# Genetic parameters for milk production and parasite resistance traits in Manech Red Head sheep

Sophie Aguerre

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Sophie Aguerre. Genetic parameters for milk production and parasite resistance traits in Manech Red Head sheep. 2016, 26 p. hal-01605789

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Master 2 PRedictive and Integrative AniMal biology (PRIAM)

# Genetic parameters for milk production and parasite resistance traits in Manech Red Head sheep

Sophie Aguerre

Supervisor :

Dr Carole Moreno Romieux
INRA UMR1388 GenPhySE
Génétique, Physiologie, Systèmes d'Elevage
B.P. 52627 31326 Castanet Tolosan
Acknowledgments

I would like to take this opportunity to thank INRA and in particular GenPhySE for welcoming me during this internship.

I would like to thank Carole Moreno-Romieux, who supervised my work, for the time she awarded to me and her advice during this internship and the writing of this report.

My thanks also go to Jean-Michel Astruc (IDELE) who provided the data and Andrés Legarra (INRA) who helped me with the programs for the attention they have given to my work.

I also thank all the members of the breeding company CDEO, in particular Claude Soulas and Francis Fidelle for their warm welcome during my visit to Ordiap.

I also would like to thank INRA, particularly Carole Moreno-Romieux, Zulma Vitezica and Andrés Legarra, who gave me the opportunity to attend a course at University of Georgia.

Finally I would like to thank all the members of INRA, the interns and Ph.D. students for making this internship so pleasant.
**Abstract**

Gastrointestinal parasites (GIN) are one of the main issue in sheep breeding responsible for substantial economic losses. The use of anthelminthic treatments to control GIN has led to widespread development of resistance in the parasite populations. Alternative control strategies are now required, among which genetic selection of sheep with enhanced resistance to parasites. It requires the estimation of genetic variation for these traits and correlations with other traits of interest. The objective of this study was to estimate genetic parameters for faecal egg count (FEC), packed cell volume (PCV) and the three following milk production traits : quantity of milk fat and protein contents in Manech Red Head (MRH) breed. Rams were experimentally challenged with *H.contortus* fourth stage larvae twice. Heritabilities estimated for FEC and PCV were not significantly different from 0 in first infestation. But they were moderate in second infestation, suggesting that parasite resistance can be selected for in Manech Red Head sheep. The correlation between fat content and FEC in first infestation was positive and significant. It also tended to be positive between fat content and FEC in second infestation. But the correlations were not significant between parasite resistance traits and milk quantity and protein content.

**Key words :** gastrointestinal parasites, fecal egg count, milk production traits, genetic parameters, sheep

**Introduction**

Gastrointestinal parasites (GIN) are one of the major constraints in sheep breeding throughout the world. They are responsible for substantial economic losses estimated at several million dollars per year because of a reduction in performance, treatment costs and extra work and can lead to the death of the severely affected animals (McLeod, 1995; Nieuwhof and Bishop, 2005; Vlassoff and McKenna, 1994).

Anthelmintic products have often been used as the main means of control against gastrointestinal nematodes. This has led to a widespread development of resistance to the main anthelmintics in parasite populations. Their use has also been questioned for a few years because of their impact on micro-fauna and the possible presence of residues in animal products. An efficient and sustainable control of GIN parasites requires now the integration of several methods such as nutritional supplementation, grazing management, breeding parasite-resistant animals and a reasonable use of anthelmintics (Getachew et al., 2007; Hoste and Torres-Acosta, 2011).

Many studies reported the existence of genetic differences both among and within breeds which make breeding for resistance possible. The number of parasite eggs in the faeces called fecal egg count (FEC) is the most commonly used indicator of host resistance to GIN nematodes, while packed cell volume (PCV) is an indicator of host resilience (Jacquiet *et al.*, 2009). Estimations of heritability for FEC reported in the literature were generally moderate (Bishop and Morris, 2007). Resistance to different parasites appeared to be highly correlated (Gruner *et al.*, 2004a). This is important
because in natural infestation, animals ingest different species of parasites and the proportion of the species vary with season. High correlations between experimental and natural infestations were also showed (around 0.9) (Gruner et al., 2004b). In natural infestation, the animals may ingest different doses of parasites. Experimental infestation allows us to perform a challenge with controlled infestation doses and thus distinguish resistant and sensitive animals. These results suggest that a selection for parasite resistance would be efficient. Resistant sheep excrete fewer eggs which lead to a reduced contamination of the pastures. Consequently, breeding resistant animals would decrease parasite infection of resistant and susceptible animals grazing the same pasture (Bishop 2011; Moreno et al., 2015). FEC is the best phenotype to select resistance but a routine collection of this phenotypic indicator will be difficult to implement and costly for a large number of animals (Moreno et al., 2015). Another possibility is to use information based on molecular markers. But no major gene has been identified so far. Many studies on the detection of quantitative trait loci (QTL) for parasite resistance in sheep have been undertaken. Several regions throughout the genome seem to be involved (Bishop and Morris, 2007) but the results are generally not consistent because of the variety of sheep breeds and parasite species considered.

Before including parasite resistance as a new trait in a selection scheme, we need to estimate the correlations between parasite resistance traits and production and reproduction traits. Then, the impact of selection on parasite resistance can be evaluated. Most studies related to the estimation of these correlations have been carried out in sheep breeds specialised for meat or wool production. No estimate has been published for correlations between parasite resistance traits and milk production traits so far.

In this study, we assessed indicators for parasite resistance traits and milk production traits in the dairy breed Manech Red Head from the Pyrénées-Atlantiques. The oceanic climate of the department with mild temperatures and regular rainfalls is favourable to the development of the parasites. Studies have reported several cases of parasites resistant to benzimidazoles, one of the few anthelmintics which can be used during lactation (Jacquiet et al., 2014). Genetic selection for resistant animals is therefore becoming an important issue for the Manech Red Head breed.

The first aim of this study was to compute the heritability of FEC and packed cell volume as parasite resistance traits and milk quantity, fat and protein content for milk production traits. The second objective was to estimate phenotypic and genetic correlations between these traits.

