- Research article
- Open Access
Both candidate gene and neutral genetic diversity correlate with parasite resistance in female Mediterranean mouflon
BMC Ecology volume 19, Article number: 12 (2019)
Parasite infections can have substantial impacts on population dynamics and are accordingly a key challenge for wild population management. Here we studied genetic mechanisms driving parasite resistance in a large herbivore through a comprehensive approach combining measurements of neutral (16 microsatellites) and adaptive (MHC DRB1 exon 2) genetic diversity and two types of gastrointestinal parasites (nematodes and coccidia).
While accounting for other extrinsic and intrinsic predictors known to impact parasite load, we show that both neutral genetic diversity and DRB1 are associated with resistance to gastrointestinal nematodes. Intermediate levels of multi-locus heterozygosity maximized nematodes resistance, suggesting that both in- and outbreeding depression might occur in the population. DRB1 heterozygosity and specific alleles effects were detected, suggesting the occurrence of heterozygote advantage, rare-allele effects and/or fluctuating selection. On the contrary, no association was detected between genetic diversity and resistance to coccidia, indicating that different parasite classes are impacted by different genetic drivers.
This study provides important insights for large herbivores and wild sheep pathogen management, and in particular suggests that factors likely to impact genetic diversity and allelic frequencies, including global changes, are also expected to impact parasite resistance.
Parasites are an important component of ecosystems and can have substantial impacts on host fitness and population dynamics. Parasites can affect body condition (e.g. [1,2,3]), reproductive success (e.g., [4, 5]), survival (e.g., ), feeding behavior (e.g., ) and/or interspecific interactions (e.g., [8, 9]). While parasitism causes significant economic losses in animal production around the world (e.g. gastrointestinal nematodes (GINs)) [10, 11], in wild populations its impact on individual and population viability  can lead to management and conservation issues [13, 14].
Resistance to parasites, defined as the “host’s ability to interact with and control the lifecycle of the parasite” [15, 16], depends in part on the genetically determined immune system of hosts and hence involves both the genetic characteristics (e.g. presence of specific alleles) and variability of hosts [17,18,19,20]. The influence of genetics on parasite resistance is also mediated by other extrinsic and intrinsic factors such as population density, environmental conditions, age, sex and body condition [18, 20,21,22,23]. Consequently, all the elements likely to impact genetic diversity are expected to impact parasite resistance as well. In the current context of habitat fragmentation [24, 25] impacting population sizes, gene flow and thus genetic diversity [26,27,28] and of climate change modifying parasite environmental persistence and dynamics [29,30,31], gathering knowledge on the genetics of parasite resistance has become crucial for population management and conservation purposes.
A large body of literature on the genetics of parasite resistance investigates heterozygosity-fitness correlations (HFCs) using heterozygosity as a measure of genetic diversity and parasite resistance as a fitness proxy. Positive relationships between pathogen resistance and heterozygosity have been evidenced in numerous taxa (e.g. wild boars, Sus scrofa, ; raccoons, Procyon lotor, ; Alpine ibex, Capra ibex, ; mongooses, Mungos mungo, ). Effects of specific loci and especially candidate genes (i.e. encoding genes associated with immunity) on pathogen resistance have also been documented (see e.g., [36,37,38,39,40,41]). For instance, Luikart et al.  had shown that the link between heterozygosity and parasite burden relies on microsatellites located in candidate genes instead of on microsatellites in genome portions assumed as neutral. Although a large majority of studies evidenced positive correlations between parasite resistance and heterozygosity, contrasting results can nevertheless be observed: inconclusive studies , negative correlations (e.g., [44, 45]) or no correlation between pathogen resistance and heterozygosity and/or specific loci/alleles (e.g., [46,47,48,49]) can be found.
Three main hypotheses might explain HFCs : (i) the direct effect hypothesis positing a direct link of genetic markers with fitness (e.g. encoding genes), (ii) the local effect hypothesis (or indirect effect hypothesis) claiming that the markers considered are in linkage disequilibrium (non-random association of alleles at different loci) with fitness-linked loci and (iii) the general effect hypothesis asserting that the heterozygote advantage is due to a genome-wide effect of fitness loci with more diverse individuals thought to be more efficient in coping with infections (e.g., ). However, since the existence and detection of HFCs are largely environment- and context-dependent , distinguishing between the three hypotheses is a challenging task. In particular, HFCs depend on the inbreeding level of the population (identity disequilibrium, ), the genetic markers and fitness components used and the ability of these markers to capture genome-wide diversity [53,54,55]. In the case of parasite resistance, HFCs may also depend on the parasites and hosts species studied (e.g., [48, 56]). Indeed, not all parasites have the same effects on hosts and thus the effects of genetic diversity on resistance may vary from one class to another and according to co-infections [33, 57]. In addition, immunocompetence of individuals is a highly polygenic trait involving numerous genes associated with immunity functions (e.g., X-chromosome ; gamma interferon ; Toll-like receptors ; major histocompatibility complex (MHC) ; reviewed by [18, 20]). Comparative studies combining different approaches and different parasites types are thus needed to better understand functional links between genetics and pathogen resistance.
Here, we proposed to gain better knowledge on the genetics of resistance and underlying mechanisms by combining candidate genes and neutral diversity approaches for two parasites classes, gastrointestinal nematodes (GINs) and protozoan parasites (Coccidia, Eimeria spp.) in female Mediterranean mouflon (Ovis gmelini musimon × Ovis sp.). GINs and coccidia are common parasites of small ruminants [62, 63] and are known to impact fitness (e.g., [64, 65]) and cause important economic losses in domestic livestock [66, 67]. While they have been the object of numerous studies on genetic parasite resistance in domestic sheep (e.g., [68,69,70], see also  for a review), they have been much less investigated in wild sheep species (but see [36, 58, 59, 72] for examples in feral Soay sheep, Ovis aries, and  for an example in bighorn sheep, Ovis canadensis) despite similar expected detrimental effects and the existence, for these wild species, of both conservation (e.g., [73, 74]) and management issues (e.g., [75,76,77]).
In both the neutral diversity and candidate gene approaches, we first accounted for other extrinsic and intrinsic predictors known to impact parasite load (e.g., socio-spatial organization ; population density ; age, sex ; body condition ). We then assessed, for the neutral diversity approach, if multi-locus heterozygosity from a set of neutral markers (16 microsatellites) was associated with parasite resistance as measured by fecal egg or fecal oocyst counts (FEC or FOC, for GINs and coccidia, respectively). In line with most HFC studies, we expected the more heterozygous individuals to be more resistant to parasite infection because more diverse individuals are expected to carry more adaptive alleles to resist parasites and/or to less express deleterious recessive alleles (e.g., [34, 36, 79]). For the candidate gene approach, we focused on MHC DRB1 class II gene, known to encode for binding proteins presenting extracellular antigens to T-lymphocytes  and to be linked to parasite resistance in sheep and mammals (see e.g., [61, 68, 81]). A high variation at MHC class II loci is often considered advantageous since it should enable an increased number of pathogens to be recognized and subsequent immune response  (see also [83, 84] for reviews). However, the presence of certain genotypes or alleles at candidate loci has also been shown to be associated with parasite resistance or susceptibility (e.g., [69, 70]). We thus independently tested for the effects on parasite resistance of genotypes, heterozygosity and the presence of specific alleles at DRB1 locus in order to discriminate between the diverse possible effects. We expected homozygous individuals at candidate locus to be more susceptible to parasite infections while specific association with genotypes and/or alleles could also be observed. In order to disentangle between genome-wide or immune gene associations, neutral multi-locus heterozygosity and immune gene were all considered in the same analyses. Finally, since GINs and coccidia are two very different classes of parasites (macro-parasites and protozoan micro-parasites, respectively) driven by diverse immune mechanisms [85, 86], results between them were expected to be different (see e.g., [33, 87, 88]).
The multi-locus heterozygosity sMLH ranged from 0.36 to 1.36 and had an average value of 0.91. The set of 16 microsatellites showed a g2 not significantly different from zero, neither when the whole population was considered (g2 = 0.008 ± 0.009, p = 0.10) nor when analyses were performed for each socio-spatial unit separately (Nf: g2 = − 0.009, p = 0.69; Cf: g2 = − 0.007, p = 0.16; Sf: g2 = 0.06, p = 0.07). Three DRB1 alleles, which have all been previously described in domestic sheep, were identified (Ovar-DRB1*0324, Ovar-DRB1*07012 and Ovar-DRB1*0114, see , GeneBank accession numbers: Ovar-DRB1 *0324, DQ659119.2, Ovar-DRB1 *07012, AY884017.2 and Ovar-DRB1*0114, DQ659116.2) leading to six different genotypes (named from A to F, see Table 1). The two individuals presenting genotype F were removed from the dataset before analyses to avoid false positive effects caused by a too small sample size. A total of 77 individuals representing 118 observations were thus considered in subsequent analyses.
