Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

HLA DNA Sequence Variation among Human Populations: Molecular Signatures of Demographic and Selective Events

HLA DNA Sequence Variation among Human Populations: Molecular Signatures of Demographic and Selective Events

  • Stéphane Buhler, 
  • Alicia Sanchez-Mazas
PLOS
x

Abstract

Molecular differences between HLA alleles vary up to 57 nucleotides within the peptide binding coding region of human Major Histocompatibility Complex (MHC) genes, but it is still unclear whether this variation results from a stochastic process or from selective constraints related to functional differences among HLA molecules. Although HLA alleles are generally treated as equidistant molecular units in population genetic studies, DNA sequence diversity among populations is also crucial to interpret the observed HLA polymorphism. In this study, we used a large dataset of 2,062 DNA sequences defined for the different HLA alleles to analyze nucleotide diversity of seven HLA genes in 23,500 individuals of about 200 populations spread worldwide. We first analyzed the HLA molecular structure and diversity of these populations in relation to geographic variation and we further investigated possible departures from selective neutrality through Tajima's tests and mismatch distributions. All results were compared to those obtained by classical approaches applied to HLA allele frequencies.

Our study shows that the global patterns of HLA nucleotide diversity among populations are significantly correlated to geography, although in some specific cases the molecular information reveals unexpected genetic relationships. At all loci except HLA-DPB1, populations have accumulated a high proportion of very divergent alleles, suggesting an advantage of heterozygotes expressing molecularly distant HLA molecules (asymmetric overdominant selection model). However, both different intensities of selection and unequal levels of gene conversion may explain the heterogeneous mismatch distributions observed among the loci. Also, distinctive patterns of sequence divergence observed at the HLA-DPB1 locus suggest current neutrality but old selective pressures on this gene. We conclude that HLA DNA sequences advantageously complement HLA allele frequencies as a source of data used to explore the genetic history of human populations, and that their analysis allows a more thorough investigation of human MHC molecular evolution.

Introduction

The Human Leukocyte Antigen (HLA) loci are among the most polymorphic genes currently described in the human genome, with more than 4,000 observed alleles according to release 2.27.1 of IMGT/HLA database. These loci are located on the short arm of chromosome 6 within the Major Histocompatibility Complex (MHC) and are extensively studied due to their critical role in organ or hematopoietic stem cell transplantation and clinical medicine. Allelic variation of HLA genes is characterized at the DNA sequence level since the mid of 1990s [1], [2]. This approach started with the development of molecular typing methods for class II loci (e.g. PCR-SSP, direct or reverse PCR-SSO, PCR-SSCP, and SBT), and more recently for class I loci, HLA-Cw being the last gene studied with such typing strategies [3], [4], [5], [6], [7]. The characterization and classification of alleles follow strict rules recommended by the WHO HLA Nomenclature Committee For Factor of the HLA System [8], [9], and make use of a hierarchic numerical code for allele naming introduced shortly after the 10th Histocompatibility Workshop and Conference. The DNA sequence of each HLA allele is now accessible to the whole scientific community through an online database which also provides genomic and coding sequence alignments for every locus, nomenclature guidelines, complete listings of alleles and new allele reports, among other information [8], [10].

Despite the availability of such an important amount of data, thus far the information on HLA nucleotide sequences has been mainly used to investigate the evolutionary history of the human MHC [11], [12], [13], [14], [15], [16], [17], [18] or to interpret individual HLA genotypic profiles for clinical (i.e. transplantation) purposes, but it has seldom been used to study the genetic variation among populations. Many studies have shown that the HLA polymorphism is very informative to reconstruct past human migration events [19], [20], [21], [22], [23], [24], [25]. However, these works are based on the interpretation of allelic frequency distributions, with all alleles being considered as equidistant molecular units in each population; the main reason being that there is still a lot of uncertainty associated to HLA molecular typings due to the extreme level of polymorphism at these loci. Indeed, HLA typings usually generate numerous ambiguities which correspond to situations where either several allelic pairs (i.e. potential genotypes) can explain the reactivity profile obtained for an individual, or the resolution of the typing protocol is too low to allow discrimination between some alleles. As a consequence, establishing a direct relationship between “allele names” (generally, groups including several ambiguous alleles) and their corresponding nucleotide sequences has been very hazardous, preventing the use of the latter in population studies. Moreover, when a study includes many population samples that were not characterized by the same molecular typing methods, the existence of different ambiguity patterns in different population samples further increases the complexity of the data.

However, new perspectives have emerged to use the HLA nucleotide sequence information for anthropological purposes: firstly, through large scale homogeneous treatments of ambiguous genotypes in population samples by the International Histocompatibility Working Group – Anthropology/Human Genetic Diversity (IHWG-AHGD) component of the 13th Histocompatibility Workshop and Conference [26]; and secondly, through the recent development of computer programs (i.e. Gene[rate] tools at http://geneva.unige.ch/generate/) that implement powerful methods of allelic frequency estimation on data that contain genotyping ambiguities [27], [28], [29], [30] within the scope of the Analysis of HLA Population Data (AHPD) component of the 15th Histocompatibility Workshop and Conference [31] and of the HLA-NET European COST project (http://w3.cost.esf.org/index.php?id=212&action_number=BM0803).

In this study we analyzed the nucleotide diversity of seven HLA genes (HLA-A, -B, -Cw, -DPB1, -DQA1, -DQB1, and -DRB1) in more than 23'500 individuals from about two hundred populations of all continents (Table 1 and File S1). We first applied an extensive treatment of the data at the genotypic and nucleotide levels to ensure compatibility with the latest updates of the official HLA allele nomenclature. We then analyzed the DNA molecular variation of HLA at several geographic scales (i.e. worldwide, continental, and regional) to investigate its congruence with the observed genetic diversity profiles based on allelic frequencies and explore the additional information brought by DNA sequences. Despite the complex evolution of the HLA system and the difficulty to disentangle the effects of molecular mechanisms such as balancing selection, gene conversion and recombination, our results suggest a strong influence of demographic factors and past human migrations on its DNA polymorphism. Nevertheless, natural selection acts by maintaining highly divergent alleles within populations, probably as a consequence of asymmetric overdominance of heterozygote individuals. Furthermore, ancient traces of selective pressures were detected in DNA sequences of HLA-DPB1, a locus whose evolution is usually assumed to be close to neutral expectations.

thumbnail
Table 1. Summary of the population and DNA sequence data used in this study.

https://doi.org/10.1371/journal.pone.0014643.t001

Results

Nucleotide diversity and heterozygosity

Expected heterozygosity (h) and nucleotide diversity (πn) within populations averaged on each geographic region are given in File S2 and plotted in Figure 1A and 1B, respectively. Values ranged from 0.525 (HLA-DPB1 in NAM) to 0.961 (HLA-B in NAF) for h, and from 0.011 (HLA-DPB1 in NAM) to 0.082 (HLA-DQA1 and -DRB1 in NEA) for πn. For h, HLA-B was the most diversified and HLA-DPB1 the less diversified locus in almost all regions, but standard deviations overlapped between loci in many population groups (results not shown). For πn, on the contrary, a clear-cut difference appeared between loci, with HLA-DRB1, -DQA1 and -DQB1 (class II loci) exhibiting higher values than HLA-B, -A, -Cw (class I loci) and -DPB1 (class II locus) in all but one (SAM) geographic regions. Reduced diversity values of both πn and h were observed in aboriginal populations from SEA (i.e. TW), as well as in PAC, AUS, NAM and SAM compared to EUR, SAF, NAF, SWA, NEA and CSEA, except for πn at HLA-Cw. This reflects peculiar demographic histories for Taiwanese aborigines, Oceanian and Amerindian populations, which most certainly underwent rapid genetic drift due to small population sizes and geographic and/or cultural barriers.

thumbnail
Figure 1. HLA genetic diversity within populations.

A) heterozygosity (h) and B) nucleotide diversity (πn) within populations grouped according to their geographic location, at each locus under study. Only the regions represented by populations at every locus are illustrated on the graphs. See File S2 for detailed values (means and standard deviations). Sub-Saharan Africa (SAF), Europe (EUR), Southwest Asia (SWA), Northeast Asia (NEA), Southeast Asia (SEA), , Pacific (PAC), Australia (AUS), North America (NAM), and South America (SAM). See Supporting Information S1 for the list of populations included in each region.

https://doi.org/10.1371/journal.pone.0014643.g001

Distributions of mean pairwise differences between allele sequences within populations

Figure 2 shows the distributions of pairwise differences (i.e. mismatch distributions) between allelic DNA sequences of each HLA locus averaged on all populations in each geographic region. The proportion of sequence pairs diverging by 0, 1 to 10, 11 to 20, 21 to 30, 31 to 40, and more than 40 nucleotides is also plotted in a box at the top-right of each graphic.

thumbnail
Figure 2. Distributions of pairwise differences between HLA sequences (mismatch distributions).