# Material and methods

## Sampled animals

The phenotyped animals were selection candidate rams of the Manech Red Head (MRH) breed. These rams are tested by progeny-testing using artificial insemination and belong to the CDEO (Ordiap, Pyrénées-Atlantiques, France). They have been
<table><caption>Table 1 : Number of animals by infection doses and phenotyping years.</caption><thead><tr><th rowspan="2">Series</th><th rowspan="2">Phenotyping year</th><th colspan="2">Doses</th><th rowspan="2">Number of animals</th></tr><tr><th>Infestation 1</th><th>Infestation 2</th></tr></thead><tbody><tr><td>1</td><td>2008</td><td>5000</td><td>5000</td><td>30</td></tr><tr><td>2</td><td>2008</td><td>7500</td><td>7500</td><td>20</td></tr><tr><td>3</td><td>2009</td><td>3500</td><td>5000</td><td>101</td></tr><tr><td>4</td><td>2011</td><td>3000</td><td>3000</td><td>52</td></tr><tr><td>5</td><td>2013</td><td>3500</td><td>5000</td><td>132</td></tr><tr><td>6</td><td>2015</td><td>3500</td><td>5000</td><td>137</td></tr></tbody><tfoot><tr><td colspan="4">TOTAL</td><td>472</td></tr></tfoot></table>
kept indoors since their birth. Between 2 and 4 years of age, 472 rams were experimentally inoculated and phenotyped for parasite resistance traits.

## *Parasite resistance traits*

The phenotypes for parasite resistance traits were collected between 2008 and 2015.

Rams were artificially challenged twice with *Haemonchus contortus* larvae. The *H.contortus* strain used in the experiment, called the “Humeau” strain, has been isolated in South-West of France and maintained in laboratory ever since. This strain is sensitive to all classes of anthelmintics. *H.contortus* is a nematode that feeds on blood through the abomasal mucosa of the host animal. The females are highly fecund therefore FEC is a good indicator of worm burden (Coyne *et al.*, 1991). It has also been chosen for this study because of the high correlation between resistance to this parasite and resistance to other parasites (Gruner, 2004a).

Different *H.contortus* doses were used during the experiments (Table 1). In 2008, two infesting doses were tested : a group of rams was infested twice with 5,000 larvae and the other group with 7,500 larvae. The same dose was given in first and second infestation. In 2009, 2013 and 2015, the animals received 3,500 larvae in first infestation and 5,000 in second infestation. In 2011, the animals were infested twice with 3,000 larvae.

At the beginning of the experiment, faecal samples were collected to check the absence of eggs in the faeces (FEC) and measure the initial packed cell volume (PCV). The animals were then infested with a first dose of larvae. Blood samples and faecal samples were taken at 34 days post-challenge. At the end of the first infestation, the rams were drenched with a dose of 0.2 mg/kg of body weight of ivermectine (Oramec®, Merial) and remained uninfected for a 15 days period before being infected again. The same protocol was followed for the second infestation.

FEC were determined using the modified MacMaster technique (Raynaud 1970). A sample of faeces was taken from the rectum, crushed and diluted in saturated saline water. After filtration, two samples were put into the chambers of a MacMaster slide. The number of eggs excreted per gram of faeces was obtained by multiplying the number of eggs counted in the slide by 50. L4 stage larvae and adult worms feed on blood from their host. Therefore PCV is correlated to worm burden. Blood samples were taken in EDTA coated tubes. Then they were put into capillary tubes that were centrifuged at 12000 rpm for 10 minutes. After centrifugation, PCV was determined by measuring the length of red blood cells on a reference grid.

## *Milk production traits*

The phenotypes for milk production traits were composed of Daughter Yield Deviation (DYD) for the quantity of milk, protein content and fat content. The DYD are the average performance of the daughters of each ram, corrected for the environmental effects and the Estimated Breeding Value (EBV) of the dam. For each ram, the DYD were weighted by the number of daughters used to compute them. The DYD were based on the data collected during milk recording. Only DYD estimated
with more than 10 daughters were considered. For milk quantity, the data of 4,310 rams were used. They had on average 61.7 daughters (between 10 and 1,018). The data of 3,633 rams with on average 46.2 daughters (between 10 and 916) were included in the analysis for fat and protein content.

## Genotypes

Part of the rams was also genotyped with the IlluminaOvineSNP50 Beadchip (Illumina, Inc., San Diego, CA) : 350 out of the 472 rams phenotyped for parasite resistance traits and 1,841 rams phenotyped for milk production traits. After quality control, a total of 38,158 markers distributed along the 26 sheep chromosomes were kept for our study. The quality of the genotypic data was also checked within our population. The quality criteria removed markers with a call rate inferior to 90% and a Minimum Allele Frequency (MAF) inferior to 5%.

## Statistical analysis

Descriptive statistics and distributions of the parasite resistance traits were performed using univariate procedure of SAS® (SAS Institute, Cary, NC, USA).

A fourth root transformation was applied to the FEC data to correct the deviations from a Gaussian distribution. The basal PCV can differ between two individuals. For each infestation the difference between the PCV at day 0 and the PCV at day 35 was computed (PCV\_day35 - PCV\_day0). DYD were multiplied by two because they include only half of the sire genetic value (Legarra, 2014).

For the transformed traits related to parasite resistance, the extreme values were checked for each group of animals according to the combined factor year-dose. All values different from three standard deviations to the mean were excluded from future analyses.

The effect of sex, age, infection dose and series on parasite resistance traits were tested using variance analyses with the GLM procedure of SAS®. Data were fitted to the following model:

$$y = X\beta + \varepsilon$$

where y is the vector of observations for the trait, X the matrix relating the observations to the fixed effects, β the vector of the fixed effects and ε the vector of residuals.

Larvae vigour depends on environmental conditions and changes from year to year. It can lead to heterogeneity of means and standard deviations when the data are collected during several years like in our experimental design. Therefore, FEC and
PCV data were reduced to test if heterogeneity of variances between the 6 series of data had an impact on the estimation of the phenotypic and genetic parameters.

Different relationship matrices were used to compute the genetic and phenotypic parameters : one using pedigree information only, one using genomic information only and one including both pedigree and genomic information. The latter used a relationship matrix between the animals computed according to VanRaden (2008) with the following weights : 0.95G+0.05A22 ; where G is the genomic relationship matrix among genotyped animals and A22 the relationship matrix computed using the pedigree information. Four generations were used to compute the genetic parameters with the methods including pedigree information.