Parasite prevalence and abundance
The prevalence of coccidia was 100% with FOC ranging from 25 to 11,300 OPG (median FOC = 925). GINs were present in 76 out of 77 individuals with FEC ranging from 0 to 5100 EPG (median FEC = 350). Repeated measurements were available for 29 individuals (70 observations) and mean repeatability for FOC was 0.08 ([0.00–0.44]95%), while it was higher for FEC with an average value of 0.41 ([0.13–0.70]95%).
For FOC, the five first models were equivalent (ΔAICc < 2) and included age, body condition, time lapse between sampling and coproscopy, and Julian date (see Additional files 1 and 2 for more details). The best non-genetic model retained for coccidia thus accounted for these four non-genetic variables. For GINs, the best non-genetic model included only the effect of body condition (see Additional files 1 and 2 for more details).
HFC and locus-specific effects
In the second step of the inferential approach, we added genetic predictors to the best non-genetic models previously retained. None of the genetic predictors showed a VIF higher than three in any of the model sets for both parasite types, indicating no correlation issues (Additional file 3: Table S4). When considering coccidia, no quadratic relationship between sMLH and FOC was detected (Additional file 3: Table S5) and the best model was the non-genetic model (Table 2) indicating that the genetic predictors we studied were not significantly linked to coccidia resistance. For GINs, a quadratic relationship between sMLH and FEC was detected in the three sets of models (i) DRB1 heterozygosity status, (ii) presence of specific DRB1 alleles and (iii) DRB1genotypes (Fig. 1, Additional file 3: Table S5). In all models where sMLH and sMLH2 appeared, estimates were negative for sMLH and positive for sMLH2 (Table 3) indicating a U-shaped relationship (Fig. 1).
Almost all GINs models including genetic predictors (16 out of 19) had a lower AICc than the non-genetic model, highlighting the strong relationship between GINs resistance and genetics. In particular, the model including DRB1 heterozygosity (model set (i)) was the best model (lowest AICc), indicating that among the three DRB1 characteristics evaluated (heterozygosity, alleles and genotypes), heterozygosity was the best descriptor of parasite resistance for GINs. The model including both sMLH/sMLH2 and DRB1 heterozygosity was better than the models including only DRB1 heterozygosity or sMLH/sMLH2 (∆AICc > 2, Table 2). A significant difference of 52% in averaged FEC was detected between heterozygous and homozygous individuals (Fig. 2a). When testing the effects of specific alleles at DRB1 locus on FEC (model set (ii)), the best model was the model including sMLH/sMLH2 and DRB1*0114 allele (Table 2). Estimate was negative for the presence of this allele (Table 3) and its presence led to a 56% decrease in FEC between individuals carrying or not carrying this allele (Fig. 2b). Finally, in the model set (iii), the models including sMLH/sMLH2 and DRB1 genotypes or only DRB1 genotypes were better than the non-genetic model (∆AICc > 2, Table 2). We found a marked gradient (Fig. 2c) between the most parasitized DRB1 genotype (D) and the least parasitized genotype (C) with a statistically significant difference between A and C genotypes, leading to a 57.2% decrease in averaged FEC. The F-ratio test between “local” and “global” models revealed no significant differences, indicating stronger support for the global hypothesis (F = 0.96, d.f. = 37, p = 0.54).
As illustrated here, parasite resistance in the female Mediterranean mouflon is a complex trait controlled by several non-genetic and genetic predictors. For both parasite classes, individuals in better condition were less parasitized. Multi-locus heterozygosity was linked to GINs resistance through a U-shaped relationship suggesting the presence of both in- and outbreeding depression in our population. However, since g2 and the “global/local” test did not lead to same conclusions, we were not able to distinguish between local and global effects of neutral genetic variation. It seemed that DRB1 candidate locus conferred a heterozygote advantage and that rare alleles and/or fluctuating selection might also occur in the study population . These results confirm that the three main hypotheses about HFCs are not mutually exclusive . In contrast, while coccidia burden appeared as simultaneously driven by age, day of sampling and time lapse between sampling and coproscopy, we detected no genetic predictor effects for that class of parasites, illustrating that resistances to different parasite classes (here GINs and coccidia) are driven by different characteristics (see also [85, 86]), emphasizing the importance of performing multi-specific studies.
Different characteristics are determining different parasite resistances
None of the genetic predictors studied were linked with coccidia resistance. The absence of correlation between genetic diversity and parasite resistance was also observed in other host-parasite systems (e.g., [41, 92, 93]). Although a lack of statistical power cannot be excluded to explain this result, the genetic effects detected for GINs with the same dataset suggested that genetics had much less effect on variation in micro-parasite resistance than in macro-parasite resistance. Repeatability was notably lower for FOC than FEC (yet comparable to other studies, e.g., ), indicating that variation in FOC is primarily driven by short-term effects or measurement errors, rather than genetic effects.
Differences between results for coccidia and GINs may be due to the fact that coccidia are intracellular protozoa, while GINs are macro-parasitic nematodes. Micro- and macro-parasites are thought to be controlled by different immune responses (Th1 and Th2 respectively [85, 86]) that can be involved in trade-offs and thus not active at the same time (e.g., [86, 87], see also  for a review). Different immune pathways may be impacted by different genetic factors explaining the differences observed between GINs and coccidia in the present study. MHC class II genes such as DRB1 seem also more specifically linked to an extracellular parasite-derived peptide presentation ([80, 95]) that may explain the impacts of DRB1 on GINs but not on coccidia.
Neutral genetic diversity effects on nematode resistance
We observed a U-shaped relationship between sMLH and GINs burden with a maximal parasite resistance obtained for individuals with intermediate heterozygosity levels. Parasite burden decreased with increasing heterozygosity until a threshold (~ 1), after which highly heterozygous individuals were parasitized as much as highly homozygous individuals, suggesting the presence of both positive and negative HFCs. While a positive relationship between parasite resistance and genetic diversity is the rule (e.g., [34, 35, 45, 79, 96]), quadratic relationships have also been previously reported (e.g., in Soay sheep ; lesser kestrel, Falco naumanni ; rostrum dace, Leuciscus leuciscus ; raccoons ; blue tits, Cyanistes caeruleus ) but most often in the opposite direction with individuals carrying intermediate heterozygosity levels being less resistant (see e.g., [33, 97, 98]). Optimal parasite resistance was nevertheless observed for an intermediate level of genetic diversity in studies considering the number of MHC alleles [61, 99]. Indeed, when considering encoding genes such as MHC genes, theory predicts that while a high diversity of alleles enables a large spectrum of pathogen recognition (diversifying selection), it could also limit the immune response efficiency by causing self-reacting . Accordingly, an intermediate number of alleles is expected to confer the highest fitness to individuals due to the two contradictory evolutionary forces acting on MHC diversity. The U-shaped relationship observed here for multi-locus heterozygosity might thus suggest that two contradictory evolutionary forces are also acting on neutral genetic diversity.
A positive relationship between genetic diversity and fitness-related traits such as parasite resistance can be explained by inbreeding depression with more inbred individuals exhibiting lower levels of heterozygosity and fitness . On the other hand, negative HFCs and thus heterozygote disadvantage might be explained by outbreeding depression (i.e. reduced fitness in offspring originating from highly differentiated parents) . Negative HFCs have been documented much less than positive ones [103,104,105] (but see e.g., [45, 106, 107]) but the U-shaped relationship observed here may suggest the presence of both inbreeding and outbreeding depression in our population. In- and outbreeding depression co-occurrence have been observed within the same populations (e.g., [108, 109]) and on the same fitness traits [103, 110,111,112]. It requires that population structure (e.g. philopatry, founder events) induce both local adaptation and inbreeding in the population . Due to high female philopatry in the study population [113, 114], moderate inbreeding (a low number of individuals exhibiting low sMLH) is likely to occur in females. On the other hand, the release of founders originating from three diverse origins  is likely to have generated outbreeding depression that still persists as observed in this population for other genetic signals . Outbreeding depression might result from underdominance, disruption of epistatic interactions leading to break-down of co-adapted gene complexes and/or loss of local adaptations by disruption of advantageous gene × environment interactions .
Finally, the absence of support for the local effect hypothesis suggested that the observed HFC was due to a genome-wide diversity effect. However, g2 was not significantly different from zero, preventing us from coming to a conclusion about global or local effect of multi-locus heterozygosity. Detection of significant identity disequilibrium using g2 is only rarely achieved (see ) and numerous studies have evidenced significant HFCs despite no detectable identity disequilibrium [34, 52, 116, 117]. Accordingly, studies where g2 and global/local tests  highlighted opposing results are not scarce (see e.g., [91, 118]). However, even when not detected, local effects cannot be fully discarded since their detection is very difficult due to dilution effects of unlinked loci on linked loci (see  but see e.g., ).
