Exploring the assumptions underlying genetic variation in host nematode resistance(Open Access publication)

Abstract

-The wide range of genetic parameter estimates for production traits and nematode resistance in sheep obtained from field studies gives rise to much speculation. Using a mathematical model describing host -parasite interactions in a genetically heterogeneous lamb population, we investigated the consequence of: (i) genetic relationships between underlying growth and immunological traits on estimated genetic parameters for performance and nematode resistance, and (ii) alterations in resource allocation on these parameter estimates. Altering genetic correlations between underlying growth and immunological traits had large impacts on estimated genetic parameters for production and resistance traits. Extreme parameter values observed from field studies could only be reproduced by assuming genetic relationships between the underlying input traits. Altering preferences in the resource allocation had less pronounced effects on the genetic parameters for the same traits. Effects were stronger when allocation shifted towards growth, in which case worm burden and faecal egg counts increased and genetic correlations between these resistance traits and body weight became stronger. Our study has implications for the biological interpretation of field data, and for the prediction of selection response from breeding for nematode resistance. It demonstrates the profound impact that moderate levels of pleiotropy and linkage may have on observed genetic parameters, and hence on outcomes of selection for nematode resistance. gastro-intestinal parasites

Modelling gastrointestinal parasitism infection in a sheep flock over two reproductive seasons:in silicoexploration and sensitivity analysis

“…Compared to previous models, our model also has the advantage of including between-animal variations that simulate a flock rather than a single animal. In addition, the previously published model from which our model is derived (Vagenas et al 2007a;Laurenson et al 2011Laurenson et al , 2012a is the only one which allows simulating a flock more or less resistant to gastrointestinal parasites and enables to test for the impact of breeding for resistance (Vagenas et al 2007c;Doeschl-Wilson et al 2008;Laurenson et al 2012a, b). However, the uniqueness of our model compared with these (Vagenas et al 2007a;Laurenson et al 2011Laurenson et al , 2012a is reflected by its capacity to follow a flock over several years and to estimate the impact of the selection of resistant sires at the flock level.…”

“…In this case, no protein was allocated to immunity or production (growth and wool or pregnancy/lactation) and protein loss was considered equal to the estimated potential protein loss caused by larvae and adult worms. On the other hand, if protein was available in excess of maintenance requirements, it was allocated to immunity and production traits proportionally to the requirements of growing lambs (Doeschl-Wilson et al 2008;Laurenson et al 2011).…”

“…However, although the immune parameters of the model were normally distributed, as individual animals developed and expressed immunity, the output immune traits, for example the WB and fecal egg count (FEC), became skewed and 'overdispersed' with a small number of animals having an extremely high FEC and WB (Bishop and Stear, 1997). According to Doeschl-Wilson et al (2008), the traits associated with immune acquisition were assumed to be strongly genetically and phenotypically correlated (r = +0·5). However, all other traits were assumed to be uncorrelated.…”

The role of mathematical models of host–pathogen interactions for livestock health and production – a review

“…The model, and its extension to a population level, satisfied many of the functions listed in Table 1 (Vagenas et al, 2007a(Vagenas et al, , 2007bDoeschl-Wilson et al, 2008). By describing all biological processes in a nutritional context, the interactive effects of host genetics and nutrition on gastrointestinal parasitism, which are difficult to study experimentally, could be explored in silico (Table 1, points 1, 5, 6 and 7).…”

“…different pathogen challenge, nutrient availability or diet composition) affect nutrient intake and allocation and consequently also the observable phenotypes related to health, reproduction and production (e.g. van der Waaij et al, 2000;van der Waaij, 2004;Vagenas et al, 2007aVagenas et al, , 2007bVagenas et al, and 2008Doeschl-Wilson et al, 2008 and2009a). In particular, the models aim to shed light on the ongoing debate about how genetic selection influences the relationship between disease resistance and (re-) production ( Knap and Bishop, 2000;Houdijk and Bü nger, 2006;DoeschlWilson et al, 2009b).…”