Genetic and residual variances (σ_u² and σ_e² respectively) were estimated for each trait. Heritabilities were then computed as follows : h² = σ_u²/( σ_u²+ σ_e² ). Data were analyzed with the following model :

$$y = X\beta + Z\alpha + e$$

where y is the vector of observations, β the vector of fixed effects : series for FEC and PCV, α the vector of random additive genetic effects, X and Z are the matrices connecting y to the effects in the model and e is the vector of residuals.

Genetic correlations between each pair of traits were computed with bivariate animal models following the same type of model.

The observations for milk trait production were the DYD multiplied by two, which have a genetic variance σ_u² and a pseudo-residual variance σ_e² equal to 2σ_u² + 4σ_e² where σ_e² is the unobserved residual variance of daughter observations. The residual variance σ_e² was then calculated as (σ_e² - 2 σ_u²)/4.

All sires did not have the same number of daughters. Therefore the pseudo-residual variance is estimated assuming a weight equal to the number of daughters nᵢ. The pseudo-residual ε follows a normal distribution with mean 0 and standard deviation Wσ_ε², where W is a diagonal matrix including the weight of each sire i wᵢ equal to 1/ nᵢ.

The program RENUMF90 was used to renumber the animals and create input files (data, pedigree and parameter) for AIREMLF90 and REMLF90 (Misztal, 1999). Genetic and residual variances and covariances were estimated by restricted maximum likelihood (REML) with AIREMLF90 program. Phenotypic and genetic correlations, heritabilities and their standard errors were deducted from the estimated variances and covariances (Appendix I). AIREMLF90 uses Average Information REML and is an approximation of the REML method. When AIREMLF90 did not converge, the computation was performed with REMLF90. REMLF90 was also used to confirm the results for the parasite resistance traits.
<table><caption>Table 2 : Abbreviations used in the tables and corresponding traits</caption><thead><tr><td>Abbreviation</td><td>Corresponding trait</td></tr></thead><tbody><tr><td>FEC_inf1</td><td>Faecal Egg Count in first infestation</td></tr><tr><td>FEC_inf2</td><td>Faecal Egg Count in second infestation</td></tr><tr><td>rac_FEC_inf1</td><td>Faecal Egg Count in first infestation after fourth root transformation</td></tr><tr><td>rac_FEC_inf2</td><td>Faecal Egg Count in second infestation after fourth root transformation</td></tr><tr><td>PCV_inf1</td><td>Packed Cell Volume in first infestation</td></tr><tr><td>PCV_inf2</td><td>Packed Cell Volume in second infestation</td></tr><tr><td>2DYD_milk</td><td>Daughter Yield Deviation for milk quantity multiplied by 2</td></tr><tr><td>2DYDFat</td><td>Daughter Yield Deviation for fat content multiplied by 2</td></tr><tr><td>2DYD_protein</td><td>Daughter Yield Deviation for protein content multiplied by 2</td></tr></tbody></table>

<table><caption>Table 3 : Descriptive statistics of phenotypes related to parasite resistance.</caption><thead><tr><td>Variable</td><td>Number of animals</td><td>Mean</td><td>Standard deviation</td><td>Minimum</td><td>Maximum</td></tr></thead><tbody><tr><td>FEC_inf1</td><td>462</td><td>2075.13</td><td>1817.65</td><td>0</td><td>10900</td></tr><tr><td>FEC_inf2</td><td>459</td><td>1655.24</td><td>1826.28</td><td>0</td><td>14900</td></tr><tr><td>rac_FEC_inf1</td><td>462</td><td>6.15</td><td>1.77</td><td>0</td><td>10</td></tr><tr><td>rac_FEC_inf2</td><td>459</td><td>5.55</td><td>2.05</td><td>0</td><td>11</td></tr><tr><td>PCV_inf1</td><td>468</td><td>-3.43</td><td>3.70</td><td>-15</td><td>8</td></tr><tr><td>PCV_inf2</td><td>462</td><td>-0.98</td><td>3.12</td><td>-13</td><td>11</td></tr></tbody></table>

The number of observations, mean, standard deviation and range are given for each trait.

<table><caption>Table 4 : Descriptive statistics for the daughter yield deviations computed for milk traits.</caption><thead><tr><td>Trait</td><td>Number of observations</td><td>Mean</td><td>Standard deviation</td><td>Minimum</td><td>Maximum</td></tr></thead><tbody><tr><td rowspan="3">Unweighted data</td><td>2DYD_milk</td><td>4310</td><td>385,04</td><td>398,09</td><td>-815,86</td><td>1893,00</td></tr><tr><td>2DYDFat</td><td>3633</td><td>0,03</td><td>4,56</td><td>-15,86</td><td>18,03</td></tr><tr><td>2DYD_protein</td><td>3633</td><td>0,06</td><td>2,41</td><td>-7,99</td><td>9,60</td></tr><tr><td rowspan="3">Weighted data</td><td>2DYD_milk</td><td>4310</td><td>425,84</td><td>3123,53</td><td>-815,86</td><td>1893,34</td></tr><tr><td>2DYDFat</td><td>3633</td><td>-0,26</td><td>29,19</td><td>-15,86</td><td>18,03</td></tr><tr><td>2DYD_protein</td><td>3633</td><td>-0,16</td><td>15,89</td><td>-7,99</td><td>9,60</td></tr></tbody></table>

The number of observations, mean, standard deviation and range are given for each trait.
**Results :**

The abbreviations used in the result tables and their meaning are listed in Table 2.

*Descriptive statistics*

*Parasite resistance traits*

The mean, standard deviation, minimum and maximum values are shown in Table 3. Both untransformed and fourth root transformed data are shown for FEC. FEC were significantly higher in infestation 1 than in infestation 2 with mean values of 2075.13 and 1655.24 eggs per gram respectively. Mean values for PCV were significantly higher in infestation 2 (-0.98) than infestation 1 (-3.43). These observations were made for the six series of data (Appendix II).

*Milk production traits*

The mean, standard deviation, minimum and maximum values for milk production traits are shown in Table 4. Both results obtained for unweighted data and data weighted with the number of daughters are reported.

The means of DYD for milk quantity were significantly higher with weighted data than with unweighted data (mean values of 425.84 and 385.04 respectively). On the contrary, DYD for fat and protein content were significantly lower with weighted data in comparison with unweighted data (mean values of -0.26 and 0.03 respectively for fat content and -0.16 and 0.06 respectively for protein content).

