International Journal for Parasitology 45 (2015) 237–242

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Estimation of genetic parameters for resistance to gastro-intestinal nematodes in pure blood Arabian horses Sławomir Kornas´ a,1, Guillaume Sallé b,c,⇑,1, Marta Skalska a, Ingrid David d, Anne Ricard e, Jacques Cabaret b,c a

Department of Zoology and Ecology, University of Agriculture of Krakow, 30-059 Krakow, Poland INRA, UMR1282 Infectiologie et Santé Publique, F-37380 Nouzilly, France Université François Rabelais de Tours, UMR1282 Infectiologie et Santé Publique, F-37000 Tours, France d INRA, UMR1388 Génétique, Physiologie et Systèmes d’élevage, F-31326 Castanet-Tolosan, France e INRA, UMR1313 Génétique Animale et Biologie Intégrative, F-78352 Jouy-en-Josas, France b c

a r t i c l e

i n f o

Article history: Received 28 July 2014 Received in revised form 20 November 2014 Accepted 24 November 2014 Available online 12 January 2015 Keywords: Genetic resistance Horse Nematode Heritability Repeatability

a b s t r a c t Equine internal parasites, mostly cyathostomins, affect both horse welfare and performance. The appearance of anthelmintic-resistant parasites creates a pressing need for optimising drenching schemes. This optimization may be achieved by identifying genetic markers associated with host susceptibility to infection and then to drench carriers of these markers. The aim of our study was to characterise the genetics of horse resistance to strongyle infection by estimating heritability of this trait in an Arabian pure blood population. A population of 789 Arabian pure blood horses from the Michałów stud farm, Poland were measured for strongyle egg excretion twice a year, over 8 years. Low repeatability values were found for faecal egg counts. Our analyses showed that less than 10% of the observed variation for strongyle faecal egg counts in this population had a genetic origin. However, additional analyses highlighted an agedependent increase in heritability which was 0.04 (±0.02) in young horses (up to 3 years of age) but 0.21 (±0.04) in older ones. These results suggest that a significant part of the inter-individual variation has a genetic origin. This paves the way to a genomic dissection of horse-nematode interactions which might provide predictive markers of susceptibility, allowing individualised drenching schemes. Ó 2015 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved.

1. Introduction Gastro-intestinal nematodes impair both horse health and welfare, representing an important economic burden for horse breeders. Among the species of interest, digestive strongyles are responsible for growth retardation in young horses and unthriftiness in both young and adult horses (Taylor et al., 2007). This family can be separated into two subfamilies (Lichtenfels et al., 2008): Cyathostominae (common name cyathostomins, also known as ‘‘small strongyles’’) and Strongylidae (common name strongylins or large strongyles). Young horses between 1 and 5 years of age (Kornas´ et al., 2010; Wood et al., 2012) excrete more eggs, and a lifelong susceptibility to potential clinical disease is usually reported (Matthews et al., 2004; Corning, 2009).

⇑ Corresponding author at: Guillaume Sallé, INRA Val de Loire, 37380 Nouzilly, France. Tel.: +33 247427567. E-mail address: [email protected] (G. Sallé). 1 Authors contributed equally to this work.

Over the past decades, management of these parasites has relied primarily on the use of anthelmintic treatments. It has now become apparent that drug-resistant populations of cyathostomins have become widespread throughout the world due to the application of systematic, frequent drenching systems (von Samson-Himmelstjerna, 2012; Peregrine et al., 2014). Their pathogenic potential in conjunction with their extensive drug resistance has resulted in their ranking as one of the top 10 pathogens of equids by the French Public Health Institute (Anonymous, 2012). One obvious strategy for limiting the rise of drug-resistant populations is to optimise drenching schemes by only targeting the horses in need of treatment (Nielsen et al., 2014). Such a strategy relies heavily on using indicators of infection that actually estimate the level of infection. To date, no better indicator has been proposed than a faecal egg count (FEC), i.e. counting the number of parasite eggs which appear in horse faeces. Also, the epidemiological features of horse infection suggest an overdispersion of faecal egg output, with 10–15% of horses excreting 80% of the eggs (Relf et al., 2013, 2014). Therefore, a genetic component of resistance to gastro-intestinal nematodes might play a role, as has already been

http://dx.doi.org/10.1016/j.ijpara.2014.11.003 0020-7519/Ó 2015 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved.