Candidate gene effects on nematode resistance
Links between MHC heterozygosity and fitness were evidenced across a wide range of taxa (e.g., [38, 39] but see ). Three main mechanisms that can co-occur have been proposed to explain the impacts of MHC diversity on pathogen resistance: (i) heterozygote advantages (i.e. heterozygote recognizing and binding a wider range of antigens than homozygotes, through overdominance or dominance), (ii) rare-allele advantages (negative frequency-dependence) in which new alleles confer advantages since selection favors parasites overcoming the more common resistance alleles and (iii) fluctuating selection proposing that spatio-temporal variability of pathogen types and abundances induce fluctuating selection on MHC, inducing differential links between pathogen resistance and MHC diversity (see  for a review). Heterozygous advantage can be detected when MHC heterozygosity and parasite resistance are associated, while rare-allele and fluctuating selection will be detected through specific MHC allele effects on resistance . In the present study, genotypes, specific alleles and heterozygosity effects were evidenced suggesting that heterozygote advantage, rare-allele effects (e.g. DRB1*0114 allele was the rarest) and/or fluctuating selection might occur. Genotypes effects were the weakest and seemed mostly linked to heterozygosity effects. Indeed, although differences between genotypes were not significant, heterozygous genotypes (genotypes B, C, and E) were significantly less parasitized than homozygous genotypes (see Fig. 2a, c). Heterozygosity effects were also stronger than specific allele effects, supporting a predominant heterozygote advantage. Distinguishing between the overdominance and dominance explanation for heterozygote advantage is challenging , but heterozygous individuals were less parasitized than both types of homozygous individuals, suggesting that the heterozygote advantage we observed was due to overdominance (see [38, 90]). Evaluating impacts of parasitism on survival and/or reproductive success might help to determine through which trait heterozygote advantage occurs.
Specific allele effects might also be explained in the light of heterozygosity effects. Indeed, the negative effects of DRB1*0114 on FEC could be attributed to the fact that it was only present in heterozygous individuals (genotype F individuals removed from the dataset because of a too-small sample size). However, models containing alleles were among the best models, and specific allele effects might more likely be due to the immunological properties of their products (i.e. peptide binding sites in our case). Specific MHC and DRB1 allele effects on fitness and parasite resistance were observed elsewhere (e.g., [41, 81, 119]). However, to our knowledge, the three DRB1 alleles sequences identified in our population were previously observed in only one study . Hermann-Hoesing et al.  studied the impacts of DRB1 alleles on ovine progressive pneumonia virus resistance in domestic ewes. They evidenced that allele DRB1*0324 and DRB1*0114 were associated with a higher provirus level, while DRB1*07012 allele was associated with a lower provirus level. The authors explained that these differences were linked with specific amino-acid encoded by the diverse alleles and determining the immune response. Indeed, immunological theory predicts that specific alleles could be advantageous (disadvantageous) if their products are more (less) effective in presenting pathogen-derived peptides . Thus our results suggested that protein binding sites encoded by DRB1*0114 conferred an advantage against GINs infections. Since different functional links between genetics and resistance could indeed be expected when considering different parasite classes (see e.g., [33, 48, 56, 57]), the opposing effects of DRB1*0114 observed between Herrmann-Hoesing et al.  and the present study are not surprising since provirus and macro-parasitic strongyles are very different pathogen types.
Our findings brought important insights for Mediterranean mouflon and more generally for large ungulate management. Firstly, the positive impact of genetic diversity on parasite resistance detected emphasizes the importance of promoting genetic diversity and preventing inbreeding in populations. Gene flow [28, 121,122,123,124] and thus genetic diversity (e.g., [125, 126]) might be impacted by landscape in wild sheep and ungulates. Accordingly, careful attention must be given to maintaining landscape connectivity, especially in threatened populations (e.g. Corsican mouflon , Argali, Ovis ammon [75, 127, 128], Cypriot mouflon, Ovis orientalis ophion , Sierra Nevada bighorn sheep, Ovis canadensis sierra [129, 130]). Secondly, when planning introductions or translocations in conservation and genetic reinforcement strategies, maximizing the admixture of founder/translocated individuals might increase parasite resistance by increasing genetic diversity . Similarly, in accordance with the direct effects of the DRB1 gene, translocated individuals might be chosen according to their parasite resistance characteristics (e.g. carrying resistance alleles). We nevertheless also evidenced that outbreeding depression can decrease parasite resistance. Wildlife managers must thus be careful regarding local adaptations when choosing individuals and source populations. In addition, in wild populations, another concern when introducing new individuals might be the introduction of alien parasite species which might have substantial negative consequences [132,133,134]. Finally, gathering more data on males would allow us to determine if genetic effects are sex-specific, and to measure the impacts of selective hunting on parasite resistance. Indeed, parasite-mediated sexual selection  posits that secondary sexual characteristics, such as horns, are an honest signal about parasitism rates of males (see e.g., [34, 136] but see ). Since in most wild sheep and Mediterranean mouflon populations males are hunted for their trophies, hunting can counter natural selection and could modify resistance allele frequencies [59, 138,139,140].
Study population and data collection
The Mediterranean mouflon study population originates from the release of 19 individuals between 1956 and 1960  in a National Hunting and Wildlife Reserve (1658 ha, 532–1124 m above sea level; hereafter called “reserve”) in the Caroux-Espinouse massif (43°38′N, 2°58′E, 17,000 ha, 130–1124 m asl, southern France). Vegetation is composed of beech, chestnut and coniferous forests in this low mountain area where deep valleys and plateaus draw a mosaic of ridges and talwegs (i.e. lines of lowest elevation within a valley, see [141, 142] for details). Local climate is under the influence of Mediterranean, oceanic and mountainous weather patterns  with dry and hot summers, autumns with lots of precipitation and cold winters .
The population has been monitored each year since 1974, mainly during spring and early summer (April–July), by capture-mark-recapture. Animals were baited with salt and captured using individual or collective traps and dropping nets. When captured, animals were marked with a numbered/colored collar; biometric measurements were made and hairs and faeces were sampled for genetic and coproscopic analyses. Genetic analyses revealed that gene flow is mostly insured by male reproductive dispersal (reproductive excursions, ), while ewes are philopatric [113, 145, 146]. Females exhibit a significant socio-spatial genetic structure consisting primarily of three spatially disconnected and genetically differentiated units (Nf, Cf and Sf, see ) and gene flow has been shown to be impacted by several landscape features .
Neutral genetic diversity
Individuals were genotyped at 16 microsatellite markers (see  for details) using hair samples. Genotyping was performed by the Antagene laboratory (Limonest, France, www.antagene.com) following the procedure presented in Portanier et al. . To assess if genome-wide genetic diversity was associated with parasite resistance, we calculated the standardized multi-locus heterozygosity (sMLH) for individuals having at least 13 microsatellite markers. sMLH was calculated as the ratio between the proportion of loci at which an individual was heterozygous and the mean heterozygosity of typed loci (see ) using the inbreedR package for R software . To determine if our set of markers was a good proxy for genome-wide heterozygosity and discriminate between global and local effects of sMLH, we quantified identity disequilibrium in the whole population and within each socio-spatial unit by estimating g2, a measure of the covariance in heterozygosity using Robust Multi-locus Estimates of Selfing software (RMES ) with 10,000 iterations. RMES tests whether g2 is significantly different from zero. If g2 = 0, HFCs are not expected to appear because identity disequilibrium is not expected to be present in the population.
Candidate gene approach
The second exon of the MHC-DRB class II gene encoding the ligand-binding domain of the protein was amplified and sequenced for all individuals. Each sample was analyzed twice by at least two independent technical replicates. Briefly, we performed the two-step PCR strategy combined with the dual-index paired-end sequencing approach described in Galan et al. . During the first PCR, we used a modified version of the primers LA31 (5′-GATCCTCTCTCTGCAGCACATTTCCT-3′) and LA32 (5′-TTCGCGTCACCTCGCCGCTG-3′) initially designed for cattle , with the addition of a partial overhang Illumina sequencing primers in 5′-end. The first PCRs were carried out in a 10 µL reaction volume using 5 µL of Multiplex PCR Kit (Qiagen) and 0.5 mM of each primer. We added to each well a volume of 1.5 µL of DNA. This PCR consists of an initial denaturation at 95 °C for 15 min, followed by 40 cycles of denaturation at 94 °C for 30 s, annealing at 55 °C for 30 s and extension at 72 °C for 30 s, with a final extension phase at 72 °C for 10 min. The second PCR consists of a limited-cycle amplification step to add multiplexing indices i5 and i7 and Illumina sequencing adapters P5 and P7 at both ends of each DNA fragment (see  for details). The PCR products were verified by electrophoresis in a 1.5% agarose gel. One negative control for extraction, one PCR blank and one negative control for indexing were systematically added to each of the PCR microplates. Each DNA extraction was amplified and indexed in two independent PCR reactions. These PCR replicates were used as technical replicates to confirm the genotypes and further remove the false-positive results . PCR products were pooled by volume and a 2 × 250 bp paired-end MiSeq (Illumina) run was conducted. The SESAME barcode software (SEquence Sorter & AMplicon Explorer ) was used to sort sequences, identify and discard artefactual variants, and generate the haplotypes and individual genotypes.