Average mismatch distributions within populations grouped according to their geographical location, at each HLA locus under study. Histograms at the top-right indicate the proportion of allele pairs with 0, 1–10, 11–20, 21–30, 31–40 and more than 40 nucleotides of divergence within each group of populations.

https://doi.org/10.1371/journal.pone.0014643.g002

HLA-B exhibited the highest number of nucleotide differences within PBR coding exons, with up to 51 nucleotides diverging between alleles, followed by HLA-A, -DRB1 and -DQB1 alleles (up to 38 diverging nucleotides), and HLA-DQA1 (34), -Cw (27) and -DPB1 (20).

Several peculiarities distinguished one locus from another, notably for HLA-DPB1 and -DQB1, both described hereafter, or for HLA-Cw compared to the other two class I loci -A and -B (i.e. less divergent alleles were observed for HLA-Cw). However, some similarities also appeared. First, there were higher proportions (up to twice) of identical allele pairs (i.e. class 0) in AME, PAC and AUS (and TW, results not shown) than in populations from EUR, SAF, NAF, SWA, NEA (and CSEA, results not shown). This result reflects the fact that Amerindian, Oceanian and Taiwanese aboriginal populations usually exhibited one or two alleles at high frequencies and only a small number of less frequent ones, probably as a consequence of rapid genetic drift. As each DNA sequence present in a population sample is compared to the others depending on their respective absolute frequency, the presence of one or two predominant alleles is weighting strongly on the percentage of pair comparisons included in class 0. By contrast, European, continental Asian and African populations generally showed many alleles with more even frequencies. Second, closely related alleles (<10 diverging nucleotides) were uncommon, while divergent ones (>20 diverging nucleotides) were abundant within all population groups, at most loci. This result was very clear for HLA-A, -B and -DQA1, with averages on all geographic regions of 57%, 71.6% and 58% of allele pairs differing by more than 20 nucleotides, respectively. HLA-Cw and -DRB1 also showed a pattern of low relatedness between alleles, but it was less pronounced (allele pairs differing by more than 20 nucleotides took values ranging from 8% at HLA-Cw in SAF to 57% at HLA-DRB1 in EUR, respectively). HLA-DQB1 exhibited a slightly to much higher proportion of related alleles compared to HLA-Cw and -DRB1, depending on the region of interest (values ranged from 0% in AUS to 40% in NAF for allele pairs differing by more than 20 nucleotides, respectively). HLA-DPB1 contrasted with the other loci as the proportion of sequence pairs that display 10 or less than 10 divergent nucleotides within population groups varied between 59.3% (SAF) and 92.9% (AUS). This can be related to the fact that HLA-DPB1 exhibited the lowest πn and h in most geographic regions (see File S2 and Figure 1A and B), and may indicate a particularity of this locus with respect to balancing selection, as shown by other kinds of evidence (see next section and discussion).

Tests of selective neutrality

The results of the two tests of selective neutrality (Tajima's D and EW) are summarized in Table 2 and described in more details in File S1. Almost all significant outcomes indicated an excess of heterozygotes (i.e. D>0 for Tajima and Fobs<Fexp for EW), suggesting balancing selection as the main cause of the deviation. However, variable proportions of significant tests were found among the different genes (HLA-B>-DQA1>-DRB1>-A>-Cw>-DQB1>-DPB1 for Tajima's D, and HLA-DQA1>-Cw>-DRB1>-B>-DQB1>-A≫-DPB1 for EW). Interestingly, we observed higher proportions of significant outcomes with Tajima's D (64.3 to 96.7%) than with EW (1.8 to 48.3%) for all genes. The discrepancy was even higher after Bonferroni's correction for multiple tests (Figure 3), with 0 to 5.9% of significant outcomes for EW and 1.47 to 65.5% for Tajima's D. Furthermore, for Tajima's D, class II loci, in particular -DQA1 and to a lesser extent -DQB1 and -DRB1, exhibited higher proportions of significant outcomes than HLA-A, -B, -Cw and -DPB1. This pattern can be related to the one described above for nucleotide diversity (πn).

thumbnail
Figure 3. Tests of selective neutrality.

Percentages of significant outcomes for Tajima's D statistic and Ewens-Watterson test after Bonferroni's correction, at each locus under study (ordered according to their location on chromosome 6, from telomere to centromere).

https://doi.org/10.1371/journal.pone.0014643.g003

thumbnail
Table 2. Tajima's D and Ewens-Watterson (EW) tests of selective neutrality at each of the seven HLA loci analyzed.

https://doi.org/10.1371/journal.pone.0014643.t002

We also detected a few significant cases showing D<0 with Tajima's test (2 populations at HLA-DPB1, including one which remained significant after Bonferroni's correction, and 1 population at HLA-DQA1), and significant Fobs>Fexp with EW (6 populations at HLA-DPB1, 3 at HLA-A, 1 at HLA-B, and 1 at HLA-DRB1).

Molecular genetic diversity within and among geographic regions

ΦST values for populations grouped according to their geographic location are given in Table 3 and File S3. At the worldwide scale, HLA-DPB1 was the locus for which populations were the most differentiated (ΦST = 14.5%), followed by HLA-DQA1 (13.1%), -DQB1 (8.8%), -A (8.2%), -DRB1 (7.4%), -Cw (6.6%), and -B (6%). When TW, OCE and AME were excluded, HLA-DPB1 was again the gene for which populations were the most differentiated (ΦST = 8.6%), but this time it was followed by -Cw (4.5%), -B (4.1%), and -A, -DRB1, -DQB1 and -DQA1 (≤3.8%).

thumbnail
Table 3. Molecular genetic diversity indexes among populations (ΦST), within geographic groups (ΦSC) and among geographic groups (ΦCT) and correlation coefficients between genetic and geographic distances (r) in all populations taken together and when excluding TW, OCE and AME, at each HLA locus under study.

https://doi.org/10.1371/journal.pone.0014643.t003

A general tendency emerged for all loci at the regional scale, with NAM, SAM, PAC and TW standing as the most diversified regions in terms of population differentiations (File S3). On the contrary, EUR, NAF and SWA were genetically very homogeneous, with similarly low ΦST values for all loci (yet significant, except for NAF at HLA-A and -B and for SWA at HLA-DQA1, but these two regions included only 2 populations each). The remaining regions, i.e. SAF, NEA, CSEA and AUS, exhibited intermediate ΦST values between these two extremes. These ΦST values were always significant except for NEA at HLA-DQA1 (but this group included only 3 populations).

Overall, ΦST values were very close to FSTs both at the worldwide scale and in the different geographic regions (File S3), yet with generally more marked variations in PAC, AUS, NAM and SAM than in other population groups. Except in two cases where differences can be explained by low sample sizes (non-significant FST for SWA at HLA-DPB1 and non significant ΦST for SWA at HLA-DQA1, these groups including 3 and 2 populations, respectively), the significance of these two indexes was also similar.

The results of the hierarchical analyses of molecular genetics variance (AMOVA) are given in Table 3. We found a significant geographic structure (significant ΦCT's) for all loci, but the structure was stronger (ΦCT's>ΦSC's) for HLA-Cw, -DPB1, -DQA1 and -DQB1 at the worldwide scale, and for HLA-A, -B, -Cw, -DPB1 and -DQA1 when TW, OCE and AME were excluded. No significant differences were observed with analyses of genetic variance (ANOVA) based only on allelic frequencies (i.e. similar FCT and FSC values with no changes in significance, results not shown).

Correlation between genetics and geography

Correlation coefficients between geographic and genetic distances based on the molecular approach were computed both for all populations, and for each geographic region taken separately (Table 3 and File S3). We also estimated these coefficients for all regions minus TW, OCE and AME, because we expected a low correlation with geography in the latter regions due to high population divergence resulting from genetic drift, which may reduce the correlation existing at the global scale. Moreover, correlation coefficients within regions were only computed for regions represented by a minimum of 5 populations in the dataset. This criterion was chosen so as to avoid, on the one hand, a too drastic reduction of the number of regions analyzed, and the computation of meaningless correlation coefficients on the other hand.

