ImmuneDEX: a strategy for the genetic improvement of immune competence in Australian Angus cattle

On the holobiont 'predictome' of immunocompetence in pigs

BACKGROUND

Gut microbial composition plays an important role in numerous traits, including immune response. Integration of host genomic information with microbiome data is a natural step in the prediction of complex traits, although methods to optimize this are still largely unexplored. In this paper, we assess the impact of different modelling strategies on the predictive capacity for six porcine immunocompetence traits when both genotype and microbiota data are available.

METHODS

We used phenotypic data on six immunity traits and the relative abundance of gut bacterial communities on 400 Duroc pigs that were genotyped for 70 k SNPs. We compared the predictive accuracy, defined as the correlation between predicted and observed phenotypes, of a wide catalogue of models: reproducing kernel Hilbert space (RKHS), Bayes C, and an ensemble method, using a range of priors and microbial clustering strategies. Combined (holobiont) models that include both genotype and microbiome data were compared with partial models that use one source of variation only.

RESULTS

Overall, holobiont models performed better than partial models. Host genotype was especially relevant for predicting adaptive immunity traits (i.e., concentration of immunoglobulins M and G), whereas microbial composition was important for predicting innate immunity traits (i.e., concentration of haptoglobin and C-reactive protein and lymphocyte phagocytic capacity). None of the models was uniformly best across all traits. We observed a greater variability in predictive accuracies across models when microbiability (the variance explained by the microbiome) was high. Clustering microbial abundances did not necessarily increase predictive accuracy.

CONCLUSIONS

Gut microbiota information is useful for predicting immunocompetence traits, especially those related to innate immunity. Modelling microbiome abundances deserves special attention when microbiability is high. Clustering microbial data for prediction is not recommended by default.

Targeting improved resilience in Merino sheep - Correlations between immune competence and health and fitness traits

Resilience can be defined as the ability of an animal to remain productive in the face of diverse environmental challenges. Several factors contribute to an animal's resilience including its ability to resist disease, cope with climatic extremes and respond to stressors. Immune competence, a proxy trait for general disease resistance, is expected to contribute to an animal's resilience. This research aimed to develop a practical method to assess immune competence in Merino sheep which would not restrict the future sale of tested animals, and to estimate genetic parameters associated with the novel trait. We also aimed to explore associations between immune competence and other industry-relevant disease resistance and fitness-related traits and to assess the ability of immune competence phenotypes to predict health outcomes. Here, the ability of Merino wethers (n = 1 339) to mount both an antibody-mediated and cell-mediated immune response was used to define their immune competence phenotype. For that purpose, antigens in a commercial vaccine were administered at the commencement of weaning and their responses were assessed. Univariate sire models were used to estimate variance components and heritabilities for immune competence and its component traits. Bivariate sire models were used to estimate genetic correlations between immune competence and a range of disease resistance and fitness-related traits. The heritability of immune competence and its component traits, antibody-mediated immune response and cell-mediated immune response were estimated at 0.49 ± 0.14, 0.52 ± 0.14 and 0.36 ± 0.11, respectively. Immune competence was favourably genetically correlated with breech flystrike incidence (-0.44 ± 0.39), worm egg count (-0.19 ± 0.23), dag score (-0.26 ± 0.31) and fitness compromise (-0.35 ± 0.24) but not fleece rot (0.17 ± 0.23). Results suggest that selection for immune competence has the potential to improve the resilience of Merino sheep; however, due to the large standard errors associated with correlation estimates reported here, further studies will be required in larger populations to validate associations between immune competence and disease resistance and fitness traits in Australian Merino sheep.

Leveraging host-genetics and gut microbiota to determine immunocompetence in pigs

Background

The gut microbiota influences host performance playing a relevant role in homeostasis and function of the immune system. The aim of the present work was to identify microbial signatures linked to immunity traits and to characterize the contribution of host-genome and gut microbiota to the immunocompetence in healthy pigs.

