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. 2011 Jan 26;107(2):143–154. doi: 10.1038/hdy.2010.177

Contrasting responses to selection in class I and class IIα major histocompatibility-linked markers in salmon

S Consuegra 1,2,*, E de Eyto 3, P McGinnity 3,5, R J M Stet 4,6, W C Jordan 1
PMCID: PMC3178404  PMID: 21266985

Abstract

Comparison of levels and patterns of genetic variation in natural populations either across loci or against neutral expectation can yield insight into locus-specific differences in the strength and direction of evolutionary forces. We used both approaches to test the hypotheses on patterns of selection on major histocompatibility (MH)-linked markers. We performed temporal analyses of class I and class IIα MH-linked markers and eight microsatellite loci in two Atlantic salmon populations in Ireland on two temporal scales: over six decades and 9 years in the rivers Burrishoole and Delphi, respectively. We also compared contemporary Burrishoole and Delphi samples with nearby populations for the same loci. On comparing patterns of temporal and spatial differentiation among classes of loci, the class IIα MH-linked marker was consistently identified as an outlier compared with patterns at the other microsatellite loci or neutral expectation. We found higher levels of temporal and spatial heterogeneity in heterozygosity (but not in allelic richness) for the class IIα MH-linked marker compared with microsatellites. Tests on both within- and among-population differentiation are consistent with directional selection acting on the class IIα-linked marker in both temporal and spatial comparisons, but only in temporal comparisons for the class I-linked marker. Our results indicate a complex pattern of selection on MH-linked markers in natural populations of Atlantic salmon. These findings highlight the importance of considering selection on MH-linked markers when using these markers for management and conservation purposes.

Keywords: MHC, microsatellites, natural selection, temporal variation, Atlantic salmon, Salmo salar

Introduction

Identifying loci under selection, and therefore potentially involved in adaptation, is a major aim in evolutionary ecology (Nielsen, 2005). Often, approaches to identifying selected loci entail comparing levels and patterns of variation in natural populations, with an expectation under a neutral model of evolution (Kimura, 1985; Vitalis et al., 2001; Ford, 2002; Beaumont and Balding, 2004; Goldringer and Bataillon, 2004). Alternatively, contrasting variation among loci (or classes of loci) from the same individuals may reveal locus-specific selection (e.g. McDonald, 1991; Karl and Avise, 1992; Pogson et al., 1995; Jordan et al., 1997; Dufresne et al., 2002; Dhuyvetter et al., 2004; Vasemagi et al., 2005).

As a result of using both approaches, the genes of the major histocompatibility complex (MHC) are widely believed to be subject to strong balancing selection (Hedrick, 1994; Apanius et al., 1997; Hughes and Yeager, 1998). MHC molecules play a central role in the T-cell-mediated specific immune response (Klein, 1986; Parham and Ohta, 1996), encoding molecules that bind small self or non-self peptides within the cell and then present them on the cell surface to T cells (Hedrick, 1994). Although MHC genes are among the most studied loci in vertebrates, the mechanisms that maintain their high levels of polymorphism remain vigorously debated (Spurgin and Richardson, 2010). Sexual selection (Potts and Wakeland, 1990, 1993; Jordan and Bruford, 1998; Landry et al., 2001; Reusch et al., 2001; Consuegra and García de Leániz, 2008) and neutral forces (Seddon and Baverstock, 1999; Landry and Bernatchez, 2001; Miller and Lambert, 2004; Seddon and Ellegren, 2004) may have a role in determining levels of variability in MHC genes. However, pathogen-driven balancing selection (through overdominance, negative frequency dependence or temporal/spatial heterogeneity in pathogen phenotype) is thought by many to be the main force driving MHC evolution (Klein and O'Huigin, 1994; Parham and Ohta, 1996; Edwards and Hedrick, 1998; Hedrick and Kim, 2000; Jeffery and Bangham, 2000). Evidence of selection on MHC genes has traditionally come from four sources (Hughes and Yeager, 1998): (a) long persistence times for MHC alleles compared with neutral expectation (often resulting in trans-specific polymorphism) (Figueroa et al., 1988; Lawlor et al., 1988; McConnell et al., 1988; Klein et al., 2007); (b) frequency distributions of MHC alleles in natural populations that are more even than that expected under a neutral model (Hedrick and Thompson, 1983; Markow et al., 1993); (c) high levels of non-synonymous versus synonymous substitutions in codons for peptide binding residues (Hughes and Nei, 1988, 1989, 1990); and (d) homogenization of introns with concurrent diversification of exons at MHC loci (Cereb et al., 1997; Reusch and Langefors, 2005).

More recently, an increasing number of descriptions of the geographic distribution of MHC variation (or variation at markers tightly linked to MHC loci)—often in conjunction with the analysis at other (putatively), neutral loci—have provided further insight into the selective influences on MHC loci in a range of species (Miller et al., 2001; Aguilar and Garza, 2006; Bryja et al., 2007; Ekblom et al., 2007; Alcaide et al., 2008). In general, MHC heterozygosity within populations is higher than that for neutral loci (Huang and Yu, 2003; Aguilar et al., 2004; but see, Boyce et al., 1997). However, there appears to be a great deal of species-to-species variation in the relative levels of MHC and neutral variability among populations. Among-population differentiation at MHC loci has been observed to range from lower than (Sommer, 2003; Aguilar et al., 2004), similar to (Boyce et al., 1997; Parker et al., 1999; Hedrick et al., 2001; Huang and Yu, 2003), to higher than that at neutral loci (Miller et al., 2001; Beacham et al., 2004), reflecting differences in the relative strengths of natural selection, genetic drift and gene flow across species (Eizaguirre et al., 2010). Indeed, the relative levels of neutral and MHC variation within and among populations can vary across closely related species (Hambuch and Lacey, 2002; Jarvi et al., 2004), and even within a species depending on the spatial scale of analysis (Landry and Bernatchez, 2001). In comparison to studies of geographic patterns in MHC variation, there have been relatively few studies on MHC which also include a temporal dimension, despite the possibility that selective forces across time within a population may differ from those across populations (Smulders et al., 2003; Sommer, 2003; Beacham et al., 2004; Seddon and Ellegren, 2004; Westerdahl et al., 2004; Coughlan et al., 2006; Oliver et al., 2009). Moreover, most studies focus on a single class of MHC gene (most commonly class IIβ), whereas selection can affect differentially the genetic diversity of both genes (Bryja et al., 2007).

The Atlantic salmon (Salmo salar) has proven to be an excellent model for the study of MHC evolution for a number of reasons. First, as in teleosts in general, MHC class I and class II loci are not physically linked in the species, allowing for independent evolution of these classes of genes (Grimholt et al., 2002). As MHC genes do not form a single complex, they are therefore known simply as MH genes in teleosts (Stet et al., 2002). Second, while MH pseudogenes exist in the genome, and several non-classical class I genes have been described recently (Lukacs et al., 2010), the major classical MH genes expressed are class I (Sasa-UBA), class IIα (Sasa-DAA) and class IIβ (Sasa-DAB) (Grimholt et al., 1993, 2000; Stet et al., 2002), making analyses of functional MHC variation relatively simple. Third, the molecular structure of MH genes has been extensively studied in Atlantic salmon (Grimholt et al., 1993, 2000, 2002; Hordvik et al., 1993; Stet et al., 2002) and there is evidence of balancing selection acting on potential peptide binding residues in both class I and class IIα loci (Consuegra et al., 2005a, 2005b). Fourth, a dinucleotide microsatellite repeat located in the 3′-untranslated region (UTR) of the MH class I locus (termed here Sasa-UBA-3UTR) (Grimholt et al., 2002) and a 10-base pair minisatellite repeat in the 3′-UTR of the MH class IIα locus (termed here Sasa-DAA-3UTR) (Stet et al., 2002) can be used to assay rapidly variation at these loci in Atlantic salmon. Fifth, the Atlantic salmon is one of the few species where MH polymorphism has been experimentally shown to be associated with resistance/susceptibility to specific pathogens (Grimholt et al., 2003). Furthermore, historical samples are widely available from Atlantic salmon populations, often as dried scales that are suitable for extraction of DNA, allowing analysis of temporal variation within populations (Nielsen et al., 1999; Consuegra et al., 2002; Ciborowski et al., 2007).

The aims of this study were to test the hypotheses that (a) MH-linked markers in natural Atlantic salmon populations show temporal and spatial levels of heterogeneity that differ from a sample of neutral microsatellite loci and fall outside the range expected from neutral models and (b) MH class I- and class II-linked markers show differential responses to selective forces as expected from the different evolutionary rates of the MH genes (Consuegra et al., 2005a, 2005b). To test these hypotheses, two case studies involving Atlantic salmon populations in western Ireland were used: (1) based on the Burrishoole (BRH) river system for temporal analysis and the nearby rivers Moy, Owenmore and Owenduff for spatial analysis and (2) based on the Delphi (DPH) river system for temporal analysis and the nearby rivers Owenwee (OWW) and Carrowinskey for spatial analysis.

Materials and methods

Samples

Samples were obtained from both contemporary and historical sources. For contemporary analysis, juveniles were sampled from all study rivers by electro-fishing of different sections, each approximately 200 m in length, in 2002, and fin clips stored in 95% ethanol for subsequent extraction of DNA. The maximum distance between the focal rivers and other spatial samples was 182 km (BRH-Moy) and 57 km (Delphi-OWW) (Figure 1).

Figure 1.

Figure 1

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Map showing the location of the study rivers for the Burrishoole and Delphi case studies.