Both at the worldwide scale and when excluding TW, OCE and AME, correlation with geography was significant for all genes and reached intermediate to high values (e.g. r = 0.167 for HLA-B and r = 0.468 for HLA-DQB1 worldwide, respectively). However, geography seemed to be a better predictor of the genetic structure when TW, OCE and AME were excluded, as in this case correlation with geography was higher at all loci except HLA-DQA1 and -DQB1. Correlation between genetics and geography was usually lower within regions. This may be the consequence of reducing the sets of populations at this geographic scale, lessening the power of the statistical test, but may also reflect a variable impact of geography among regions in shaping HLA genetic profiles. Yet, except for SEA and NAF (AUS was not tested because of insufficient data), correlation with geography was significant for at least one HLA gene in every geographic region. In SEA, a significant correlation was observed when populations were subdivided into CSEA (at HLA-Cw) and TW (at HLA-B and -Cw), respectively.

The correlation between genetics and geography was also significant at the worldwide scale (both when considering all populations and when excluding TW, OCE and AME) when genetic distances were computed according to the allelic approach (results not shown).

Differences in genetic distances between the molecular and the allelic approaches

At the regional scale, however, a few differences were observed between the molecular and the allelic approaches. Indeed, correlation coefficients were significant in PAC at HLA-B, SAM at HLA-DQA1, and SWA and EUR at HLA-DRB1 with the molecular but not with the allelic approach, and the reverse situation was observed in SEA at HLA-A, SWA at HLA-B, EUR and NAM at HLA-DQA1, and NAF and SAM at HLA-DQB1. Actually, some populations showed a majority of greater genetic distances to the other populations when using the molecular approach, while others showed a majority of lesser genetic distances (Figure 4 and File S4). Two such examples are given on Figure 5, where genetic distances based on molecular data are plotted against genetic distances based on allele frequencies for two different loci (HLA-B and -DRB1) and where the points corresponding to the distances of two different populations (Samoans and Lebanese Arabs, respectively) are highlighted. For Samoans at HLA-B (Figure 5A), genetic distances were clearly skewed toward higher values with the molecular approach than with the allelic approach, whether the opposite situation was observed for Lebanese Arabs at HLA-DRB1 (Figure 5B). Detailed comparisons of distance matrices obtained with the molecular and allelic approaches (Figure 4 and File S4) showed that HLA-DQA1 was the only locus for which genetic distances were generally underestimated with the allelic approach when all populations were considered together (i.e. column “All” in Figure 4 where more than 60% of the populations exhibit a majority of higher molecular than allelic distances for HLA-DQA1). On the contrary, genetic distances were usually overestimated for HLA-A, -DRB1, and -DPB1 with the allelic approach (i.e. <30% of the populations exhibit a majority of higher molecular than allelic distances). This was also true but to a lesser extent for HLA-Cw (>40%), while HLA-B and -DQB1 were close to equality between the two approaches (about 50%). At the regional scale, a general pattern emerged (Figure 4). In TW, PAC, NAM and SAM, genetics distances between populations tended to be higher at most loci when molecular distances between alleles were considered, while the opposite situation was observed for SAF, NAF, EUR, SWA and CSEA, where genetic distances were commonly overestimated on the sole basis of allelic frequencies.

thumbnail
Figure 4. Summary of the comparisons of Reynolds genetic distance matrices between the molecular and allelic approaches.

Percentage of populations exhibiting a majority of higher Reynolds genetic distances computed with the molecular approach compared to Reynolds genetic distances computed with the allelic approach (see “Statistical analyses” in Materials and Methods for the definition of the two approaches) within geographic regions, at each locus under study. The detailed comparisons of Reynolds matrices are given in File S4. Percentages were only computed for regions including at least 5 populations (n≥5).

https://doi.org/10.1371/journal.pone.0014643.g004

thumbnail
Figure 5. Plot comparisons of Reynolds genetic distance matrices in two populations.

Plot comparisons of Reynolds genetic distance matrices computed with either the molecular (X-axis) or the allelic (Y-axis) approach (see “Statistical analyses” in Materials and Methods for the definition of the two approaches) in Samoans for HLA-B (A) and in Lebanese Arabs for HLA-DRB1 (B). The red squares represent the genetic distances between the single out population and the other populations available in the databank at this locus. The blue circles represent the genetic distances between each pair of populations at this locus, excluding the single out population. The black line on the plot indicates the equality of distance between both approaches. The detailed comparisons of Reynolds matrices are given in File S4.

https://doi.org/10.1371/journal.pone.0014643.g005

These distance differences may explain why, in some regions and for some loci (i.e. the ten cases listed above), the molecular and allelic approaches differed in the significance of the correlation with geography.

Discussion

In this study, nucleotide diversity in PBR coding exons of HLA genes was extensively analyzed for a dataset of 2,062 sequences in more than 23,500 individuals from about 200 human populations spread worldwide. To our knowledge, this is the first time that nucleotide variability within the MHC is used to analyze human genetic diversity on such a large dataset. Indeed, the information on HLA nucleotide sequences has mostly been studied to discover the evolution of this multigenic family [11], [12], [13], [14], [15], [16], [17], [18], [32], [33], [34], and to understand its functionality [35], [36], [37]. As DNA sequences represent the highest possible level of resolution available for studying the polymorphism of these genes (with amino-acid sequences to whom they are directly related), it should bring more insights and information than the common approach which consists in comparing HLA alleles on the basis of their frequencies, without acknowledging for their molecular divergence. Currently, the availability of databanks containing population samples already screened to reduce typing ambiguities [26], as well as the development of programs allowing for an optimal treatment of ambiguous cases to estimate allele frequencies [27], [28], [29], [30], have allowed to undertake this study, and to include this information with more confidence. Of course, we still had to statistically resolve remaining typing ambiguities in the data, update the allele names to the successive nomenclature changes, and work with missing data in the nucleotide sequences to obtain comparable DNA sequences for all alleles detected in the population data (i.e. formatting the DNA sequences of the alleles not existing anymore in the current sequence alignments following their split into many synonymous variants, with names extending from a 4-digits nomenclature to 6 or more digits). Furthermore, we limited our analyses to the PBR coding exons of HLA genes, and we had to assume the absence of recessive data (commonly known as “blank”) by considering all apparent homozygotes as true homozygotes. However, thanks to the development of molecular typing methods, the presence of blank alleles within HLA data has been greatly reduced (frequencies are generally observed well below 5%) compared to serological typings, where blank could reach frequencies higher than 30% for the HLA-Cw locus [38], [39]. Thus, although it is probable that the above assumption might cause some biases in the results of the present study, we expect them to be minimal.

The first objective of this study was to determine whether the molecular profiles of HLA genetic diversity among human populations (the molecular approach) were congruent or not with those based only on frequency data for the same polymorphism (the allelic approach) [19], [20], [22], [24], [25], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50]. Our results suggest that this is the case at the global scale. Indeed, we find a high and significant correlation between genetics and geography for all HLA genes, both when all populations are considered and when particularly divergent regions are excluded (i.e. TW, OCE and AME). Correlation with geography is lower in these latter regions where populations are more differentiated from each other, in close agreement with the works cited above and our own analyses based on allelic frequencies. Furthermore, a significant geographic structure is observed at all loci, confirming the major role played by geography in shaping HLA molecular variability among populations.

More detailed analyses performed at the regional scale, however, gives fairly different results. When considering different geographic regions and different HLA loci, we observe a total of 10 cases out of 40 (see Results) where taking into account nucleotide information leads to a change of significance (i.e. gain or loss) of the correlation coefficient between genetics and geography. This indicates that the estimation of the genetic distance between some populations closely depends on whether or not the molecular variability of their alleles is considered. For instance, Amerindian, Oceanian and Taiwanese populations are generally more distantly related to other populations if one weights the genetic distance by the molecular distance between their alleles, while the opposite situation is observed for Europeans, continental Asians and Africans (Figure 4). This is indirect evidence that despite the fact that lower numbers of alleles are usually detected in the former group of populations compared to the ones in Europe, Africa and continental Asia, these alleles are highly divergent in the PBR exons, which is probably a result of some functional constraints (see below). On the other hand, due to the complexity of the HLA polymorphism, the changes towards either greater or lesser genetic distances seem hardly predictable for a given population as they vary among loci and also within geographic regions (File S4). As a consequence, to analyze systematically the DNA sequence variation of HLA alleles in population studies may become essential for detailed genetic comparisons among populations.