Results

To achieve this goal, we undertook a combination of network, mixed model and microbial-wide association studies (MWAS) for 21 immunity traits and the relative abundance of gut bacterial communities in 389 pigs genotyped for 70K SNPs. The heritability (h²; proportion of phenotypic variance explained by the host genetics) and microbiability (m²; proportion of variance explained by the microbial composition) showed similar values for most of the analyzed immunity traits, except for both IgM and IgG in plasma that was dominated by the host genetics, and the haptoglobin in serum which was the trait with larger m² (0.275) compared to h² (0.138). Results from the MWAS suggested a polymicrobial nature of the immunocompetence in pigs and revealed associations between pigs gut microbiota composition and 15 of the analyzed traits. The lymphocytes phagocytic capacity (quantified as mean fluorescence) and the total number of monocytes in blood were the traits associated with the largest number of taxa (6 taxa). Among the associations identified by MWAS, 30% were confirmed by an information theory network approach. The strongest confirmed associations were between Fibrobacter and phagocytic capacity of lymphocytes (r = 0.37), followed by correlations between Streptococcus and the percentage of phagocytic lymphocytes (r = -0.34) and between Megasphaera and serum concentration of haptoglobin (r = 0.26). In the interaction network, Streptococcus and percentage of phagocytic lymphocytes were the keystone bacterial and immune-trait, respectively.

Conclusions

Overall, our findings reveal an important connection between gut microbiota composition and immunity traits in pigs, and highlight the need to consider both sources of information, host genome and microbial levels, to accurately characterize immunocompetence in pigs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42523-021-00138-9.

Bias, dispersion, and accuracy of genomic predictions for feedlot and carcase traits in Australian Angus steers

Background

Improving feedlot performance, carcase weight and quality is a primary goal of the beef industry worldwide. Here, we used data from 3408 Australian Angus steers from seven years of birth (YOB) cohorts (2011–2017) with a minimal level of sire linkage and that were genotyped for 45,152 SNPs. Phenotypic records included two feedlot and five carcase traits, namely average daily gain (ADG), average daily dry matter intake (DMI), carcase weight (CWT), carcase eye muscle area (EMA), carcase Meat Standard Australia marbling score (MBL), carcase ossification score (OSS) and carcase subcutaneous rib fat depth (RIB). Using a 7-way cross-validation based on YOB cohorts, we tested the quality of genomic predictions using the linear regression (LR) method compared to the traditional method (Pearson’s correlation between the genomic estimated breeding value (GEBV) and its associated adjusted phenotype divided by the square root of heritability); explored the factors, such as heritability, validation cohort, and phenotype that affect estimates of accuracy, bias, and dispersion calculated with the LR method; and suggested a novel interpretation for translating differences in accuracy into phenotypic differences, based on GEBV quartiles (Q1Q4).

Results

Heritability (h²) estimates were generally moderate to high (from 0.29 for ADG to 0.53 for CWT). We found a strong correlation (0.73, P-value < 0.001) between accuracies using the traditional method and those using the LR method, although the LR method was less affected by random variation within and across years and showed a better ability to discriminate between extreme GEBV quartiles. We confirmed that bias of GEBV was not significantly affected by h², validation cohort or trait. Similarly, validation cohort was not a significant source of variation for any of the GEBV quality metrics. Finally, we observed that the phenotypic differences were larger for higher accuracies.

Conclusions

Our estimates of h² and GEBV quality metrics suggest a potential for accurate genomic selection of Australian Angus for feedlot performance and carcase traits. In addition, the Q1Q4 measure presented here easily translates into possible gains of genomic selection in terms of phenotypic differences and thus provides a more tangible output for commercial beef cattle producers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-021-00673-8.

