

2022 ◽  
Author(s):  
Tingting Ma ◽  
Yan Liu ◽  
Xingyu Wei ◽  
Qianjin Xue ◽  
Zhiwei Zheng ◽  
...  
2013 ◽  
Vol 38 (1) ◽  
pp. 64-68
Author(s):  
Ji ZHU ◽  
Jian LIU ◽  
Jian-bang SUN ◽  
Shi-liu YANG ◽  
Jing-ru LI ◽  
...  

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Simone Savoia ◽  
Andrea Albera ◽  
Alberto Brugiapaglia ◽  
Liliana Di Stasio ◽  
Alessio Cecchinato ◽  
...  

Abstract Background The possibility of assessing meat quality traits over the meat chain is strongly limited, especially in the context of selective breeding which requires a large number of phenotypes. The main objective of this study was to investigate the suitability of portable infrared spectrometers for phenotyping beef cattle aiming to genetically improving the quality of their meat. Meat quality traits (pH, color, water holding capacity, tenderness) were appraised on rib eye muscle samples of 1,327 Piemontese young bulls using traditional (i.e., reference/gold standard) laboratory analyses; the same traits were also predicted from spectra acquired at the abattoir on the intact muscle surface of the same animals 1 d after slaughtering. Genetic parameters were estimated for both laboratory measures of meat quality traits and their spectra-based predictions. Results The prediction performances of the calibration equations, assessed through external validation, were satisfactory for color traits (R2 from 0.52 to 0.80), low for pH and purge losses (R2 around 0.30), and very poor for cooking losses and tenderness (R2 below 0.20). Except for lightness and purge losses, the heritability estimates of most of the predicted traits were lower than those of the measured traits while the genetic correlations between measured and predicted traits were high (average value 0.81). Conclusions Results showed that NIRS predictions of color traits, pH, and purge losses could be used as indicator traits for the indirect genetic selection of the reference quality phenotypes. Results for cooking losses were less effective, while the NIR predictions of tenderness were affected by a relatively high uncertainty of estimate. Overall, genetic selection of some meat quality traits, whose direct phenotyping is difficult, can benefit of the application of infrared spectrometers technology.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Giovanni Bittante ◽  
Simone Savoia ◽  
Alessio Cecchinato ◽  
Sara Pegolo ◽  
Andrea Albera

AbstractSpectroscopic predictions can be used for the genetic improvement of meat quality traits in cattle. No information is however available on the genetics of meat absorbance spectra. This research investigated the phenotypic variation and the heritability of meat absorbance spectra at individual wavelengths in the ultraviolet–visible and near-infrared region (UV–Vis-NIR) obtained with portable spectrometers. Five spectra per instrument were taken on the ribeye surface of 1185 Piemontese young bulls from 93 farms (13,182 Herd-Book pedigree relatives). Linear animal model analyses of 1481 single-wavelengths from UV–Vis-NIRS and 125 from Micro-NIRS were carried out separately. In the overlapping regions, the proportions of phenotypic variance explained by batch/date of slaughter (14 ± 6% and 17 ± 7%,), rearing farm (6 ± 2% and 5 ± 3%), and the residual variances (72 ± 10% and 72 ± 5%) were similar for the UV–Vis-NIRS and Micro-NIRS, but additive genetics (7 ± 2% and 4 ± 2%) and heritability (8.3 ± 2.3% vs 5.1 ± 0.6%) were greater with the Micro-NIRS. Heritability was much greater for the visible fraction (25.2 ± 11.4%), especially the violet, blue and green colors, than for the NIR fraction (5.0 ± 8.0%). These results allow a better understanding of the possibility of using the absorbance of visible and infrared wavelengths correlated with meat quality traits for the genetic improvement in beef cattle.


2018 ◽  
Vol 96 (suppl_3) ◽  
pp. 84-84
Author(s):  
M Abo-Ismail ◽  
J Crowley ◽  
E Akanno ◽  
C Li ◽  
P Stothard ◽  
...  

BMC Genetics ◽  
2012 ◽  
Vol 13 (1) ◽  
pp. 66 ◽  
Author(s):  
Marion T Ryan ◽  
Ruth M Hamill ◽  
Aisling M O’Halloran ◽  
Grace C Davey ◽  
Jean McBryan ◽  
...  

2013 ◽  
Vol 12 (3) ◽  
pp. 3643-3650 ◽  
Author(s):  
Y.T. Hui ◽  
Y.Q. Yang ◽  
R.Y. Liu ◽  
Y.Y. Zhang ◽  
C.J. Xiang ◽  
...  

2011 ◽  
Vol 39 (3) ◽  
pp. 2329-2335 ◽  
Author(s):  
Mu Qiao ◽  
Hua-Yu Wu ◽  
Ling Guo ◽  
Shu-Qi Mei ◽  
Peng-Peng Zhang ◽  
...  

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