- Research article
- Open Access
Adaptive molecular evolution of the Major Histocompatibility Complex genes, DRA and DQA, in the genus Equus
© Kamath and Getz; licensee BioMed Central Ltd. 2011
Received: 30 November 2010
Accepted: 18 May 2011
Published: 18 May 2011
Major Histocompatibility Complex (MHC) genes are central to vertebrate immune response and are believed to be under balancing selection by pathogens. This hypothesis has been supported by observations of extremely high polymorphism, elevated nonsynonymous to synonymous base pair substitution rates and trans-species polymorphisms at these loci. In equids, the organization and variability of this gene family has been described, however the full extent of diversity and selection is unknown. As selection is not expected to act uniformly on a functional gene, maximum likelihood codon-based models of selection that allow heterogeneity in selection across codon positions can be valuable for examining MHC gene evolution and the molecular basis for species adaptations.
We investigated the evolution of two class II MHC genes of the Equine Lymphocyte Antigen (ELA), DRA and DQA, in the genus Equus with the addition of novel alleles identified in plains zebra (E. quagga, formerly E. burchelli). We found that both genes exhibited a high degree of polymorphism and inter-specific sharing of allele lineages. To our knowledge, DRA allelic diversity was discovered to be higher than has ever been observed in vertebrates. Evidence was also found to support a duplication of the DQA locus. Selection analyses, evaluated in terms of relative rates of nonsynonymous to synonymous mutations (dN/dS) averaged over the gene region, indicated that the majority of codon sites were conserved and under purifying selection (dN < dS). However, the most likely evolutionary codon models allowed for variable rates of selection across codon sites at both loci and, at the DQA, supported the hypothesis of positive selection acting on specific sites.
Observations of elevated genetic diversity and trans-species polymorphisms supported the conclusion that balancing selection may be acting on these loci. Furthermore, at the DQA, positive selection was occurring at antigen binding sites, suggesting that a few selected residues may play a significant role in equid immune function. Future studies in natural equid populations will be valuable for understanding the functional significance of the uniquely diverse DRA locus and for elucidating the mechanism maintaining diversity at these MHC loci.
Genes of the Major Histocompatibility Complex (MHC) are ideal candidates for investigating the influence of selection in promoting patterns of genetic diversity [1, 2], due to their ecological significance. This multi-gene family has been widely demonstrated to play a fundamental role in gnathostome (i.e. jawed vertebrate) immune response by modulation of resistance to parasites and pathogens [1, 3, 4]. More specifically, class I and II MHC genes encode cell-surface glycoproteins that recognize foreign antigen molecules and, subsequently, present them to T-lymphocytes to initiate an immune system response in the host . The MHC is known to be the most polymorphic gene region in vertebrates and, in humans, exhibits levels of nucleotide diversity that are two times higher than the genomic average . Evidence from studies of natural populations suggests that this elevated genetic diversity is driven and maintained by exposure to pathogens and parasites in the environment. For example, studies on sheep [7, 8], mice , voles  and lemurs  have found relationships between gastrointestinal parasites and MHC diversity or associations between specific alleles and infection levels. This vital role for the MHC in pathogen recognition has been the subject of much investigation. Further study of selection at the molecular level, however, is imperative to facilitate understanding of the mechanistic basis for adaptation in natural systems.
The MHC is believed to be under strong selective balancing pressure (reviewed in ) under the key hypothesized mechanisms of negative frequency-dependent [13, 14] and overdominant selection [3, 15, 16]. Balancing selection is often supported by three lines of evidence: (1) elevated levels of polymorphism, (2) higher rates of nonsynonymous (dN) to synonymous (dS) nucleotide substitutions than what would be expected under neutral evolution [15, 16] and (3) trans-species polymorphisms with alleles among species maintained over longer evolutionary time than those observed at neutral loci . In support of the latter observation, MHC allelic lineages of some mammals are thought to be millions of years old and allele divergences often pre-date species divergences . As a result, alleles from different species may be more closely related than alleles within a species . MHC trans-specific diversity has been demonstrated in many natural systems, including fish , rodents [18, 21–24], ungulates [25, 26], carnivores [27, 28] and primates . The persistence of highly divergent alleles over time may be explained by the hypothesis that increased diversity confers a fitness advantage to the host with an ability to recognize a broader spectrum of pathogens .
The extent to which selection is responsible for the observed mode of MHC evolution requires an in-depth look at patterns of variation occurring across the gene. The nonsynonymous/synonymous substitution rate ratio (ω = dN/dS) has been widely used as a measure of selective pressure on a gene (reviewed in ). Whereas ratios larger than one indicate a fitness advantage for mutations resulting in an amino acid change (i.e. positive selection), ratios smaller than one suggest selection against deleterious mutations (i.e. purifying selection). Within a MHC molecule only a limited proportion of amino acids have been found to be involved in antigen recognition and binding  and, thus, dN/dS estimates averaged across the gene can be misleading. Site-specific selection analyses have proven to be useful for elucidating how rates of evolution can vary across a gene region and for pin-pointing particular sites under selection [31, 32], such as those that specifically interact and recognize foreign peptides. Site-specific methods have found elevated dN/dS ratios at these antigen binding sites (ABS), suggesting substantially differing rates of evolution across the MHC .
MHC genes of the family Equidae, also called the Equine Lymphocyte Antigen (ELA), are similar in organization to those of humans, with adjacent class I, II and III regions , and their structure and overall function are believed to be conserved . Despite these similarities, the evolution of the ELA has been shown to differ in some ways from other species. For example, the most striking observation is that the ELA comprises at least two homologues of the class II DQA locus distributed on two different chromosomes, a phenomenon which has never been observed in any other mammalian species . In situ hybridization studies have localized the ELA to chromosome 20q14-q22 [37, 38], except for a single DQA homologue which was localized to chromosome 5 . Further examination of differences in the ELA revealed that the class II DRA locus, exon 2, has greater allelic variation in Equidae than in most other taxa [40, 41]. For example, in the domestic horse (Equus callabus), ass (E. asinus), mountain zebra (E. zebra) and plains zebra (E. quagga, formerly E. burchelli), 5-6 alleles per species have been detected [41–44], in contrast to the majority of species which have little to no sequence variation at this locus (e.g. [45–48]). The DRA and DQA loci are known to be paralogous, encoding the α-chain of a MHC class II molecule, and have a similar function in presenting peptides derived from extracellular proteins. However, the considerable difference in levels of diversity between these genes remains unexplained. Several studies have described the variability of ELA-DRA and DQA loci of the MHC [36, 40, 41], but there is still little understanding of the functional significance of these observed differences in ELA genes and how selection may be acting at the molecular level (but see ).
In this study, we investigated the molecular evolution of two MHC class II genes, ELA-DRA and DQA, within the genus Equus. This study combined previously discovered allelic data [36, 41, 44] with new genetic data collected from natural populations of plains zebra (E. quagga/E. burchelli). Our objectives were to: (1) characterize inter-specific genetic variation, (2) elucidate evolutionary relationships among alleles and (3) detect molecular-level patterns of selection at these loci. We hypothesize that these genes are highly variable and under balancing selection, and that positive selection is occurring at specific functional codon sites in equids. A better understanding of the variability and evolution of ELA genes will provide valuable background for future studies that aim to examine the genetic basis of susceptibility or resistance to pathogens in both domestic and wild equids.