Standard deviation computed with weighted data were significantly higher than the ones computed with unweighted data for the three milk production traits (3123.53 and 389.09 respectively for milk quantity, 29.19 and 4.56 respectively for fat content, 15.89 and 2.41 for protein content).

*Environmental factors*

The results of the GLM procedure showed that parasite resistance traits were affected by the series corresponding to the combined factor year-dose (P<0.0001). Age was also tested but did not have a significant influence on the traits.

DYD are already corrected for environmental effects so no effect was tested.

*Heterogeneity of means and standard deviations*

The heterogeneity of variances between series was tested and found non-significant except for PCV in second infestation (Appendix III). The estimations for phenotypic and genetic parameters were also computed for reduced data but were not different from the results shown above.
<table><caption>Table 5: Genetic and phenotypic parameters for parasite resistance traits and milk production traits with pedigree information.</caption><thead><tr><th></th><th>rac_FEC_inf1</th><th>rac_FEC_inf2</th><th>PCV_inf1</th><th>PCV_inf2</th><th>2DYD_milk</th><th>2DYD_fat</th><th>2DYD_protein</th></tr></thead><tbody><tr><td>rac_FEC_inf1</td><td><strong>0.06 ± 0.08</strong></td><td>0.99 ± 0.53</td><td>-0.99 ± 0.66</td><td>-0.99 ± 0.62</td><td>-0.02 ± 0.09</td><td>0.97 ± 0.25</td><td>0.16 ± 0.54</td></tr><tr><td>rac_FEC_inf2</td><td>0.33 ± 0.04</td><td><strong>0.31 ± 0.12</strong></td><td>-0.75 ± 0.62</td><td>0.20 ± 0.75</td><td>0.35 ± 0.31</td><td>0.24 ± 0.43</td><td>-0.25 ± 0.42</td></tr><tr><td>PCV_inf1</td><td>-0.27 ± 0.04</td><td>-0.22 ± 0.05</td><td><strong>0.09 ± 0.11</strong></td><td>0.36 ± 2.5</td><td>-0.18 ± 0.47</td><td>-0.10 ± 0.49</td><td>0.12 ± 0.43</td></tr><tr><td>PCV_inf2</td><td>-0.04 ± 0.05</td><td>-0.20 ± 0.05</td><td>0.01 ± 0.05</td><td><strong>0.10 ± 0.11</strong></td><td>0.09 ± 0.30</td><td>0.15 ± 0.39</td><td>0.27 ± 0.44</td></tr><tr><td>2DYD_milk</td><td>-0.05 ± 0.03</td><td>0.11 ± 0.06</td><td>-0.01 ± 0.06</td><td>0.06 ± 0.06</td><td><strong>NA</strong></td><td>-0.35 ± NA</td><td>-0.44 ± NA</td></tr><tr><td>2DYD_fat</td><td>0.13 ± 0.04</td><td>0.08 ± 0.06</td><td>-0.02 ± 0.06</td><td>-0.05 ± 0.06</td><td>NA</td><td><strong>0.43 ± NA</strong></td><td>0.55 ± NA</td></tr><tr><td>2DYD_protein</td><td>0.03 ± 0.06</td><td>-0.07 ± 0.06</td><td>-0.05 ± 0.06</td><td>0.02 ± 0.06</td><td>NA</td><td>NA</td><td><strong>NA</strong></td></tr></tbody></table>

Heritabilities are written in bold on the diagonal, genetic correlations are above the diagonal and phenotypic correlations are below the diagonal. The standard errors are also given when the computation was made with AIREMLF90. For milk production traits, AIREMLF90 did not converge and REMLF90 was used instead. No standard error is provided for these estimates. The estimated residual variances for milk quantity and protein content were inconsistent so the heritabilities were not computed.

<table><caption>Table 6: Genetic and phenotypic parameters for the parasite resistance traits and milk production traits with genomic information.</caption><thead><tr><th></th><th>rac_FEC_inf1</th><th>rac_FEC_inf2</th><th>PCV_inf1</th><th>PCV_inf2</th><th>2DYD_milk</th><th>2DYD_fat</th><th>2DYD_protein</th></tr></thead><tbody><tr><td>rac_FEC_inf1</td><td><strong>7.6E-06 ± 6E-07</strong></td><td>0.99 ± 1.3</td><td>-0.99 ± 1.2</td><td>0.99 ± 0.61</td><td>-0.93 ± 0.46</td><td>1.0 ± 0.45</td><td>0.99 ± 0.88</td></tr><tr><td>rac_FEC_inf2</td><td>0.33 ± 0.04</td><td><strong>0.19 ± 0.12</strong></td><td>-0.99 ± 1.26</td><td>0.23 ± 0.56</td><td>0.09 ± 0.41</td><td>0.25 ± 0.35</td><td>0.01 ± 0.31</td></tr><tr><td>PCV_inf1</td><td>-0.26 ± 0.04</td><td>-0.18 ± 0.05</td><td><strong>2.3E-06 ± 3.5E-04</strong></td><td>-0.99 ± 5.3</td><td>0.46 ± 1.13</td><td>-0.99 ± 0.99</td><td>-0.79 ± 0.70</td></tr><tr><td>PCV_inf2</td><td>-0.08 ± 0.05</td><td>-0.20 ± 0.05</td><td>0.01 ± 0.05</td><td><strong>0.20 ± 0.14</strong></td><td>0.03 ± 0.27</td><td>0.19 ± 0.33</td><td>0.04 ± 0.29</td></tr><tr><td>2DYD_milk</td><td>-0.07 ± 0.05</td><td>0.08 ± 0.06</td><td>0.01 ± 0.05</td><td>0.05 ± 0.06</td><td><strong>0.43 ± NA</strong></td><td>-0.34 ± NA</td><td>-0.41 ± NA</td></tr><tr><td>2DYD_fat</td><td>0.14 ± 0.05</td><td>0.07 ± 0.06</td><td>-0.03 ± 0.05</td><td>-0.05 ± 0.06</td><td>NA</td><td><strong>0.49 ± NA</strong></td><td>0.56 ± NA</td></tr><tr><td>2DYD_protein</td><td>0.03 ± 0.05</td><td>-0.07 ± 0.06</td><td>-0.08 ± 0.06</td><td>-0.02 ± 0.06</td><td>NA</td><td>NA</td><td><strong>0.32 ± NA</strong></td></tr></tbody></table>

Heritabilities are written in bold on the diagonal, genetic correlations are above the diagonal and phenotypic correlations are below the diagonal. The standard errors of the estimates are also given when the computation was made with AIREMLF90. For milk production traits, AIREMLF90 did not converge and REMLF90 was used instead. No standard error is provided for these estimates.
## Phenotypic and genetic parameters

The estimates obtained with genomic information only and the ones obtained with both pedigree and genomic information were very close. The results shown here are the estimates obtained with pedigree information only (Table 5) and genomic information only (Table 6). The results obtained with both pedigree and genomic information are shown in appendix IV.