Historical samples from the BRH system were obtained from the Marine Institute (Newport) as dried scales from both angled fish and fish caught in a research fish trap in 1956, 1968, 1973, 1983 and 1995. All selected samples were from grilse salmon (adults returning to the river after a single winter in the sea) and, given the predominance of 2-year smolts in the BRH, they mostly represented single cohorts. For the DPH system, fin clips for DNA analyses were collected from juvenile salmon sampled by electro-fishing fish in 1993 and 2002, representing four different cohorts. Fish were assigned to either the 0+ or 1+ age class using length frequency analysis (cutoff length was 7 cm).

DNA isolation and microsatellite genotyping

Genomic DNA was isolated from fin and scale samples (two scales per individual) using the Wizard SV96 Genomic Purification kit (Promega, Southampton, UK), eluted in 500 μl (fin samples) or 100 μl (scale samples) elution buffer and stored at 4 °C until use in PCR amplifications. All samples were amplified for eight microsatellite loci: SsoSL 85 (Slettan et al., 1995), Ssa 171, Ssa 197, Ssa 202 (O'Reilly et al., 1996), Ssa D144b, Ssa D170 (King et al., 2005), Ssa 2215SP and Ssa 1G7SP (Paterson et al., 2004). These unlinked microsatellites were selected as they behaved in a neutral manner in previous studies in Atlantic salmon (de Eyto et al., 2007). Samples were also amplified for a microsatellite repeat located in the 3′-UTR of the MHC class I locus (Sasa-UBA-3UTR) and a minisatellite repeat located in the 3′-UTR of the MHC class IIα locus (Sasa-DAA-3UTR) (Stet et al., 2002). Previous studies (e.g. Grimholt et al., 2002; Stet et al., 2002; de Eyto et al., 2007) have shown that the micro/minisatellite markers used in this study are uniquely and tightly linked to the expressed MH loci in Atlantic salmon.

All loci were amplified with the Qiagen Multiplex Kit (Qiagen, Crawley, UK) in three multiplex reactions with the following proportions of each 100 pM primer combined in a final volume of 500 μl primer stock solution: (a) SsoS 85 (20 μl), Ssa2215SP (5 μl), Ssa171 (10 μl), SsaD144b (5 μl); (b) Ssa197 (10 μl), SsaD170 (10 μl), Ssa1G7SP (10 μl), Ssa202 (10 μl); and (c) Sasa-UBA-3UTR (10 μl), Sasa-DAA-3UTR (10 μl).

Each reaction consisted of 4 μl of the multiplex mix (containing hot-start Taq polymerase, buffer and dNTPs), 0.8 μl of the primer stock; 1.2 μl of nuclease-free water and 2–3 μl of template DNA. PCR conditions were as follows: denaturation step at 95 °C for 15 min, followed by 30/35 cycles (modern/historical samples): 94 °C 30 s; 58 °C 90 s; 72 °C 60 s; and a final extension at 60 °C for 30 min. Fragment sizes were then analyzed on an Applied Biosystems ABI377 automatic sequencer and estimated with the aid of GeneScan and Genotyper software (Applied Biosystems, Warrington, UK) using an internal molecular size marker (TAMRA 350/500) as a reference standard.

All historical samples and half of the modern samples were replicated at least once and only repeatable peaks were counted as real alleles. Error rates (allelic dropouts (ADO) and false alleles) were estimated using GIMLET v.1.3.3 (Valière, 2002), which was also used to construct consensus genotypes from the PCR replicates of each sample.

Statistical analysis

Intra-population genetic diversity

Concordance with Hardy–Weinberg expectation (significance of FIS values) and linkage disequilibrium between pairs of loci were tested for each locus in all samples with GENEPOP 3.2 (Raymond and Rousset, 1995). Observed heterozysity (HO) was also calculated in GENEPOP 3.2, whereas allelic richness (NA) was calculated using FSTAT (Goudet, 1995).

After testing for deviation from normality and heterogeneity of variances, temporal and spatial heterogeneity in mean HO and NA for microsatellites was tested using one-way ANOVA, with post hoc tests of differences between sample means where appropriate, using SYSTAT v.10. Spatial and temporal heterogeneity in the frequency of heterozygotes and deviation from mean allelic richness at MH-linked loci was assessed using G tests.

For temporal and spatial samples from each case study Mantel tests were performed between matrices of genetic differentiation (FST) at microsatellite and MH-linked markers and the significance of the test statistic was assessed by performing 10 000 permutations of the data using GENETIX (Belkhir et al., 2001). Given that FST values can underestimate the differentiation between populations with highly polymorphic microsatellites and when variability differs between marker classes, we also estimated Hedrick's standardized G′ST (Hedrick, 2005) that provide more robust estimates of population difference and the Dest estimate of differentiation (Jost, 2008) based on allele identities, which also accounts for the fixation of alleles in different populations. G′ST and Dest were estimated in SMOGD (Crawford, 2010).

Deviations of the distribution of genetic variability within populations from that expected under neutrality were tested using the Ewens–Watterson homozyosity test of neutrality (Ewens, 1972; Watterson, 1978), with significance assessed by the Slatkin exact P-test (Slatkin, 1994, 1996) implemented in Arlequin v.3 (Excoffier et al., 2005). The Ewens–Watterson test compares the observed homozygosity (Fo) with the equilibrium homozygosity under the neutral theory (Fe) from a simulation of randomly generated populations (1000 in this case). Significant negative values are indicative of balancing selection, while positive values indicate directional selection.

Inter-population genetic diversity

Concordance to an isolation-by-distance spatial model of population structure between all contemporary samples was estimated with IBD Web Service (at http://ibdws.sdsu.edu/) (Jensen et al., 2005) for the microsatellite loci and MH-linked markers separately using logarithm transformations of genetic (FST, G′ST, Dest) and geographic distance (Km) between rivers. We also performed partial Mantel tests using zt (Bonnet and Van de Peer, 2002) to estimate the correlation between pairwise population differentiation (FST, G′ST) at MH-linked markers and geographical distance while keeping differentiation at microsatellites constant, to test for significant positive correlation between geographical distance and MH differentiation independent of demographic and stochastic factors.

Further temporal and spatial analysis of evidence for selection at MH-linked loci was performed using BayeScan (Foll and Gaggiotti 2008). BayeScan uses an extension of the (Beaumont and Balding, 2004) method to detect outlier loci by employing a Bayesian likelihood method. To identify loci under selection, we used 10 pilot runs of 5000 iterations to estimate the distribution of α (the locus-specific component of FST), followed by a burn-in period of 50 000 iterations and 150 000 iterations, with sample size of 5000 and a thinning interval of 20 between samples. The identification of loci under selection is based on the Bayes factor (BF), the ratio between the posterior probabilities of two models (i.e. with and without selection). According to the scale of evidence for BF, a BF above 100 (log 10>2; posterior probabilities ranging between 0.99 and 1) is interpreted as decisive evidence for selection.

Results

Intra-population variability

Numbers of alleles in the two case studies were similar: mean number per locus for the BRH study was 23.9 (range 12–52) and that for the DPH study was 21.0 (range 9–33). Allele frequencies for each sample are given in Supporting Information (Tables S1 and S2).

There were a number of significant deviations from Hardy–Weinberg equilibrium in the samples, even after correcting for the number of tests carried out using a Bonferroni procedure (Tables 1 and 2). These significant deviations were almost exclusively associated with a positive value for FIS, indicating an excess of homozygotes in the sample. In general, the significant deviations were not associated with any particular locus. However, the earlier sample (1956) from the BRH case study (Table 1) and the OWW sample from the DPH case study (Table 2) displayed relatively high levels of deviation from Hardy–Weinberg equilibrium across many loci. Only 33 of 184 tests for linkage disequilibrium gave significant results, 15 of them in the DPH spatial study, mostly involving Ssa171 (five tests) and SasaDAA (five tests) (Supporting Information; Table S3). The average value of allelic dropouts was 3.1% and of false alleles 0.1% suggesting genotyping error rate was low.

Table 1. Values of FIS (Weir and Cockerham, 1984) for samples from the BRH case study and the significance of deviation of genotype frequencies from Hardy–Weinberg equilibrium (P).