Our second objective was to investigate the molecular evolution of the HLA polymorphism by comparing the molecular profiles of populations to those depicted on the basis of allelic frequencies. Here, we were mainly interested by the potential effects of evolutionary mechanisms such as balancing selection and gene conversion on the patterns of HLA molecular variability, compared to the influence of demographic factors such as gene flow and genetic drift. While we observe a reduced genetic diversity in TW, OCE and AME (explained by demography) both at the molecular and allelic levels, HLA-Cw exhibits a singular pattern. Indeed, for this locus, mean values of nucleotide diversity are roughly similar in TW, OCE and AME and in EUR, NAF, SAF, SWA, NEA and CSEA. As HLA-Cw is the last HLA gene which was characterized at the DNA level, its nucleotide diversity may currently not be as well described as for the other genes. However, a more likely explanation is that HLA-Cw evolves under specific mechanisms compared to other HLA genes. Actually, HLA-Cw molecules are exposed to fewer pathogenic pressures, in relation to their lower cellular expression than other class I molecules [51]; however, they are also the main KIR ligands and a co-evolution of both polymorphisms is a likely hypothesis [52]. For instance, disease studies and population genetics analyses have reported a significant correlation (either positive or negative) between the frequencies of HLA-Cw alleles and some KIR genes (see [53], [54] and references therein). In addition, HLA-Cw has a more recent origin than other class Ia genes (i.e. classical class I genes), as it is thought that the Cw and B loci shared a common ancestor before the speciation of great apes, but after divergence of apes and Old World monkeys [11], [55]. As a consequence, HLA-Cw had less time to accumulate polymorphism within its DNA sequences than HLA-A and -B, which might be related to our results on nucleotide diversity (Figure 1B) and distributions of pairwise differences between sequences (Figure 2).

Although explainable by demography, the drop of genetic diversity in many aboriginal populations is intriguing in the light of the crucial functions played by HLA genes in the immune surveillance of our organism. Indeed, reduced allele subsets, as those observed in several populations, could affect in some way the overall range of peptide binding specificity. In this respect, relevant results emerge from the mismatch distributions (Figure 2). A majority of alleles are distantly related among each other in all geographic regions and at all loci except HLA-DPB1 and (to a much lesser extent) -DQB1. This pattern may be the consequence of balancing selection in the form of asymmetric heterozygous advantage [56], where individuals carrying distantly related alleles would benefit from a better immunological protection than homozygous ones [57], [58], [59]. Natural selection favoring functionally effective HLA phenotypes would thus explain why populations with reduced HLA genetic diversity may survive, which is in agreement with two previous studies focusing on Amerindians [40], [60]. Of course, our approach is based on the assumption that alleles greatly differing at the DNA level in PBR coding exons cover a larger antigenic repertoire than closely related ones, which has to be confirmed. The fact that HLA-DPB1 exhibits a distinct profile than other classical HLA genes is another support to this hypothesis, as this gene evolves (almost) neutrally [49], [50], [61], [62]. A recent theory of MHC evolution called Associative Balancing Complex (i.e. ABC), where polymorphism in immune genes is maintained both by epistasis among loci and purifying selection against recessive deleterious mutations located nearby MHC loci [63], [64], has been shown (with simulated data) to result in large differentiations between extant alleles. Thus, ABC evolution might also explain the shape of the mismatch distributions observed for classical HLA genes. Furthermore, ABC can act alongside balancing selection, as both evolutionary forces are not mutually exclusive.

Yet, another molecular mechanism may also be involved in the shape of the mismatch distributions observed for HLA genes. Indeed, in addition to DNA substitutions, gene conversion has been proposed as a putative mechanism in the generation of new HLA alleles [57], [65], [66], [67], [68], [69], [70], [71], [72], [73], even though its precise role and molecular process in the evolution of the MHC remain controversial [74]. However, several studies have shown that recombination and gene conversion can affect the shape of a sequence phylogeny and its related mismatch distribution in a similar way as a demographic expansion [75], [76], [77], [78], [79], i.e. resulting in star-like phylogenies and bell-shaped mismatch distributions [80], [81], [82]. Thus, to better understand the possible effect of gene conversion on the molecular profiles observed for HLA genes, we tested for the presence of significant conversion fragments within HLA sequence alignments using the GENECONV program [83], [84] (Table 4). In agreement with other studies [74], [85], [86], HLA-B and HLA-DPB1 stand out as being the most affected by gene conversion as they exhibit the highest number of significant conversion fragments (611 and 99, respectively, after correction for multiple tests by the BLAST method). Other HLA genes exhibit fewer significant conversion fragments after correction (HLA-DRB1>-A>-Cw>-DQA1) or none (HLA-DQB1). Therefore, as HLA-B and -DPB1 are the most prone to gene conversion but display very different mismatch distributions, gene conversion alone does not explain the different patterns observed for those genes. As suggested above, asymmetric overdominant selection and/or ABC evolution must have played a prominent role, even though the effects of recombination, gene conversion and balancing selection are very hard to disentangle from each other and may have acted simultaneously [87].

thumbnail
Table 4. Testing for the presence of significant conversion fragments within HLA genes (ordered according to their location on chromosome 6, from telomere to centromere) with the GENECONV program package.

https://doi.org/10.1371/journal.pone.0014643.t004

We have shown (Figure 2) and mentioned above that for HLA-DQB1, alleles are more related to each other than for HLA-A, -B, -Cw, -DQA1 and -DRB1, but still much less than for HLA-DPB1. For HLA-DQB1, the result might be related to selective pressures acting on the generation of HLA-DQ heterodimers. Indeed, class II HLA molecules are usually formed by any association of cis and trans allelic products, except for HLA-DQA1 and -DQB1 where particular allele combinations have been shown to lead to unstable dimers [88], [89]. HLA-DQB1 would have been submitted, then, to some kind of purifying selection due to structural constraints on DQ proteins and slowing down the rate of nucleotide divergence. Further studies are needed to test this hypothesis, as the assumption of co-evolution of HLA-DQA1 and HLA-DQB1 was initially based on serological assignations of their alleles, and has not yet been investigated in relation to DNA sequences.

To complement the results obtained for the mismatch distributions, we tested putative departures from selective neutrality by the Tajima's D statistic and the Ewens-Watterson (EW) test. Evidence for balancing selection is detected at all loci except HLA-DPB1 for EW, but the level of selection appears to be heterogeneous across the HLA region. HLA-B exhibits the highest percentage of significant departures for Tajima's D among the seven loci (which can be related to its mismatch distributions showing very high proportions of unrelated alleles), although we do not detect any significant outcomes in EW after correction for multiple tests. As some studies suggest that this locus is the most selected one [90], [91], we suspect that the apparent neutrality indicated with EW test after correction is due to a lack of power of this test, which is too conservative when the number of alleles is high (HLA-B is by far the gene showing the greatest number of alleles within populations, see File S1) and the sample size not substantially increased [61]. As a matter of fact, the proportions of Tajima's significant outcomes observed at HLA-A, -B and -Cw are also in agreement with a study based on pathogen-driven balancing selection [51]. Among class II genes, HLA-DRB1 exhibits the highest percentage of rejections for Tajima's D, closely followed by HLA-DQA1, whereas HLA-DQB1 exhibits smaller percentages of rejections for both tests, in contradiction with some studies [49], [62], [92], [93]. When taking into account correction for multiple tests for Tajima's D (Figure 3), class II genes, in particular HLA-DQA1, exhibit at least tenfold higher proportions of significant outcomes than class I loci and HLA-DPB1. This might be related to the fact that HLA-DQA1 is the locus with the smallest number of alleles within populations (mean value of 7.4 compared to values of 12.1 to 31.6 at other loci), and that selection acts strongly on maintaining highly divergent sequences at intermediate frequencies as some sort of compensation. Results on EW (see hereafter), nucleotide diversity within populations (Figure 1B) and mismatch distributions (Figure 2) are concordant with this assumption.

Concerning EW, and in agreement with published results [47], [62] of which a recent survey on 497 populations spread worldwide [50], HLA-Cw and -DQA1 show the highest proportions of significant outcomes.