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demonstrated in small ruminants (Bishop et al., 2004; Kemper et al., 2011). In sheep, this genetic variability has been exploited for breeding purposes (Kemper et al., 2011; Sallé et al., 2012). Deciphering the genetic architecture of resistance to gastro-intestinal nematodes in horses may enable us to identify genetic markers of susceptibility to nematodes, and thus target horses in need of anthelmintic treatment. A herd of pure blood Arabian horses with a recorded pedigree were measured for faecal production of gastro-intestinal nematode eggs. The aim of the study was to quantify the genetic component of susceptibility to gastro-intestinal nematodes in this pure blood Arabian horse population. 2. Materials and methods 2.1. Parasitological measurements Data collection was conducted from 1999 to 2008 (except in 2003 and in 2007) from pure blood Arabian Horses in the Michałòw stud farm in southern Poland. These pure blood Arabian horses descended from ancestors imported to Poland during the 17th, 18th and 19th centuries (Chmiel et al., 1999). In reference to parasite control programs, horses were dewormed twice per year before and after the pasture season, in April and October, mostly with ivermectin, and no treatment was given during the pasture season. Each year of the study, faecal samples were collected twice during the pasture season which began, depending on weather conditions, at the end of April and ended at the beginning of November. Nematode eggs were counted using a modified McMaster technique (Raynaud, 1970) in which each egg counted represented 50 eggs/g of faeces. Strongyle egg counts (SECs) which included both Cyathostominae and Strongylidae, were recorded. A previous epidemiological study had already demonstrated through larval cultures that cyathostomins make up the bulk of strongyle species in horses (Kornas´ et al., 2007). In total, 768 horses (269 males and 499 females) were sampled and resulted in a total of 2,691 records available for analysis (543 and 2,148 from males and females, respectively), which were collected in April (before the spring drenching, n = 1,384) and August (n = 1,307). On average, each horse was recorded three times in the timeframe considered (with a maximum of 14 occurrences), while for each time point considered, 179 SEC data were available on average. No geldings were present in the dataset. 2.2. Pedigree information Heritability is the component of the observed variation that is due to additive genetic variance (Falconer and Mackay, 1996). It is estimated from the degree of phenotypic resemblance between relatives (Falconer and Mackay, 1996). This information is contained in a pedigree file that lists the parents of each individual. The more records in the pedigree, the more relatives can be identified and the better the heritability estimate. In this case, every sampled horse had a recorded mother and father so that a complete pedigree could be built. The stallions and mares which were not sampled had no pedigree information provided and were therefore considered to be ancestors, i.e. individuals with unknown parents. In the end, the pedigree file contained 945 individuals, 61 stallions and 292 mares. Among these, 17 stallions and 204 mares had at least one record in the data file. Other individuals with no phenotype contributed to estimating the genetic relationship within the whole population. However, additional pedigree information from the Michałòw stud farm, i.e. the entire family tree for every sampled individual, could not be obtained.

2.3. Data analysis 2.3.1. Variables considered SEC data distribution was checked using the Shapiro–Wilk test implemented in R software (R Development Core Team, 2008. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria).The overdispersion of measured FECs was corrected by applying a ln(X + 10) transformation. The transformed trait was subsequently denoted ln(SEC) and used for analysis. Due to the high number of zeros, FEC data were also considered as a binary trait (denoted Sbin), taking the value of 1 if the horse was infected and 0 if not. A limited set, comprising data recorded on individuals up to 3 years old, was also prepared to investigate age-related variation of heritability. 2.3.2. Model Transformed continuous FECs were analysed using a linear mixed model, and binary infection statuses were analysed using a threshold model. The model was the following:

Y ijkl ¼ l þ YMj þ AGEk þ SEXl þ BYi þ WYij þ Ai þ eijkl

ð1Þ

where Yijkl is the transformed continuous FEC or the underlying variable of the threshold model for the binary infection status; l is the population mean; YMj is a fixed effect of year by month interaction (2 months repeated over 8 years); AGEk is the age at sampling in years (horses of 2 and 3 years of age were grouped into one category; horses older than 15 years were grouped into the final category; nine different levels were available in total); SEXl is the sex of the horses, being either stallions or mares; BYi is the random permanent environmental effect of animal i over the years; WYij is the random permanent environmental effect of animal i over the months within year j; Ai is the random additive genetic effect; eijkl is the random residual effect. 2.3.3. Bivariate analyses By applying this model to our data, the heritability of each trait was estimated, using both the whole dataset and the subset of data recorded for young horses, i.e. up to 3 years old. To assess the extent to which two traits possessed a similar genetic architecture, pair-wise analyses between the two defined traits, i.e. SEC considered as a continuous or a binary trait, were performed to estimate additive genetic correlations (rg). Analyses were carried out using the whole dataset or the subset of data recorded in young horses. Immunity takes some time to develop. Therefore, a bivariate analysis between records of young and adult horses was performed to assess whether the genetic architecture of nematode egg excretion differed between these two age groups. The phenotypic correlation (rp) between traits X and Y was subsequently derived as (Falconer and Mackay, 1996):

rp ¼

ðrg  raX  raY þ re  reX  reY þ rr  rrY Þ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ; r2aX þ r2eX  r2aY þ r2eY

where rg, re and rr are the additive genetic, random environmental and residual correlations, r2aX, r2aY, r2eX, r2eY, r2rX, r2rY, are the additive genetic, random environmental and residual S.D.s for traits X and Y, respectively. 2.3.4. Estimation methods Estimation of genetic parameters was performed using a Bayesian framework implemented in TM (Threshold Model) software, freely available at http://snp.toulouse.inra.fr/alegarra/. A chain of 500,000 iterations was used and 20% of the estimates were discarded in a burn-in procedure. Estimates for the fixed and random effects were saved every100 iterations from the a posteriori

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of strongyle egg excretion are plotted in Fig. 1. Estimates of horse sex, month of sampling and horse age at sampling for faecal egg excretion are plotted in Fig. 2. SEC data were overdispersed with an average prevalence of strongyles of 58% (Table 1). Peaks in strongyle infection occurred in April 2000 and August 2004 (prevalence of 89.2% and 85.6%, respectively) (Fig. 1). A seasonal pattern with a decreased prevalence between April and August was observed (P = 0.002, Fig. 2), except in 2004 and 2005 (Fig. 1). No significant differences were found between stallions and mares for SECs (P = 0.55, Fig. 2A). However, young females excreted fewer strongyle eggs (P < 0.001, data not shown). A higher prevalence (63%) was observed in horses less than 3 years of age. In addition, shedding of strongyle eggs also decreased with age (P < 0.001), but horses of 15 or more years of age were still infected (Fig. 2B).

Table 1 Basic statistics for measured strongyle egg count data including those for measured faecal egg counts for strongyles (SEC) and corresponding analysed traits, i.e. lntransformed variable (ln(SEC)) and binary infection status (Sbin), are provided. Statistics correspond to the whole dataset, the youngster-only dataset (horses up to 3 years old) or the adult-only dataset (horses more than 3 years of age).

Whole Data Youngsters

Adults

SEC ln(SEC) Sbin SEC ln(SEC) Sbin SEC ln(SEC) Sbin

n

Mean

S.D.

Min.

Max.

2692 2692 2692 1344 1344 1344 1593 1593 1593

305 4.2 0.58 425 4.55 0.63 222 3.95 0.54

622 1.9 0.49 759 2 0.48 489 1.7 0.5

0 2.3 0 0 2.3 0 0 2.3 0

5750 8.6 1 5750 8.6 1 5650 8.6 1

239

n, number of processed samples; mean, arithmetic mean. The minimum (Min.) and maximum (Max.) data are shown.

3.2. Heritability and repeatability distribution. Parameter values were inferred by taking the mean of the posterior density. P < 0.05 was considered to reject the null hypothesis.

Estimated variance components of heritability and repeatability for strongyle egg excretion are given in Table 2. Repeatability for ln(SEC) in the whole dataset (Table 2) was slightly higher within years (0.06 ± 0.02) than between years (0.04 ± 0.02). High across- (0.10 ± 0.04) and within-year (0.15 ± 0.05) repeatability of SECs was estimated in adults, although none of the repeatability estimates were significantly different from zero in young horses (Table 2). Estimated heritability values were low (Table 2). Considering the whole dataset, 8–9% of the observed variation had a genetic

3. Results 3.1. Data description SEC data structure according to year of study, horse age and horse sex is provided in Supplementary Fig. S1. Basic statistics for SEC data are summarized in Table 1 and prevalence dynamics 1.00

Prevalence

0.75

0.50

0.25

08/2008

04/2008

08/2006

04/2006

08/2005

04/2005

08/2004

04/2004

08/2002

08/2001

04/2001

08/2000

04/2000

08/1999

04/1999

0.00

Time of sampling Fig. 1. Variations in strongyle prevalence at sampling time points from April 1999 (04/1999) to August 2008 (08/2008). Prevalence of strongyle infection, i.e. the proportion of horses shedding strongyle eggs, is shown for each sampling time.