In the candidate gene approach, we also genotyped a microsatellite located in the gamma interferon gene (chromosome 3, o(IFN)-γ) known to be linked with parasite resistance in wild sheep (see e.g., ). However, based on the analysis of a representative subset of 48 individuals, we found the o(IFN)-γ to be monomorphic in our population and it was thus not considered in subsequent analyses (data not shown).
Fecal parasite egg and oocyst counts
Mediterranean mouflon might be infected by a large diversity of endoparasites such as Trichuris spp., Moniezia spp. or Dicrocoelium spp. but the most prevalent are strongylid nematodes and coccidia (Eimeria spp., [30, 153]). We accordingly limited our analyses to these last two parasites types. Strongyles and coccidia abundances were estimated by counting the number of eggs and oocysts in fecal samples (FEC and FOC, respectively, are widespread parasite resistance measurements often used in HFCs studies; see e.g., [3, 34]). FEC and FOC represented the abundances of all strongyles and coccidia species present in the samples, respectively. Coproscopic analyses were performed between 2010 and 2017. Faeces samples were individually stored in a refrigerated container before analyses. FEC and FOC were estimated using a modified MacMaster procedure (modified from ). After sample homogenization, 5 grams of faeces were weighed and mixed with 70 mL of zinc sulfate (d = 1.36). The sample was then filtered through a sieve lined with a compress and the homogenized filtrate was immediately loaded in two 0.15 mL chambers of a MacMaster slide. After allowing them to float at the surface for at least one minute, eggs and oocysts were counted using a compound microscope (magnification ×100). The number of eggs or oocysts per gram of faeces (EPG and OPG, respectively) was obtained by multiplying the total number of counted eggs by 50. In order to perform a qualitative examination (“control slide” hereafter), we filled a 14 mL tube with the remaining solution until a meniscus was obtained; a cover slide was then placed on the tube. After 5 min of centrifugation at 1200 rpm, the cover slide was recovered and placed on a microscope slide. We searched for parasite propagules using a microscope (magnification ×40–400). The theoretical sensitivity of the MacMaster is 50 EPG/OPG of fecal matter. When, for an individual, no eggs or oocysts were observed using the MacMaster technique, but at least one egg or oocyst was observed on the control slide, we attributed the value of 25 EPG/OPG for FEC or FOC. FEC and FOC had skewed distribution and were log-transformed to obtain a normal distribution. To avoid log of zero for GINs, 10 was added to FEC values. We assessed repeatability of FEC and FOC measurements for a given animal by computing intra-class correlation coefficients (for unbalanced design because number of measurements differed among animals ).
Prior to testing for genetic effects on FEC and FOC we first identified (and accounted for) other intrinsic and extrinsic variables known to impact parasitism [18, 20, 22, 78, 156,157,158]. Variables considered included the year and the Julian date of sampling to account for intra and inter-annual variations in environmental conditions and population densities. We corrected for part of the sampling variance by adding the number of days between sampling and coproscopic analyses in the models since it can impact the number of fecal egg and oocyst counted [159, 160]. We only considered individuals for which less than 30 days elapsed between the sampling and laboratory analyses. Body condition (Scaled Mass Index, calculated based on individual mass and metatarsus length ) was included to account for heterogeneity in individual quality, and age was included to account for changes in immunity with age (e.g., [22, 162,163,164]). Since females cannot be accurately aged when > 3 years old , ages were divided into 4 classes: 1, 2, 3 and ≥ 4-year-old individuals. However, due to the paucity of data on males and juveniles, we focused in this study on sexually mature females only (i.e. 2 or more years old ). A total of 79 individuals representing 120 observations were included in subsequent analyses. Finally, the socio-spatial unit (SSU) of individuals was also included (80, 28 and 10 observations from the Cf, Nf and Sf socio-spatial units, respectively) since spatial structure of the population, when overlooked, can lead to spurious HFCs .
We applied a two-stage procedure in a linear mixed model selection framework, first identifying for each response variable the best non-genetic model (including both extrinsic factors and other, not purely molecular, intrinsic factors) to which genetic predictors (both neutral and adaptive) were then added. The predictors included in non-genetic models can also have a genetic basis (e.g., body condition ) but are different from direct genetic measurements such as the ones included in genetic models. Such two-stage procedure enabled measurement of the importance of genetic predictors relative to other factors known to impact parasite resistance, and to avoid over-parameterization of models. Model selection was done by comparing corrected Akaike’s Information Criterion (AICc) values of the possible models. All variables included in models being in a range of ∆AICc < 2 were included in the optimal non-genetic model. We then evaluated the improvement of models through the addition of genetic predictors. Since there could be specific and/or general effects of genetic diversity on FEC and FOC, three sets of genetic models were created and included (i) sMLH and/or the DRB1 heterozygosity status of individuals coded as 1 if heterozygous and 0 if homozygous, (ii) sMLH and/or the presence of specific DRB1 alleles, coded as 1 if present and 0 if absent (see e.g.,  for a similar approach on survival), and (iii) sMLH and/or the DRB1 genotypes of individuals.
All continuous variables (sMLH, day of sampling, body condition, time elapsed between sampling and coproscopy) were centered and scaled (mean = 0, standard deviation = 1) before analyses and individual identity and year of sampling were included as random effects to account for repeated measurements and measurements made in different years, respectively. We also tested for possible non-linear relationships between FEC and FOC and continuous variables by adding their quadratic terms in models. If the addition of the quadratic term did not improve the model by more than two AICc units (ΔAICc > 2), only the linear term was retained to perform model selection. Multi-collinearity of predictors was checked using variance inflation factors (VIF, vif.mer function ). Following Zuur et al. , if a predictor showed a VIF > 3, it was not kept in model selection. In both non-genetic and genetic steps, residuals structure and normality of the best models were tested and visually assessed (see Additional file 2). We measured the relative likelihood of each model using the AIC weights. Goodness-of-fit was assessed using conditional (R2c) and marginal (R2m) R2, representing the variance explained by the fixed and random effects and by the fixed effects alone, respectively.
Finally, in order to discriminate between the hypotheses of local or global effects of neutral genetic diversity on parasite resistance, we followed the method proposed by Szulkin et al. . We built two models including the non-genetic predictors retained and (i) sMLH, and its quadratic term if necessary, as the sole genetic predictor (“global” model) or (ii) all single locus heterozygosities, with each individual coded as 1 if heterozygous or 0 if homozygous (“local” model). The two models were then compared using a F-ratio test. If the “local” model explains more variance than the “global” model, the local effect hypothesis will receive more support than the global one. Since there are relatively large differences in loci heterozygosity levels (see ), we also performed the test using the standardized approach introduced by Szulkin et al. , in which more weight is given to more heterozygous loci. Both non-standardized and standardized approaches led to the same results; only results of the standardized approach are given in the following. All modeling and model selection were performed using lme4  and MuMIn packages  of R 3.3.1 software . Significance tests were performed using the lmerTest package  and model plots were carried out using the visreg package  of R 3.3.1 software . The code used for all the statistical analyses is available in the Additional file 4.
Delahay RJ, Speakman JR, Moss R. The energetic consequences of parasitism: effects of a developing infection of Trichostrongylus tenuis (Nematoda) on red grouse (Lagopus lagopus scoticus) energy balance, body weight and condition. Parasitology. 1995;110:473–82.
Hakkarainen H, Huhta E, Koskela E, Mappes T, Soveri T, Suorsa P. Eimeria-parasites are associated with a lowered mother’s and offspring’s body condition in island and mainland populations of the bank vole. Parasitology. 2007;134:23–31.
Debeffe L, Mcloughlin PD, Medill SA, Stewart K, Andres D, Shury T, et al. Negative covariance between parasite load and body condition in a population of feral horses. Parasitology. 2016;143:983–97.
Mulvey M, Aho JM, Rhodes E. Parasitism and white-tailed deer: timing and components of female reproduction. Oikos. 1994;70:177–82.
Pelletier F, Page KA, Ostiguy T, Festa-Bianchet M. Fecal counts of lungworms larvae and reproductive effort in bighorn sheep, Ovis canadensis. Oikos. 2005;110:473–80.
Hudson PJ, Dobson AP, Newborn D. Prevention of population cycles by parasite removal. Science. 1998;282:2256–8.
Forbes AB, Huckle CA, Gibb MJ, Rook AJ, Nuthall R. Evaluation of the effects of nematode parasitism on grazing behaviour, herbage intake and growth in young grazing cattle. Vet Parasitol. 2000;90:111–8.