Most of the results are thus congruent with previously published works, but, yet, some contradictions appear between Tajima's D and EW results and at HLA-DQB1 for EW. For the latter discrepancies, the use of unequal datasets is a probable explanation (i.e. there is an agreement on the significant rejects of neutrality observed for HLA-DQB1, it is only the proportion which do vary between our study and previous works and this is clearly dependent on the number of populations analysed), while we should keep in mind that Tajima's D and EW tests do not apply to the same source of variability. As a matter of fact, the former statistic takes into account the nucleotide differences between alleles, while the latter is only based on allelic frequency distributions. Moreover, the outcomes of Tajima's test may be interpreted in different manners, i.e. either by demographic effects or selection. In particular, a significant positive D may result from balancing selection but also from a demographic contraction, while a negative D value may be caused by purifying selection against deleterious alleles or by a demographic expansion. In the seven datasets presently studied, all significant rejects of D are towards positive values, except for 3 cases (2 at HLA-DPB1 and 1 at -DQB1, see Table 2). In populations of small size like Amerindians and Oceanians, a positive D value may be interpreted either by a demographic contraction, and/or by balancing selection, and it is not possible to disentangle the effects caused by both alternatives. At the opposite, most populations included in our analyses (or populations from close geographic areas) have been described as evolving under demographic expansion on the basis of mitochondrial DNA data [76], [79], and thus negative D values were expected under neutral conditions. The fact that D remains significantly positive in most populations from Europe, Africa and continental Asia is a proof that balancing selection is acting significantly on HLA genes and, in the same process, conceals some of the effects of demographic factors.

Our results merely suggest the existence of fluctuating selection across loci, but also over time at the human MHC. According to some authors [94], Tajima's test is more powerful to detect the magnitude of selection acting on HLA genes, as it is better suited to detect ancient traces of selection (history of species timescale) than EW (history of populations timescale). The fact that we observe higher proportions of significant outcomes at every locus according to Tajima's D sustains this assumption. In this context, the singular profile exhibited by HLA-DPB1 can be discussed. Indeed, as already stated, this gene is described as evolving under neutrality, which is concordant with our results of the EW tests. However, we detect a high percentage of significant outcomes according to Tajima's D, including 3 populations remaining significant after correction for multiple tests. Our hypothesis is that HLA-DPB1 presently evolves under neutrality, but retains ancient traces of balancing selection within its nucleotide sequences. The fact that strong evidences for balancing selection at some amino acid sites within the β-1 domain of HLA-DPB1 molecules have been previously identified brings support to this conclusion [92].

In addition to balancing selection, we find evidence for directional selection in some populations. However, most of the populations displaying negative D values and/or excess homozygosity (i.e. Fobs>Fexp) are, or have been, prone to genetic drift (see File S1 for details on outcomes for individual populations), suggesting that demography and stochastic forces, rather than directional selection in response to specific pathogens, are the main factors behind these peculiar statistical results. A similar situation was discussed in a previous study [95].

To conclude, this study presents a large scale survey of HLA molecular polymorphism among and within human populations. While our analyses support previous results on the global genetic structure of human populations based on HLA allelic frequencies, we show that taking into account the molecular divergence of HLA DNA sequences leads to different patterns of genetic relationships between populations at regional scales. Notably, populations that are usually described as very distant genetically from each other on the basis of their HLA frequency distributions (e.g. Taiwanese aborigines, Pacific islanders and Amerindians) are even more discriminated when one weights their genetic distances with the nucleotide differences among alleles, while the opposite pattern is observed for the other populations. Furthermore, our investigation reveals new information on the putative mechanisms involved in the evolution of HLA genes. For instance, we confirm that balancing selection left a strong signature on intra-population diversity profiles at most loci and is the main force acting on the maintenance of HLA polymorphism, even though gene conversion and/or recombination may also have some influence. Our analysis of pairwise differences between HLA alleles indicates that balancing selection acting on this system is asymmetric, i.e. stronger for heterozygotes having molecularly distant alleles, even in populations exhibiting a low level of internal diversity. In addition, other kinds of selection (notably at HLA-Cw and -DQB1, which exhibit peculiar profiles compared to other loci) are not excluded. We also detect ancient traces of selection in DNA sequences of the HLA-DPB1 locus, which was assumed to evolve close to neutral conditions on the basis of allelic frequencies distributions. Nonetheless, despite accumulating evidence of natural selection acting on HLA and while some challenging questions remain unanswered (e.g. the intensity and time frame of balancing selection acting on HLA [94]), the history and demography of populations still appear to be the strongest factors lying behind their genetic differentiations. In this context, working with nucleotide sequences from PBR neighboring exons and introns, that may evolve more neutrally, could provide new perspectives.

Materials and Methods

Population samples

Population data used in this study are mainly taken from the database of the 12th and 13th International Histocompatibility Workshops (IHWs) [48], [96], completed with additional data from published reports and from our own laboratory (Table 1). A total of 97, 90, 68, 106, 58, 89 and 56 populations were gathered for HLA-A, -B, -Cw, -DRB1, -DQA1, -DQB1 and -DPB1, respectively. We allocated each of these populations to one of ten geographic groups defined a priori by the IHWG-AHGD component: North Africa (NAF), sub-Saharan Africa (SAF), Europe (EUR), Southwest Asia (SWA), Northeast Asia (NEA), Southeast Asia (SEA), Pacific (PAC), Australia (AUS), North America (NAM), and South America (SAM); for SEA, we sometimes distinguished the aboriginal populations of Taiwan (TW, not including the Hakka and Minnan populations which are descendent of early Chinese settlers in Taiwan and speak Sino-Tibetan languages, while Taiwanese indigenous peoples speak Austronesian languages) from the continental Southeast Asian populations (CSEA); we also considered an additional category called Other (OTH) for known admixed populations. This last category was not taken into account in most analyses notably because we lack precise geographic information on most populations. Furthermore, we referred to Oceania (OCE) for PAC and AUS taken together, and to America (AME) for NAM and SAM taken together (see File S1 for population names, geographic regions, references, sample sizes and more). Population samples were selected according to two criteria: (1) being characterized at the allelic level (i.e. having all alleles coded on at least 4 digits), and (2) lacking typing uncertainties corresponding to ambiguous allele groups. Indeed, currently used coding techniques for HLA data often do not allow any distinction between groups of alleles that are undistinguishable for a given typing method and alleles that are usually discriminated from each other, except in specific combinations. As a consequence, taking into account ambiguous allele groups often leads to incongruous estimated frequencies (i.e. subdividing the group results in sharing the frequencies equally among all the alleles composing it). On the other hand, samples with individuals showing ambiguities that correspond to multiple possible genotypes were allowed because such ambiguities can easily be treated by the Gene[rate] computer programs. A maximum of 3 to 5 individuals genotyped at the generic level (i.e. 2-digits typing) were allowed in samples of very large size to avoid a drastic reduction in the number of populations included in the analyses. Due to the use of different typing strategies and to the heterogeneity of the data sources, the data included in this study were submitted to automated scripts written in GNU/Linux shell bash to check for the latest updates and changes in the official WHO HLA Nomenclature (e.g. allele renaming, deletion and/or extension from 5 digits to 6 digits adopted in 2002).

While these quality standards were chosen to retain only the best characterized population samples, assuring a solid basis for subsequent statistical analyses, this may have lead to a relative heterogeneity in the datasets used for this study. Drawbacks of this kind are hardly avoidable when data are gathered from various sources and have been characterized in a time span of several years. However, two considerations lessen this problem; first, despite variable numbers of populations from one locus to another, a closer look at the geographic maps provided in File S1 shows that the overall coverage of geographic areas is quite similar for the seven datasets. This is particularly true among class I, and among class II genes, respectively. Indeed, data were principally taken from the 12th and 13th IHWs database, and, thus, as class I and class II genes were mainly genotyped within the scope of the 13th IHW, and 12th IWH, respectively, with some additional typings provided by the 13th IHW, population datasets overlap for HLA-A, -B and -Cw on the one hand, and HLA-DRB1, -DQA1, -DQB1 and -DPB1 on the other hand.

DNA sequences

A total of 2,062 DNA sequences for alleles of class I (exons 2 and 3) and class II (exon 2) loci were downloaded from the IMGT official database (release 2.13) [10], [97]. This version of the database was chosen so as to be more recent than the genotyping data in hand, thus providing reference DNA sequences for all the alleles detected in the population samples used for this study. Yet, additional sequence formatting was necessary for some of the alleles, as described below. We restricted our analyses to the PBR (Peptide Binding Region) coding exon(s) which are the most polymorphic amongst HLA classical genes and which are screened through most typing protocols (Table 1). As a matter of fact, the PBR exons contain less undetermined positions within sequence alignments than neighboring exons and introns, as alleles have generally been fully sequenced for PBR before being processed to the databank, whereas other DNA regions are often incompletely described and are thus less reliable for sequence comparisons.