Sample collection and DNA isolation
Fecal, blood and tissue samples were collected from plains zebra (E. quagga/E. burchelli) in two parks of southern Africa: Etosha National Park, Namibia (n = 38) and Kruger National Park, South Africa (n = 33). For the purposes of consistency with historical ELA allele nomenclature, we hereafter refer to the species by its former scientific name, E. burchelli. With fecal samples, three to five pellets were collected from each individual and allowed to dry. Epithelial cells from the outermost mucosal layer were scraped from the desiccated pellets using a sterile razor blade. Tissue samples were preserved in DMSO/EDTA/Tris/salt solution and blood samples in ethylenediaminetetraacetic acid (EDTA). All samples were stored at-20°C until DNA extraction. Sample collection was approved by the Animal Care and Use Committee (Protocol #R217-0510B) at UC Berkeley.
Whole genomic DNA was extracted from blood and tissue using Qiagen kits (Valencia, CA). Non-invasive samples, collected from feces, are subject to contamination, enzyme degradation (e.g. ), and hydrolytic and oxidative damage that may result in lower DNA yield and increased error rates (most commonly allele dropout ). Thus, we used the AquaGenomics protocol (MultiTarget Pharmaceuticals, Inc.) optimized for fecal DNA extraction. A few fecal samples suffered degradation which resulted in failed PCR-amplifications. These degraded samples were re-extracted using the QIAmp fecal extraction kit (Qiagen), also designed specifically for fecal DNA extraction.
PCR-amplification and sequencing
We targeted two MHC loci of the Equine Lymphocyte Antigen (ELA) system, ELA-DRA and DQA, by polymerase chain reaction (PCR)  and genotyped these loci through direct sequence-based typing. We amplified 246 bp of the DRA using equid-specific primers, Be3 and Be4 , and 205 bp of the DQA using the primers DQA-2e and DQA-2f . These primers targeted the functionally significant exon 2 of both genes, a region consisting of antigen binding sites (ABS) as predicted by their human lymphocyte antigen (HLA) equivalent . PCR mixes (total reaction volume of 15 μL) for both genes contained approximately 25-50 ng DNA, 2 uL GeneAmp 10 × PCR buffer (100 mM Tris-Cl, pH 8.3, 500 mM KCl, 15 mM MgCl2, 0.01% (w/v) gelatin), 1 U AmpliTaq Gold DNA polymerase (Applied Biosystems), 0.4 mM dNTPs, 15 μg bovine serum albumin (New England BioLabs) and 0.50 μM of each primer.
Amplification of the DRA locus used the following "touch-down" thermocycling profile: an initial denaturation at 95°C for 10 min; 2 cycles of 94°C for 1 min, 60°C for 1 min, and 70°C for 35 s; 18 cycles of 93°C for 45 s, 59°C for 45 s, and 70°C for 45 s, with the annealing temperature decreasing by 0.5°C with each cycle; 35 cycles of 92°C for 30 s, 50°C for 30 s, and 70°C for 1 min; final extension at 72°C for 10 min to allow for complete amplification of the targeted gene. PCR-amplification of the DQA locus used the following thermocycling profile: an initial denaturation at 95°C for 6 min; 40 cycles of 94°C for 45 s, 56°C for 45 s, and 72°C for 1 min; final extension at 72°C for 5 min.
DRA amplicons were purified prior to sequencing by incubating with Exonuclease I and Shrimp Alkaline Phosphatase at 37°C for 30 minutes. Purified products were cycle-sequenced in both forward and reverse directions using the Big Dye® Terminator v.3.1 kit and run on an ABI 3730 automated sequencer (Applied Biosystems).
Identification of MHC alleles
Sequence chromatograms were edited and aligned using the software Geneious 4.7 . Allelic phase for DRA heterozygous sequences was determined by computational inference with the haplotype reconstruction program PHASE v.2.1 . This program has been found to be accurate in determining allelic phase even in extremely variable loci, such as the MHC  and, therefore, is considered to be a reliable method for allele identification. We conducted five runs, using different initial random seed values, and compared phase results across runs. A threshold posterior probability of 0.9, a value considered significantly higher than the standard (see ), was used to assess the accuracy of the allelic phase determination. Individuals not meeting this threshold were dropped from use in further analyses.
Given the large number of heterozygous sites in the DQA locus and previous evidence for multiple loci , all PCR-amplicons were cloned and sequenced to identify novel haplotypes. PCR products were extracted and purified with the QIAquick Gel Extraction Kit, (Valencia, CA) and cloning was performed using a TOPO-TA® cloning kit with Mach 1™-T1R competent cells (Invitrogen). Amplicons were ligated into pCR®4 TOPO vectors and transformed into E. coli competent cells. Sixteen to twenty-three positive clones per individual were picked with a sterile toothpick and screened by sequencing (protocol described above). The high number of PCR-amplified clones was sufficient to avoid errors, such as recombinant sequences generated during PCR . Each allele was confirmed with at least two observations, meaning that it had to be found in at least one homozygous individual or two heterozygous individuals to be included in the following analyses.
Sequence data and alignments
Novel MHC alleles identified in E. burchelli were compiled with a reference panel of Equidae sequences (GenBank, NCBI), including horse (E. callabus), ass (E. asinus), onager (E. hemionus), kiang (E. kiang), plains zebra (E. burchelli), mountain zebra (E. zebra), Grevy's zebra (E. grevyi) and Przewalski's horse (E. przewalski). A list of ELA-DRA and DQA sequences from each equid species and their respective GenBank accession numbers are listed in Additional file 1. As the ELA-MHC nomenclature is currently in revision, names for previously discovered alleles follow designations given in Janova et al. (2009) and novel sequences discovered here were named based on the recommendations outlined by the MHC allele nomenclature committee . The new nomenclature is expected to be established soon on the IPD-MHC Database (http://www.ebi.ac.uk/ipd/mhc). Identical alleles shared between species were given species-specific numbering. Reference and novel nucleotide, and corresponding amino acid sequences were aligned using the Geneious 4.7 sequence alignment tool and editor .
Statistical analyses of diversity and evolution
Standard descriptive diversity indices for each locus within the genus Equidae were calculated using MEGA4 . These indices included the number of alleles (A), variable nucleotide positions (VNP), parsimony informative positions (PIP), transition/transversion bias ratio (R), Kimura 2-parameter gamma (K2P+Γ) evolutionary distance (d) and Poisson-corrected amino acid distance. The K2P+Γ model accounts for multiple hits, differences in transitional and transversional substitution rates and variation in substitution rates among sites following a gamma-shaped distribution. Estimates of the gamma shape parameter (α) were determined in PAUP*v4.0b0  to be α = 0.9872 for the DRA data and α = 0.4181 for the DQA data. Standard error of distance estimates were obtained by using a bootstrap procedure with 10,000 pseudoreplicates.
