Microbiability of meat quality and carcass composition traits in swine

Khanal, Piush; Maltecca, Christian; Schwab, Charles V.; Fix, Justin; Tiezzi, Francesco

doi:10.1101/833731

Cited by 5 publications

(6 citation statements)

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“…Estimates of variance components, heritabilities, and microbiabilities (proportion of variance explained by the microbiome) for the meat quality and carcass traits are in Additional file 5: Tables S9 and S10 [see Additional file 5: Tables S9 and S10]. Estimates of heritabilities and microbiabilities for these data were previously reported by Khanal et al [22]. Briefly, the estimate of the proportion of variance explained by the microbiome was higher for carcass composition traits than for meat quality traits.…”

Section: Variances Explained By G M and Their Interactionmentioning

confidence: 81%

“…Thus, the objectives of this study were: (i) to evaluate genomic and microbial predictions of phenotypes for meat quality and carcass traits, and (ii) to evaluate the effect of including a host-microbiome interaction for the prediction of these traits in swine. This study is a follow-up of the study by Khanal et al [22] that used data from the same experiment. In this paper, we expanded the results on the estimates of microbiability and microbial correlations among carcass quality traits by focusing on prediction of carcass quality with the inclusion of genomic and microbial information as well as their interaction.…”

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confidence: 98%

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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: Variances Explained By G M and Their Interactionmentioning

confidence: 81%

mentioning

confidence: 98%

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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“…Evidence of host-microbiome interaction has been reported in humans, dairy cattle, and mice 47,48 . Furthermore, the possible exploitation of fecal microbial heritability and host genetics in swine selection programs have also been discussed previously 49,50 . To further explore the genomic architecture of host microbiome control, we performed a genome-wide association study.…”

Section: Discussionmentioning

confidence: 99%

Heritability and genome-wide association of swine gut microbiome features with growth and fatness parameters

Bergamaschi

Maltecca

Schillebeeckx³

et al. 2020

Sci Rep

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fix 3 & francesco tiezzi 1 ✉ Despite recent efforts to characterize longitudinal variation in the swine gut microbiome, the extent to which a host's genome impacts the composition of its gut microbiome is not yet well understood in pigs. The objectives of this study were: i) to identify pig gut microbiome features associated with growth and fatness, ii) to estimate the heritability of those features, and, iii) to conduct a genome-wide association study exploring the relationship between those features and single nucleotide polymorphisms (SNP) in the pig genome. A total of 1,028 pigs were characterized. Animals were genotyped with the Illumina PorcineSNP60 Beadchip. Microbiome samples from fecal swabs were obtained at weaning (Wean), at mid-test during the growth trial (MidTest), and at the end of the growth trial (OffTest). Average daily gain was calculated from birth to week 14 of the growth trial, from weaning to week 14, from week 14 to week 22, and from week 14 to harvest. Backfat and loin depth were also measured at weeks 14 and 22. Heritability estimates (±SE) of Operational Taxonomic Units ranged from 0.025 (±0.0002) to 0.139 (±0.003), from 0.029 (±0.003) to 0.289 (±0.004), and from 0.025 (±0.003) to 0.545 (±0.034) at Wean, MidTest, and OffTest, respectively. Several SNP were significantly associated with taxa at the three time points. These SNP were located in genomic regions containing a total of 68 genes. This study provides new evidence linking gut microbiome composition with growth and carcass traits in swine, while also identifying putative host genetic markers associated with significant differences in the abundance of several prevalent microbiome features.

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“…Microbiota studies have hugely benefitted from these NGS techniques, as culture-free sequencing allows obtaining a lot more information from microbiome communities. Gut microbiota has become a key focus in animal breeding industry, as well as in human health, since gut microbiota and host are linked in a bidirectional way; first, diet composition and other environmental elements, as well as animal genetics, might modify microbial population structure [12]; second, microbiota has been reported to have an impact on animal physiology, on multiple animal productive traits and also on the final products' quality and composition [13,14]. This intricate correlation network makes gut microbiota composition a key factor for understanding the relationships between genotype, phenotype and environment.…”

Section: Introductionmentioning

confidence: 99%

Influence of genetic background and dietary oleic acid on gut microbiota composition in Duroc and Iberian pigs

et al. 2021

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Background Phenotypic variability for productive and meat quality traits has been largely studied in Iberian pigs, especially in genetic selection and nutritional experiments. Complex interactions among genetic background, diet composition and gut microbiota hinder the correct assessment of each factor’s contribution on phenotypes. In order to disentangle these interactions, we evaluated changes in gut microbiota composition comparing 48 Iberian and Duroc pigs fed diets with different energy source (standard diet with carbohydrates vs sunflower oil-enriched diet with high oleic acid content). Results A higher richness was observed for Iberian pigs (p < 0.05) and compositional analysis was applied for beta-diversity, differential abundance and pairwise log-ratio analyses. We found significant differences in overall microbiota composition between breeds, and also between diets inside breeds, to a lesser extent. Differential abundance analysis revealed that Duroc animals have more proportion of Actinobacteria and Prevotella, while Iberian replace those microorganisms with other more variable taxa. According to dietary differences, high-oleic fed animals were richer in Prevotella. We also found microbial ratios capable of separating animals by breeds and diets, mostly related to Actinobacteria. Conclusion This study reveals that both genetic background and diet composition might have a relevant impact in gut microbiota composition. The application of compositional data analysis has facilitated the identification of microorganisms and ratios as possibly related to metabolic changes due to genetic background and, to a lower extent, to dietary changes. This may lead to a relevant progress in the knowledge of interactions between pig genetics, environment and gut microbiota.

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Microbiability of meat quality and carcass composition traits in swine

Cited by 5 publications

References 51 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

Heritability and genome-wide association of swine gut microbiome features with growth and fatness parameters

Influence of genetic background and dietary oleic acid on gut microbiota composition in Duroc and Iberian pigs

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