Because the population data used in this study were genotyped during a time span of several years and with different typing protocols, we used automated scripts written in GNU/Linux shell bash to process HLA allele sequences at each possible level of resolution (e.g. HLA-A*01, A*0101, A*010101 and A*01010101). Indeed, whereas official sequence alignments include up-to-date allelic characterization, the molecular resolution of alleles in population data may vary (e.g. a population sample may contains typings referring to A*0101 while this allele name is not anymore listed in the sequence alignments following its splits into several 6 and 8 digits alleles, see hereafter). Basically, the scripts replace nucleotides within sequence alignments by a “?” (i.e. an undetermined nucleotide) for any polymorphic position between two (or more) alleles that are to be grouped when converted back to a lower level of resolution (e.g. a C to ? replacement at position 144 of exon 2 for HLA-A*01010101, A*01010102N, A*010102, A*010103, A*010104, A*010106, A*010107 and A*010108, and a G to ? replacement at the same position for HLA-A*010105, respectively, when all these alleles are reduced to the sequence corresponding to HLA-A*0101). This process increases the number of indeterminations within the sequences, thus explaining why only populations characterized at the allelic level (i.e. 4 or more digits) were considered in this work. A maximum level of 5% missing data at each nucleotide site within the sequence alignments was allowed in the estimation of all molecular statistics used in this study, meaning that sites above that threshold were discarded from the analyses (see next section). For the sites below that threshold and containing undetermined nucleotides, a “?” for a given allele was considered as identical to any determined nucleotide (A,G,T,C) at the same site for another allele. Note that the number of nucleotide sites with less than 5% of missing data vary both among different loci as well as for a given locus depending on the number of populations considered in each analysis (e.g. population comparisons at the worldwide scale or on subsets of populations defined on a geographic criterion; analyses at the intra-population level).

Statistical analyses

All analyses done for this study were performed twice, once using the nucleotide sequences of PBR exons (hereafter the “molecular approach”), and once using only the allelic frequencies (hereafter the “allelic approach”). For the molecular approach, a distance matrix of pairwise differences among DNA sequences was computed and was used to weight all molecular statistics, while all alleles were considered as molecularly equidistant for the allelic approach. We estimated allelic frequencies in each population with an EM algorithm by using the Gene[rate] program allowing to take multiple genotypes (i.e. typing ambiguities) into account [27], [28], [29]. Significant departures from selective neutrality were tested by Tajima's D statistic at the nucleotide level [98] and by Ewens-Watterson's (EW) test on allelic frequency distributions [99], [100]. The distribution of pairwise nucleotide differences between HLA alleles (i.e. mismatch distribution) was obtained for each population, and average mismatch distributions were generated on groups of populations defined on a geographic criterion. The distributions were plotted to depict mean nucleotide divergences between alleles within each population subset (i.e. geographic region). This allowed exploring whether molecular distances between alleles were skewed towards greater values, an expected pattern of asymmetric balancing selection (i.e. heterozygous with highly divergent alleles would be functionally advantaged because they would be able to cover wider spectrums of pathogen's peptides), or not. We estimated HLA molecular genetic diversity at three levels: (1) within populations by computing nucleotide diversity (πn) indices [101]; (2) among populations by computing ΦST fixation indices; (3) using the analysis of molecular variance (AMOVA) hierarchical framework to estimate both the diversity among populations within geographic groups (ΦSC index) and the diversity between geographic groups (ΦCT index) [102]. HLA genetic diversity based on allelic frequencies was estimated at these same levels with the expected heterozygozity (h) [103], FST, FSC, and FCT indices [104], respectively. Statistical significance of the fixation indices was tested by the non-parametric permutation procedure implemented in the Arlequin computer package [105], which permutes individual genotypes among populations for ΦSC/FSC and ΦST/FST, or populations among groups for ΦCT/FCT. Coancestry coefficients (i.e. Reynolds genetic distances), which are directly derived from ΦSTs or FSTs [106], and geographic distances based on the arc length of a sphere and transformed to natural logarithms [107] were computed between all pairs of populations. Correlation coefficients between genetic and geographic distances were computed and tested by a Mantel test [108] using the NTSYS software [109]. Reynolds distance matrixes obtained for the molecular and the allelic approaches were compared using a script written with the R statistical software (http://www.r-project.org/).

Supporting Information

File S1.

Population data and neutrality tests.

https://doi.org/10.1371/journal.pone.0014643.s001

(12.21 MB DOC)

File S2.

Nucleotide diversity and heterozygosity within populations.

https://doi.org/10.1371/journal.pone.0014643.s002

(0.09 MB DOC)

File S3.

Genetic diversity among populations and correlation coefficient between genetic and geographic distances in different geographic groups.

https://doi.org/10.1371/journal.pone.0014643.s003

(0.23 MB DOC)

File S4.

Comparisons of Reynolds genetic distance matrixes.

https://doi.org/10.1371/journal.pone.0014643.s004

(0.86 MB DOC)

Acknowledgments

We thank José Manuel Nunes for his precious inputs and comments on working with ambiguous genotypic data, as well as for providing computer programs, and Steve Mack, Jean-Marie Tiercy, David Comas, Erika Hagelberg, André Langaney and Estella S. Poloni for their useful comments to a previous version of this work.

Author Contributions

Conceived and designed the experiments: SB ASM. Performed the experiments: SB. Analyzed the data: SB. Contributed reagents/materials/analysis tools: ASM. Wrote the paper: SB ASM.