  BRH
 OWD (N=57) OWM (N=57) MOY (N=48)
  1956 (N=50) 1968 (N=44) 1973 (N=48) 1983 (N=47) 1995 (N=40) 2002 (N=57)      
Ssa D144b
 FIS +0.245 +0.277 +0.170 +0.183 +0.160 +0.028 +0.023 +0.081 +0.150
 P <0.0001 0.0007 <0.0001 <0.0001 0.0179 0.0476 0.2994 0.0805 0.0417
                   
Ssa 171
 FIS +0.134 +0.120 −0.026 −0.117 +0.012 +0.067 +0.026 +0.089 +0.299
 P 0.1450 <0.0001 0.8374 0.4654 0.2267 <0.0001 0.2880 0.0512 <0.0001
                   
Ssa 2215SP
 FIS −0.026 −0.009 +0.076 +0.116 +0.326 +0.205 −0.001 +0.092 +0.130
 P 0.1413 0.0520 0.0655 0.0119 <0.0001 0.0014 0.1354 0.0621 0.0145
                   
SsoSL 85
 FIS +0.275 +0.189 +0.087 −0.049 +0.158 +0.248 +0.119 +0.142 +0.407
 P <0.0001 0.0094 0.0160 0.2018 0.0142 <0.0001 0.1858 <0.0001 <0.0001
                   
Ssa 197
 FIS +0.113 +0.085 +0.162 −0.067 +0.002 +0.034 +0.123 +0.099 +0.090
 P 0.0010 0.0025 0.0324 0.4183 0.3111 0.0034 0.1167 0.1488 0.5526
                   
Ssa 1G7SP
 FIS +0.079 +0.259 +0.292 −0.101 +0.075 +0.025 +0.293 +0.128 +0.156
 P 0.0018 0.0211 0.0113 0.559 0.5648 0.0789 <0.0001 0.1692 0.0238
                   
Ssa 202
 FIS +0.447 +0.230 +0.251 +0.139 +0.158 +0.004 −0.027 +0.066 −0.025
 P <0.0001 0.0010 0.0234 0.0023 0.1124 0.0298 0.4732 0.5981 0.6107
                   
Ssa D170
 FIS +0.319 +0.112 +0.189 +0.213 +0.086 −0.031 +0.137 +0.098 −0.040
 P <0.0001 <0.0001 <0.0001 <0.0001 0.0334 0.3977 0.0243 0.3857 0.8574
                   
Sasa-DAA-3UTR
 FIS +0.082 +0.125 −0.044 +0.051 +0.148 −0.065 +0.203 +0.123 +0.062
 P 0.5158 0.4199 0.1343 0.8610 0.0873 0.3966 0.0013 0.4553 0.3670
                   
Sasa-UBA-3UTR
 FIS +0.052 −0.102 +0.161 +0.297 +0.028 −0.039 +0.111 +0.182 +0.294
 P 0.1460 0.2183 0.2725 <0.0001 0.0746 0.1530 0.0078 <0.0001 <0.0001
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Abbreviations: FIS, inbreeding coefficient; 3-UTR, 3-untranslated region.

River abbreviations: BRH, Burrishoole; CAW, Carrowinskey; DPH, Delphi; MOY, Moy; OWD, Owenduff; OWM, Owenmore; OWW, Owenwee.

Values in bold are those that remained significant after Bonferroni correction for the number of tests in this series (α=0.05/90=0.0006).

Table 2. Values of FIS (Weir and Cockerham 1984) for samples from the DPH case study and the significance of deviation of genotype frequencies from Hardy–Weinberg equilibrium (P).

  DPH
 OWW (N=55) CAW (N=48)
  1993 0+ (N=45)
 1993 1+ (N=50)
 2002 0+ (N=45)
 2002 1+ (N=51)
    
             
Ssa D144b
 FIS −0.007 +0.114 +0.121 +0.157 +0.031 +0.029
 P 0.2089 0.0055 0.1098 0.0190 >0.0001 0.0545
             
Ssa 171
 FIS +0.013 −0.012 −0.147 +0.028 +0.028 +0.051
 P 0.0460 0.5265 0.9571 0.2574 0.0062 0.0357
             
Ssa 2215SP
 FIS −0.009 +0.070 +0.113 +0.074 +0.281 −0.032
 P 0.8879 0.3332 0.0044 0.2369 0.0002 0.1871
             
SsoSL 85
 FIS +0.025 +0.082 +0.004 −0.003 +0.063 +0.183
 P 0.1160 0.0158 0.5275 0.5162 0.0066 0.0037
             
Ssa 197
 FIS +0.012 +0.036 −0.049 −0.002 +0.575 −0.028
 P 0.5630 0.2662 0.4270 0.6161 >0.0001 0.7733
             
Ssa 1G7SP
 FIS +0.069 +0.013 +0.067 +0.172 +0.304 +0.032
 P 0.7914 0.0505 0.3465 >0.0001 0.0001 0.0592
             
Ssa 202
 FIS +0.176 +0.365 −0.040 +0.012 +0.271 +0.210
 P 0.0013 >0.0001 0.5938 0.1613 0.0033 >0.0001
             
Ssa D170
 FIS +0.279 +0.130 +0.059 +0.129 +0.240 +0.007
 P >0.0001 0.0036 >0.0001 0.0011 >0.0001 >0.0001
             
Sasa-DAA-3UTR
 FIS +0.132 +0.303 +0.104 −0.005 +0.118 −0.223
 P 0.0038 0.0029 0.0674 0.6681 0.6304 0.0013
             
Sasa-UBA-3UTR
 FIS +0.113 +0.114 +0.115 +0.262 +0.313 +0.027
 P 0.3261 0.0055 0.1193 >0.0001 >0.0001 0.0390
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Abbreviations: FIS, inbreeding coefficient; 3-UTR, 3-untranslated region.

River abbreviations: BRH, Burrishoole; CAW, Carrowinskey; DPH, Delphi; MOY, Moy; OWD, Owenduff; OWM, Owenmore; OWW, Owenwee.

Values in bold are those that remained significant after Bonferroni correction for the number of tests in this series (α=0.05/60=0.0008).

Comparisons across loci

Mean HO values for microsatellite loci were high (>0.70) in all samples, and while values of HO for Sasa-UBA-3UTR tended to fall within the 95% confidence limits for the mean value for microsatellites, those for Sasa-DAA-3UTR were generally lower than the lower 95% bound for the distribution of HO among microsatellites (Figure 2; Supplementary Table S4). Significant heterogeneity in HO was found among temporal samples in the BRH and DPH case studies at the Sasa-DAA-3UTR locus (Table 3, Figure 2) and at spatial samples at the microsatellite loci in the DPH spatial comparison. Post hoc tests showed that most of the heterogeneity in mean HO of microsatellites in the DPH case study was due to the OWW sample having a significantly lower value of HO than the samples from the DPH and Carrowniskey. Conversely, there was no evidence for significant heterogeneity in HO among spatial or temporal samples in both case studies at Sasa-UBA-3UTR (Table 3, Figure 2).

Figure 2.

Figure 2

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Patterns of HO at the microsatellite loci (mean±95% confidence intervals) and MH-linked markers across time in the (a) Burrishoole and (b) Delphi river systems, and across space for samples in the (c) Burrishoole and (d) Delphi case studies.

Table 3. Results of tests of temporal and spatial heterogeneity in mean HO and NA at microsatellite loci, the frequency of heterozygotes at MH-linked makers and deviation from mean NA at MH-linked makers.

  Temporal
 Spatial
  Burrishoole Delphi Burrishoole Delphi
Microsatellites F5,42=1.24, P=0.309 F3,28=0.292, P=0.786 F3,28=0.98, P=0.415 F2,21=3.80, P=0.039
         
Heterozygosity (HO)
 Sasa-DAA-3UTR G5=23.07, P<0.001 G3=8.17, P=0.043 G3=1.35, P=0.715 G2=0.172, P=0.917
 Sasa-UBA-3UTR G5=3.595, P=0.606 G3=0.719, P=0.868 G3=3.06, P=0.380 G2=3.07, P=0.214
         
Microsatellites F5,42=0.95, P=0.461 F3,28=0.218, P=0.883 F3,28=0.71, P=0.553 F2,21=0.13, P=0.876
         
Allelic richness (NA)
 Sasa-DAA-3UTR G5=1.59, P=0.896 G3=0.99, P=0.306 G3=0.67, P=0.879 G2=0.29, P=0.866
 Sasa-UBA-3UTR G5=1.61, P=0.893 G3=0.61, P=0.892 G3=4.48, P=0.208 G2=0.18, P=0.913
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Abbreviations: MH, major histocompatibility; 3-UTR, 3-untranslated region.

Numbers in subscript indicate degrees of freedom associated with each test. Values in bold were significant at the α=0.05 level.

There was relatively little fluctuation in values of NA over space and time compared with that for HO (Figure 3), with no significant heterogeneity detected at any locus or class of loci at any scale (Table 3). As for HO, values of NA for Sasa-DAA-3UTR were generally lower than those for other loci (Figure 3; Supplementary Table S5).

Figure 3.

Figure 3

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Patterns of NA at the microsatellite loci (mean±95% confidence intervals) and MH-linked markers across time in the (a) Burrishoole and (b) Delphi river systems, and across space for samples in the (c) Burrishoole and (d) Delphi case studies.

Matrices of genetic distance measured as FST, G′ST and Dest among temporal and spatial samples in both case studies were not correlated with each other in the comparisons between microsatellites and Sasa-DAA 3UTR (BRH temporal FST: Z=0.04, P=0.074; G′ST: Z=0.649, P=0.056; Dest: Z=0.080, P=0.074; BRH spatial FST: Z=0.019, P=0.432; G′ST: Z=0.515, P=0.450; Dest: Z=0.426, P=0.432; DPH temporal FST: Z=0.01, P=0.074; G′ST: Z=0.014, P=0.220; Dest: Z=0.014, P=0.208; DPH spatial FST: Z=0.001, P=0.866; G′ST: Z=0.012, P=0.886; Dest: Z=0.012, P=0.867). In contrast, most matrices of genetic distances measured with microsatellites and Sasa-UBA-3UTR were correlated (BRH temporal FST: Z=0.010, P=0.043; G′ST: Z=0.649, P=0.038; Dest: Z=0.584, P=0.016; BRH spatial FST: Z=0.024, P=0.141; G′ST: Z=1.272, P=0.450; Dest: Z=1.160, P=0.383; DPH temporal FST: Z=0.012, P=0.039; G′ST: Z=0.292, P=0.049; Dest: Z=0.265, P=0.049; DPH spatial FST: Z=0.002, P=0.036; G'ST: Z=0.882, P=0.025; Dest: Z=1.160, P=0.025).