Four different methods, implemented in RDP v.3.44 beta package , were used to test for recombination and detect potential recombinant events: (1) RDP, (2) GENECONV, (3) Maximum Chi, and (4) BootScan. The highest acceptable p-value for all methods was set at a conservative value of 0.10, with a Bonferroni correction for multiple comparisons and a window size of 30 variable nucleotides for all approaches except BootScan. For analyses in BootScan, 1,000 bootstrap replicates were conducted under the Kimura model (transition/transversion ratio = 1.341), with a window size of 100 bp, step size of 20 nucleotides and cut-off value of 0.70.
Selection, averaged across the gene, was estimated using MEGA4  in terms of the relative rates of nonsynonymous (dN) and synonymous (dS) base pair substitutions, according to Nei and Gojobori (1986) with the Jukes and Cantor correction for multiple hits . Z-tests of selection were performed over all sites, and separately at ABS and non-ABS, under the null hypothesis of neutrality (dN = dS) and the alternative hypotheses of non-neutrality (dN ≠ dS), positive selection (dN > dS), and purifying selection (dN < dS).
Site-specific selection analyses
As selection will realistically act on only a small subset of amino acids in a protein, averaging substitution rates over entire gene regions is considered to be a conservative indicator of positive selection . Therefore, we used a more powerful maximum-likelihood based method, implemented in the CodeML subroutine of the software PAML  which allows the rates of ω = dN/dS to vary among codons [31, 64]. This method has been suggested to be more sensitive than other methods for detection of molecular evidence of selection . The models employed here, called 'random-sites' models, do not require a priori information on the functional significance of each site and estimate the nonsynonymous to synonymous rate ratio (ω) to indicate selective pressure at the protein level (ω < 1: purifying selection, ω = 1: neutral evolution, ω > 1: positive selection). In this analysis, we used the Equidae alignments to assess heterogeneity in ω across the two MHC genes (DRA and DQA) and to identify codons under positive selection. We fit the alignment to the following codon 'random-sites' models, in PAML: M0 (one ratio: best average ω across all sites), M1a (nearly neutral: estimates the proportion of sites that best-fit ω = 0 versus those best-fit by ω = 1), M2a (positive selection: adds a third set of sites to M1a that have ω > 1 and estimates the best-fit for this added ω value and associated proportion of sites), M3 (discrete: fits proportions and ω values assuming three classes of sites labeled 0, 1, and 2 such that ω0 < ω1 ≤ ω2), M7 (beta: ω is beta-distributed on [0, ]) and M8 (beta and omega: a proportion of sites are beta-distributed on [0, ] and the remaining proportion have an average ω2 > 1 ). M0 is the only model that does not allow for variation in ω across codon sites. Whereas M1a and M7 allow only for neutral evolution and purifying selection at some proportion of sites, M2a, M3, and M8 also allow for the possibility of positive selection at a proportion of sites.
Likelihood ratio tests (LRT) were used to compare nested models based on their log-likelihood . We compared M0 and M3 to test for the significance of heterogeneity in ω across sites, whereas M1a was compared with M2a, and M7 with M8 to test for positive selection. Significant adaptive evolution was inferred if twice the difference in log-likelihood values was greater than the chi-square critical value for the given degrees of freedom. We used the Bayes empirical Bayes (BEB) approach  to estimate mean ω and standard errors across codon positions. Specific sites under positive selection were indicated by estimates of ω > 1 and posterior probabilities > 0.95. This approach accounts for sampling errors in the maximum likelihood estimates of the parameters and has a low false positive rate. Tree files used in PAML analyses were generated using a maximum likelihood approach in PhyML , under the Kimura 3-parameter and the Kimura 2-paramter model of nucleotide substitution for the DRA and DQA locus, respectively. Models of nucleotide substitution and the distribution of rate variation across nucleotide sites (gamma) were estimated in PAUP*v4.0b0 .
Phylogenetic relationships among Equidae DRA and DQA sequences were reconstructed using a Bayesian approach implemented in MrBayes 3.1 . The data set was partitioned and the best-fit models were determined for each codon position using the Akaike Information Criterion (AIC) in MODELTEST v.3.7 . Bayesian inference involved running six Metropolis-coupled MCMC chains (1 cold and 5 heated) simultaneously at n incremental temperature of 0.1, and chains were run for seven and sixteen million generations for the DRA and DQA data, respectively. Trees were sampled every 100 generations and the first 25% of trees found were discarded, leaving the remaining trees to be used for estimating the consensus tree. Two independent analyses were conducted and results were compared to check for convergence by confirming that the average deviation of split frequencies approached 0 (with values less than 0.01). We also checked that the potential scale reduction factor (PSRF) approached 1 and that chains mixed sufficiently (with chain mixing values greater than 0.2 between chain pairs). Finally, we used the program Tracer v1.4  to ensure whether sampling from the posterior distribution of each parameter was sufficient and had reached a large enough effective sample size (ESS > 200) for accurate parameter estimation. Posterior probabilities, representing the probability that a specific node is observed, were recorded. This analysis was run on both non-partitioned and partitioned data, and the optimal model was determined using Bayes Factors.
DRA sequences from Bos taurus (DQ821713), Ovis aries (Z11600) and Sus scrofa (AY754888) obtained from GenBank (NCBI) were used as outgroups. For DQA trees, available sequences from B. taurus (AB548942), O. aries (M33304) and S. scrofa (EU195146) were used as outgroups.
Alleles amplified from the DRA, exon 2, in E. burchelli represented a single locus. Overall, we found 9 unique DRA alleles with haplotype phase certainties greater than the threshold probability value of 90% and which were observed at least twice in our sample. Of the alleles observed, five were novel sequences (DRA*07-*11) never seen before in plains zebra [GenBank: HQ637392-HQ637396]. Two of these newly discovered alleles have previously been found in other equid species (Eqbu-DRA*07 is identical to Eqas-DRA*01 of E. asinus; Eqbu-DRA*08 identical to Eqca-DRA*04 of E. callabus).
In the E. burchelli sample, 21 unique DQA alleles were found through cloning which met our requirements for this study. We found 13 novel alleles in E. burchelli, Eqbu-DQA*09-*21 [GenBank: HQ637397- HQ637409]. One of these, Eqbu-DQA*09, is identical to the E. callabus allele, Eqca-DQA*07. Cloning of the DQA revealed between 1-4 different alleles in each individual, indicating the presence of at least two DQA homologous loci.