## Heritabilities

The heritabilities estimated for parasite resistance traits and milk production traits are given on the diagonal of Tables 5 and 6. The results reported here were computed for each trait.

The estimated heritabilities for FEC and PCV were not different from 0 in first infestation. The FEC heritabilities were 0.06 (± 0.08) with the pedigree only and 7.6E-06 (± 6E-07) with the genomic information. The estimated heritabilities for PCV were 0.09 (± 0.11) and 2.3E-06 (± 3.5E-04) with pedigree information and genomic information respectively.

The heritabilities of these two traits were higher in second infestation than in first infestation with both pedigree and genomic methods. The estimates were 0.31 (± 0.12) with pedigree information and 0.19 (± 0.12) with genomic information for FEC. We obtained heritabilities of 0.10 (± 0.11) with pedigree information and 0.20 (± 0.14) with genomic information for PCV.

AIREMLF90 was found to have problems converging for milk production traits. The results reported here were obtained with REMLF90. Consequently, the standard errors were not computed for these estimates.

We were not able to estimate heritabilities for milk quantity and protein content with pedigree information. REMLF90 converged but the estimated values for genetic and pseudo-residual variances were inaccurate. The deducted residual variance $σ_e^2$ was negative, which is impossible. Though, we estimated a heritability of 0.43 for fat content with pedigree information.

With genetic information, we obtained heritabilities of 0.43, 0.49 and 0.32 for milk quantity, fat content and protein content respectively.

## Genetic correlations

The genetic correlations between the two parasite resistance traits and the three milk production traits are shown in Tables 5 and 6 (above the diagonal), together with the
standard error of each estimate except for the correlations between milk production
traits. They were estimated for each pair of traits.

The correlations between parasite resistance traits obtained with the two models
were in general consistent, except for the correlation between FEC in first infestation
and PCV in second infestation which was negative with pedigree information and
positive with genomic information.

We obtained a correlation of 0.99 between FEC in first and second infestations with
both pedigree information and genomic information. The correlations between FEC
and PCV tended to be negative. But the standard errors were rather large.

The estimates for correlations between milk production traits obtained with the two
models were very close. The estimated correlations were -0.35 and -0.34 between
milk quantity and fat content ; -0.44 and -0.41 between milk quantity and protein
content ; 0.55 and 0.56 between fat and protein content with pedigree information
and genetic information respectively.

The estimates for the correlation between PCV in second infestation and milk
quantity, parasite resistance traits (except for PCV in second infestation) and fat
content were consistent with both pedigree and genomic models. According to their
standard errors, the correlations were not significantly different from zero except for
the correlation between FEC in first infestation and fat content. They tended to be
positive between FEC and DYD for fat content with the two models.

Phenotypic correlations

The phenotypic correlations between parasite resistance traits and milk production
traits are shown in Table 5 and 6 (below the diagonal). The standard errors are also
provided except for the correlations between milk production traits. They were
estimated for each pair of traits.

The phenotypic correlations between parasite resistance traits obtained with the two
models were consistent. According to their standard errors, the correlations between
FEC in first infestation and PCV in second infestation and between PCV in first and
second infestation were not significantly different from zero.
The other correlations were moderate and ranged from -0.27 (± 0.04) to 0.33 (± 0.04)
with pedigree information and from -0.18 (± 0.05) and 0.33 (± 0.04) with genomic
information.

The correlations between milk production traits were not estimated.

In general, the phenotypic correlations between parasite resistance traits and milk production traits were close to zero. We found significant correlations between FEC in first infestation and fat content and between FEC in second infestation and milk quantity but they remained low.

**Discussion :**

In this study, we estimated phenotypic and genetic parameters for the parasite
resistance traits FEC and PCV and the milk production traits : quantity of milk, protein
and fat content.

For milk production traits, means and standard deviations obtained with unweighted
data and data weighted with the number of daughters were quite different.
A higher mean for milk quantity was expected with weighted data since the rams with
high DYD are the best rams and have more daughters than the others. On the
contrary, we observed lower means with weighted data for protein and fat content.
This is probably due to the negative correlations between milk quantity and protein
and fat content. The rams with high DYD for milk quantity generally have lower DYD
for fat and protein content than the others.

We also obtained higher standard deviations for the three traits with weighted data.
This is due to the different number of daughters for each rams that ranged from 10 to
1,018 for milk quantity and 10 to 916 for fat and protein content. But standard
deviations with weighted data were 6 to 7 times the standard deviations obtained with
unweighted data. This ratio, which is quite large, could be due to the use of the
number of daughters as weights for DYD.

We found higher heritabilities for FEC and PCV in second infestation than in first
infestation, which is consistent with the results obtained by Assenza et al. (2014). But
in first infestation FEC showed lower values than those generally reported, probably
because several doses of *H.contortus* larvae were given during the experiments. It
seems to have a higher impact in first infestation than in the second one. In natural
infestation, Gutiérrez-Gil et al. (2010) obtained an estimated heritability of 0.09±0.03 for FEC with pedigree information in Spanish Churra dairy breed. The animals were not naïve so this result can be compared to our heritability in second infestation. In this study, the animals were managed with daily grazing so that they were exposed to a natural infestation by different species of parasites, which can explain the difference with our result. Gruner et al. (2004a) experimentally challenged Romane sheep with 10,000 *H.contortus* larvae and measured FEC at 28 and 35 days post challenge. Their estimated heritabilities ranged between 0.44 and 0.48 in first and second infestation. Morris et al. (2005) estimated heritabilities for FEC after a natural mixed-species parasite challenge in Perendale sheep. They found heritabilities of 0.22±0.03 and 0.16±0.03 in first and second infestations respectively.