Evidence for selection on MH-linked markers: comparison with neutral models

The Ewens–Watterson test rejected the null hypothesis of neutrality for Sasa-UBA-3UTR in the Owenduff sample (Fo=0.219, Fe=0.122 Slatkin exact P=0.014).

The geographical distribution of variation at the microsatellite loci conformed to an isolation-by-distance model of population structure (FST: Z=−13.84, P=0.049; G′ST: Z=−18.99, P=0.032; Dest: Z=−477.82, P=0.042), whereas this was not true of either Sasa-DAA-3UTR (FST: Z=−50.05, P=0.382; G′ST: Z=−50.66, P=0.331; Dest: Z=−1327.82, P=0.355) or Sasa-UBA-3UTR (FST: Z=−49.62, P=0.101; G′ST: Z=−25.69, P=0.098; Dest: Z=−663.46, P=0.099).

Partial Mantel tests controlling for differentiation in microsatellites did not reveal any significant correlation between differentiation at MH-linked markers between populations and geographical distance (FST: Sasa-DAA-3UTR: r=−0.0095, P=0.5029 (one tailed); Sasa-UBA-3UTR: r=0.2406, P=0.1113 (one tailed); G′ST: Sasa-DAA-3UTR: r=0.1627, P=0.2610 (one tailed); Sasa-UBA-3UTR: r=0.3113, P=0.0970 (one tailed)).

Sasa-DAA-3UTR displayed evidence of selection with high log 10 BF, with values above 2 in spatial and temporal samples in both BRH and DPH case studies (BRH temporal α=1.42; BRH spatial α=0.98; DPH temporal α=1.14; DPH spatial α=0.83) (Figure 4). Sasa-UBA-3UTR also showed evidence of selection, but only in the temporal samples of the BRH and DPH case studies, respectively (BRH temporal α=0.76; DPH temporal α=0.83). SasaD144b showed evidence for balancing selection (α=−0.72) in the Burrishole spatial study (Figure 4).

Figure 4.

Figure 4

Open in a new tab

Spatial and temporal analyses for the identification of loci potentially subject to differential selection. (a) Burrishole temporal case, (b) Delphi temporal case, (c) Burrishole spatial case and (d) Delphi spatial case. Vertical lines mark the log 10 of the BFs estimated using BAYESCAN, log 10 (BF)=2, corresponding to posterior probabilities of locus effects of 0.99 (decisive). Only loci identified as under selection are labelled.

Discussion

Previous studies had shown that both neutral forces and natural selection can act in shaping variability of MHC genes (Landry and Bernatchez, 2001; Seddon and Ellegren, 2004; Charbonnel and Pemberton, 2005; de Eyto et al., 2007; Oliver et al., 2009). To our knowledge, this is the first study that compares the effects of selection and neutral forces at both class I and class IIα MH-linked markers spatial and temporally. Here, using two case studies involving Atlantic salmon populations in western Ireland, we tested two hypotheses on patterns of selection on MH-linked makers. To do this, we compared temporal and spatial heterogeneity at the microsatellite loci, which were presumed to be neutral to the effects of natural selection, with variation at MHC-linked markers using three measures of genetic variability: heterozygosity, allelic richness and variance in allele frequency among samples. We also tested levels of temporal and spatial differentiation at MH-linked markers and microsatellites against neutral models of genetic differentiation. Finally, we compared patterns of variability between classes of MH-linked markers to look for evidence of differential selection.

Comparison among loci

Our first hypothesis was that MH-linked markers would show higher levels of heterogeneity over temporal and spatial scales than microsatellites. We found differences among loci in two out of three measures of variability. Comparison of levels of heterozygosity across time suggested that the class IIα MH-linked locus (Sasa-DAA-3UTR) showed more heterogeneity than the microsatellite loci. However, this contention requires careful scrutiny and justification as heterogeneity in heterozygosity could arise as a technical or sampling artifact. Analysis of the pattern of deviation from Hardy–Weinberg equilibrium suggested that older samples from the BRH and samples from the OWW were exceptional in that they displayed significantly higher levels of homozygosity across several loci (although not in Sasa-DAA-3UTR). In particular, the OWW was identified by post hoc tests as significantly lower in microsatellite heterozygosity than other samples from the same population or case study. Given the known problems with PCR amplification of DNA extracted from old-scale samples (Nielsen et al., 1997) and the potential for sampling a small number of families from salmonid populations (Hansen et al., 1997), it may be that allelic dropout (in older BRH samples) or sampling effects (in the OWW sample) were responsible for some of the observed temporal and spatial heterogeneity at microsatellite and MH-linked loci. However, repeated genotyping (with low levels of genotyping error rate) and sampling across several kilometers of river were specifically employed in this study to avoid these potential problems. Moreover, there is no a priori reason to believe, and no evidence from our data, that MH-linked markers were more susceptible to these problems than other loci. On this basis, it appears that heterogeneity in heterozygosity was indeed greater at Sasa-DAA-3UTR than at microsatellites in our case studies.

In contrast with heterozygosity, the lack of heterogeneity observed in allelic richness is perhaps surprising given that allelic richness is often assumed to be a more sensitive measure of demographic and selective effects than heterozygosity (Leberg, 1992). Correlations between allelic richness and heterozygosity are scale dependent, and tend to be higher in nearby populations that share similar environments and selective pressures (Comps et al., 2001), but in general allelic richness correlates better with demographic patterns as a consequence of the effect of genetic drift in rare alleles, which contribute little to heterozygosity values but are more easily lost by random mating (Leberg, 2002). In fact, demographic effects such as post-glacial history and bottlenecks can result in important population structuring at MHC genes (e.g. Miller et al., 1997) and selection on particular alleles also result in important allele differences between populations (e.g. MHC class II in Fundulus heteroclitus; Cohen, 2002). In our study, we did not observe allelic number reductions, but we found some degree of population structuring at MH-linked markers, suggesting that neutral forces are also important in shaping MH variability.

Analysis of FST, G′ST and Dest among samples showed no correlation between matrices of genetic differentiation based on microsatellites and the MH class II-linked locus (Sasa-DAA-3UTR). However, there was a correlation between matrices of genetic differentiation from microsatellites and the MH class I-linked locus (Sasa-UBA-3UTR) in the BRH temporal case study and in both DPH case studies (all the three distances). Results using the three estimates of genetic diversity were in general consistent. Only microsatellites conformed to an isolation-by-distance model of population structure, suggesting that neutral forces such as gene flow and migration played a more important role in shaping the variability of the neutral microsatellites than of the MH-linked markers (Dionne et al., 2007). Higher levels of population differentiation for MH-linked markers could be the result of directional selection and local adaptation (Landry and Bernatchez, 2001; Cohen, 2002; Heath et al., 2006).

Comparison with neutral models

We also hypothesized that MH-linked markers would lie outside the expectation from neutral models of population structure. Indeed, comparisons with neutral models provided additional evidence for differences in response to selection of class I- and class IIα-linked markers. The Ewens–Watterson test confirmed that directional selection was acting on the Sasa-UBA-3UTR with higher homozygosity than expected under neutrality, albeit in only one of the populations (Owenduff). However, variable results of this test are commonly found when several populations are compared, probably as a result of differences in the strength of selection in different environments (Bernatchez and Landry, 2003) and among different subpopulations (Garrigan and Hedrick, 2003). In addition, the power of the Ewens–Watterson test decreases rapidly when beneficial mutations reach fixation, the time to fixation for positively selected alleles being commonly short (Zhai et al., 2009).

Perhaps, the most unequivocal evidence for selection on the MH-linked markers comes from the Bayesian analysis of deviation from a neutral model of genetic variation. In all tests Sasa-DAA-3UTR was a strong outlier from the neutral expectation, whereas Sasa-UBA-3UTR was an outlier in the two temporal tests. The strong evidence for selection on Sasa-DAA-3UTR in this analysis is perhaps surprising given its relatively low level of polymorphism, which could reduce the power of the analysis to detect selection (Foll and Gaggiotti, 2008). In contrast, microsatellites conformed to the neutral model, with only one exception (Sasa-D144b in the BRH spatial analysis). Although assumed neutral, microsatellites can appear as outliers as a consequence of hitchhiking selection, and this has been seen previously in salmonids with microsatellites known to be associated to life history traits (Aguilar and Garza, 2006). However, we did not find evidence in the literature, suggesting that this could be the case for Sasa-D144b and given that this only occurred in one case, although the evidence for selection at Sasa-UBA-3UTR and Sasa-DAA-3UTR was supported by results of several tests, we consider that this result does not invalidate the evidence found here for selection acting on the MHC-linked markers.

Directional selection acting on MH class II has been previously observed in Atlantic salmon within a river drainage, and was attributed to local adaptation to environmental conditions or pathogen composition (Landry and Bernatchez, 2001). An extensive study in populations of sockeye salmon found evidence for directional and balancing selection acting on MH class II (Miller et al., 2001), with a high degree of population differentiation at the MH locus that may have resulted from directional selection spatial and/or temporal variability in pathogen composition. The directional selection we observed could also be the result of spatial and temporal variation in pathogen composition, if the selective advantage of MH alleles differed among environments (Bernatchez and Landry, 2003), and this was reflected in the linked markers studied here. In the BRH system a previous study linking MH variability to survival and fitness in Atlantic salmon under natural conditions found evidence for disease-mediated selection acting on both the MH class II locus and the linked marker Sasa-DAA-3UTR (de Eyto et al., 2007). Moreover, the results suggested that additive allelic effects were more important than heterozygote advantage for individual survival (de Eyto et al., 2007). Sexual selection could also favor individuals carrying specific resistance alleles against common parasites in different populations, and differences in MHC composition due to divergent parasite-mediated selection could be further maintained by assortative mating of females with locally adapted males (Eizaguirre et al., 2009, 2010).