References

  1. 1. Bunce M, Young N, Welsh K (1997) Molecular HLA typing-the brave new world. Transplantation 64: 1505–1513.M. BunceN. YoungK. Welsh1997Molecular HLA typing-the brave new world.Transplantation6415051513
  2. 2. Marsh S, Parham P, Barber L (2000) The HLA FactsBook. Academic Press. 398 p.S. MarshP. ParhamL. Barber2000The HLA FactsBookAcademic Press398
  3. 3. Mytilineos J, Christ U, Lempert M, Opelz G (1997) Comparison of typing results by serology and polymerase chain reaction with sequence-specific primers for HLA-Cw in 650 individuals. Tissue Antigens 50: 395–400.J. MytilineosU. ChristM. LempertG. Opelz1997Comparison of typing results by serology and polymerase chain reaction with sequence-specific primers for HLA-Cw in 650 individuals.Tissue Antigens50395400
  4. 4. Bunce M, Barnardo M, Procter J, Marsh S, Vilches C, et al. (1997) High resolution HLA-C typing by PCR-SSP: identification of allelic frequencies and linkage disequilibria in 604 unrelated random UK Caucasoïds and a comparison with serology. Tissue Antigens 50: 100–111.M. BunceM. BarnardoJ. ProcterS. MarshC. Vilches1997High resolution HLA-C typing by PCR-SSP: identification of allelic frequencies and linkage disequilibria in 604 unrelated random UK Caucasoïds and a comparison with serology.Tissue Antigens50100111
  5. 5. Grundschober C, Rufer N, Sanchez-Mazas A, Madrigal A, Jeannet M, et al. (1997) Molecular characterization of HLA-C incompatibilities in HLA-ABDR-matched unrelated bone marrow donor-recipient pairs. Tissue Antigens 49: 612–623.C. GrundschoberN. RuferA. Sanchez-MazasA. MadrigalM. Jeannet1997Molecular characterization of HLA-C incompatibilities in HLA-ABDR-matched unrelated bone marrow donor-recipient pairs.Tissue Antigens49612623
  6. 6. Sanchez-Mazas A, Steiner QG, Grundschober C, Tiercy JM (2000) The molecular determination of HLA-Cw alleles in the Mandenka (West Africa) reveals a close genetic relationship between Africans and Europeans. Tissue Antigens 56: 303–312.A. Sanchez-MazasQG SteinerC. GrundschoberJM Tiercy2000The molecular determination of HLA-Cw alleles in the Mandenka (West Africa) reveals a close genetic relationship between Africans and Europeans.Tissue Antigens56303312
  7. 7. Buhler S, Sanchez-Mazas A, Zanone R, Djavad N, Tiercy JM (2002) PCR-SSOP molecular typing of HLA-C alleles in an Iranian population. Tissue Antigens 59: 525–530.S. BuhlerA. Sanchez-MazasR. ZanoneN. DjavadJM Tiercy2002PCR-SSOP molecular typing of HLA-C alleles in an Iranian population.Tissue Antigens59525530
  8. 8. Marsh SG (2008) Nomenclature for Factors of the HLA System, Update June 2008. Hum Immunol. SG Marsh2008Nomenclature for Factors of the HLA System, Update June 2008.Hum Immunol
  9. 9. Marsh SG, Albert ED, Bodmer WF, Bontrop RE, Dupont B, et al. (2005) Nomenclature for factors of the HLA system, 2004. Tissue Antigens 65: 301–369.SG MarshED AlbertWF BodmerRE BontropB. Dupont2005Nomenclature for factors of the HLA system, 2004.Tissue Antigens65301369
  10. 10. Robinson J, Marsh SG (2007) The IMGT/HLA database. Methods Mol Biol 409: 43–60.J. RobinsonSG Marsh2007The IMGT/HLA database.Methods Mol Biol4094360
  11. 11. Adams E, Parham P (2001) Species-specific evolution of MHC class I genes in the higher primates. Immunol Rev 183: 41–64.E. AdamsP. Parham2001Species-specific evolution of MHC class I genes in the higher primates.Immunol Rev1834164
  12. 12. Bergström T, Erlandsson R, Engkvist H, Josefsson A, Erlich H, et al. (1999) Phylogenetic history of hominoid DRB loci and alleles inferred from intron sequences. Immunological Reviews 167: 351–365.T. BergströmR. ErlandssonH. EngkvistA. JosefssonH. Erlich1999Phylogenetic history of hominoid DRB loci and alleles inferred from intron sequences.Immunological Reviews167351365
  13. 13. Bergström T, Josefsson A, Erlich H, Gyllensten U (1998) Recent origin of HLA-DRB1 alleles and implications for human evolution. Nature Genetics 18: 237–242.T. BergströmA. JosefssonH. ErlichU. Gyllensten1998Recent origin of HLA-DRB1 alleles and implications for human evolution.Nature Genetics18237242
  14. 14. Bontrop RE (2006) Comparative genetics of MHC polymorphisms in different primate species: duplications and deletions. Hum Immunol 67: 388–397.RE Bontrop2006Comparative genetics of MHC polymorphisms in different primate species: duplications and deletions.Hum Immunol67388397
  15. 15. Cereb N, Hughes A, Yang S (1997) Locus-specific conservation of the HLA class I introns by intra-locus homogenization. Immunogenetics 47: 30–36.N. CerebA. HughesS. Yang1997Locus-specific conservation of the HLA class I introns by intra-locus homogenization.Immunogenetics473036
  16. 16. Kelley J, Walter L, Trowsdale J (2005) Comparative genomics of major histocompatibility complexes. Immunogenetics 56: 683–695.J. KelleyL. WalterJ. Trowsdale2005Comparative genomics of major histocompatibility complexes.Immunogenetics56683695
  17. 17. Kulski J, Shiina T, Anzai T, Kohara S, Inoko H (2002) Comparative genomic analysis of the MHC: the evolution of class I duplication blocks, diversity and complexity from shark to man. Immunol Rev 190: 95–122.J. KulskiT. ShiinaT. AnzaiS. KoharaH. Inoko2002Comparative genomic analysis of the MHC: the evolution of class I duplication blocks, diversity and complexity from shark to man.Immunol Rev19095122
  18. 18. von Salome J, Gyllensten U, Bergstrom TF (2007) Full-length sequence analysis of the HLA-DRB1 locus suggests a recent origin of alleles. Immunogenetics 59: 261–271.J. von SalomeU. GyllenstenTF Bergstrom2007Full-length sequence analysis of the HLA-DRB1 locus suggests a recent origin of alleles.Immunogenetics59261271
  19. 19. Sanchez-Mazas A (2006) 13th International Histocompatibility Workshop Anthropology/Human Genetic Diversity Joint Report - Chapter 7: HLA genetic differentiation of the 13th IHWC population data relative to worldwide linguistic families. In: Hansen J, editor. Immunobiology of the Human MHC: Proceedings of the 13th International Histocompatibility Workshop and Conference. Seattle: IHWG Press. pp. 758–766.A. Sanchez-Mazas200613th International Histocompatibility Workshop Anthropology/Human Genetic Diversity Joint Report - Chapter 7: HLA genetic differentiation of the 13th IHWC population data relative to worldwide linguistic families.J. HansenImmunobiology of the Human MHC: Proceedings of the 13th International Histocompatibility Workshop and ConferenceSeattleIHWG Press758766
  20. 20. Mack S, Erlich H (2006) 13th International Histocompatibility Workshop Anthropology/Human Genetic Diversity Joint Report - Chapter 6: Population relationships as inferred from classical HLA genes. In: Hansen J, editor. Immunobiology of the Human MHC: Proceedings of the 13th International Histocompatibility Workshop and Conference. Seattle: IHWG Press. pp. 747–757.S. MackH. Erlich200613th International Histocompatibility Workshop Anthropology/Human Genetic Diversity Joint Report - Chapter 6: Population relationships as inferred from classical HLA genes.J. HansenImmunobiology of the Human MHC: Proceedings of the 13th International Histocompatibility Workshop and ConferenceSeattleIHWG Press747757
  21. 21. Buhler S, Megarbane A, Lefranc G, Tiercy JM, Sanchez-Mazas A (2006) HLA-C molecular characterization of a Lebanese population and genetic structure of 39 populations from Europe to India-Pakistan. Tissue Antigens 68: 44–57.S. BuhlerA. MegarbaneG. LefrancJM TiercyA. Sanchez-Mazas2006HLA-C molecular characterization of a Lebanese population and genetic structure of 39 populations from Europe to India-Pakistan.Tissue Antigens684457
  22. 22. Meyer D, Single R, Mack S, Lancaster A, Nelson M, et al. (2006) 13th International Histocompatibility Workshop Anthropology/Human Genetic Diversity Joint Report - Chapter 4: Single locus polymorphism of classical HLA genes. In: Hansen J, editor. Immunobiology of the Human MHC: Proceedings of the 13th International Histocompatibility Workshop and Conference. Seattle: IHWG Press. pp. 653–704.D. MeyerR. SingleS. MackA. LancasterM. Nelson200613th International Histocompatibility Workshop Anthropology/Human Genetic Diversity Joint Report - Chapter 4: Single locus polymorphism of classical HLA genes.J. HansenImmunobiology of the Human MHC: Proceedings of the 13th International Histocompatibility Workshop and ConferenceSeattleIHWG Press653704
  23. 23. Abdennaji Guenounou B, Loueslati BY, Buhler S, Hmida S, Ennafaa H, et al. (2006) HLA class II genetic diversity in southern Tunisia and the Mediterranean area. Int J Immunogenet 33: 93–103.B. Abdennaji GuenounouBY LoueslatiS. BuhlerS. HmidaH. Ennafaa2006HLA class II genetic diversity in southern Tunisia and the Mediterranean area.Int J Immunogenet3393103
