Immunity traits in pigs: substantial genetic variation and limited covariation

Abstract

Background: Increasing robustness via improvement of resistance to pathogens is a major selection objective in livestock breeding. As resistance traits are difficult or impossible to measure directly, potential indirect criteria are measures of immune traits (ITs). Our underlying hypothesis is that levels of ITs with no focus on specific pathogens define an individual's immunocompetence and thus predict response to pathogens in general. Since variation in ITs depends on genetic, environmental and probably epigenetic factors, our aim was to estimate the relative importance of genetics. In this report, we present a large genetic survey of innate and adaptive ITs in pig families bred in the same environment.

Methodology/principal findings: Fifty four ITs were studied on 443 Large White pigs vaccinated against Mycoplasma hyopneumoniae and analyzed by combining a principal component analysis (PCA) and genetic parameter estimation. ITs include specific and non specific antibodies, seric inflammatory proteins, cell subsets by hemogram and flow cytometry, ex vivo production of cytokines (IFNα, TNFα, IL6, IL8, IL12, IFNγ, IL2, IL4, IL10), phagocytosis and lymphocyte proliferation. While six ITs had heritabilities that were weak or not significantly different from zero, 18 and 30 ITs had moderate (0.1<h2≤0.4) or high (h2>0.4) heritability values, respectively. Phenotypic and genetic correlations between ITs were weak except for a few traits that mostly include cell subsets. PCA revealed no cluster of innate or adaptive ITs.

Conclusions/significance: Our results demonstrate that variation in many innate and adaptive ITs is genetically controlled in swine, as already reported for a smaller number of traits by other laboratories. A limited redundancy of the traits was also observed confirming the high degree of complementarity between innate and adaptive ITs. Our data provide a genetic framework for choosing ITs to be included as selection criteria in multitrait selection programmes that aim to improve both production and health traits.

Figure 1. Position of the measured ITs on a global scheme of immunity.

The global scheme of immunity was obtained using Ingenuity Pathway Analysis software v 8.8 (Ingenuity Systems Inc., USA, http://www.ingenuity.com/). Cellular subsets and membrane proteins considered in this study are in yellow and cell activity traits in green. For each trait, the heritability estimate rounded to one decimal place is shown in red.

Figure 2. Normed PCA on 32 ITs.

A. Histogram of eigenvalues. The five first components, which explain more than 50% of the total variance, are in red. B. Plot of the Bayesian Information Criterion (BIC) calculated with different models according to number of clusters. Six models are compared: EII (spherical with equal volume and equal shape), VII (spherical with variable volume and equal shape), EEI (diagonal with equal shape and equal volume), VEI (diagonal with variable shape and equal volume), EVI (diagonal with equal shape and variable volume), VVI (diagonal with variable shape and variable volume). C. First factorial plan (1: first component, 2: second component) with three clusters identified by multivariate normal mixture modelling and model-based clustering taking into account the 32 components (clusters K1, K2 and K3 are in blue, green and red, respectively).

Figure 3. Heatmap of the genetic correlations between 32 ITs.

The correspondence between colour scale and genetic correlation levels are presented on the right-hand side of the heatmap.