Differences between MH-linked markers

Our second hypothesis was that MH-linked markers would show different patterns of selection. To our knowledge, this is the first time that the response to selection has been compared between MHC class I- and class II-linked markers at both temporal and spatial scales. Collectively, our results indicate that the diversity of both MH-linked markers may be affected by selection, whereas undoubtedly neutral forces (genetic drift) also play a role. We found strong evidence for directional selection acting on Sasa-DAA-3UTR; we also found some evidence for directional selection on Sasa-UBA-3UTR, but at a different scale. Although Sasa-DAA-3UTR showed evidence for directional selection at both temporal and spatial studies, we only found evidence of selection acting on Sasa-UBA-3UTR mostly in the temporal comparisons and only in one of the populations for the BRH spatial study.

Previous studies in salmonids found temporal stability at MHC class I (Hansen et al., 2007) and class II (Miller et al., 2001) genes and higher levels of population structuring at MHC class II (Landry and Bernatchez, 2001; Heath et al., 2006), although guppies seem to show low levels of differentiation on MH class II genes (Van Oosterhout et al., 2006), and evidence from mammals and birds suggests that there is temporal variation in selection acting at MHC class II and class I genes (Westerdahl et al., 2004; Charbonnel and Pemberton, 2005). Our finding of different responses to selection of an MH class IIα-linked marker and a class I-linked marker might reflect an association between level of environmental heterogeneity (within and among rivers) and possibly differences in pathogen-driven selective pressures acting on variability at each class of MH locus (Paterson, 1998; Wegner et al., 2003, 2008). Although we cannot automatically assume that the genetic variation at the MHC loci is reflected in the linked microsatellite loci, previous studies (e.g. Grimholt et al., 2002; Stet et al., 2002) have shown that the micro/minisatellite markers used in this study are uniquely and tightly linked to the expressed MH loci in Atlantic salmon, and can be used as good proxies for functional variation in MH genes. Class I and class II genes have different patterns of variability in teleosts. For example, Consuegra et al. (2005a) observed greater divergence of alleles in class I in contrast with an overlap of most of the allelic composition of class II in two isolated Atlantic salmon populations. In contrast, Kruiswijk et al. (2005) found a complete divergence in class II, but not in class I of a barbs species flock. Differences in evolutionary rates and the response to selection between both genes have been observed not only in fish but also in mammals and birds (Go et al., 2003; Bryja et al., 2007). Class I and class II molecules differ in the nature of the peptides that they bind, in how they bind and process them (Castellino et al., 1997; Kaufman et al., 1999; Grommé and Neefjes, 2002; Go et al., 2003) and in the T cells they react with (Housset and Malissen, 2003; Huseby et al., 2003). Our results suggest that differences in the response to selection of class I and class II markers could reflect structural and functional differences in the genes to which they are linked. Given the increasing importance of using MHC-linked markers for conservation and management purposes (Hedrick et al., 2001; Bos et al., 2008), our results highlight the need to consider both loci in population and evolutionary studies.

Acknowledgments

Funding for this study was supplied by a contract from the European Commission (Salimpact: QLRT-2000-01185). We thank the field staff of the Marine Institute Newport for assistance with sampling and Carlos García de Leániz, Christophe Eizaguirre and an anonymous referee for their useful comments on the manuscript. PMcG was in part supported by the Beaufort Marine Research award in Fish Population Genetics funded by the Irish Government under the Sea Change Programme.

The authors declare no conflict of interest.

Footnotes

Supplementary Information accompanies the paper on Heredity website (http://www.nature.com/hdy)

Supplementary Material

Supplementary Tables S1–S5
Click here for additional data file. (771KB, doc)