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240

A

P = 0.55

4.8

3.3. Phenotypic and genetic correlations

P = 0.002

Table 3 shows phenotypic and genetic correlations for the traits considered. Both phenotypic and genetic correlations showed good agreement between continuous traits and their binary equivalent with no correlations below 0.99 (Table 3). There was a high genetic correlation (0.70 ± 0.24) between FECs from adults and those from horses less than 3 years old, whereas the phenotypic correlation was positive but weak (Table 2).

Ln(SEC)

4.3 3.8 3.3 2.8 2.3

Males

Females

April

Sex

August Month

Fixed effect

Ln(SEC)

B

6.3 5.8 5.3 a 4.8 4.3 3.8 3.3 2.8 2.3

4. Discussion

b a

a

a

a

a c

c

Age class Fig. 2. Considered fixed effects, i.e. horse sex, month of sampling and horse age, on strongyle spp. egg excretion. (A) For each of the sexes and each month of study between 1999 and 2008 considered, the corresponding transformed egg count average estimate (ln(SEC)) is plotted on the Y-axis. P values indicate the significance of the difference between males and females, and between April and August. (B) The average transformed egg count for each age class considered. Different letters indicate significant differences, P < 0.05.

origin depending on the trait considered (Table 2). Higher heritability estimates were found for adult SECs (0.21 ± 0.04 for ln(SEC)) while estimates from the young horses dataset were not significantly different from zero (Table 2).

Other reports on strongyle egg shedding consistency over time have been made previously (Döpfer et al., 2004; Nielsen et al., 2006; Becher et al., 2010; Wood et al., 2012) but no known evaluation of the genetic variation for this trait has been published to date. Weak repeatability estimates were found from our data, which corroborates the findings by Wood et al. (2012), who reported a between-individual variation of less than 10% of the total variance. This low repeatability tends to contrast with the seemingly high consistency of egg shedding, i.e. variation relative to each individual’s successive records, reported elsewhere (Döpfer et al., 2004; Nielsen et al., 2006; Becher et al., 2010). Indeed, between 55% (Becher et al., 2010) and 91% of horses (Nielsen et al., 2006) showed consistently low SECs. However, these estimates differ from repeatability estimates as the variations in individuals’ SECs were not taken into account. These papers also reported lower consistencies when considering horses above the 200 eggs/g of faeces threshold, with only 32% (Becher et al., 2010) or 59% (Nielsen et al., 2006) of horses shedding consistently more than the cut-off value, which might favour a higher within-individual variance and thus a lower repeatability. Two conclusions can be drawn from our repeatability estimates. Firstly, even if the data were corrected for known fixed effects such as age at sampling or seasonal effects, additional environmental

Table 2 Genetic parameters and repeatability estimates for strongyle egg excretion. Observations were recorded on horses up to 3 years of age (Youngster), on horses more than 3 years old (Adult) and whole data.

Whole Data Youngster Adult

ln(SEC) Sbin ln(SEC) Sbin ln(SEC) Sbin

vp

va

vpeacr

vpewit

h2

S.E.

racr

S.E.

rwit

S.E.

2.42 1.17 1.91 1.11 2.31 1.50

0.21 0.09 0.07 0.05 0.50 0.31

0.11 0.05 0.02 0.02 0.18 0.12

0.14 0.07 0.08 0.06 0.28 0.19

0.09 0.08 0.04 0.04 0.21 0.21

0.02 0.02 0.02 0.02 0.04 0.05

0.04 0.05 0.01 0.02 0.08 0.08

0.02 0.02 0.01 0.01 0.03 0.03

0.06 0.06 0.04 0.06 0.12 0.13

0.02 0.02 0.02 0.03 0.04 0.04

ln(SEC), transformed strongyles faecal egg count; Sbin, infection status by strongyles; vp, phenotypic variance; va, random genetic additive variance; vpeacr, between-years random permanent environmental variance; vpewit, within-year random permanent environmental variance; h2, heritability (reported value for binary trait is the heritability on the underlying reliability scale); S.E., standard error; racr, repeatability across years; rwit, repeatability within-year.