Hudson PJ, Dobson AP, Newborn D. Do parasites make prey vulnerable to predation? Red grouse and parasites. J Anim Ecol. 1992;61:681–92.
Packer C, Holt RD, Hudson PJ, Lafferty DJ, Dobson AP. Keeping the herds healthy and alert: implications of predator control for infectious disease. Ecol Lett. 2003;6:797–802.
Roeber F, Jex AR, Gasser RB. Impact of gastrointestinal parasitic nematodes of sheep, and the role of advanced molecular tools for exploring epidemiology and drug resistance—an Australian perspective. Parasites Vect. 2013;6:1–13.
Karrow NA, Goliboski K, Stonos N, Schenkel F, Peregrine A. Review: genetics of helminth resistance in sheep. Can J Anim Sci. 2014;94:1–9.
Smith KF, Acevedo-Whitehouse K, Pedersen AB. The role of infectious diseases in biological conservation. Anim Conserv. 2009;12:1–12.
Grenfell B, Gulland FMD. Introduction: ecological impact of parasitism on wildlife host populations. Parasitology. 1998;111(Suppl 1):3–14.
Daszak P, Cunningham AAA, Hyatt AD. Emerging infectious diseases of wildlife—threats to biodiversity and Human health. Science. 2000;287:443–9.
Bishop SC, Stear MJ. Modeling of host genetics and resistance to infectious diseases: understanding and controlling nematode infections. Vet Parasitol. 2003;115:147–66.
Bishop SC. Possibilities to breed for resistance to nematode parasite infections in small ruminants in tropical production systems. Animal. 2012;6:741–7.
Saddiqi HA, Jabbar A, Sarwar M, Iqbal Z, Muhammad G, Nisa M, Shahzad A. Small ruminant resistance against gastrointestinal nematodes: a case of Haemonchus contortus. Parasitol Res. 2011;109:1483–500.
Hayward AD. Causes and consequences of intra- and inter-host heterogeneity in defence against nematodes. Parasite Immunol. 2013;35:362–73.
Sweeney T, Hanrahan JP, Ryan MT, Good B. Immunogenomics of gastrointestinal nematode infection in ruminants - breeding for resistance to produce food sustainably and safely. Parasite Immunol. 2016;38:569–86.
Benavides MV, Sonstegard TS, Van Tassell C. Genomic regions associated with sheep resistance to gastrointestinal nematodes. Trends Parasitol. 2016;32:470–80.
Bertolino S, Wauters LA, De Bruyn L, Canestri-Trotti G. Prevalence of coccidia parasites (Protozoa) in red squirrels (Sciurus vulgaris): effects of host phenotype and environmental factors. Oecologia. 2003;137:286–95.
Body G, Ferté H, Gaillard JM, Delorme D, Klein F, Gilot-Fromont E. Population density and phenotypic attributes influence the level of nematode parasitism in roe deer. Oecologia. 2011;167:635–46.
Aleuy OA, Ruckstuhl K, Hoberg EP, Veitch A, Simmons N, Kutz SJ. Diversity of gastrointestinal helminths in Dall’s sheep and the negative association of the abomasal nematode, Marshallagia marshalli, with fitness indicators. PLoS ONE. 2018;57:158–65.
Lande R. Anthropogenic, ecological and genetic factors in extinction and conservation. Res Popul Ecol. 1998;40:259–69.
Fahrig L. Effects of habitat fragmentation on biodiversity. Annu Rev Ecol Evol Syst. 2003;34:487–515.
Frankham R, Ballou JD, Briscoe DA. A primer of conservation genetics. New York: Cambridge University Press; 2004.
Heller R, Okello JBA, Siegismund H. Can small wildlife conservancies maintain genetically stable populations of large mammals? Evidence for increased genetic drift in geographically restricted populations of Cape buffalo in East Africa. Mol Ecol. 2010;19:1324–34.
Creech TG, Epps CW, Landguth EL, Wehausen JD, Crowhurst RS, Holton B, Monello RJ. Simulating the spread of selection-driven genotypes using landscape resistance models for desert bighorn sheep. PLoS ONE. 2017;12:e0176960.
Van Dijk J, Sargison ND, Kenyon F, Skuce PJ. Climate change and infectious disease: helminthological challenges to farmed ruminants in temperate regions. Animal. 2010;4:377–92.
Morgan ER, van Dijk J. Climate and the epidemiology of gastrointestinal nematode infections of sheep in Europe. Vet Parasitol. 2012;189:8–14.
Rose H, Wang T, Van Dijk J, Morgan E. GLOWORM-FL: a simulation model of the effects of climate and climate change on the free-living stages of gastro-intestinal nematode parasites of ruminants. Ecol Modell. 2015;297:232–45.
Acevedo-Whitehouse K, Vicente J, Gortazar C, Höfle U, Fernández-de-mera I, Amos W. Genetic resistance to bovine tuberculosis in the Iberian wild boar. Mol Ecol. 2005;14:3209–17.
Ruiz-Lopez MJ, Monello RJ, Gompper ME, Eggert LS. The effect and relative importance of neutral genetic diversity for predicting parasitism varies across parasite taxa. PLoS ONE. 2012;9:e45404.
Brambilla A, Biebach I, Bassano B, Bogliani G, von Hardenberg A. Direct and indirect causal effects of heterozygosity on fitness-related traits in Alpine ibex. Proc R Soc B Biol Sci. 2015;82:20141873.
Mitchell J, Vitikainen EIK, Wells DA, Cant MA, Nichols HJ. Heterozygosity but not inbreeding coefficient predicts parasite burdens in the banded mongoose. J Zool. 2017;302:32–9.
Coltman DW, Pilkington JG, Smith JA, Pemberton JM. Parasite-mediated selection against inbred Soay sheep in a free-living, island population. Evolution. 1999;53:1259–67.
MacDougall-Shackleton EA, Derryberry EP, Foufopoulos J, Dobson AP, Hahn TP. Parasite-mediated heterozygote advantage in an outbred songbird population. Biol Lett. 2005;1:105–7.
Oliver M, Telfer S, Piertney S. Major histocompatibility complex (MHC) heterozygote superiority to natural multi-parasite infections in the water vole (Arvicola terrestris). Proc R Soc B Biol Sci. 2009;276:1119–28.
Sin YW, Annavi G, Dugdale HL, Newman C, Burke T, Macdonald DW. Pathogen burden, co-infection and major histocompatibility complex variability in the European badger (Meles meles). Mol Ecol. 2014;23:5072–88.
Osborne AJ, Pearson J, Negro SS, Chilvers BL, Kennedy MA, Gemmel NJ. Heterozygote advantage at MHC DRB may influence response to infectious disease epizootics. Mol Ecol. 2015;24:1419–32.
Bateson ZW, Hammerly SC, Johnson JA, Morrow ME, Whittingham LA, Dunn PO. Specific alleles at immune genes, rather than genome-wide heterozygosity, are related to immunity and survival in the critically endangered Attwater’s prairie-chicken. Mol Ecol. 2016;25:4730–44.
Luikart G, Pilgrim K, Visty J, Ezenwa VO, Schwartz MK. Candidate gene microsatellite variation is associated with parasitism in wild bighorn sheep. Biol Lett. 2008;4:228–31.
Brown EA, Pilkington JG, Nussey DH, Watt KA, Hayward AD, Tucker A, Graham AL, Paterson S, Beraldi D, Pemberton JM, Slate J. Detecting genes for variation in parasite burden and immunological traits in a wild population: testing the candidate gene approach. Mol Ecol. 2013;22:757–73.
Ilmonen P, Penn DJ, Damjanovich K, Morrison L, Ghotbi L, Potts WK. Major histocompatibility complex heterozygosity reduces fitness in experimentally infected mice. Genetics. 2007;176:2501–8.
Olano-Marin J, Mueller JC, Kempenaers B. Heterozygosity and survival in blue tits (Cyanistes caeruleus): contrasting effects of presumably functional and neutral loci. Mol Ecol. 2011;20:4028–41.
Côté SD, Stien A, Irvine RJ, Dallas JF, Marshall F, Halvorsen O, et al. Resistance to abomasal nematodes and individual genetic variability in reindeer. Mol Ecol. 2005;14:4159–68.
Schwensow N, Fietz J, Dausmann KH, Sommer S. Neutral versus adaptive genetic variation in parasite resistance: importance of major histocompatibility complex supertypes in a free-ranging primate. Heredity. 2007;99:265–77.
Ortego J, Cordero PJ, Aparicio JM, Calabuig G. No relationship between individual genetic diversity and prevalence of avian malaria in a migratory kestrel. Mol Ecol. 2007;16:4858–66.
Hoeck PEA, Keller LF. Inbreeding, immune defence and ectoparasite load in different mockingbird populations and species in the Galapagos Islands. J Avian Biol. 2012;43:423–34.