We found a genetic correlation of 0.99 between FEC in first and second infestations, which is consistent with the literature (Gruner et al., 2004a; Morris et al., 2005). The genetic and phenotypic correlations between FEC and PCV tended to be negative. It is due to the fact that *H.contortus* feeds on blood from the host. When the worm burden of an animal increases, its PCV decreases. But we are not able to conclude on the genetic correlations because of the high standard errors of our estimates.

For milk production traits, the estimated heritabilities were not consistent with the literature (Barillet *et al.*, 2016). The residual variances were not well estimated which led to poor estimates for heritabilities. We used the DYD as ram phenotypes for milk production instead of milk phenotypes measured on their daughters in order to have both milk and parasite phenotypes on the rams to estimate the correlations. However, the use of DYD does not allow estimating correctly phenotypic variances and then heritabilities. In fact, DYD are pseudo-phenotypes computed as the average of the daughters performances corrected for environmental effects and the merit of their dam. DYD consist of both additive polygenic and residual components. So genetic and residual components computed with DYD are different than the component that we would have obtained with the performances of each daughter. Besides, all DYD did not have the same precision because, as previously mentioned, the number of daughters per ram varied from 10 to around 1,000. Another possible source of error is the difference between the number of phenotyped rams and genotyped rams. When genomic information was used, we only took into account the rams which were both genotyped and phenotyped. The use of all phenotyped animals in the analysis should lead to better estimates for heritabilities and correlations and to smaller range for standard errors.

However, only the genetic variance components were used to compute the genetic correlations and our estimates are in accordance with the results of Barillet *et al.* (2016) who reported correlations of -0.3, -0.5 and 0.4 to 0.5 between milk quantity
and fat content, milk quantity and protein content and fat and protein content respectively.

The genetic correlation between fat content and FEC tended to be positive during both infestations. This could be explained by the principle of resource allocation. If ewes allocate more energy to produce fat in milk, their capacity to reduce the number of parasites in its abomasum should decrease. In first infestation, the animals are naïve so developing an immune response probably requires more energy than in second infestation. It can explain why the correlation between FEC and fat content was significantly different from 0 in first infestation but not in second infestation. But the standard errors for genetic correlations were quite large and we are not able to conclude on the impact of a genetic selection for parasite resistance on milk quantity and protein content with our dataset. Moreover, for each ram, the phenotypes for parasite resistance traits were its own measure whereas milk production traits were phenotyped using several daughters. But it was not possible to take into account different weights for different traits in multitrait model with AIREMLF90 and REMLF90. So we computed the correlation between FEC and milk production traits with unweighted data for milk production traits, which may produce misleading results.

No estimation of correlation between parasite resistance traits and milk production
traits was found in the literature but several studies estimated the correlations
between parasite resistance traits and other production traits. In their study, Assenza
et al. (2014) found that genetic correlation between FEC in first infestation and
growth was null but it was positive between FEC in second infestation and growth in
a *H.contortus* free environment and negative in a contaminated environment. Bishop
et al. (1996) found phenotypic correlations between FEC and live weight close to
zero but genetic correlations were close to -1 in Scottish Blackface lambs bred in a
contaminated environment. But other studies reported low correlations between
these traits (Bisset et al., 1992 ; Douch et al., 1995). The use of different parasite
species, sheep breeds and experimental designs in the studies may explain that no
consensus has been found so far on the correlations between production traits and
parasite resistance traits. Parasite resistance and production are two functions in
competition for resources. The correlations between them result from a trade-off
between resistance and production depending on the costs and benefits of being
resistant. Besides, the parasite will also use host resources for its own growth and
reproduction. So the impact of the infestation also depends on the parasite virulence
and larvae vigour which can vary from year to year.

In general, the results obtained with pedigree information were consistent with the
estimates obtained with genomic information. With genomic information, we usually
expect an increased precision of the estimates since the proportion of the genome
that two individuals have in common is better known (Assenza et al., 2014;
Veerkamp et al., 2011). But we did not obtain smaller standard errors with genomic information in comparison with the results obtained with pedigree information. This is certainly due to the difference between the number of phenotyped rams and genotyped rams. Only 350 rams out of the 472 phenotyped rams for resistance traits were also genotyped. The last 122 rams should be genotyped within the next months. For milk production traits, the difference is even larger with 1,841 rams genotyped out of 4,310 rams phenotyped for milk quantity and 3,633 rams for fat and protein content.

Our results showed that parasite resistance traits are moderately heritable. Jacquiet et al. (2015) measured FEC in resistant ram daughters and sensitive ram daughters. They found that resistant ram daughters excreted 30 to 70% less eggs in their faeces than sensitive ram daughters. These results suggest that a selection on these traits is possible. The integration of the parasite resistance in selection schemes requires a routine collection of phenotypes. But an experimental infestation protocol as described above is too difficult to implement on a large scale. It would represent a substantial cost for breeding companies : around 50 euros per candidate rams (Moreno et al., 2015). But a correlation of 0.99 between FEC in first and second infestations and higher heritabilities for FEC and PCV in second infestation were estimated in our study. It suggests that breeding companies could phenotype candidate rams for FEC and PCV only in second infestation and so reduce the cost of parasitism phenotyping.

It will also be necessary to evaluate the economic weight of parasite resistance. We estimated a positive and significant genetic correlation between FEC in first infestation and fat content. The correlation between FEC in second infestation and fat content also tended to be positive. As previously discussed, we did not take weights into account and our residual variance components were incorrectly estimated for milk production traits. But if this relationship is confirmed, it will be necessary to take it into account before assigning an economic weight to parasite resistance.

## Conclusion

According to the results obtained with both pedigree model and genomic model, parasite resistance traits are moderately heritable in Manech Red Head sheep. This suggests that genetic selection is possible for these traits.

The genetic correlation between fat content and FEC in first infestation was positive and significant and it also tended to be positive with FEC in second infestation. It means that selection for parasite resistance traits may have a negative impact on the traits already included in the breeding goal. But further analyses are needed to
properly evaluate the relationship between milk production traits and parasite
resistance traits. A QTL detection could also be performed to identify possible major
genes or genome regions common to parasite resistance and fat content that could
be responsible for the observed relationship between these traits.