ImmuneDEX: updated genomic estimates of genetic parameters and breeding values for Australian Angus cattle

Context Immune competence is a proxy trait for general disease resistance and is based on combined measures of an animal’s ability to mount both a cell-mediated immune response (Cell-IR) and an antibody-mediated immune response (Ab-IR). On the basis of previously described arithmetic, we combined these measures into a single proxy trait for immune competence, named ImmuneDEX (IDEX). Aims Using a population of 3715 Australian Angus steers (n = 2395) and heifers (n = 1320) with genotypes for 45 364 single-nucleotide polymorphisms, we provide the latest genomic estimates of heritability and genetic correlations for IDEX and the components Cell-IR and Ab-IR immune competence phenotypes. Accuracy and bias of genomic predictions of breeding values are also presented and discussed. Methods Measures of Cell-IR, Ab-IR and IDEX were analysed jointly in a tri-variate genomic restricted maximum-likelihood model that contained the fixed effects of contemporary group with 80 levels, the linear covariates of age at measurement and change in skin thickness at control site, and the random polygenic (genomic estimated breeding value, GEBV) and residual effects. Following Method LR procedures, we estimate accuracy, bias and dispersion of genomic predictions using a cross-validation scheme based on five year-of-birth cohorts. Key results We report genomic restricted maximum-likelihood model estimates of heritability of 0.247 ± 0.040 for Cell-IR, 0.326 ± 0.059 for Ab-IR, 0.275 ± 0.046 for IDEX. While a small positive genetic correlation (rg) was estimated between Cell-IR and Ab-IR (rg = 0.138 ± 0.095), strongly positive estimates were obtained between IDEX and Cell-IR (rg = 0.740 ± 0.044) and between IDEX and Ab-IR (rg = 0.741 ± 0.036). Averaged across the five validation sets, the accuracy of GEBV for Cell-IR, Ab-IR and IDEX was 0.405, 0.443 and 0.411 respectively. Also, some significant bias or dispersion can be expected depending on the cohort used as the validation population. Conclusions Consistent with previous findings, immune competence phenotypes are moderately heritable and accurate GEBV can be generated to allow the selection of cattle with an improved ability to mount a general immune response. Implications Our analyses suggest that ImmuneDEX will provide a tool to underpin long-term genetic strategies aimed at improving the immune competence of Australian Angus cattle in production systems, which, in turn, is expected to reduce the incidence of disease and our reliance on antibiotics to treat disease.

Development of Angus SteerSELECT: a genomic-based tool to identify performance differences of Australian Angus steers during feedlot finishing: Phase 1 validation

Context Genomic-based technologies are allowing commercial beef producers to predict the genetic merit of individual animals of unknown pedigree with increased ease and accuracy. Genomic selection tools that can accurately predict the feedlot and carcass performance of steers have the potential to improve profitability for the beef supply chain. Aims To validate the ability of the Angus SteerSELECT genomic product to predict differences in performance of Australian Angus steers, in terms of carcass weight, marbling score, ossification score and carcass value, using a short-fed (100 days) or long-fed (270 days) finishing protocol at a commercial feedlot. Methods A reference population of 2763 Australian Angus steers was used to generate genomic prediction equations for three carcass traits, namely, carcass weight, marbling score and ossification. The accuracy and bias of genomic predictions of breeding values were then evaluated using a validation population of 522 Angus steers, either short- or long-fed at a commercial feedlot, by comparing breeding values to measured phenotypes. The potential economic benefits for feedlot operators when using Angus SteerSELECT were estimated on the basis of the ability of the tool to predict the carcass value of steers in the validation population. Key results The accuracy of genomic predictions of breeding values for carcass weight, marbling score and ossification score were 0.752, 0.723 and 0.734 respectively. When steers were ranked in quartiles for predicted carcass value, calculated using genomic predictions of breeding values for carcass weight and marbling score, the least-square mean carcass value for steers in each quartile, from bottom 25% predicted performers to top 25% predicted performers, were estimated at A$1794, A$1977, A$2021 and A$2148 for short-fed steers and A$3546, A$3780, A$3864 and A$4258 for long-fed steers. Differences in the carcass value least-squares mean between the bottom and top quartile were highly significant (P &lt; 0.001) for both short-fed and long-fed steers. Conclusions Genomic prediction equations used in Angus SteerSELECT can predict differences in carcass weight, marbling score, ossification score and carcass value in both short-fed and long-fed Australian Angus steers. Implications Genomic selection tools that can predict differences in performance, in terms of growth and carcass characteristics, of commercial feedlot cattle have the potential to significantly increase profitability for the beef supply chain by improving the quality and consistency of the beef products they produce.