Hansson B, Westerberg L. On the correlation between heterozygosity and fitness in natural populations. Mol Ecol. 2002;11:2467–74.
Luong LT, Heath BD, Polak M. Host inbreeding increases susceptibility to ectoparasitism. J Evol Biol. 2007;20:79–86.
Szulkin M, Bierne N, David P. Heterozygosity-fitness correlations: a time for reappraisal. Evolution. 2010;64:1202–17.
DeWoody YD, DeWoody JA. On the estimation of genome-wide heterozygosity using molecular markers. J Hered. 2005;96:85–8.
Väli Ü, Einarsson A, Waits L, Ellegren H. To what extent do microsatellite markers reflect genome-wide genetic diversity in natural populations? Mol Ecol. 2008;17:3808–17.
Miller JM, Malenfant RM, David P, Davis CS, Poissant J, Hogg JT, et al. Estimating genome-wide heterozygosity: effects of demographic history and marker type. Heredity. 2014;112:240–7.
Ortego J, Aparicio JM, Calabuig G, Cordero PJ. Risk of ectoparasitism and genetic diversity in a wild lesser kestrel population. Mol Ecol. 2007;16:3712–20.
Ezenwa VO, Etienne RS, Luikart G, Beja-Pereira A, Jolles AE. Hidden consequences of living in a wormy world: nematode-induced immune suppression facilitates tuberculosis invasion in African buffalo. Am Nat. 2010;176:613–24.
Beraldi D, McRae AF, Gratten J, Pilkington JG, Slate J, Visscher PM, Pemberton JM. Quantitative trait loci (QTL) mapping of resistance to strongyles and coccidia in the free-living Soay sheep (Ovis aries). Int J Parasitol. 2007;37:121–9.
Coltman DW, Wilson K, Pilkington JG, Stear MJ, Pemberton JM. A microsatellite polymorphism in the gamma interferon gene is associated with resistance to gastrointestinal nematodes in a naturally-parasitized population of Soay sheep. Parasitology. 2001;122:571–82.
Lin YS, Zhou H, Forrest RHJ, Frampton CM, Burrows LER, Hickford JGH. Association between variation in faecal egg count for a natural mixed field-challenge of nematode parasites and TLR4 variation. Vet Parasitol. 2016;218:5–9.
Kloch A, Babik W, Bajer A, Sinski E, Radwan J. Effects of an MHC-DRB genotype and allele number on the load of gut parasites in the bank vole Myodes glareolus. Mol Ecol. 2010;19:255–65.
Samuel WM, Pybus MJ, Kocan AA. Parasitic diseases of wild mammals. 2nd ed. Ames: Iowa State University Press; 2001.
Taylor M, Coop RL, Wall R. Veterinary parasitology. 4th ed. Hoboken: Wiley-Blackwell; 2015.
Gulland FMD. The role of nematode parasites in Soay sheep (Ovis aries L.) mortality during a population crash. Parasitology. 1992;105:493–503.
Colditz IG. Six costs of immunity to gastrointestinal nematode infections. Parasite Immunol. 2008;30:63–70.
Charlier J, Morgan ER, Rinaldi L, van Dijk J, Demeler J, Hoglund J, et al. Practices to optimise gastrointestinal nematode control on sheep, goat and cattle farms in Europe using targeted (selective) treatments. Vet Rec. 2014;175:250–5.
Chartier C, Paraud C. Coccidiosis due to Eimeria in sheep and goats, a review. Small Rumin Res. 2012;103:84–92.
Sayers G, Good B, Hanrahan JP, Ryan M, Sweeney T. Intron 1 of the interferon c gene: its role in nematode resistance in Suffolk and Texel sheep breeds. Res Vet Sci. 2005;79:191–6.
Figueroa Castillo JA, Medina RDM, Villalobos JMB, Gayosso-Vázquez A, Ulloa-Arvízu R, Rodríguez RA, et al. Association between major histocompatibility complex microsatellites, fecal egg count, blood packed cell volume and blood eosinophilia in Pelibuey sheep infected with Haemonchus contortus. Vet Parasitol. 2011;177:339–44.
Valilou RH, Rafat SA, Notter DR, Shojda D, Moghaddam G, Nematollahi A. Fecal egg counts for gastrointestinal nematodes are associated with a polymorphism in the MHC-DRB1 gene in the Iranian Ghezel sheep breed. Front Genet. 2015;6:1–11.
Dukkipati V, Blair H, Garrick D, Murray A. ‘Ovar-Mhc‘— Ovine major histocompatibility complex: role in genetic resistance to diseases. N Z Vet J. 2006;54:153–60.
Paterson S, Wilson K, Pemberton JM. Major histocompatibility complex variation associated with juvenile survival and parasite resistance in a large unmanaged ungulate population. Proc Natl Acad Sci. 1998;95:3714–9.
Shackleton, DM, IUCN/SSC Caprinae Specialist Group. Wild sheep and goats and their 849 relatives: status survey and conservation action plan for Caprinae. IUCN, Gland, Switzerland and Cambridge, UK; 1997.
Valdez R. Ovis orientalis. The IUCN red list of threatened species 2008: 867 e.T15739A5076068.
Harris RB, Pletscher DH. Incentives toward conservation of argali Ovis ammon: a case study of trophy hunting in western China. Oryx. 2002;36:373–81.
Chapuis JL, Boussès P, Barnaud G. Alien mammals, impact and management in the French subantarctic islands. Biol Conserv. 1994;67:97–104.
Bertolino S, Di Montezemolo NC, Bassano B. Food-niche relationships within a guild of alpine ungulates including an introduced species. J Zool. 2009;277:63–9.
Altizer S, Nunn CL, Thrall PH, Gittleman JL, Dobson AP, Ezenwa V, et al. Social organization and parasite risk in mammals: integrating theory and empirical studies. Annu Rev Ecol Evol Syst. 2003;34:517–47.
Cassinello J, Gomendio M, Roldan ERS. Relationship between coefficient of inbreeding and parasite burden in endangered gazelles. Conserv Biol. 2001;15:1171–4.
Dukkipati V, Blair H, Garrick D, Murray A. “Ovar-Mhc”—ovine major histocompatibility complex: structure and gene polymorphisms. Genet Mol Res. 2006;5:581–608.
Schwaiger FW, Gostomski D, Stear MJ, Duncan JL, McKellar QA, Epplen JT, Buitkamp J. An ovine major histocompatibility complex DRB1 allele is associated with low faecal egg counts following natural, predominantly Ostertagia circumcincta infection. Int J Parasitol. 1955;25:815–22.
Doherty P, Zinkernagel RM. Enhanced immunological surveillance in mice heterozygous at the H-2 gene complex. Nature. 1975;256:50–2.
Bernatchez L, Landry C. MHC studies in nonmodel vertebrates: what have we learned about natural selection in 15 years? J Evol Biol. 2003;16:363–77.
Sommer S. The importance of immune gene variability (MHC) in evolutionary ecology and conservation. Front Zool. 2005;2:1–18.
Cox FE. Concomitant infections, parasites and immune responses. Parasitology. 2001;122(Suppl 1):23–38.
Cizauskas CA, Turner WC, Wagner B, Kusters M, Vance RE, Getz WM. Gastrointestinal helminths may affect host susceptibility to anthrax through seasonal immune trade-offs. BMC Ecol. 2014;14:1–15.
Carmo AM, Vicentini MA, Dias AT, Alves LL, Alves CCS, Brandi JS, et al. Increased susceptibility to Strongyloides venezuelensis in mice due to Mycobacterium bovis co-infection which modulates production of Th2 cytokines. Parasitology. 2009;136:1357–65.
Ezenwa VO. Helminth–microparasite co-infection in wildlife: lessons from ruminants, rodents and rabbits. Parasite Immunol. 2016;38:527–34.
Herrmann-Hoesing LM, White SN, Mousel MR, Lewis GS, Knowles DP. Ovine progressive pneumonia provirus levels associate with breed and Ovar-DRB1. Immunogenetics. 2008;60:749–58.
Spurgin LG, Richardson DS. How pathogens drive genetic diversity: MHC, mechanisms and misunderstandings. Proc R Soc B Biol Sci. 2010;277:979–88.
García-Navas V, Cáliz-Campal C, Ferrer ES, Sanz JJ, Ortego J. Heterozygosity at a single locus explains a large proportion of variation in two fitness-related traits in great tits: a general or a local effect? J Evol Biol. 2014;27:2807–19.
Gutierrez-Espeleta GA, Hedrick PW, Kalinowski ST, Garrigan D, Boyce WM. Is the decline of desert bighorn sheep from infectious disease the resulot of low MHC variation. Heredity. 2001;86:439–50.
Boyce WM, Weisenberger ME, Penedo MCT, Johnson CK. Wildlife translocation: the conservation implications of pathogen exposure and genetic heterozygosity. BMC Ecol. 2011;11:5–11.