Inter-and intra-specific analyses of diversity
Diversity of the ELA-DRA, exon 2, by taxon
No. of nucleotide sequences
No. of protein sequences
Indices of diversity and selection at the ELA-DRA and DQA
K2P distance (%)
AA distance (%)
ELA-DRA and DQA diversity and selection within Equus spp
d N /d S
d N /d S
Global selection analyses
Selection tests over all sites, antigen binding sites (ABS) and non-antigen binding sites (non-ABS)
Z; dN ≠ dS
Z; dN >dS
Z; dN <dS
Z; dN ≠ dS
Z; dN >dS
Z; dN <dS
Site-specific selection analyses
Parameter estimates, log-likelihood values and predicted sites under selection for codon evolution models
Sites under positive selection
2Δℓ ( p -value)
M0 (one ratio)
ω = 0.353
14.34 (p = 0.006)
ω0 = 0.044, p0 = 0.589,
ω1 = 0.044, p1 = 0.315,
ω 2 = 3.40 p2 = 0.096
M1a (nearly neutral)
ω0 = 0, p0 = 0.787,
ω1 = 1, p1 = 0.213
3.34 (p = 0.188)
M2a (positive selection)
ω0 = 0.044, p0 = 0.904,
ω1 = 1, p1 = 0
ω 2 = 3.40 p2 = 0.096
14,19, 47, 49
p = 0.005, q = 0.020
3.18 (p = 0.204)
M8 (beta and omega)
p0 = 0.904, p1 = 0.096,
p = 4.61, q = 99.0, ω = 3.40
14,19, 47, 49
M0 (one ratio)
ω = 0.984
197.68 (p < 0.001)
ω0 = 0.078, p0 = 0.556,
ω 1 = 1.68, p1 = 0.364,
ω 2 = 6.80, p2 = 0.080
M1a (nearly neutral)
ω0 = 0.043, p0 = 0.556,
ω1 = 1, p1 = 0.444
66.8 (p < 0.001)
M2a (positive selection)
ω0 = 0.047, p0 = 0.521,
ω1 = 1, p1 = 0.389,
ω 2 = 4.91, p2 = 0.090
2*, 43*, 53*, 57*, 67*
p = 0.104, q = 0.119
29.65 (p < 0.001)
M8 (beta and omega)
p0 = 0.909, p1 = 0.091,
p = 0.02, q = 0.02, ω = 5.15
2*, 43*, 52, 53*, 57*, 64, 67*
At the DRA, both M2a and M8 had equivalent likelihoods and suggested that approximately 10% of sites were possibly under positive selection (ω = 3.40) with the remaining sites under purifying selection (ω = 0.04) (Table 5). Using a LRT, the model of one evolutionary rate across sites (M0) was rejected (p = 0.006) for the alternative model predicting variable rates of evolution (M3) across DRA codons. However, the models of neutral evolution (M1a, M7) could not be rejected (p = 0.188, p = 0.204). Posterior means of ω estimated across DRA codons under positive selection models predicted four sites (positions 14, 19, 47, 49) that may be under selection (ω > 1), two of which are also putative ABS based on the HLA equivalents . However, as posterior probabilities for these site predictions were less than 95% and positive selection models (M2a and M8) by which these sites were identified were not significant, the hypothesis that positive selection is occurring at these specific DRA codons requires further investigation.
There was no evidence for recombination occurring at either MHC locus, even when using a very conservative cutoff for the highest acceptable p-value (p = 0.10) and small window sizes. Despite these measures, which are known to increase the potential for detecting false positive recombinant events , no recombination was detected. This supports the conclusion that recombination does not play a major role in the generation of diversity at these loci.
Phylogenetic reconstructions and inter-specific allele sharing
We observed a large number of identical alleles across species (Table 3). Overall, there were 33 and 55 alleles in DRA and DQA, respectively, when accounting for all unique alleles in each Equus species (i.e. allowing for identical alleles across species). Identical allele sharing was more prevalent at the DRA locus, with 7 of the 22 unique alleles found in multiple species, whereas a lower proportion of the unique haplotypes (5 out of 48) were shared by two or more species at the DQA locus.
The characterization of diversity and selection patterns within MHC genes is imperative for understanding their adaptive significance in host immune function. This study found elevated levels of polymorphism and compelling evidence for selection acting on the class II MHC genes, DRA and DQA, within the genus Equus with the contribution of many novel alleles identified in E. burchelli. In particular, the average pair-wise amino acid distance among alleles was observed to be greater than nucleotide-based distances in both loci, reflecting an excess of nonsynonymous mutations relative to synonymous mutations. Although global estimates of dN/dS averaged across all codon sites contradict the hypothesis of positive selection at these loci, codon-based evolution models that allowed for heterogeneous selection pressure across codon sites best-fit the data. Furthermore, codon models incorporating positive selection were also significant at the DQA. Most notably, site-specific selection analyses at this locus suggested that positive selection is occurring at particular codons associated with foreign antigen binding.
Selection at antigen binding sites
Despite the observation of high levels of functional diversity, whole gene-level selection analyses based on the nonsynonymous/synonymous substitution rate ratios (dN/dS) revealed no evidence for positive selection at either locus in Equus. However, it is well known that for many functional proteins dS is often greater than dN (i.e. purifying selection) due to strong functional and structural constraints. Consequently, selection detection methods that average over entire coding regions can be misleading when selective pressures differ substantially across codons; They are unlikely to find elevated nonsynonymous mutation rates and, therefore, have low power to detect signatures of positive selection (e.g. [72, 73]). The codon models implemented in this study, however, allowed for selection to vary across codon sites and did, in fact, suggest that a large proportion of sites were conserved, particularly at the DRA. More importantly, as even small, single amino acid changes can have a significant impact on gene function these models proved to be valuable for detecting specific targets of selection.
The primary function of classical MHC molecules is to initiate host immune response through the presentation of foreign and self-peptides to T-cells. Studies have shown incredible diversity and elevated nonsynonymous mutations at the ABS of these genes, which is believed to increase the host's ability to recognize a diverse range of pathogens [15, 16]. This underlies the hypothesis that pathogen-driven selection is a primary mechanism sustaining extreme diversity at the MHC [1, 3]. In agreement with this, we found that all five DQA codons under significant positive selection were also predicted to be ABS (Figure 3). Of the two sites where weaker statistical support for selection was found, only one of these (positions 52) was not a putative ABS. However, this codon was noted to be proximate to an ABS and may play a potential associative role in peptide recognition. This finding is significant as it not only supports the hypothesized pathogen-driven mechanism driving the diversity observed at the DQA, but also identifies candidate amino acid residues that may play a significant role in equid immune response.
Effect of recombination
Although the maximum likelihood based approach used in this study has proven to be powerful in testing for site heterogeneity in selection and in identifying critical amino acids under positive selection [74, 75], the presence of recombination can violate the assumptions of the codon-models. We expect that, even if recombination has occurred during the evolution of these genes, the effects on the outcome of our results would be minimal. Anisimova et al. (2003) tested the effect of recombination through simulations and concluded that the likelihood-ratio test (LRT) was robust to the presence of low levels of recombination in a dataset. At higher levels of recombination, however, false positive detection rate could be extremely high (up to 90%). Recombination can be difficult to detect, but we found no evidence for its occurrence when using four different approaches. Moreover, M7 and M8 in CodeML, have been shown to be relatively robust to the influence of recombination on selection estimates . As our results from LRTs of all three sets of nested models on the DQA were highly significant, including M7 versus M8, we conclude that our conclusions hold up even under the low likelihood of undetected recombination.
Trans-species polymorphisms and balancing selection
Balancing selection is expected to preserve high levels of polymorphisms at MHC loci by retaining alleles during species diversification events [77, 78]. The lack of allele clustering by species, in reconstructions of DQA and DRA phylogenies, suggests that MHC allele divergence pre-dates that of species divergence in Equidae. This pattern contrasts that previously found in equid phylogenies based on neutral genetic markers, including microsatellites  and mitochondrial DNA , as well as non-neutral globin gene trees , all of which have shown distinct allele segregation by taxon. The discordance between MHC gene phylogenies and other gene phylogenies has similarly been seen among other vertebrate taxa (e.g. ) and has been attributed to balancing selecting acting on these loci due to their role in foreign peptide recognition. Trans-species polymorphisms were well supported in the Equidae DQA phylogeny, providing evidence for balancing selection acting on this locus. However, our DRA data revealed only one well supported clade (posterior probability > 95%) and, thus, caution must be used in its interpretation. Specifically, the limited availability of sequence variation at the DRA largely affected our ability to predict the phylogenetic relationships among alleles and, thus, further examination of diversity in flanking regions of this locus would be useful for clarifying the mode of evolution occurring at this locus. However, the observations of extensive allele sharing among species, in conjunction with unique levels of DRA amino acid diversity in Equus relative to other taxa (see further discussion below), is compatible with the hypothesis that selection is acting to promote or maintain diversity at this locus in equids.