Table 3 Genetic and phenotypic correlations between the different variables considered for strongyle egg counts. Correlations are estimated for the whole range of data (whole data), for youngsters only (horses 1–3 years of age) or adults only (horses more than 3 years of age). Phenotypic and genetic correlations are reported above and below the diagonal, respectively. Standard errors of genetic correlations are provided in brackets. Whole data

Whole data Youngsters Adults

ln(SEC) Sbin ln(SEC) Sbin ln(SEC) Sbin

Youngsters

ln(SEC)

Sbin

– 0.99a (0.01)

1a (0.002) –

ln(SEC), transformed strongyle faecal egg count; Sbin, infection status by strongyles. a Correlations significantly different from zero.

Adults

ln(SEC)

Sbin

ln(SEC)

– 0.95a (0.07) 0.70a (0.24)

0.99a (0.005) –

0.05 (0.52) – 1a (0.04)

Sbin

1a (0.002) –

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interactions, such as animal by season interactions or variation in exposure, may have impacted egg excretion. In turn, this residual variation prevents the correct estimation of within- and between-individual variations. Secondly, the weak repeatability suggests that it would be difficult to accurately predict SECs in horses based on their previous records, especially in horses less than 3 years old. Low heritability estimates were found throughout the data, i.e. less than 0.10, suggesting that only a slight proportion of the observed variation in faecal egg excretion had a genetic basis. However, restricting the dataset to only the egg counts from adults (horses older than 3 years) resulted in an increased heritability of 0.21. This value is consistent with the usual range of 0.20–0.40 for faecal egg excretion found in small ruminants (Davies et al., 2006; Kemper et al., 2011; Sallé et al., 2012; Assenza et al., 2014) or other host-nematode systems such as hookworm infections in humans (Pullan et al., 2010; Quinnell et al., 2010), ascaridiosis in humans (Williams-Blangero et al., 1999, 2002) or pigs (Nejsum et al., 2009). This also corroborates the age-dependent excretion pattern of strongyle eggs in horses (Chapman et al., 2003; Relf et al., 2012; Wood et al., 2012). This result is also consistent with other findings in young lambs which suggest that heritability in the early years of life is around zero and increases after 6 months of age (Bishop et al., 1996). The genetic mechanisms underlying resistance to strongyles in horses may only become apparent with acquired immunity. However, a high genetic correlation was found between egg counts from young and adult horses, thus suggesting similarities in the genetic architecture of resistance to strongyles over the years. The reported results are in favour of a small to moderate genetic component for nematode egg excretion in the Michałòw pure blood Arabian horse population. As far as we know, no heritability estimates for nematode egg excretion have been proposed for horses, thus additional data from other populations would help to draw conclusions about this low heritability. Indeed, the variation in exposure associated with natural horse infestation and the imperfect sensitivity of faecal egg counting may have contributed to underestimating heritability in the Michałòw Arabian horse population (Quinnell, 2003). Firstly, the egg count technique is associated with suboptimal precision and accuracy, which can lead to substantial variation between samples taken from the same horse (Denwood et al., 2012). Secondly, exposure to nematodes varies from one horse to another, which in turn hampers the drawing of a standardized comparison of the true level of horse resistance to nematodes. Thirdly, the species composition of the strongyle burden may fluctuate from one year to the next, which may lead to variation in SECs due to the variations in species fecundity (Kuzmina et al., 2012). The imperfections in horse phenotypic ranking mask the inter-individual variation to some extent and thus hamper heritability estimation (Bishop and Woolliams, 2010). Experimental infection of a horse population could help to standardize the evaluation of resistance to nematodes and may lead to higher heritability estimates as reported in pigs (Nejsum et al., 2009) or sheep (Assenza et al., 2014), although the feasibility of such an experiment seems far from achievable. Resistance to parasites, i.e. the ability to prevent parasite establishment or to limit their development or both (Detilleux, 2011), is known to be a sex-biased trait, females being generally more resistant than males (Poulin, 1996). For horses, this trend does not seem so obvious. A few recent papers provide insights into the epidemiological features of nematode infection in horses and evaluate the effect of sex on egg excretion (Kornas´ et al., 2010; Relf et al., 2012; Wood et al., 2012). Our results corroborated observations by Relf et al. (2012) with a sex-biased excretion being observed in young horses only (1–3 years of age in this paper and 2–4 years of age in the Relf et al. (2012) study). However these differences diminish