Gauly M, Krauthahn C, Bauer C, Erhardt G. Pattern of Eimeria oocyst output and repeatability in naturally infected suckling Rhön lambs. J Vet Med Ser B. 2001;48:665–73.
Piertney SB, Oliver MK. The evolutionary ecology of the major histocompatibility complex. Heredity. 2006;96:7–21.
Olano-Marin J, Mueller JC, Kempenaers B. Correlations between heterozygosity and reproductive success in the blue tit (Cyanistes caeruleus): an analysis of inbreeding and single locus effects. Evolution. 2011;65:3175–94.
Blanchet S, Rey O, Berthier P, Lek S, Loot G. Evidence of parasite-mediated disruptive selection on genetic diversity in a wild fish population. Mol Ecol. 2009;18:1112–23.
Ferrer ES, García-Navas V, Sanz JJ, Ortego J. Individual genetic diversity and probability of infection by avian malaria parasites in blue tits (Cyanistes caeruleus). J Evol Biol. 2014;27:2468–82.
Wegner KM. Parasite selection for immunogenetic optimality. Science. 2003;301:1343.
Nowak MA, Tarczy-Hornoch K, Austyn JM. The optimal number of major histocompatibility complex molecules in an individual. Proc Natl Acad Sci. 1992;89:10896–9.
Keller L, Waller DM. Inbreeding effects in wild populations. Trends Ecol Evol. 2002;17:230–41.
Edmands S. Between a rock and a hard place: evaluating the relative risks of inbreeding and outbreeding for conservation and management. Mol Ecol. 2007;16:463–75.
Marshall TC, Spalton JA. Simultaneous inbreeding and outbreeding depression in reintroduced Arabian oryx. Anim Conserv. 2000;3:241–8.
Chapman JR, Nakagawa S, Coltman DW, Slate J, Shekdon BC. A quantitative review of heterozygosity-fitness correlations in animal populations. Mol Ecol. 2009;18:2746–65.
Szulkin M, David P. Negative heterozygosity-fitness correlations observed with microsatellites located in functional areas of the genome. Mol Ecol. 2011;20:3949–52.
Goldberg TL, Grant EC, Inendino KR, Kassler TW, Claussen JE, Philipp DP. Increased infectious disease susceptibility resulting from outbreeding depression. Conserv Biol. 2005;19:455–62.
Soulsbury CD, Lebigre C. Viability selection creates negative heterozygosity–fitness correlations in female Black Grouse Lyrurus tetrix. J Ornithol. 2018;159:93–101.
Dolgin ES, Charlesworth B, Baird SE, Cutter AD. Inbreeding and outbreeding depression in Caenorhabditis nematodes. Evolution. 2007;61:1339–52.
Escobar JS, Nicot A, David P. The different sources of variation in inbreeding depression, heterosis and outbreeding depression in a metapopulation of Physa acuta. Genetics. 2008;180:1593–608.
Neff BD. Stabilizing selection on genomic divergence in a wild fish population. Proc Natl Acad Sci. 2004;101:2381–5.
Phillips KP, Jorgensen TH, Jolliffe KG, Richardson DS. Evidence of opposing fitness effects of parental heterozygosity and relatedness in a critically endangered marine turtle? J Evol Biol. 2017;30:1953–65.
Atalay D, Schausberger P. Balancing in- and out-breeding by the predatory mite Phytoseiulus persimilis. Exp Appl Acarol. 2018;74:159–69.
Dupuis J, Badia J, Maublanc ML, Bon R. Survival and spatial fidelity of mouflon (Ovis gmelini): a bayesian analysis of an age-dependent capture-recapture model. J Agric Biol Environ Stat. 2002;7:277–98.
Portanier E, Garel M, Devillard S, Marchand P, Andru J, Maillard D, Bourgoin G. Introduction history overrides social factors in explaining genetic structure of females in Mediterranean mouflon. Ecol Evol. 2017;7:9580–91.
Garel M, Cugnasse JM, Gaillard JM, Loison A, Gibert P, Douvre P, Dubray D. Reproductive output of female mouflon (Ovis gmelini musimon × Ovis sp.): a comparative analysis. J Zool. 2005;266:65–71.
Miller JM, Coltman DW. Assessment of identity disequilibrium and its relation to empirical heterozygosity fitness correlations: a meta-analysis. Mol Ecol. 2014;23:1899–909.
Kardos M, Allendorf FW, Luikart G. Evaluating the role of inbreeding depression in heterozygosity-fitness correlations: how useful are tests for identity disequilibrium? Mol Ecol Resour. 2014;14:519–30.
Arct A, Sudyka J, Podmokła E, Drobniak SM, Gustafsson L, Cichon M. Heterozygosity–fitness correlations in blue tit nestlings (Cyanistis caeruleus) under contrasting rearing conditions. Evol Ecol. 2017;31:803–14.
Tollenaere C, Bryja J, Galan M, Cadet P, Deter J, Chaval Y, et al. Multiple parasites mediate balancing selection at two MHC class II genes in the fossorial water vole: insights from multivariate analyses and population genetics. J Evol Biol. 2008;21:1307–20.
Potts WK, Slev PR. Pathogen-based models favoring MHC genetic diversity. Immunol Rev. 1995;143:181–97.
Epps CW, Wehausen JD, Bleich VC, Torres SG, Brashares JS. Optimizing dispersal and corridor models using landscape genetics. J Appl Ecol. 2007;44:714–24.
Wilson RE, Farley SD, McDonough TJ, Talbot SL, Barboza PS. A genetic discontinuity in moose (Alces alces) in Alaska corresponds with fenced transportation infrastructure. Conserv Genet. 2015;16:791–800.
Roffler GH, Schwartz MK, Pilgrim KL, Talbot S, Sage GK, Adams LG, Luikat G. Identification of landscape features influencing gene flow: how useful are habitat selection models? Evol Appl. 2016;9:805–17.
Portanier E, Larroque J, Garel M, Marchand P, Maillard D, Bourgoin G, Devillard S. Landscape genetics matches with behavioral ecology and brings new insight on the functional connectivity in Mediterranean mouflon. Landsc Ecol. 2018;33:1069–85.
Epps CW, Palsbøll PJ, Wehausen JD, Roderick GK, Ramey RR II, McCllough DR. Highways block gene flow and cause a rapid decline in genetic diversity of desert bighorn sheep. Ecol Lett. 2005;8:1029–38.
Gubili C, Mariani S, Weckworth BV, Galpern P, McDevitt AD, Hebblewhite M, et al. Environmental and anthropogenic drivers of connectivity patterns: a basis for prioritizing conservation efforts for threatened populations. Evol Appl. 2017;10:199–211.
Harris RB, Reading R. Ovis ammon. The IUCN Red List of Threatened Species 2008: e.T15733A5074694. http://dx.doi.org/10.2305/IUCN.UK.2008.RLTS.T15733A5074694.en. Downloaded on 08 June 2018.
Singh NJ, Yoccoz NG, Lecomte N, Côté SD, Fox JL. Scale and selection of habitat and ressources: Tibetan argali (Ovis ammon hodgsoni) in high-altitude rangelands. Can J Zool. 2010;8:436–47.
U.S. Fish andWildlife Service. Recovery plan for Sierra Nevada bighorn sheep. U.S. Fish and Wildlife Service, Sacramento, California, USA. 2007.
Cahn ML, Conner MM, Schmitz OJ, Stephenson TR, Wehausen JD, Johnson HE. Disease, population viability, and recovery of endangered Sierra Nevada bighorn sheep. J Wildl Manage. 2011;75:1753–66.
Biebach I, Keller L. Genetic variation depends more on admixture than number of founders in reintroduced Alpine ibex populations. Biol Conserv. 2012;147:197–203.
Woodford MH, Rossiter PB. Disease risks associated with wildlife translocation projects. Rev Sci Tech. 1993;12:115–35.
Cunningham AA. Disease risks of wildlife translocations. Conserv Biol. 1996;10:349–53.
Kock RA, Woodford MH, Rossiter PB. Disease risks associated with the translocation of wildlife. Rev Sci Tech l’OIE. 2010;29:329–50.
Hamilton W, Zuk M. Heritable true fitness and bright birds: a role for parasites? Science. 1982;218:384–7.
Ezenwa V, Jolles A. Horns honestly advertise parasite infection in male and female African buffalo. Anim Behav. 2008;75:2013–21.
Buczek M, Okarma H, Demiaszkiewicz AW, Radwan J. MHC, parasites and antler development in red deer: no support for the Hamilton & Zuk hypothesis. J Evol Biol. 2016;29:617–32.
Coltman DW, Pilkington JG, Pemberton JM. Fine-scale genetic structure in a free-living ungulate population. Mol Ecol. 2003;12:733–42.