MHC gene evolution and evidence for DQAduplication
Cloning results suggested at least two DQA loci in E. burchelli, corroborating a previous study in the domestic horse . Fraser and Bailey (1998) discovered that the horse allele, Eqca-DQA*13, is derived from a DQA homologue localized to chromosome 5, separate from the primary MHC cluster on chromosome 20. This represented the first time MHC genes were found on more than one chromosome ; although, there was a recent report of MHC genes distributed over four chromosomes in zebra finch (Taeniopygia guttata) . Little is known about whether the DQA homologue is polymorphic in equids. In our phylogeny, the plains zebra allele, Eqbu-DQA*08, clustered with this putative duplicate allele basal to the primary clades and, therefore, could be a variant of the duplicate locus. Further study is necessary to determine the functionality and expression of this second DQA locus.
Bayesian phylogenies showed at least two DQA allele clades (Figure 5), one of which encompasses the majority of all equid DQA alleles known to date. The second smaller clade is more divergent and includes the putative 'pseudogene' allele, Eqbu-DQA*21. This allele may be the result of a deleterious mutation that arose relatively recently, as the other alleles in this cluster encode potentially functional alleles (i.e. without stop codons). It is possible the alleles of this clade are derived from a paralogous locus which is gradually becoming dysfunctional through an accumulation of deleterious mutations, as would be expected under the 'birth and death' model which has been a hypothesized mode of evolution for MHC gene families . This model suggests that new genes are created by gene duplication and either are maintained over long periods of time or become non-functional through mutations. However, it is alternatively possible that the DQA*21 allele has acquired a new, unknown function as the frame-shift mutation present in the allele generated a stop codon present at the very end of the gene, thus only truncating the protein by four amino acid residues (Additional file 2; Figure 2). In addition, Eqbu-DQA*18 was found to be highly divergent from the other DQA alleles in Equidae and could also potentially be an allele derived from a DQA homologue.
Unique DRAdiversity in Equidae
Inter-specific analyses of diversity and divergence in MHC alleles revealed that the DQA is considerably more polymorphic than the DRA in Equidae, with elevated nonsynonymous substitution rates. This finding is concordant with previous studies in other vertebrate species on DQA orthologs (e.g. [83–85]). However, the nucleotide and functional diversity in the DRA locus was shown to be unusually high relative to what has been observed in other taxonomic groups (Table 1), supporting the results of previous equid MHC studies [40–42, 44]. This observation is particularly compelling because little to no variation in the DRA locus has been found in most vertebrate species, for example in humans , dogs , cats , goats  and pigs . Chu et al. (1994) found that although very low levels of DRA polymorphisms exist in mice, these molecules remain involved with peptide binding and suggested that the DRA is under strong functional constraints, such that any mutations would be deleterious to peptide-presenting function. Similarly, it is possible that the reduced DRA diversity observed in other taxa may be the result of multiple selective sweeps occurring independently across vertebrate lineages. Alternatively, but not exclusive of this hypothesis, functional constraints that are present in other taxa may have become relaxed in equids. The significance of this unique level of diversity within the Equidae DRA remains unclear, though we hypothesize that this locus plays a vital role in response to a unique suite of pathogens or parasites specific to the genus. MHC diversity has also been suggested to be associated with mate recognition and preference (or inbreeding avoidance) in some species [87, 88]. Therefore, further research is necessary to address the potential role sexual selection and parasite-mediated selection could play in the patterns of diversity at the DRA.
Much of the research on Equidae MHC, to date, has been conducted using samples from captive or domestic individuals (e.g. [41, 44, 89]). Here, focused sampling from natural populations of plains zebra substantially increased the number of known MHC alleles, nearly doubling and tripling that which has previously been identified in this species at the DRA and DQA, respectively. Wild equid populations are subject to strong selective pressure by parasites and pathogens (e.g. nematode infections and anthrax in Etosha National Park, Namibia), and therefore further study on these populations would substantially advance our knowledge of immune gene evolution and its role in host fitness under natural conditions. This study also highlights the need for more extensive sampling from wild vertebrates in order to capture the full extent of variation at MHC genes. Elucidating patterns of selective pressure across functional immune genes can be especially informative for identifying candidate disease genes and significant protein residues. However, future research linking these results to gene function and ecology is necessary to better understand the mechanisms underlying adaptation in nature.
We would like to thank the Getz Lab, Bowie Lab and two anonymous reviewers for valuable comments on the manuscript. We would also like to specifically acknowledge R. Bowie, K. Goodman, M. Hadjistylli and E. Rubidge for discussions concerning the genetic methods and analyses; O. Putzeys, H. Ganz, and members of the Getz Lab for help collecting samples in the field; E. DeFranco and K. Hall for assistance in the laboratory. Finally, we are grateful to South African National Parks, the Namibian Ministry of Environment and Tourism and the Etosha Ecological Institute for supporting the sample collection phase of this project. The funding for this research was provided by the National Institute of Health Ecology and Evolution of Infectious Disease (NIH-EEID) GM083863 to WMG and the Carolyn Meek Memorial Scholarship and National Science Foundation Doctoral Dissertation Improvement Grant (NSF-DDIG) MCINS-20091291 to PLK.