References

  1. Aguilar A, Garza JC. A comparison of variability and population structure for major histocompatibility complex and microsatellite loci in California coastal steelhead (Oncorhynchus mykiss Walbaum) Mol Ecol. 2006;15:923–937. doi: 10.1111/j.1365-294X.2006.02843.x. [DOI] [PubMed] [Google Scholar]
  2. Aguilar A, Roemer G, Debenham S, Binns M, Garcelon D, Wayne RK. High MHC diversity maintained by balancing selection in an otherwise genetically monomorphic mammal. Proc Natl Acad Sci USA. 2004;101:3490–3494. doi: 10.1073/pnas.0306582101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Alcaide M, Edwards SV, Negro JJ, Serrano D, Tella JOS, L Extensive polymorphism and geographical variation at a positively selected MHC class II B gene of the lesser kestrel (Falco naumanni) Mol Ecol. 2008;17:2652–2665. doi: 10.1111/j.1365-294X.2008.03791.x. [DOI] [PubMed] [Google Scholar]
  4. Apanius V, Penn D, Slev PR, Ruff LR, Potts WK. The nature of selection on the major histocompatibility complex. Crit Rev Immunol. 1997;17:179–224. doi: 10.1615/critrevimmunol.v17.i2.40. [DOI] [PubMed] [Google Scholar]
  5. Beacham TD, Lapointe M, Candy JR, McIntosh B, MacConnachie C, Tabata A, et al. Stock identification of Fraser river sockeye salmon using microsatellites and major histocompatibility complex variation. Trans Am Fish Soc. 2004;133:1117–1137. [Google Scholar]
  6. Beaumont MA, Balding DJ. Identifying adaptive genetic divergence among populations from genome scans. Mol Ecol. 2004;13:969–980. doi: 10.1111/j.1365-294x.2004.02125.x. [DOI] [PubMed] [Google Scholar]
  7. Belkhir K, Borsa P, Chikhi L, Raufaste N, Bonhomme F. Laboratoire Génome, Populations, Interactions. CNRS UMR. Université de Montpellier II, Montpellier (France); 2001. [Google Scholar]
  8. Bernatchez L, Landry C. MHC studies in non-model vertebrates: What have we learned about natural selection in 15 years. J Evol Biol. 2003;16:363–377. doi: 10.1046/j.1420-9101.2003.00531.x. [DOI] [PubMed] [Google Scholar]
  9. Bonnet E, Van de Peer Y. zt: a software tool for simple and partial Mantel tests. J Stat Soft. 2002;7:1–12. [Google Scholar]
  10. Bos DH, Gopurenko D, Williams RN, DeWoody AJ, Knowles L. Inferring population history and demography using microsatellites, mitochondrial DNA, and major histocompatibility complex (MHC) genes. Evolution. 2008;62:1458–1468. doi: 10.1111/j.1558-5646.2008.00364.x. [DOI] [PubMed] [Google Scholar]
  11. Boyce WM, Hedrick PW, Muggli-Cockett NE, Kalinowski S, Penedo MCT, Ramey-Ii RR. Genetic variation of major histocompatibility complex and microsatellite loci: a comparison in bighorn sheep. Genetics. 1997;145:421–433. doi: 10.1093/genetics/145.2.421. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Bryja J, Charbonnel N, Berthier K, Galan M, Cosson JF. Density-related changes in selection pattern for major histocompatibility complex genes in fluctuating populations of voles. Mol Ecol. 2007;16:5084–5097. doi: 10.1111/j.1365-294X.2007.03584.x. [DOI] [PubMed] [Google Scholar]
  13. Castellino F, Zhong G, Germain RN. Antigen presentation by MHC class II molecules: invariant chain function, protein trafficking, and the molecular basis of diverse determinant capture. Hum Immunol. 1997;54:159–169. doi: 10.1016/s0198-8859(97)00078-5. [DOI] [PubMed] [Google Scholar]
  14. Cereb N, Hughes AL, Yang SY. Locus-specific conservation of the HLA class I introns by intra-locus homogenization. Immunogenetics. 1997;47:30–36. doi: 10.1007/s002510050323. [DOI] [PubMed] [Google Scholar]
  15. Charbonnel N, Pemberton J. A long-term genetic survey of an ungulate population reveals balancing selection acting on MHC through spatial and temporal fluctuations in selection. Heredity. 2005;95:377–388. doi: 10.1038/sj.hdy.6800735. [DOI] [PubMed] [Google Scholar]
  16. Ciborowski K, Consuegra S, García de Leániz C, Wang J, Beaumont M, Jordan W. Stocking may increase mitochondrial DNA diversity but fails to halt the decline of endangered Atlantic salmon populations. Conserv Genet. 2007;8:1355–1367. [Google Scholar]
  17. Cohen S. Strong positive selection and habitat-specific amino acid substitution patterns in MHC from an estuarine fish under intense pollution stress. Mol Biol Evol. 2002;19:1870–1880. doi: 10.1093/oxfordjournals.molbev.a004011. [DOI] [PubMed] [Google Scholar]
  18. Comps B, Gomory D, Letouzey J, Thiebaut B, Petit RJ. Diverging trends between heterozygosity and allelic richness during postglacial colonization in the European beech. Genetics. 2001;157:389–397. doi: 10.1093/genetics/157.1.389. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Consuegra S, García de Leániz C. MHC-mediated mate choice increases parasite resistance in salmon. Proc R Soc Biol Sci Ser B. 2008;275:1397–1403. doi: 10.1098/rspb.2008.0066. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Consuegra S, GarcíaDeLeániz C, Serdio A, Morales MG, Straus LG, Knox D, et al. Mitochondrial DNA variation in Pleistocene and modern Atlantic salmon from the Iberian glacial refugium. Mol Ecol. 2002;11:2037–2048. doi: 10.1046/j.1365-294x.2002.01592.x. [DOI] [PubMed] [Google Scholar]
  21. Consuegra S, Megens HJ, Leon K, Stet RJM, Jordan WC. Patterns of variability at the major histocompatibility class II alpha locus in Atlantic salmon contrast with those at the class I locus. Immunogenetics. 2005a;57:16–24. doi: 10.1007/s00251-004-0765-z. [DOI] [PubMed] [Google Scholar]
  22. Consuegra S, Megens HJ, Schaschl H, Leon K, Stet RJM, Jordan WC. Rapid evolution of the MH class I locus results in different allelic compositions in recently diverged populations of Atlantic salmon. Mol Biol Evol. 2005b;22:1095–1106. doi: 10.1093/molbev/msi096. [DOI] [PubMed] [Google Scholar]
  23. Coughlan J, McGinnity P, O′Farrell B, Dillane E, Diserud O, de Eyto E, et al. Temporal variation in an immune response gene (MHC I) in anadromous Salmo trutta in an Irish river before and during aquaculture activities. ICES J Mar Sci. 2006;63:1248–1255. [Google Scholar]
  24. Crawford NG. smogd: software for the measurement of genetic diversity. Mol Ecol Res. 2010;10:556–557. doi: 10.1111/j.1755-0998.2009.02801.x. [DOI] [PubMed] [Google Scholar]
  25. de Eyto E, McGinnity P, Consuegra S, Coughlan J, Tufto J, Farrell K, et al. Natural selection acts on Atlantic salmon major histocompatibility (MH) variability in the wild. Proc R Soc Biol Sci Ser B. 2007;274:861–869. doi: 10.1098/rspb.2006.0053. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Dhuyvetter H, Gaublomme E, Desender K. Genetic differentiation and local adaptation in the salt-marsh beetle Pogonus chalceus: a comparison between allozyme and microsatellite loci. Mol Ecol. 2004;13:1065–1074. doi: 10.1111/j.1365-294X.2004.02134.x. [DOI] [PubMed] [Google Scholar]
  27. Dionne M, Miller KM, Dodson JJ, Caron F, Bernatchez L. Clinal variation in MHC diversity with temperature: evidence for the role of host-pathogen interaction on local adaptation in Atlantic salmon. Evolution. 2007;61:2154–2164. doi: 10.1111/j.1558-5646.2007.00178.x. [DOI] [PubMed] [Google Scholar]
  28. Dufresne F, Bourget E, Bernatchez L. Differential patterns of spatial divergence in microsatellite and allozyme alleles: further evidence for locus-specific selection in the acorn barnacle, Semibalanus balanoides. Mol Ecol. 2002;11:113–123. doi: 10.1046/j.0962-1083.2001.01423.x. [DOI] [PubMed] [Google Scholar]
  29. Edwards SV, Hedrick PW. Evolution and ecology of MHC molecules: from genomics to sexual selection. Trends Ecol Evol. 1998;13:305–311. doi: 10.1016/s0169-5347(98)01416-5. [DOI] [PubMed] [Google Scholar]
  30. Eizaguirre C, Lenz T, Sommerfeld R, Harrod C, Kalbe M, Milinski M. Parasite diversity, patterns of MHC II variation and olfactory based mate choice in diverging three-spined stickleback ecotypes. Evol Ecol. 2010. pp. 1–18.
  31. Eizaguirre C, Yeates SE, Lenz TL, Kalbe M, Milinski M. MHC-based mate choice combines good genes and maintenance of MHC polymorphism. Mol Ecol. 2009;18:3316–3329. doi: 10.1111/j.1365-294X.2009.04243.x. [DOI] [PubMed] [Google Scholar]
  32. Ekblom R, SÆTher SA, Jacobsson P, Fiske P, Sahlman T, Grahn M, et al. Spatial pattern of MHC class II variation in the great snipe (Gallinago media) Mol Ecol. 2007;16:1439–1451. doi: 10.1111/j.1365-294X.2007.03281.x. [DOI] [PubMed] [Google Scholar]
  33. Ewens WJ. The sampling theory of selectively neutral alleles. Theor Popul Biol. 1972;3:87–112. doi: 10.1016/0040-5809(72)90035-4. [DOI] [PubMed] [Google Scholar]
  34. Excoffier L, Laval G, Schneider S. Arlequin (version 3.0): an integrated software package for population genetics data analysis. Evol Bioinform. 2005;1:47–50. [PMC free article] [PubMed] [Google Scholar]
  35. Figueroa F, Gunther E, Klein J. MHC polymorphism pre-dating speciation. Nature. 1988;335:265–267. doi: 10.1038/335265a0. [DOI] [PubMed] [Google Scholar]
  36. Foll M, Gaggiotti O. A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: a Bayesian perspective. Genetics. 2008;180:977–993. doi: 10.1534/genetics.108.092221. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Ford MJ. Applications of selective neutrality tests to molecular ecology. Mol Ecol. 2002;11:1245–1262. doi: 10.1046/j.1365-294X.2002.01536.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Garrigan D, Hedrick PW. Perspective: detecting adaptive molecular polymorphism: lessons from the MHC. Evolution. 2003;57:1707–1722. doi: 10.1111/j.0014-3820.2003.tb00580.x. [DOI] [PubMed] [Google Scholar]
  39. Go Y, Satta Y, Kawamoto Y, Rakotoarisoa G, Randrianjafy A, Koyama N, et al. Frequent segmental sequence exchanges and rapid gene duplication characterize the MHC class I genes in lemurs. Immunogenetics. 2003;55:450–461. doi: 10.1007/s00251-003-0613-6. [DOI] [PubMed] [Google Scholar]