Ditchkoff SS, Lochmiller RL, Masters RE, Hoofer SR, Van Den Bussche RA. Major-histocompatibility-complex-associated variation in secondary sexual traits of white-tailed deer (Odocoileus virginianus): evidence for good-genes advertisement. Evolution. 2001;55:616–25.
Ditchkoff SS, Hoofer SR, Lochmiller RL, Masters RE, Van Den Bussche RA. Mhc-Drb evolution provides insight into parasite resistance in white-tailed deer. Southwest Nat. 2005;50:57–64.
Marchand P, Garel M, Bourgoin G, Dubray D, Maillard D, Loison A. Sex-specific adjustments in habitat selection contribute to buffer mouflon against summer conditions. Behav Ecol. 2015;26:472–82.
Marchand P, Garel M, Bourgoin G, Duparc A, Dubray D, Maillard D, Loison A. Combining familiarity and landscape features helps break down the barriers between movements and home ranges in a non-territorial large herbivore. J Anim Ecol. 2017;86:371–83.
Baudière A. Le Parc National du Caroux – Essai de synthèse climatique. Bulletin de la Société Languedocienne de Géographie. 1962;33:169–86.
Garel M, Loison A, Gaillard JM, Cugnasse JM, Maillard D. The effects of a severe drought on mouflon lamb survival. Proc R Soc B Biol Sci. 2004;271:S471–3.
Dubois M, Bon R, Cransac N, Maublanc ML. Dispersal patterns of Corsican mouflon ewes: importance of age and proximate influences. Appl Anim Behav Sci. 1994;42:29–40.
Dubois M, Gerard J, Maublanc ML. Seasonal movements of females Corsican mouflon (Ovis ammon) in a Mediterranean mountain range, southern France. Behav Processes. 1992;26:155–66.
Stoffel MA, Esser M, Kardos M, Humble E, Nichols H, David P, Hoffma JI. inbreedR: an R package for the analysis of inbreeding based on genetic markers. Methods Ecol Evol. 2016;7:1331–9.
David P, Pujol B, Viard F, Castella V, Goudet J. Reliable selfing rate estimates from imperfect population genetic data. Mol Ecol. 2007;16:2474–87.
Galan M, Pons JB, Tournayre O, Pierre E, Leuchtma M, Pontier D, Charbonnel N. Metabarcoding for the parallel identification of several hundred predators and their prey: application to bat species diet analysis. Mol Ecol Resour. 2018;18:474–89.
Sigurdardottir S, Borsch C, Gustafsson K, Andersson L. Cloning and sequence analysis of 14 DRB alleles of the bovine major histocompatibility complex by using the polymerase chain reaction. Anim Genet. 1991;22:199–209.
Robasky K, Lewis NE, Church GM. The role of replicates for error mitigation in next-generation sequencing. Nat Rev Genet. 2014;15:56–62.
Piry S, Guivier E, Realini A, Martin JF. SE|S|AM|E| Barcode: NGS-oriented software for amplicon characterization—application to species and environmental barcoding. Mol Ecol Resour. 2012;12:1151–7.
Cockenpot A. Etude des facteurs de variation de l’excrétion parasitaire mesurée par analyse coproscopique chez le mouflon Méditerranéen (Ovis gmelini musimon × Ovis sp.) dans le massif du Caroux-Espinouse. Veterinary dissertation, VetAgro Sup, Campus vétérinaire de Lyon, Université de Lyon, France. 2013.
Raynaud JP. Etude de l’efficacité d’une technique de coproscopie quantitative pour le diagnostic de routine et le contrôle des infections parasitaires des bovins, ovins, équins et porcins. Ann Parasitol Hum Comp. 1970;45:321–42.
Burdick RK, Quiroz J, Iyer HK. The present status of confidence interval estimation for one-factor random models. J Stat Plan Inference. 2006;136:4307–25.
Vander Wal E, Paquet PC, Andraés JA. Influence of landscape and social interactions on transmission of disease in a social cervid. Mol Ecol. 2012;21:1271–82.
Mejía-Salazar MF, Goldizen AW, Menz CS, Dwyer RG, Plomberg SP, Waldner CL, et al. Mule deer spatial association patterns and potential implications for transmission of an epizootic disease. PLoS ONE. 2017;12:1–21.
Marchand P, Freycon P, Herbaux J, Game Y, Toigo C, Gilot-Fromont E, et al. Sociospatial structure explains marked variation in brucellosis seroprevalence in an Alpine ibex population. Sci Rep. 2017;7:15592.
Nielsen MK, Vidyashankar AN, Andersen UV, DeLisi K, Pilegaard K, Kaplan RM. Effects of fecal collection and storage factors on strongylid egg counts in horses. Vet Parasitol. 2010;167:55–61.
Drimtzia A, Papadopoulos E. Reduction rate of nematode egg counts and third-stage larvae development from sheep and goat faeces preserved at 4 °C. J Hell Vet Med Soc. 2016;67:177–82.
Peig J, Green AJ. New perspectives for estimating body condition from mass/length data: the scaled mass index as an alternative method. Oikos. 2009;118:1883–91.
Hayward AD, Wilson AJ, Pilkington JG, Pemberton JM, Kruuk LEB. Ageing in a variable habitat: environmental stress affects senescence in parasite resistance in St Kilda Soay sheep. Proc R Soc B. 2009;276:3477–85.
Cheynel L, Lemaître JF, Gaillard JM, Rey B, Bourgoin G, Ferté H, Jégo M, Débias F, Pellerin M, Jacob L, Gilot-Fromont E. Immunosenescence patterns differ between populations but not between sexes in a long-lived mammal. Sci Rep. 2017;7:13700.
Plowright RK, Manlove KR, Besser TE, Paez DJ, Andrews KR, Matthews PE, Waits LP, Hudson PJ, Cassirer EF. Age-specific infectious period shapes dynamics of pneumonia in bighorn sheep. Ecol Lett. 2017;20:1325–36.
Ryder ML. Sheep and man. London: Duckworth; 1983.
Slate J, Pemberton J. Does reduced heterozygosity depress sperm quality in wild rabbits (Oryctolagus cuniculus)? Curr Biol. 2006;16:790–1.
Wilson AJ, Nussey DH. What is individual quality? An evolutionary perspective. Trends Ecol Evol. 2010;25:207–14.
Frank AF (2011) R-hacks/mer-utils.R. https://github.com/aufrank/R-hacks/blob/master/mer-utils.R. Accessed June 2018.
Zuur AF, Ieno EN, Elphick CS. A protocol for data exploration to avoid common statistical problems. Methods Ecol Evol. 2010;1:3–14.
Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67:1–48.
Barton K. MuMIn: multi-model inference. R package version 1.15.6. 2016.
R Core Team (2016) R: a language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. https://www.R-project.org/.
Kuznetsova A, Brockhoff PB, Christensen RHB. lmerTest: Tests in linear mixed effects models. R package version 2.0-33. 2016.
Breheny P, Burchett W. Visualization of regression models using visreg. R J. 2017;9:56–71.
EP, M. Garel, SD, DM and GB conceptualized and designed the research. CI, JD and GB conducted field sampling. MTP, SB, GB, M. Galan and JP conducted laboratory experimentations. EP, M. Garel, GB and SD conducted data analyses. All authors contributed in interpreting the results and writing the paper. All authors read and approved the final manuscript.
We warmly thank all the professionals from the Office National de la Chasse et de la Faune Sauvage (Service Départemental 34) and numerous trainees for their technical support in trapping, sampling and tagging mouflons. We would like to thank all the technical personnel of LBBE for helping with laboratory steps of the study, and the Antagene laboratory (Limonest, France, http://www.antagene.com) for DNA extraction and genotyping. We would also like to thank Elodie Bonneau and Julie Andru for the first laboratory and genetic analyses. Finally, we thank two anonymous referees for helpful comments on the manuscript.
The authors declare that they have no competing interests.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Consent for publication
The population of Mediterranean mouflon inhabiting the Caroux-Espinouse massif is monitored by the Office National de la Chasse et de la Faune Sauvage according to the ethical conditions detailed in the specific accreditations delivered by the Préfecture de Paris (prefectorial decree n°2009-014) in agreement with the French environmental code (Art. R421-15 to 421-31 and R422-92 to 422-94-1).
This research project and E. Portanier’s collaboration were funded by the Office National de la Chasse et de la Faune Sauvage, Vetagro Sup and LBBE (Grants Numbers 2015-14, 2016-15).
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Results of non-genetic model selection.
Normality tests of non-genetic and genetic models.
Variance Inflation Factors values for genetic models and tests of the presence of sMLH quadratic effects on FOC and FEC.
R code used for statistical analyses.
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Portanier, E., Garel, M., Devillard, S. et al. Both candidate gene and neutral genetic diversity correlate with parasite resistance in female Mediterranean mouflon. BMC Ecol 19, 12 (2019). https://doi.org/10.1186/s12898-019-0228-x
- Heterozygosity-fitness correlations
- Gastro-intestinal nematodes