- Hedrick P, Kim T: Genetics of complex polymorphisms: parasites and maintenance of MHC variation. Genetics, Evolution, and Society. Edited by: Singh RS, Krimbas CB. 1998, Cambridge: Harvard University Press, 205-233.Google Scholar
- Meyer D, Thomson G: How selection shapes variation of the human major histocompatibility complex: a review. Ann Hum Genet. 2001, 65: 1-26. 10.1046/j.1469-1809.2001.6510001.x.View ArticlePubMedGoogle Scholar
- Doherty PC, Zinkernagel RM: Enhanced immunological surveillance in mice heterozygous at H-2 gene complex. Nature. 1975, 256 (5512): 50-52. 10.1038/256050a0.View ArticlePubMedGoogle Scholar
- Potts WK, Slev PR: Pathogen-based models favoring MHC genetic diversity. Immunol Rev. 1995, 143: 181-197. 10.1111/j.1600-065X.1995.tb00675.x.View ArticlePubMedGoogle Scholar
- Klein J: Natural History of the Major Histocompatibility Complex. 1986, New York: Wiley & SonsGoogle Scholar
- Gaudieri S, Dawkins RL, Habara K, Kulski JK, Gojobori T: SNP profile within the human major histocompatibility complex reveals an extreme and interrupted level of nucleotide diversity. Genome Res. 2000, 10 (10): 1579-1586. 10.1101/gr.127200.View ArticlePubMedPubMed CentralGoogle Scholar
- Buitkamp J, Filmether P, Stear MJ, Epplen JT: Class I and class II major histocompatibility complex alleles are associated with faecal egg counts following natural, predominantly Ostertagia circumcincta infection. Parasitol Res. 1996, 82 (8): 693-696. 10.1007/s004360050187.View ArticlePubMedGoogle Scholar
- Paterson S, Wilson K, Pemberton JM: Major histocompatibility complex variation associated with juvenile survival and parasite resistance in a large unmanaged ungulate population (Ovis aries L.). Proc Natl Acad Sci USA. 1998, 95 (7): 3714-3719. 10.1073/pnas.95.7.3714.View ArticlePubMedPubMed CentralGoogle Scholar
- Meyer-Lucht Y, Sommer S: MHC diversity and the association to nematode parasitism in the yellow-necked mouse (Apodemus flavicollis). Mol Ecol. 2005, 14 (7): 2233-2243. 10.1111/j.1365-294X.2005.02557.x.View ArticlePubMedGoogle Scholar
- 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: 255-265.View ArticlePubMedGoogle Scholar
- Schad J, Ganzhorn JU, Sommer S: Parasite burden and constitution of major histocompatibility complex in the malagasy mouse lemur, Microcebus murinus. Evolution. 2005, 59 (2): 439-450.View ArticlePubMedGoogle Scholar
- Piertney SB, Oliver MK: The evolutionary ecology of the major histocompatibility complex. Heredity. 2006, 96 (1): 7-21.PubMedGoogle Scholar
- Kojima K: Is there a constant fitness value for a given genotype? No!. Evolution. 1971, 25: 281-285. 10.2307/2406919.View ArticleGoogle Scholar
- Takahata N, Nei M: Allelic genealogy under overdominant and frequency-dependent selection and polymorphism of Major Histocompatibility Complex loci. Genetics. 1990, 124 (4): 967-978.PubMedPubMed CentralGoogle Scholar
- Hughes AL, Nei M: Pattern of nucleotide substitution at Major Histocompatibility Complex class-I loci reveals overdominant selection. Nature. 1988, 335 (6186): 167-170. 10.1038/335167a0.View ArticlePubMedGoogle Scholar
- Hughes AL, Nei M: Nucleotide substitution at Major Histocompatibility Complex class-II loci - Evidence for overdominant selection. Proc Natl Acad Sci USA. 1989, 86 (3): 958-962. 10.1073/pnas.86.3.958.View ArticlePubMedPubMed CentralGoogle Scholar
- Klein J, Satta Y, Takahata N, Ohuigin C: Trans-specific MHC polymorphism and the origin of species in primates. J Med Primatol. 1993, 22 (1): 57-64.PubMedGoogle Scholar
- Figueroa F, Gunther E, Klein J: MHC polymorphism predating speciation. Nature. 1988, 335 (6187): 265-267. 10.1038/335265a0.View ArticlePubMedGoogle Scholar
- Nei M, Rooney AP: Concerted and birth-and-death evolution of multigene families. Annu Rev Genet. 2005, 39: 121-152. 10.1146/annurev.genet.39.073003.112240.View ArticlePubMedPubMed CentralGoogle Scholar
- Ottova E, Simkova A, Martin JF, de Bellocq JG, Gelnar M, Allienne JF, Morand S: Evolution and trans-species polymorphism of MHC class II beta genes in cyprinid fish. Fish Shellfish Immunol. 2005, 18 (3): 199-222.View ArticlePubMedGoogle Scholar
- Edwards SV, Chesnut K, Satta Y, Wakeland EK: Ancestral polymorphism of MHC class II genes in mice: Implications for balancing selection and the mammalian molecular clock. Genetics. 1997, 146 (2): 655-668.PubMedPubMed CentralGoogle Scholar
- Bryja J, Galan M, Charbonnel N, Cosson JF: Duplication, balancing selection and trans-species evolution explain the high levels of polymorphism of the DQA MHC class II gene in voles (Arvicolinae). Immunogenetics. 2006, 58 (2-3): 191-202. 10.1007/s00251-006-0085-6.View ArticlePubMedGoogle Scholar
- Cutrera AP, Lacey EA: Trans-species polymorphism and evidence of selection on class II MHC loci in tuco-tucos (Rodentia: Ctenomyidae). Immunogenetics. 2007, 59 (12): 937-948. 10.1007/s00251-007-0261-3.View ArticlePubMedGoogle Scholar
- Kundu S, Faulkes CG: A tangled history: patterns of major histocompatibility complex evolution in the African mole-rats (Family: Bathyergidae). Biol J Linnean Soc. 2007, 91 (3): 493-503. 10.1111/j.1095-8312.2007.00814.x.View ArticleGoogle Scholar
- Van den Bussche RA, Hoofer SR, Lochmiller RL: Characterization of MHC-DRB allelic diversity in white-tailed deer (Odocoileus virginianus) provides insight into MHC-DRB allelic evolution within Cervidae. Immunogenetics. 1999, 49 (5): 429-437. 10.1007/s002510050516.View ArticlePubMedGoogle Scholar
- Hedrick PW, Parker KM, Gutierrez-Espeleta GA, Rattink A, Lievers K: Major histocompatibility complex variation in the Arabian oryx. Evolution. 2000, 54 (6): 2145-2151.View ArticlePubMedGoogle Scholar
- Hedrick PW, Lee RN, Parker KM: Major histocompatibility complex (MHC) variation in the endangered Mexican wolf and related canids. Heredity. 2000, 85 (6): 617-624. 10.1046/j.1365-2540.2000.00805.x.View ArticlePubMedGoogle Scholar
- Seddon JM, Ellegren H: MHC class II genes in European wolves: a comparison with dogs. Immunogenetics. 2002, 54 (7): 490-500. 10.1007/s00251-002-0489-x.View ArticlePubMedGoogle Scholar
- Otting N, de Groot NG, Doxiadis GGM, Bontrop RE: Extensive MHC-DQB variation in humans and non-human primate species. Immunogenetics. 2002, 54 (4): 230-239. 10.1007/s00251-002-0461-9.View ArticlePubMedGoogle Scholar