In the Pyrenees-Atlantiques, parasite resistance will be soon taken into account in
the genetic evaluation of candidate rams. Our results can be used for the
implementation of a selection for parasite resistance in Manech Red Head breed. In
particular, it could help designing a more suitable experimental infestation for
breeding companies : less costly and easier to use on a large-scale. It will also be
necessary to evaluate the economic weight of parasite resistance and the
consequences of the selection for these traits in farms.

References :

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# Appendices

**Appendix 1:** Parameter files for RENUMF90 describing our model for the computation of the correlations between milk quantity and fat content.

\# allows us to put comments in the parameter file.

The option `se_covar_function` was used to compute heritabilities and genetic and phenotypic correlations and their standard errors. Notation is with reference to the effect number and the trait number ($G_{eff1}$, $G_{eff2}$, $trt1$, $trt2$), where $eff1$ and $eff2$ are effect numbers 1 and 2, and $trt1$ and $trt2$ are trait numbers 1 and 2.

Parameter file used with the pedigree model:

**DATAFILE**

/parasheep/saguerre/Manech2/Param_genet/corr_lait # name of the datafile

**TRAITS**

8 11

# column number of the traits

**FIELDS_PASSED TO OUTPUT**

1 # column 1 (animal ID) in the original datafile is passed to the renumbered data without change

**WEIGHT(S)**

4

# position of weight for the two traits

**RESIDUAL_VARIANCE**

110938 -0.4 # initial values for residual variances and covariances
-0.4 14.8

**EFFECT**

2 2 cross numer

**EFFECT** # description of the animal effect

1 1 cross alpha # column number of animal ID for the two traits, type of effect and the IDs are alphanumeric

**RANDOM**

Animal #correlation structure among animals

**FILE**

/parasheep/saguerre/Manech2/Param_genet/Pedig_jm #name of the pedigree file

**PED_DEPTH**

3 # we use 3 generations in addition to our phenotyped animals

**(CO)VARIANCES**

47545 -0.4 # initial values for genetic variances and covariances
-0.4 6.3

**OPTION sol se** # store solutions and those standard errors

**OPTION missing -999** # the missing values in the datafile are equal to -999

**OPTION se_covar_function h21 G_2_2_1_1/(G_2_2_1_1+R_1_1)**

**OPTION se_covar_function h22 G_2_2_2_2/(G_2_2_2_2+R_2_2)**

**OPTION se_covar_function rg12 G_2_2_1_2/(G_2_2_1_1*G_2_2_2_2)**0.5
Parameter file used with the genomic model :

DATAFILE

/parasheep/saguerre/Manech2/Param_genet/genom

TRAITS

8 11

FIELDS_PASSED TO OUTPUT

1

WEIGHT(S)

4

RESIDUAL_VARIANCE

110938 -512

-512 14.8

EFFECT

2 2 cross numer

EFFECT

1 1 cross alpha

RANDOM

animal

FILE

/parasheep/saguerre/Manech2/Param_genet/Pedig_0 #we use a pedigree with
0 for parents' IDs

SNP_FILE

/parasheep/saguerre/Manech2/ipg Typfin # name of the SNP file

(CO)VARIANCES

47545 -219

-219 6.3

OPTION sol se

OPTION missing -999

OPTION maxrounds 10000 # number of maximum rounds (default : 5000)

OPTION verify_parentage 1 # detect animals with mendelian errors

OPTION se_covar_function h21 G_2_2_1_1/(G_2_2_1_1+R_1_1)

OPTION se_covar_function h22 G_2_2_2_2/(G_2_2_2_2+R_2_2)

OPTION se_covar_function rg12 G_2_2_1_2/(G_2_2_1_1*G_2_2_2_2)**0.5
## Appendix II: Descriptive statistics for FEC and PCV for the six series.

The series number, number of phenotyped animals, mean, standard deviation and range values are given for each trait. Values are shown for both untransformed and fourth root transformed data for FEC.