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as horses age. This is in good agreement with the findings of Kornas´ et al. (2010) but contrasts with those of Francisco et al. (2009), who found higher FECs in females (Francisco et al., 2009). This may be related to a variation in the physiological state of mares, e.g. due to pregnancy, which is usually associated with transient loss of immunity in other mammals such as ruminants or pigs (Houdijk, 2008). These observations are particularly relevant if a genetic evaluation was to be performed, since egg excretion varies with both sex and age. This study not only provides insights into the epidemiology of nematode infection in horses but also leads, to our knowledge, to the first estimation of the heritability of nematode egg excretion in horses. Even though our results identified significant heritability of horse nematode egg excretion in this population, only a small amount of the observed variation had a genetic origin. Additional insights from other horse populations would be useful to confirm the potential of breeding strategies as part of integrated nematode management. Acknowledgements The Michałòw stud farm and its staff are acknowledged for allowing this study on their pure blood Arabian horses. Data analyses were performed as part of a project funded by both the French Institute for Horse and Horse Riding (IFCE) and an Animal Health Carnot Institute program (ICSA). The authors are grateful to A. Legarra for his support while using the TM software, S.C. Bishop for his enlightened comments on the analysis of these data and R. Beech for his critical review of this manuscript. The authors are not aware of any conflict of interest. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.ijpara.2014.11. 003. References Anonymous, 2012. Hiérarchisation de 103 maladies animales présentes dans les filières ruminants, équidés, porcs, volailles et lapins en France métropolitaine. Avis ANSES 2010-SA-0280. Agence nationale de sécurité sanitaire de l’alimentation, de l’environnement et du travail, Maisons-Alfort, France. . Assenza, F., Elsen, J.-M., Legarra, A., Carré, C., Sallé, G., Robert-Granié, C., Moreno, C., 2014. Genetic parameters for growth and faecal worm egg count following Haemonchus contortus experimental infestations using pedigree and molecular information. Genet. Sel. Evol. 46, 13. http://dx.doi.org/10.1186/1297-9686-4613. Becher, A.M., Mahling, M., Nielsen, M.K., Pfister, K., 2010. Selective anthelmintic therapy of horses in the Federal states of Bavaria (Germany) and Salzburg (Austria): an investigation into strongyle egg shedding consistency. Vet. Parasitol. 171, 116–122. http://dx.doi.org/10.1016/j.vetpar.2010.03.001. Bishop, S., Woolliams, J., 2010. On the genetic interpretation of disease data. PLoS ONE 5, e8940. http://dx.doi.org/10.1371/journal.pone.0008940. Bishop, S.C., Bairden, K., McKellar, Q.A., Park, M., Stear, M.J., 1996. Genetic parameters for faecal egg count following mixed, natural, predominantly Ostertagia circumcincta infection and relationships with live weight in young lambs. Anim. Sci. 63, 423–428. http://dx.doi.org/10.1017/S1357729800015319. Bishop, S.C., Jackson, F., Coop, R.L., Stear, M.J., 2004. Genetic parameters for resistance to nematode infections in Texel lambs and their utility in breeding programmes. Animal 78, 185–194. Chapman, M.R., French, D.D., Klei, T.R., 2003. Prevalence of strongyle nematodes in naturally infected ponies of different ages and during different seasons of the year in Louisiana. J. Parasitol. 89, 309–314, 10.1645/0022-3395(2003) 089[0309:POSNIN]2.0.CO;2. Chmiel, K., Sobczuk, D., Gajewska, A., 1999. Charakterystyka polskiego stada matek _ ´ ci czystej krwi arabskiej pod wzgle˛dem niektórych wskaz´ników uzytkowos rozpłodowej. Przegl. Hodowl., 19–21 Corning, S., 2009. Equine cyathostomins: a review of biology, clinical significance and therapy. Parasites Vectors 2, S1. http://dx.doi.org/10.1186/1756-3305-2S2-S1. Davies, G., Stear, M.J., Benothman, M., Abuagob, O., Kerr, A., Mitchell, S., Bishop, S.C., 2006. Quantitative trait loci associated with parasitic infection in Scottish

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