- Wakeland EK, Boehme S, She JX, Lu CC, McIndoe RA, Cheng I, Ye Y, Potts WK: Ancestral polymorphisms of MHC class-II genes - divergent allele advantage. Immunol Res. 1990, 9 (2): 115-122. 10.1007/BF02918202.View ArticlePubMedGoogle Scholar
- Yang ZH, Bielawski JP: Statistical methods for detecting molecular adaptation. Trends Ecol Evol. 2000, 15 (12): 496-503. 10.1016/S0169-5347(00)01994-7.View ArticlePubMedGoogle Scholar
- Yang ZH, Nielsen R, Goldman N, Pedersen AMK: Codon-substitution models for heterogeneous selection pressure at amino acid sites. Genetics. 2000, 155 (1): 431-449.PubMedPubMed CentralGoogle Scholar
- Hughes AL, Hughes MK: Natural selection on the peptide-binding regions of Major Histocompatibility Complex molecules. Immunogenetics. 1995, 42 (4): 233-243.View ArticlePubMedGoogle Scholar
- Gustafson AL, Tallmadge RL, Ramlachan N, Miller D, Bird H, Antczak DF, Raudsepp T, Chowdhary BP, Skow LC: An ordered BAC contig map of the equine major histocompatibility complex. Cytogenet Genome Res. 2003, 102 (1-4): 189-195. 10.1159/000075747.View ArticlePubMedGoogle Scholar
- Madden DR: The 3-dimensional structure of peptide-MHC complexes. Annu Rev Immunol. 1995, 13: 587-622. 10.1146/annurev.iy.13.040195.003103.View ArticlePubMedGoogle Scholar
- Fraser DG, Bailey E: Polymorphism and multiple loci for the horse DQA gene. Immunogenetics. 1998, 47 (6): 487-490. 10.1007/s002510050387.View ArticlePubMedGoogle Scholar
- Mäkinen A, Chowdhary B, Mahdy E, Andersson L, Gustavsson I: Localization of the equine Major Histocompatibility Complex (ELA) to chromosome-20 by insitu hybridization. Hereditas. 1989, 110 (1): 93-96.View ArticlePubMedGoogle Scholar
- Ansari HA, Hediger R, Fries R, Stranzinger G: Chromosomal localization of the Major Histocompatibility Complex of the horse (ELA) by insitu hybridization. Immunogenetics. 1988, 28 (5): 362-364. 10.1007/BF00364235.View ArticlePubMedGoogle Scholar
- Bailey E, Marti E, Fraser DG, Antczak DF, Lazary S: Immunogenetics of the horse. The Genetics of the Horse. Edited by: Bowling A, Ruvinsky A. 2000, New York: CAB International Publishing, 123-156.View ArticleGoogle Scholar
- Bailey E: Variation within the antigen binding site of the major histocompatibility complex gene of domestic horses. Equine infectious diseases VII: Proceedings of the Seventh International Conference: 8-11 June 1994; Tokyo, Japan. 1994, R & W Publications (Newmarket) Ltd, 123-126.Google Scholar
- Brown JJ, Thomson W, Clegg P, Eyre S, Kennedy LJ, Matthews J, Carter S, Ollier WER: Polymorphisms of the equine major histocompatibility complex class II DRA locus. Tissue Antigens. 2004, 64 (2): 173-179. 10.1111/j.1399-0039.2004.00269.x.View ArticlePubMedGoogle Scholar
- AlbrightFraser DG, Reid R, Gerber V, Bailey E: Polymorphism of DRA among equids. Immunogenetics. 1996, 43 (5): 315-317.Google Scholar
- Luis C, Cothran EG, Oom MM, Bailey E: Major histocompatibility complex locus DRA polymorphism in the endangered Sorraia horse and related breeds. J Anim Breed Genet. 2005, 122 (1): 69-72. 10.1111/j.1439-0388.2004.00485.x.View ArticlePubMedGoogle Scholar
- Janova E, Matiasovic J, Vahala J, Vodicka R, Van Dyk E, Horin P: Polymorphism and selection in the major histocompatibility complex DRA and DQA genes in the family Equidae. Immunogenetics. 2009, 61 (7): 513-527. 10.1007/s00251-009-0380-0.View ArticlePubMedGoogle Scholar
- Chardon P, Renard C, Vaiman M: The major histocompatibility complex in swine. Immunol Rev. 1999, 167: 179-192. 10.1111/j.1600-065X.1999.tb01391.x.View ArticlePubMedGoogle Scholar
- Wagner JL, Burnett RC, Storb R: Organization of the canine major histocompatibility complex: Current perspectives. J Hered. 1999, 90 (1): 35-38. 10.1093/jhered/90.1.35.View ArticlePubMedGoogle Scholar
- Takada T, Kikkawa Y, Yonekawa H, Amano T: Analysis of goat MHC class II DRA and DRB genes: identification of the expressed gene and new DRB alleles. Immunogenetics. 1998, 48 (6): 408-412. 10.1007/s002510050452.View ArticlePubMedGoogle Scholar
- Yuhki N, O'Brien SJ: Nature and origin of polymorphism in feline MHC class II DRA and DRB genes. J Immunol. 1997, 158 (6): 2822-2833.PubMedGoogle Scholar
- Linn S: Deoxyribonucleases: a survey and perspectives. Enzymes. Edited by: Boyer ED. 1981, New York: Academic Press, 14: 131-145.Google Scholar
- Taberlet P, Waits LP, Luikart G: Noninvasive genetic sampling: look before you leap. Trends Ecol Evol. 1999, 14 (8): 323-327. 10.1016/S0169-5347(99)01637-7.View ArticlePubMedGoogle Scholar
- Saiki RK, Gyllensten UB, Erlich HA: The polymerase chain reaction. Genome analysis: a practical approach. 1988, Oxford UK: IRL Press, 141-152.Google Scholar
- Reche PA, Reinherz EL: Sequence variability analysis of human class I and class II MHC molecules: functional and structural correlates of amino acid polymorphisms. Journal of Molecular Biology. 2003, 331 (3): 623-641. 10.1016/S0022-2836(03)00750-2.View ArticlePubMedGoogle Scholar
- Drummond A, Ashton B, Cheung M, Heled J, Kearse M, Moir R, Stones-Havas S, Sturrock S, Thierer T, Wilson A: Geneious v5.0. 2010, [http://www.geneious.com]Google Scholar
- Stephens M, Smith NJ, Donnelly P: A new statistical method for haplotype reconstruction from population data. Am J Hum Genet. 2001, 68 (4): 978-989. 10.1086/319501.View ArticlePubMedPubMed CentralGoogle Scholar
- Bos DH, Turner SM, Dewoody JA: Haplotype inference from diploid sequence data: evaluating performance using non-neutral MHC sequences. Hereditas. 2007, 144 (6): 228-234. 10.1111/j.2007.0018-0661.01994.x.View ArticlePubMedGoogle Scholar
- Harrigan RJ, Mazza ME, Sorenson MD: Computation vs. cloning: evaluation of two methods for haplotype determination. Mol Ecol Resour. 2008, 8 (6): 1239-1248. 10.1111/j.1755-0998.2008.02241.x.View ArticlePubMedGoogle Scholar
- Bradley RD, Hillis DM: Recombinant DNA sequences generated by PCR amplification. Mol Biol Evol. 1997, 14 (5): 592-593.View ArticlePubMedGoogle Scholar
- Ellis SA, Bontrop RE, Antczak DF, Ballingall K, Davies CJ, Kaufman J, Kennedy LJ, Robinson J, Smith DM, Stear MJ, Stet RJM, Waller MJ, Walter L, Marsh SGE: ISAG/IUIS-VIC Comparative MHC Nomenclature Committee report, 2005. Immunogenetics. 2006, 57 (12): 953-958. 10.1007/s00251-005-0071-4.View ArticlePubMedGoogle Scholar