<table><thead><tr><th>Series</th><th>Variable</th><th>Number of phenotyped animals</th><th>Mean</th><th>Standard deviation</th><th>Minimum</th><th>Maximum</th></tr></thead><tbody><tr><td rowspan="6">1</td><td>FEC_inf1</td><td>30</td><td>3694.33</td><td>2184.41</td><td>400</td><td>8400</td></tr><tr><td>FEC_inf2</td><td>30</td><td>2265.00</td><td>2057.83</td><td>0</td><td>9150</td></tr><tr><td>rac_FEC_inf1</td><td>30</td><td>7.49</td><td>1.33</td><td>4.47</td><td>9.57</td></tr><tr><td>rac_FEC_inf2</td><td>30</td><td>6.00</td><td>2.46</td><td>0</td><td>9.78</td></tr><tr><td>PCV_inf1</td><td>30</td><td>-6.27</td><td>4.11</td><td>-15</td><td>3</td></tr><tr><td>PCV_inf2</td><td>30</td><td>1.83</td><td>2.93</td><td>-3</td><td>8</td></tr><tr><td rowspan="6">2</td><td>FEC_inf1</td><td>20</td><td>2830.00</td><td>1769.58</td><td>150</td><td>6700</td></tr><tr><td>FEC_inf2</td><td>20</td><td>1227.85</td><td>1176.42</td><td>7</td><td>3700</td></tr><tr><td>rac_FEC_inf1</td><td>20</td><td>6.97</td><td>1.36</td><td>3.50</td><td>9.05</td></tr><tr><td>rac_FEC_inf2</td><td>20</td><td>5.26</td><td>1.73</td><td>1.63</td><td>7.80</td></tr><tr><td>PCV_inf1</td><td>20</td><td>-5.90</td><td>3.82</td><td>-13</td><td>1</td></tr><tr><td>PCV_inf2</td><td>20</td><td>-0.05</td><td>1.73</td><td>-4</td><td>3</td></tr><tr><td rowspan="6">3</td><td>FEC_inf1</td><td>96</td><td>1132.81</td><td>706.06</td><td>0</td><td>3500</td></tr><tr><td>FEC_inf2</td><td>98</td><td>958.11</td><td>829.83</td><td>0</td><td>3500</td></tr><tr><td>rac_FEC_inf1</td><td>96</td><td>5.52</td><td>1.15</td><td>0</td><td>7.69</td></tr><tr><td>rac_FEC_inf2</td><td>98</td><td>4.79</td><td>2.00</td><td>0</td><td>7.69</td></tr><tr><td>PCV_inf1</td><td>101</td><td>-2.08</td><td>3.19</td><td>-9</td><td>8</td></tr><tr><td>PCV_inf2</td><td>100</td><td>-1.70</td><td>2.51</td><td>-8</td><td>7</td></tr><tr><td rowspan="6">4</td><td>FEC_inf1</td><td>52</td><td>419.52</td><td>333.38</td><td>0</td><td>1350</td></tr><tr><td>FEC_inf2</td><td>50</td><td>1266.50</td><td>1060.81</td><td>0</td><td>4100</td></tr><tr><td>rac_FEC_inf1</td><td>52</td><td>3.93</td><td>1.65</td><td>0</td><td>6.06</td></tr><tr><td>rac_FEC_inf2</td><td>50</td><td>5.31</td><td>1.84</td><td>0</td><td>8.00</td></tr><tr><td>PCV_inf1</td><td>52</td><td>0.42</td><td>2.64</td><td>-5</td><td>6</td></tr><tr><td>PCV_inf2</td><td>52</td><td>-2.35</td><td>2.51</td><td>-11</td><td>2</td></tr><tr><td rowspan="6">5</td><td>FEC_inf1</td><td>132</td><td>3447.73</td><td>1954.91</td><td>0</td><td>10900</td></tr><tr><td>FEC_inf2</td><td>129</td><td>2576.74</td><td>2652.74</td><td>0</td><td>14900</td></tr><tr><td>rac_FEC_inf1</td><td>132</td><td>7.31</td><td>1.52</td><td>0</td><td>10.22</td></tr><tr><td>rac_FEC_inf2</td><td>129</td><td>6.27</td><td>2.18</td><td>0</td><td>11.05</td></tr><tr><td>PCV_inf1</td><td>132</td><td>-4.47</td><td>3.13</td><td>-12</td><td>5</td></tr><tr><td>PCV_inf2</td><td>128</td><td>-1.00</td><td>2.91</td><td>-7</td><td>10</td></tr><tr><td rowspan="6">6</td><td>FEC_inf1</td><td>132</td><td>1557.69</td><td>1138.07</td><td>0</td><td>6650</td></tr><tr><td>FEC_inf2</td><td>132</td><td>1345.68</td><td>1117.55</td><td>0</td><td>4400</td></tr><tr><td>rac_FEC_inf1</td><td>132</td><td>5.91</td><td>1.37</td><td>0</td><td>9.03</td></tr><tr><td>rac_FEC_inf2</td><td>132</td><td>5.45</td><td>1.75</td><td>0</td><td>8.14</td></tr><tr><td>PCV_inf1</td><td>133</td><td>-3.91</td><td>3.35</td><td>-13</td><td>6</td></tr><tr><td>PCV_inf2</td><td>132</td><td>-0.67</td><td>3.66</td><td>-13</td><td>11</td></tr></tbody></table>
**<u>Appendix III</u>**: Distribution curves for parasite resistance traits for the six series. *rac_FEC_inf1* and *rac_FEC_inf2* refer to FEC after fourth root transformation in first and second infestation respectively.

PCV_inf1 and PCV_inf2 correspond to PCV in first and second infestation respectively.

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>
Distribution curves for PCV_inf1

<figure><img src="image_5.png" /></figure>

Distribution curves for PCV_inf2

<figure><img src="image_6.png" /></figure>
Appendix IV : Heritabilities, genetic and phenotypic correlations obtained with both pedigree and genomic information.

<table><thead><tr><th></th><th>rac_FEC_inf1</th><th>rac_FEC_inf2</th><th>PCV_inf1</th><th>PCV_inf2</th><th>2DYD_milk</th><th>2DYD_fat</th><th>2DYD_protein</th></tr></thead><tbody><tr><td>rac_FEC_inf1</td><td><strong>2.1E-05 ± 1.6E-06</strong></td><td>0.99 ± 0.83</td><td>-0.99 ± 0.98</td><td>0.99 ± 0.56</td><td>-0.89 ± 0.22</td><td>0.99 ± 0.33</td><td>0.99 ± 0.57</td></tr><tr><td>rac_FEC_inf2</td><td>0.35 ± 0.05</td><td><strong>0.19 ± 0.12</strong></td><td>-0.99 ± 0.86</td><td>0.28 ± 0.88</td><td>0.09 ± 0.41</td><td>0.24 ± 0.42</td><td>-0.02 ± 0.30</td></tr><tr><td>PCV_inf1</td><td>-0.29 ± 0.05</td><td>-0.18 ± 0.05</td><td><strong>7.1E-06 ± 3.9E-04</strong></td><td>-0.99 ± 0.76</td><td>0.81 ± 0.76</td><td>-0.98 ± 0.64</td><td>-0.76 ± 0.68</td></tr><tr><td>PCV_inf2</td><td>-0.08 ± 0.05</td><td>-0.22 ± 0.06</td><td>-0.05 ± 0.05</td><td><strong>0.19 ± 0.14</strong></td><td>0.03 ± 0.30</td><td>0.19 ± 0.36</td><td>0.05 ± 1.0</td></tr><tr><td>2DYD_milk</td><td>-0.07 ± 0.04</td><td>0.08 ± 0.06</td><td>0.02 ± 0.05</td><td>0.05 ± 0.06</td><td><strong>0.97 ± NA</strong></td><td>-0.38 ± NA</td><td>-0.47 ± NA</td></tr><tr><td>2DYD_fat</td><td>0.15 ± 0.05</td><td>0.08 ± 0.06</td><td>-0.03 ± 0.05</td><td>-0.05 ± 0.06</td><td>NA</td><td><strong>NA</strong></td><td>NA</td></tr><tr><td>2DYD_protein</td><td>0.04 ± 0.05</td><td>-0.07 ± 0.06</td><td>-0.08 ± 0.06</td><td>-0.02 ± 0.06</td><td>NA</td><td>NA</td><td><strong>0.45 ± NA</strong></td></tr></tbody></table>

Heritabilities are written in bold on the diagonal, genetic correlations are above the diagonal and phenotypic correlations are below the diagonal. The standard errors of the estimates are also given when the computation was made with AIREMLF90. For milk production traits, AIREMLF90 did not converge and REMLF90 was used instead. No standard error is provided for these estimates. The phenotypic correlations between milk production traits were not computed.