  40. Goldringer I, Bataillon T. On the distribution of temporal variations in allele frequency: consequences for the estimation of effective population size and the detection of loci undergoing selection. Genetics. 2004;168:563–568. doi: 10.1534/genetics.103.025908. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Goudet J. FSTAT (Version 1.2): a computer program to calculate F-statistics. J Hered. 1995;86:485–486. [Google Scholar]
  42. Grimholt U, Drablos F, Jorgensen SM, Hoyheim B, Stet RJ. The major histocompatibility class I locus in Atlantic salmon (Salmo salar L.): polymorphism, linkage analysis and protein modelling. Immunogenetics. 2002;54:570–581. doi: 10.1007/s00251-002-0499-8. [DOI] [PubMed] [Google Scholar]
  43. Grimholt U, Getahun A, Hermsen T, Stet RJM. The major histocompatibility class II alpha chain in salmonid fishes. Dev Comp Immunol. 2000;24:751–763. doi: 10.1016/s0145-305x(00)00034-3. [DOI] [PubMed] [Google Scholar]
  44. Grimholt U, Hordvik I, Fosse VM, Olsaker I, Endresen C, Lie Molecular cloning of major histocompatibility complex class I cDNAs from Atlantic salmon (Salmo salar) Immunogenetics. 1993;37:469–473. doi: 10.1007/BF00222473. [DOI] [PubMed] [Google Scholar]
  45. Grimholt U, Larsen S, Nordmo R, Midtlyng P, Kjoeglum S, Storset A, et al. MHC polymorphism and disease resistance in Atlantic salmon (Salmo salar); facing pathogens with single expressed major histocompatibility class I and class II loci. Immunogenetics. 2003;55:210–219. doi: 10.1007/s00251-003-0567-8. [DOI] [PubMed] [Google Scholar]
  46. Grommé M, Neefjes J. Antigen degradation or presentation by MHC class I molecules via classical and non-classical pathways. Mol Immunol. 2002;39:181–202. doi: 10.1016/s0161-5890(02)00101-3. [DOI] [PubMed] [Google Scholar]
  47. Hambuch TM, Lacey EA. Enhanced selection for MHC diversity in social tuco-tucos. Evolution. 2002;56:841–845. doi: 10.1111/j.0014-3820.2002.tb01395.x. [DOI] [PubMed] [Google Scholar]
  48. Hansen MM, Nielsen EE, Mensberg KLD. The problem of sampling families rather than populations: relatedness among individuals in samples of juvenile brown trout Salmo trutta L. Mol Ecol. 1997;6:469–474. [Google Scholar]
  49. Hansen MM, Skaala ∅, Jensen LF, Bekkevold D, Mensberg K-LD. Gene flow, effective population size and selection at major histocompatibility complex genes: brown trout in the Hardanger Fjord, Norway. Mol Ecol. 2007;16:1413–1425. doi: 10.1111/j.1365-294X.2007.03255.x. [DOI] [PubMed] [Google Scholar]
  50. Heath DD, Shrimpton JM, Hepburn RI, Jamieson SK, Brode SK, Docker MF. Population structure and divergence using microsatellite and gene locus markers in Chinook salmon (Oncorhynchus tshawytscha) populations. Can J Fish Aquat Sci. 2006;63:1370–1383. [Google Scholar]
  51. Hedrick P, Kim T. Genetics of Complex Polymorphisms: Parasites and Maintenance of MHC Variation. Harvard University Press: Cambridge, MA; 2000. pp. 204–234. [Google Scholar]
  52. Hedrick PW. A standardized genetic differentiation measure. Evolution. 2005;59:1633–1638. [PubMed] [Google Scholar]
  53. Hedrick PW. Evolutionary genetics of the major histocompatibility complex. Am Nat. 1994;143:945–964. [Google Scholar]
  54. Hedrick PW, Parker KM, Lee RN. Using microsatellite and MHC variation to identify species, ESUs, and MUs in the endangered Sonoran topminnow. Mol Ecol. 2001;10:1399–1412. doi: 10.1046/j.1365-294x.2001.01289.x. [DOI] [PubMed] [Google Scholar]
  55. Hedrick PW, Thompson G. Evidence for balancing selection at HLA. Genetics. 1983;104:449–456. doi: 10.1093/genetics/104.3.449. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Hordvik I, Grimholt U, Fosse VM, Lie ∅, Endresen C. Cloning and sequence analysis of cDNAs encoding the MHC class II β chain in Atlantic salmon (Salmo salar) Immunogenetics. 1993;37:437–441. doi: 10.1007/BF00222467. [DOI] [PubMed] [Google Scholar]
  57. Housset D, Malissen B. What do TCR-pMHC crystal structures teach us about MHC restriction and alloreactivity. Trends Immunol. 2003;24:429–437. doi: 10.1016/s1471-4906(03)00180-7. [DOI] [PubMed] [Google Scholar]
  58. Huang S-W, Yu H-T. Genetic variation of microsatellite loci in the major histocompatibility complex (MHC) region in the Southeast Asian house mouse (Mus musculus castaneus) Genetica. 2003;119:201–218. doi: 10.1023/a:1026061216816. [DOI] [PubMed] [Google Scholar]
  59. Hughes AL, Nei M. Pattern of nucleotide substitution at major histocompatibility complex class I loci reveals overdominant selection. Nature. 1988;335:167–170. doi: 10.1038/335167a0. [DOI] [PubMed] [Google Scholar]
  60. Hughes AL, Nei M. Nucleotide substitution at major histocompatibility complex class II loci: evidence for overdominant selection. Proc Natl Acad Sci USA. 1989;86:958–962. doi: 10.1073/pnas.86.3.958. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Hughes AL, Nei M. Evolutionary relationships of class II MHC genes in mammals. Mol Biol Evol. 1990;7:491. doi: 10.1093/oxfordjournals.molbev.a040622. [DOI] [PubMed] [Google Scholar]
  62. Hughes AL, Yeager M. Natural selection at the major histocompatibility complex loci of vertebrates. Annu Rev Genet. 1998;32:415–435. doi: 10.1146/annurev.genet.32.1.415. [DOI] [PubMed] [Google Scholar]
  63. Huseby ES, Crawford F, White J, Kappler J, Marrack P. Negative selection imparts peptide specificity to the mature T cell repertoire. Proc Natl Acad Sci USA. 2003;100:11565–11570. doi: 10.1073/pnas.1934636100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Jarvi SI, Tarr CL, Mcintosh CE, Atkinson CT, Fleischer RC. Natural selection of the major histocompatibility complex (Mhc) in Hawaiian honeycreepers (Drepanidinae) Mol Ecol. 2004;13:2157–2168. doi: 10.1111/j.1365-294X.2004.02228.x. [DOI] [PubMed] [Google Scholar]
  65. Jeffery KJM, Bangham CRM. Do infectious diseases drive MHC diversity. Microbes Infect. 2000;2:1335–1341. doi: 10.1016/s1286-4579(00)01287-9. [DOI] [PubMed] [Google Scholar]
  66. Jensen JL, Bohonak AJ, Kelley ST. Isolation by distance, web service. BMC Genet. 2005;6:13.v–13.16. doi: 10.1186/1471-2156-6-13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Jordan WC, Bruford MW. New perspectives on mate choice and the MHC. Heredity. 1998;81:127–133. doi: 10.1046/j.1365-2540.1998.00428.x. [DOI] [PubMed] [Google Scholar]
  68. Jordan WC, Verspoor E, Youngson AF. The effect of natural selection on estimates of genetic divergence among populations of the Atlantic salmon. J Fish Biol. 1997;51:546–560. [Google Scholar]
  69. Jost L. GST and its relatives do not measure differentiation. Mol Ecol. 2008;17:4015–4026. doi: 10.1111/j.1365-294x.2008.03887.x. [DOI] [PubMed] [Google Scholar]
  70. Karl SA, Avise JC. Balancing selection at allozyme loci in oysters: implications from nuclear RFLPs. Science. 1992;256:100–102. doi: 10.1126/science.1348870. [DOI] [PubMed] [Google Scholar]
  71. Kaufman J, Milne S, Gobel TWF, Walker BA, Jacob JP, Auffray C, et al. The chicken B locus is a minimal essential major histocompatibility complex. Nature. 1999;401:923–925. doi: 10.1038/44856. [DOI] [PubMed] [Google Scholar]
  72. Kimura M. The Neutral Theory of Molecular Evolution. Cambridge University Press: New York; 1985. p. 384pp. [Google Scholar]
  73. King TL, Eackles MS, Letcher BH. Microsatellite DNA markers for the study of Atlantic salmon (Salmo salar) kinship, population structure, and mixed-fishery analyses. Mol Ecol Notes. 2005;5:130–132. [Google Scholar]
  74. Klein J. Natural History of the Major Histocompatibility Complex. Wiley: New York; 1986. p. 775pp. [Google Scholar]
  75. Klein J, O'HUigin C. The conundrum of nonclassical major histocompatibility complex genes. Proc Natl Acad Sci USA. 1994;91:6251–6252. doi: 10.1073/pnas.91.14.6251. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Klein J, Sato A, Nikolaidis N. MHC, TSP, and the origin of species: from immunogenetics to evolutionary genetics. Annu Rev Genet. 2007;41:281–304. doi: 10.1146/annurev.genet.41.110306.130137. [DOI] [PubMed] [Google Scholar]
  77. Kruiswijk CP, Hermsen T, van Heerwaarden J, Dixon B, Savelkoul HFJ, Stet RJM. Major histocompatibility genes in the Lake Tana African large barb species flock: evidence for complete partitioning of class II B, but not class I, genes among different species. Immunogenetics. 2005;56:894–908. doi: 10.1007/s00251-005-0767-5. [DOI] [PubMed] [Google Scholar]
  78. Landry C, Bernatchez L. Comparative analysis of population structure across environments and geographical scales at major histocompatibility complex and microsatellite loci in Atlantic salmon (Salmo salar) Mol Ecol. 2001;10:2525–2539. doi: 10.1046/j.1365-294x.2001.01383.x. [DOI] [PubMed] [Google Scholar]
  79. Landry C, Garant D, Duchesne P, Bernatchez L. ‘Good genes as heterozygosity': the major histocompatibility complex and mate choice in Atlantic salmon (Salmo salar) Proc R Soc Biol Sci Ser B. 2001;268:1279–1285. doi: 10.1098/rspb.2001.1659. [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Lawlor DA, Ward FF, Ennis PD, Jackson AP, Parham P. HLA-A,-B polymorphisms predate the divergence of humans and chimpanzees. Nature. 1988;335:268. doi: 10.1038/335268a0. [DOI] [PubMed] [Google Scholar]
  81. Leberg PL. Effects of population bottlenecks on genetic diversity as measured by allozyme electrophoresis. Evolution. 1992;46:477–494. doi: 10.1111/j.1558-5646.1992.tb02053.x. [DOI] [PubMed] [Google Scholar]
  82. Leberg PL. Estimating allelic richness: effects of sample size and bottlenecks. Mol Ecol. 2002;11:2445–2449. doi: 10.1046/j.1365-294x.2002.01612.x. [DOI] [PubMed] [Google Scholar]