- Tamura K, Dudley J, Nei M, Kumar S: MEGA4: Molecular evolutionary genetics analysis (MEGA) software version 4.0. Mol Biol Evol. 2007, 24 (8): 1596-1599. 10.1093/molbev/msm092.View ArticlePubMedGoogle Scholar
- Swofford D: PAUP*. Phylogenetic Analysis Using Parsimony (*and Other Methods). Version 4. 2002, Sinauer AssociatesGoogle Scholar
- Martin DP, Lemey P, Lott M, Moulton V, Posada D, Lefeuvre P: RDP3: a flexible and fast computer program for analyzing recombination. Bioinformatics. 2010, 26 (19): 2462-2463. 10.1093/bioinformatics/btq467.View ArticlePubMedPubMed CentralGoogle Scholar
- Jukes TH, Cantor CR: Evolution of protein molecules. Mammalian protein metabolism, III. Edited by: Munro HN. 1969, New York: Academic Press, 21-132.View ArticleGoogle Scholar
- Yang ZH: PAML 4: Phylogenetic analysis by maximum likelihood. Mol Biol Evol. 2007, 24 (8): 1586-1591. 10.1093/molbev/msm088.View ArticlePubMedGoogle Scholar
- Bielawski JP, Yang Z: Maximum likelihood methods for detecting adaptive evolution after gene duplication. Journal of Structural and Functional Genomics. 2003, 3 (1-4): 201-212.View ArticlePubMedGoogle Scholar
- Anisimova M: Detecting positive selection with likelihood ratio tests and empirical Bayesian approach: An example study of the hepatitis delta antigen gene. Infection Genetics and Evolution. 2003, 2 (4): 259-Google Scholar
- Nielsen R, Yang ZH: Likelihood models for detecting positively selected amino acid sites and applications to the HIV-1 envelope gene. Genetics. 1998, 148 (3): 929-936.PubMedPubMed CentralGoogle Scholar
- Yang ZH, Wong WSW, Nielsen R: Bayes empirical Bayes inference of amino acid sites under positive selection. Mol Biol Evol. 2005, 22 (4): 1107-1118. 10.1093/molbev/msi097.View ArticlePubMedGoogle Scholar
- Guindon S, Gascuel O: A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst Biol. 2003, 52 (5): 696-704. 10.1080/10635150390235520.View ArticlePubMedGoogle Scholar
- Ronquist F, Huelsenbeck JP: MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics. 2003, 19 (12): 1572-1574. 10.1093/bioinformatics/btg180.View ArticlePubMedGoogle Scholar
- Posada D, Crandall KA: MODELTEST: testing the model of DNA substitution. Bioinformatics. 1998, 14 (9): 817-818. 10.1093/bioinformatics/14.9.817.View ArticlePubMedGoogle Scholar
- Rambaut A, Drummond A: Tracer v1.4. 2007, [http://beast.bio.ed.ac.uk/Tracer]Google Scholar
- Akashi H: Within- and between-species DNA sequence variation and the 'footprint' of natural selection. Gene. 1999, 238 (1): 39-51. 10.1016/S0378-1119(99)00294-2.View ArticlePubMedGoogle Scholar
- Crandall KA, Kelsey CR, Imamichi H, Lane HC, Salzman NP: Parallel evolution of drug resistance in HIV: Failure of nonsynonymous/synonymous substitution rate ratio to detect selection. Mol Biol Evol. 1999, 16 (3): 372-382.View ArticlePubMedGoogle Scholar
- Anisimova M, Bielawski JP, Yang ZH: Accuracy and power of the likelihood ratio test in detecting adaptive molecular evolution. Mol Biol Evol. 2001, 18 (8): 1585-1592.View ArticlePubMedGoogle Scholar
- Anisimova M, Bielawski JP, Yang ZH: Accuracy and power of Bayes prediction of amino acid sites under positive selection. Mol Biol Evol. 2002, 19 (6): 950-958.View ArticlePubMedGoogle Scholar
- Anisimova M, Nielsen R, Yang ZH: Effect of recombination on the accuracy of the likelihood method for detecting positive selection at amino acid sites. Genetics. 2003, 164 (3): 1229-1236.PubMedPubMed CentralGoogle Scholar
- Klein J: Generation of diversity, vol. 80. 1980, London: Academic PressGoogle Scholar
- Klein J: Origin of major histocompatibility complex polymorphism: the trans-species hypothesis. Human Immunology. 1987, 19: 155-162. 10.1016/0198-8859(87)90066-8.View ArticlePubMedGoogle Scholar
- Kruger K, Gaillard C, Stranzinger G, Rieder S: Phylogenetic analysis and species allocation of individual equids using microsatellite data. J Anim Breed Genet. 2005, 122: 78-86. 10.1111/j.1439-0388.2005.00505.x.View ArticlePubMedGoogle Scholar
- George M, Ryder OA: Mitochondrial-DNA evolution in the genus Equus. Mol Biol Evol. 1986, 3 (6): 535-546.PubMedGoogle Scholar
- Oakenfull EA, Clegg JB: Phylogenetic relationships within the genus Equus and the evolution of alpha and theta globin genes. J Mol Evol. 1998, 47 (6): 772-783. 10.1007/PL00006436.View ArticlePubMedGoogle Scholar
- Balakrishnan CN, Ekblom R, Volker M, Westerdahl H, Godinez R, Kotkiewicz H, Burt DW, Graves T, Griffin DK, Warren WC, Edwards SV: Gene duplication and fragmentation in the zebra finch major histocompatibility complex. BMC Biol. 2010, 8: 19-10.1186/1741-7007-8-19.View ArticleGoogle Scholar
- Chen YY, Zhang YY, Zhang HM, Ge YF, Wan QH, Fang SG: Natural selection coupled with intragenic recombination shapes diversity patterns in the Major Histocompatibility Complex class II genes of the giant panda. J Exp Zool Part B. 2010, 314B (3): 208-223.Google Scholar
- Bondinas GP, Moustakas AK, Papadopoulos GK: The spectrum of HLA-DQ and HLA-DR alleles, 2006: a listing correlating sequence and structure with function. Immunogenetics. 2007, 59 (7): 539-553. 10.1007/s00251-007-0224-8.View ArticlePubMedGoogle Scholar
- O'Connor SL, Blasky AJ, Pendley CJ, Becker EA, Wiseman RW, Karl JA, Hughes AL, O'Connor DH: Comprehensive characterization of MHC class II haplotypes in Mauritian cynomolgus macaques. Immunogenetics. 2007, 59 (6): 449-462. 10.1007/s00251-007-0209-7.View ArticlePubMedPubMed CentralGoogle Scholar
- Chu ZTE, Carswellcrumpton C, Cole BC, Jones PP: The minimal polymorphism of class-II E-alpha chains is not due to the functional neutrality of mutations. Immunogenetics. 1994, 40 (1): 9-20. 10.1007/BF00163959.View ArticlePubMedGoogle Scholar
- Jordan WC, Bruford MW: New perspectives on mate choice and the MHC. Heredity. 1998, 81: 127-133. 10.1038/sj.hdy.6884281.View ArticlePubMedGoogle Scholar
- Reusch TBH, Haberli MA, Aeschlimann PB, Milinski M: Female sticklebacks count alleles in a strategy of sexual selection explaining MHC polymorphism. Nature. 2001, 414 (6861): 300-302. 10.1038/35104547.View ArticlePubMedGoogle Scholar
- Hedrick PW, Parker KM, Miller EL, Miller PS: Major histocompatibility complex variation in the endangered Przewalski's horse. Genetics. 1999, 152 (4): 1701-1710.PubMedPubMed CentralGoogle Scholar
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