scholarly journals Carcass composition and selected meat quality traits of Pekin ducks from genetic resources flocks

2019 ◽  
Vol 98 (7) ◽  
pp. 3029-3039 ◽  
Author(s):  
D. Kokoszyński ◽  
R. Wasilewski ◽  
K. Stęczny ◽  
M. Kotowicz ◽  
C. Hrnčar ◽  
...  
2020 ◽  
Author(s):  
Piush Khanal ◽  
Christian Maltecca ◽  
Clint Schwab ◽  
Justin Fix ◽  
Francesco Tiezzi

Abstract BackgroundSwine gut microbiome constitutes a portion of the whole genome and has potential to affect different phenotypes. More recently, research is more directed towards association of gut microbiome and different traits in swine. However, the contribution of microbial composition to the phenotypic variation of meat quality and carcass composition traits in pigs has not been explored yet. The objectives of this study are to estimate the microbiabilities for different meat quality and carcass composition traits; to investigate the impact of intestinal microbiome on heritability estimates; to estimate the correlation between microbial diversity and meat quality and carcass composition traits; and to estimate the microbial correlation between the meat quality and carcass composition traits in a commercial swine population.ResultsThe contribution of the microbiome to carcass composition and meat quality traits was prominent although it varied over time, increasing from weaning to off test for most traits. Microbiability estimates of carcass composition traits were greater than that of meat quality traits. Among all of the traits analyzed, belly weight had higher microbiability estimate (0.29 ± 0.04). Adding microbiome information did not affect the estimates of genomic heritability of meat quality traits but affected the estimates of carcass composition traits. Fat depth had greater decrease (10%) in genomic heritability. High microbial correlations were found among several traits. This suggested that genomic correlation was partially contributed by genetic similarity of microbiome composition.ConclusionsResults indicate that better understanding of microbial composition could aid the improvement of complex traits, particularly the carcass composition traits in swine by inclusion of microbiome information in the genetic evaluation process.


2015 ◽  
Vol 42 (9) ◽  
pp. 1403-1407 ◽  
Author(s):  
Lupei Zhang ◽  
Hongyan Ren ◽  
Jiuguang Yang ◽  
Qianfu Gan ◽  
Fuping Zhao ◽  
...  

2009 ◽  
Vol 8 (sup3) ◽  
pp. 98-100 ◽  
Author(s):  
Kresimir Salajpal ◽  
Marija Dikic ◽  
Danijel Karolyi ◽  
Zlatko Janjecic ◽  
Ivan Juric

2000 ◽  
Vol 32 (2) ◽  
pp. 165 ◽  
Author(s):  
Pascale Le Roy ◽  
Jean-Michel Elsen ◽  
Jean-Claude Caritez ◽  
André Talmant ◽  
Hervé Juin ◽  
...  

2019 ◽  
Author(s):  
Piush Khanal ◽  
Christian Maltecca ◽  
Clint Schwab ◽  
Justin Fix ◽  
Francesco Tiezzi

AbstractThe impact of gut microbiome composition was investigated at different stages of production (Wean, Mid-test, and Off-test) on meat quality and carcass composition traits of 1,123 three-way-crossbred pigs. Data were analyzed using linear mixed models which included the fixed effects of dam line, contemporary group and gender as well as the random effects of pen, animal and microbiome information at different stages. The contribution of the microbiome to all traits was prominent although it varied over time, increasing from weaning to Off-test for most traits. Microbiability estimates of carcass composition traits were greater compared to meat quality traits. Adding microbiome information did not affect the estimates of genomic heritability of meat quality traits but affected the estimates of carcass composition traits. High microbial correlations were found among different traits, particularly with traits related to fat deposition with decrease in genomic correlation up to 20% for loin weight and belly weight. Decrease in genomic heritabilities and genomic correlations with the inclusion of microbiome information suggested that genomic correlation was partially contributed by genetic similarity of microbiome composition.


Author(s):  
SiRan Ding ◽  
GuangSheng Li ◽  
SiRui Chen ◽  
Feng Zhu ◽  
JinPing Hao ◽  
...  

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