  83. Lukacs M, Harstad H, Bakke H, Beetz-Sargent M, McKinnel L, Lubieniecki K, et al. Comprehensive analysis of MHC class I genes from the U-, S-, and Z-lineages in Atlantic salmon. BMC Genomics. 2010;11:154. doi: 10.1186/1471-2164-11-154. [DOI] [PMC free article] [PubMed] [Google Scholar]
  84. Markow T, Hedrick PW, Armstrong C, Martin J, Zuerlein K, Vyvial T, et al. HLA polymorphism in the Havasupai: evidence for balancing selection. Am J Hum Genet. 1993;53:943–952. [PMC free article] [PubMed] [Google Scholar]
  85. McConnell TJ, Talbot WS, McIndoe RA, Wakeland EK. The origin of MHC class II gene polymorphism within the genus Mus. Nature. 1988;332:651–654. doi: 10.1038/332651a0. [DOI] [PubMed] [Google Scholar]
  86. McDonald JH. Contrasting amounts of geographical variation as evidence for direct selection: the Mpi and Pgm loci in eight crustacean species. Heredity. 1991;67:215–219. [Google Scholar]
  87. Miller HC, Lambert DM. Genetic drift outweighs balancing selection in shaping post-bottleneck major histocompatibility complex variation in New Zealand robins (Petroicidae) Mol Ecol. 2004;13:3709–3721. doi: 10.1111/j.1365-294X.2004.02368.x. [DOI] [PubMed] [Google Scholar]
  88. Miller KM, Kaukinen KH, Beacham TD, Withler ME. Geographic heterogeneity in natural selection on an MHC locus in sockeye salmon. Genetica. 2001;111:237–257. doi: 10.1023/a:1013716020351. [DOI] [PubMed] [Google Scholar]
  89. Miller KM, Withler RE, Beacham TD. Molecular evolution at Mhc genes in two populations of chinook salmon Oncorhynchus tshawytscha. Mol Ecol. 1997;6:937–954. doi: 10.1046/j.1365-294x.1997.00274.x. [DOI] [PubMed] [Google Scholar]
  90. Nielsen EE, Hansen MM, Loeschcke V. Analysis of microsatellite DNA from old scale samples of Atlantic salmon Salmo salar: a comparison of genetic composition over 60 years. Mol Ecol. 1997;6:487–492. [Google Scholar]
  91. Nielsen EE, Hansen MM, Loeschcke V. Genetic variation in time and space: microsatellite analysis of extinct and extant populations of Atlantic Salmon. Evolution. 1999;53:261–268. doi: 10.1111/j.1558-5646.1999.tb05351.x. [DOI] [PubMed] [Google Scholar]
  92. Nielsen R. Molecular signatures of natural selection. Annu Rev Genet. 2005;39:197–218. doi: 10.1146/annurev.genet.39.073003.112420. [DOI] [PubMed] [Google Scholar]
  93. O'Reilly P, Hamilton L, McConnell S, Wright J. Rapid analysis of genetic variation in Atlantic salmon (Salmo salar) by PCR multiplexing of dinucleotide and tetranucleotide microsatellites. Can J Fish Aquat Sci. 1996;53:2292–2298. [Google Scholar]
  94. Oliver MK, Lambin X, Cornulier T, Piertney SB. Spatio-temporal variation in the strength and mode of selection acting on major histocompatibility complex diversity in water vole (Arvicola terrestris) metapopulations. Mol Ecol. 2009;18:80–92. doi: 10.1111/j.1365-294X.2008.04015.x. [DOI] [PubMed] [Google Scholar]
  95. Parham P, Ohta T. Population biology of antigen presentation by MHC class I molecules. Science. 1996;272:67–74. doi: 10.1126/science.272.5258.67. [DOI] [PubMed] [Google Scholar]
  96. Parker KM, Ruby JS, Hedrick PW. Molecular variation and evolutionarily significant units in the endangered Gila Topminnow. Conserv Biol. 1999;13:108–116. [Google Scholar]
  97. Paterson S. Evidence for balancing selection at the major histocompatibility complex in a free-living ruminant. J Hered. 1998;89:289–294. doi: 10.1093/jhered/89.4.289. [DOI] [PubMed] [Google Scholar]
  98. Paterson S, Piertney SB, Knox D, Gilbey J, Verspoor E. Characterization and PCR multiplexing of novel highly variable tetranucleotide Atlantic salmon (Salmo salar L.) microsatellites. Mol Ecol Notes. 2004;4:160–162. [Google Scholar]
  99. Pogson GH, Mesa KA, Boutilier RG. Genetic population structure and gene flow in the Atlantic cod Gadus morhua: a comparison of allozyme and nuclear RFLP loci. Genetics. 1995;139:375–385. doi: 10.1093/genetics/139.1.375. [DOI] [PMC free article] [PubMed] [Google Scholar]
  100. Potts WK, Wakeland EK. Evolution of diversity at the major histocompatibility complex. Trends Ecol Evol. 1990;5:181–187. doi: 10.1016/0169-5347(90)90207-T. [DOI] [PubMed] [Google Scholar]
  101. Potts WK, Wakeland EK. The evolution of MHC genetic diversity: a tale of incest, pestilence, and sexual preference. Trends Genet. 1993;9:408–412. doi: 10.1016/0168-9525(93)90103-o. [DOI] [PubMed] [Google Scholar]
  102. Raymond M, Rousset F. GENEPOP (Version 1.2): Population Genetics Software for Exact tests and ecumenicism. J Hered. 1995;86:248–249. [Google Scholar]
  103. Reusch T, Langefors A. Inter- and intralocus recombination drive MHC class IIB gene diversification in a teleost, the three-spined stickleback gasterosteus aculeatus. J Mol Evol. 2005;61:531–541. doi: 10.1007/s00239-004-0340-0. [DOI] [PubMed] [Google Scholar]
  104. Reusch TBH, Aeschlimann PB, Haberli MA, Milinski M. Female sticklebacks count alleles in a strategy of sexual selection explaining MHC-polymorphism. Nature. 2001;414:300–302. doi: 10.1038/35104547. [DOI] [PubMed] [Google Scholar]
  105. Seddon JM, Baverstock PR. Variation on islands: major histocompatibility complex (Mhc) polymorphism in populations of the Australian bush rat. Mol Ecol. 1999;8:2071–2079. doi: 10.1046/j.1365-294x.1999.00822.x. [DOI] [PubMed] [Google Scholar]
  106. Seddon JM, Ellegren H. A temporal analysis shows major histocompatibility complex loci in the Scandinavian wolf population are consistent with neutral evolution. Proc R Soc Biol Sci Ser B. 2004;271:2283–2291. doi: 10.1098/rspb.2004.2869. [DOI] [PMC free article] [PubMed] [Google Scholar]
  107. Slatkin M. An exact test for neutrality based on the Ewens sampling distribution. Genet Res. 1994;64:71–74. doi: 10.1017/s0016672300032560. [DOI] [PubMed] [Google Scholar]
  108. Slatkin M. A correction to the exact test based on the Ewens sampling distribution. Genet Res. 1996;68:259–260. doi: 10.1017/s0016672300034236. [DOI] [PubMed] [Google Scholar]
  109. Slettan A, Olsaker I, Lie Atlantic salmon, Salmo salar, microsatellites at the SSOSL25, SSOSL85, SSOSL311, SSOSL417 loci. Anim Genet. 1995;26:281–282. doi: 10.1111/j.1365-2052.1995.tb03262.x. [DOI] [PubMed] [Google Scholar]
  110. Smulders MJM, Snoek LB, Booy G, Vosman B. Complete loss of MHC genetic diversity in the common Hamster (Cricetus cricetus) population in The Netherlands. Consequences for conservation strategies. Conserv Genet. 2003;4:441–451. [Google Scholar]
  111. Sommer S. Effects of habitat fragmentation and changes of dispersal behaviour after a recent population decline on the genetic variability of noncoding and coding DNA of a monogamous Malagasy rodent. Mol Ecol. 2003;12:2845–2851. doi: 10.1046/j.1365-294x.2003.01906.x. [DOI] [PubMed] [Google Scholar]
  112. Spurgin LG, Richardson DS. How pathogens drive genetic diversity: MHC, mechanisms and misunderstandings. Proc R Soc Biol Sci Ser B. 2010;277:979–988. doi: 10.1098/rspb.2009.2084. [DOI] [PMC free article] [PubMed] [Google Scholar]
  113. Stet R, de Vries B, Mudde K, Hermsen T, van Heerwaarden J, Shum B, et al. Unique haplotypes of co-segregating major histocompatibility class II A and class II B alleles in Atlantic salmon (Salmo salar) give rise to diverse class II genotypes. Immunogenetics. 2002;54:320–331. doi: 10.1007/s00251-002-0477-1. [DOI] [PubMed] [Google Scholar]
  114. Valière N. gimlet: a computer program for analysing genetic individual identification data. Mol Ecol Notes. 2002;2:377–379. [Google Scholar]
  115. Van Oosterhout C, Joyce DA, Cummings SM, Blais J, Barson NJ, Ramnarine IW, et al. Balancing selection, random genetic drift, and genetic variation at the major histocompatibility complex in two wild populations of guppies (Poecilia reticulata) Evolution. 2006;60:2562–2574. [PubMed] [Google Scholar]
  116. Vasemagi A, Nilsson J, Primmer CR. Expressed sequence tag-linked microsatellites as a source of gene-associated polymorphisms for detecting signatures of divergent selection in Atlantic salmon (Salmo salar L.) Mol Biol Evol. 2005;22:1067–1076. doi: 10.1093/molbev/msi093. [DOI] [PubMed] [Google Scholar]
  117. Vitalis R, Dawson K, Boursot P. Interpretation of variation across marker loci as evidence of selection. Genetics. 2001;158:1811–1823. doi: 10.1093/genetics/158.4.1811. [DOI] [PMC free article] [PubMed] [Google Scholar]
  118. Watterson GA. The homozygosity test of neutrality. Genetics. 1978;88:405–417. doi: 10.1093/genetics/88.2.405. [DOI] [PMC free article] [PubMed] [Google Scholar]
  119. Wegner KM, Kalbe M, Milinski M, Reusch T. Mortality selection during the 2003 European heat wave in three-spined sticklebacks: effects of parasites and MHC genotype. BMC Evol Biol. 2008;8:124. doi: 10.1186/1471-2148-8-124. [DOI] [PMC free article] [PubMed] [Google Scholar]
  120. Wegner KM, Reusch TBH, Kalbe M. Multiple parasites are driving major histocompatibility complex polymorphism in the wild. J Evol Biol. 2003;16:224–232. doi: 10.1046/j.1420-9101.2003.00519.x. [DOI] [PubMed] [Google Scholar]
  121. Weir BS, Cockerham CC. Estimating F-statistics for the analysis of population structure. Evolution. 1984;38:1358–1370. doi: 10.1111/j.1558-5646.1984.tb05657.x. [DOI] [PubMed] [Google Scholar]
  122. Westerdahl H, Hansson B, Bensch S, Hasselquist D. Between-year variation of MHC allele frequencies in great reed warblers: selection or drift. J Evol Biol. 2004;17:485–492. doi: 10.1111/j.1420-9101.2004.00711.x. [DOI] [PubMed] [Google Scholar]
  123. Zhai W, Nielsen R, Slatkin M. An investigation of the statistical power of neutrality tests based on comparative and population genetic data. Mol Biol Evol. 2009;26:273–283. doi: 10.1093/molbev/msn231. [DOI] [PMC free article] [PubMed] [Google Scholar]

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