Genetic parameters of meat quality, carcass composition, and growth traits in commercial swine

Khanal, Piush; Maltecca, Christian; Schwab, Charles V.; Gray, Kent; Tiezzi, Francesco

doi:10.1093/jas/skz247

Cited by 38 publications

(41 citation statements)

References 46 publications

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“…The distribution of these samples across families, growth stages and sex are in Additional file 3: Table S5. The following meat quality traits were measured on each pig, as described by Khanal et al [6]: intramuscular fat content (IMF), Minolta a* (MINA), Minolta b* (MINB), Minolta L* (MINL), ultimate pH (PH), subjective color score (SCOL), subjective marbling score (SMARB), subjective firmness score (SFIRM), and shearing force (SSF)]. Minolta L*, a* and b* color scores measured the lightness (greater L* indicates a lighter as opposed to darker color), redness (greater a* indicates a redder color as opposed to green), and yellowness (greater b* indicates a yellower color as opposed to blue) and were recorded with a Minolta CR-400 Chroma meter (Minolta Camera Co. Ltd., Osaka, Japan).…”

Section: Animals and Sample Collectionmentioning

confidence: 99%

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Modeling host-microbiome interactions for the prediction of meat quality and carcass composition traits in swine

Khanal

Maltecca

Schwab³

et al. 2020

Genet Sel Evol

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Background: The objectives of this study were to evaluate genomic and microbial predictions of phenotypes for meat quality and carcass traits in swine, and to evaluate the contribution of host-microbiome interactions to the prediction. Data were collected from Duroc-sired three-way crossbred individuals (n = 1123) that were genotyped with a 60 k SNP chip. Phenotypic information and fecal 16S rRNA microbial sequences at three stages of growth (Wean, Mid-test, and Off-test) were available for all these individuals. We used fourfold cross-validation with animals grouped based on sire relatedness. Five models with three sets of predictors (full, informatively reduced, and randomly reduced) were evaluated. 'Full' included information from all genetic markers and all operational taxonomic units (OTU), while 'informatively reduced' and 'randomly reduced' represented a reduced number of markers and OTU based on significance preselection and random sampling, respectively. The baseline model included the fixed effects of dam line, sex and contemporary group and the random effect of pen. The other four models were constructed by including only genomic information, only microbiome information, both genomic and microbiome information, and microbiome and genomic information and their interaction. Results: Inclusion of microbiome information increased predictive ability of phenotype for most traits, in particular when microbiome information collected at a later growth stage was used. Inclusion of microbiome information resulted in higher accuracies and lower mean squared errors for fat-related traits (fat depth, belly weight, intramuscular fat and subjective marbling), objective color measures (Minolta a*, Minolta b* and Minolta L*) and carcass daily gain. Informative selection of markers increased predictive ability but decreasing the number of informatively reduced OTU did not improve model performance. The proportion of variation explained by the host-genome-bymicrobiome interaction was highest for fat depth (~ 20% at Mid-test and Off-test) and shearing force (~ 20% consistently at Wean, Mid-test and Off-test), although the inclusion of the interaction term did not increase the accuracy of predictions significantly. Conclusions: This study provides novel insight on the use of microbiome information for the phenotypic prediction of meat quality and carcass traits in swine. Inclusion of microbiome information in the model improved predictive ability of phenotypes for fat deposition and color traits whereas including a genome-by-microbiome term did not improve prediction accuracy significantly.

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Section: Animals and Sample Collectionmentioning

confidence: 99%

“…S m is the fixed effect m of sires (28 levels), X n is the n th marker/ OTU covariate, β represents the marker/OTU effect, and all other terms were as described previously. The markers (6)…”

Section: Predictive Ability Of Reduced Modelsmentioning

confidence: 99%

Modeling host-microbiome interactions for the prediction of meat quality and carcass composition traits in swine

Khanal

Maltecca

Schwab³

et al. 2020

Genet Sel Evol

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“…Meat quality traits (intramuscular fat content ( IMF ), Minolta a* ( MINA ), Minolta b* ( MINB ), minolta L* ( MINL ), ultimate pH ( PH ), subjective color score ( SCOL ), subjective marbling score ( SMARB ), subjective firmness score ( SFIRM ), shearing force ( SSF )) and carcass composition traits (Belly weight ( BEL ), ham weight ( HAM ), loin weight ( LOIN ), fat depth ( FD ), loin depth ( LD ) and carcass average daily gain ( CADG )) were used for the current analysis. All the traits were measured as described by Khanal et al (2019). A summary of traits used in current analysis is reported in Table 1.…”

Section: Methodsmentioning

confidence: 99%

Microbiability of meat quality and carcass composition traits in swine

Khanal

Maltecca

Schwab³

et al. 2019

Preprint

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The 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.

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“…Pork is a meat rich in lipids, cholesterol, saturated and unsaturated fatty acids. Genetics, sex, age, and dietary management influence fatty acid concentrations, ranging from 8 to 55 % (Khanal et al, 2019).…”

Section: Physicochemical Parametersmentioning

confidence: 99%

Reduction of sodium content in pork coppa: physicochemical, microbiological and sensory evaluation

Domenico

Machado‐Lunkes

Prado

et al. 2021

Sci. agric. (Piracicaba, Braz.)

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Sodium reduction and the substitution of sodium chloride by other salts have been extensively studied in order to produce healthier foods. Coppa is a pork cured product that receives high levels of sodium through the salting process. The objective of this work was to evaluate the impact of reductions in Sodium Chloride (NaCl) , substitution by Potassium Chloride (KCl) and reductions in the re-salting time on the physicochemical, microbiological and sensorial characteristics in pork coppa. Four treatments were applied: T1 with 2 days of salting, 2 days of re-salting and 35 % reduction of NaCl; T2 with 2 days of salting and 3 days of re-salting and 35 % reduction of NaCl; T3 with 2 days of salting and 2 days of re-salting and replacing 35 % of NaCl by KCl; T4 with 2 days of salting and 3 days of re-salting and 35 % replacing NaCl by KCl. Control: standard treatment was applied with 100 % of NaCL and salting time was 2 days and then 5 days to re-salting. The reduction in sodium content in processed pork Coppa produced no microbiological nor physicochemical changes. The reduction in salting together with salting and re-salting time and the partial replacement of NaCl by KCl resulted in reductions from 2,000 mg to 1,600 mg of sodium. Sensory evaluation demonstrated that the reduction in re-salting time was efficient using the ideal profile method which showed that treatments T1 and T2 were efficient in creating a product that meets consumer expectations.

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Genetic parameters of meat quality, carcass composition, and growth traits in commercial swine

Cited by 38 publications

References 46 publications

Modeling host-microbiome interactions for the prediction of meat quality and carcass composition traits in swine

Modeling host-microbiome interactions for the prediction of meat quality and carcass composition traits in swine

Microbiability of meat quality and carcass composition traits in swine

Reduction of sodium content in pork coppa: physicochemical, microbiological and sensory evaluation

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