  24. 24. Riccio M, Nunes J, Rahal M, Kervaire B, Tiercy JM, et al. The Austroasiatic Munda population from India and its enigmatic origin: a HLA diversity study. Hum Biol . M. RiccioJ. NunesM. RahalB. KervaireJM TiercyThe Austroasiatic Munda population from India and its enigmatic origin: a HLA diversity study.Hum Biol In press. In press.
  25. 25. Di D, Sanchez-Mazas AChallenging views on the peopling history of East Asia: the story according to HLA markers. Am J Phys Anthropol. D. DiA. Sanchez-MazasChallenging views on the peopling history of East Asia: the story according to HLA markers.Am J Phys AnthropolIn press. In press.
  26. 26. Mack S, Sanchez-Mazas A, Meyer D, Single R, Tsai Y, et al. (2006) 13th International Histocompatibility Workshop Anthropology/Human Genetic Diversity Joint Report - Chapter 2: Methods used in the generation and preparation of data for analysis in the 13th International Histocompatibility Workshop. In: Hansen J, editor. Immunobiology of the Human MHC: Proceedings of the 13th International Histocompatibility Workshop and Conference. Seattle: IHWG Press. pp. 564–579.S. MackA. Sanchez-MazasD. MeyerR. SingleY. Tsai200613th International Histocompatibility Workshop Anthropology/Human Genetic Diversity Joint Report - Chapter 2: Methods used in the generation and preparation of data for analysis in the 13th International Histocompatibility Workshop.J. HansenImmunobiology of the Human MHC: Proceedings of the 13th International Histocompatibility Workshop and ConferenceSeattleIHWG Press564579
  27. 27. Nunes J (2006) Generate: tools for analysis and handling of data with ambiguities. J. Nunes2006Generate: tools for analysis and handling of data with ambiguities.Laboratory of Anthropology, Genetics and Peopling History, University of Geneva, Switzerland. Laboratory of Anthropology, Genetics and Peopling History, University of Geneva, Switzerland.
  28. 28. Nunes J (2005) Counting Genes [Thesis]. University of Porto. J. Nunes2005Counting Genes [Thesis]University of Porto
  29. 29. Nunes J (2006) Tools for efficient HLA data handlings. In: Hansen J, editor. Immunobiology of the Human MHC: Proceedings of the 13th International Histocompatibility Workshop and Conference. Seattle: IHWG Press. pp. 143–147.J. Nunes2006Tools for efficient HLA data handlings.J. HansenImmunobiology of the Human MHC: Proceedings of the 13th International Histocompatibility Workshop and ConferenceSeattleIHWG Press143147
  30. 30. Nunes J, Riccio M, Tiercy JM, Sanchez-Mazas AAllele frequency estimation from ambiguous data: using resampling schema in validating frequency estimates and in selective neutrality testing. Hum Biol. J. NunesM. RiccioJM TiercyA. Sanchez-MazasAllele frequency estimation from ambiguous data: using resampling schema in validating frequency estimates and in selective neutrality testing.Hum BiolIn press. In press.
  31. 31. Nunes J, Riccio M, Buhler S, Currat M, Ries F, et al. (2010) Analysis of the HLA population data submitted to the 15th International Histocompatibilitiy and Immunogenetics Workshop by using the GENE[RATE] computer tools accommodating ambiguous data (AHPD component report). Tissue Antigens 76: 18–30.J. NunesM. RiccioS. BuhlerM. CurratF. Ries2010Analysis of the HLA population data submitted to the 15th International Histocompatibilitiy and Immunogenetics Workshop by using the GENE[RATE] computer tools accommodating ambiguous data (AHPD component report).Tissue Antigens761830
  32. 32. The MHC sequencing consortium (1999) Complete sequence and gene map of a human major histocompatibility complex. Nature 401: 921–923.The MHC sequencing consortium1999Complete sequence and gene map of a human major histocompatibility complex.Nature401921923
  33. 33. Beck S, Trowsdale J (2000) The human major histocompatability complex: lessons from the DNA sequence. Annu Rev Genomics Hum Genet 1: 117–137.S. BeckJ. Trowsdale2000The human major histocompatability complex: lessons from the DNA sequence.Annu Rev Genomics Hum Genet1117137
  34. 34. Gaudieri S, Dawkins R, Habara K, Kulski J, Gojobori T (2000) SNP profile within the human major histocompatibility complex reveals an extreme and interrupted level of nucleotide diversity. Genome Res 10: 1579–1586.S. GaudieriR. DawkinsK. HabaraJ. KulskiT. Gojobori2000SNP profile within the human major histocompatibility complex reveals an extreme and interrupted level of nucleotide diversity.Genome Res1015791586
  35. 35. Gibert M, Sanchez-Mazas A (2003) Geographic patterns of functional categories of HLA-DRB1 alleles: a new approach to analyse associations between HLA-DRB1 and disease. Eur J Immunogenet 30: 361–374.M. GibertA. Sanchez-Mazas2003Geographic patterns of functional categories of HLA-DRB1 alleles: a new approach to analyse associations between HLA-DRB1 and disease.Eur J Immunogenet30361374
  36. 36. de Bakker PI, McVean G, Sabeti PC, Miretti MM, Green T, et al. (2006) A high-resolution HLA and SNP haplotype map for disease association studies in the extended human MHC. Nat Genet 38: 1166–1172.PI de BakkerG. McVeanPC SabetiMM MirettiT. Green2006A high-resolution HLA and SNP haplotype map for disease association studies in the extended human MHC.Nat Genet3811661172
  37. 37. Reche P, Reinherz E (2003) Sequence variability analysis of human class I and class II MHC molecules: functional and structural correlates of amino acid polymorphisms. J Mol Biol 331: 623–641.P. RecheE. Reinherz2003Sequence variability analysis of human class I and class II MHC molecules: functional and structural correlates of amino acid polymorphisms.J Mol Biol331623641
  38. 38. Turner S, Ellexson M, Hickman H, Sidebottom D, Fernández-Viña M, et al. (1998) Sequence-Based Typing Provides a New Look at HLA-C Diversity. The Journal of Immunology 161: 1406–1413.S. TurnerM. EllexsonH. HickmanD. SidebottomM. Fernández-Viña1998Sequence-Based Typing Provides a New Look at HLA-C Diversity.The Journal of Immunology16114061413
  39. 39. Wang H, Tokunaga K, Akaza T, Tadokoro K, Shibata Y, et al. (1997) Identification of HLA-C alleles using PCR-single-strand-conformation polymorphism and direct sequencing. Tissue Antigens 49: 134–140.H. WangK. TokunagaT. AkazaK. TadokoroY. Shibata1997Identification of HLA-C alleles using PCR-single-strand-conformation polymorphism and direct sequencing.Tissue Antigens49134140
  40. 40. Cadavid L, Watkins D (1997) Heirs of the jaguar and the anaconda: HLA, conquest and disease in the indigenous populations of the Americas. Tissue Antigens 50: 209–218.L. CadavidD. Watkins1997Heirs of the jaguar and the anaconda: HLA, conquest and disease in the indigenous populations of the Americas.Tissue Antigens50209218
  41. 41. Gao X, Lester S, Boettcher B, McCluskey J (1997) Diversity of HLA genes in populations of Australia and the Pacific. In: Charron D, editor. Proc 12th Int Histocompatibility Workshop and Conference. Paris: EDK. pp. 298–306.X. GaoS. LesterB. BoettcherJ. McCluskey1997Diversity of HLA genes in populations of Australia and the Pacific.D. CharronProc 12th Int Histocompatibility Workshop and ConferenceParisEDK298306
  42. 42. Petzl-Erler M, Gorodezky C, Layrisse Z, Klitz W, Fainboim L, et al. (1997) Anthropology report for Region Latin-America: Amerindian and admixed populations. In: Charron D, editor. Proc 12th Int Histocompatibility Workshop and Conference. Paris: EDK. pp. 337–344.M. Petzl-ErlerC. GorodezkyZ. LayrisseW. KlitzL. Fainboim1997Anthropology report for Region Latin-America: Amerindian and admixed populations.D. CharronProc 12th Int Histocompatibility Workshop and ConferenceParisEDK337344
  43. 43. Piazza A, Lonjou C (1997) HLA in Europe and in the Mediterranean countries. In: Charron D, editor. Proc 12th Int Histocompatibility Workshop and Conference. Paris: EDK. pp. 374–384.A. PiazzaC. Lonjou1997HLA in Europe and in the Mediterranean countries.D. CharronProc 12th Int Histocompatibility Workshop and ConferenceParisEDK374384
  44. 44. Qasim Medhi S, Mohyuddin A, Qamar R, Khaliq S (1997) INPA (India and Pakistan) regional report: HLA Class I polymorphism in different Pakistani ethnic groups. In: Charron D, editor. Proc 12th Int Histocompatibility Workshop and Conference. Paris: EDK. pp. 321–322.S. Qasim MedhiA. MohyuddinR. QamarS. Khaliq1997INPA (India and Pakistan) regional report: HLA Class I polymorphism in different Pakistani ethnic groups.D. CharronProc 12th Int Histocompatibility Workshop and ConferenceParisEDK321322
  45. 45. Arnaiz-Villena A, Vargas-Alarcón G, Granados J, Gomez-Casado E, Longas J, et al. (2000) HLA genes in Mexican Mazatecans, the peopling of the Americas and the uniqueness of Amerindians. Tissue Antigens 56: 405–416.A. Arnaiz-VillenaG. Vargas-AlarcónJ. GranadosE. Gomez-CasadoJ. Longas2000HLA genes in Mexican Mazatecans, the peopling of the Americas and the uniqueness of Amerindians.Tissue Antigens56405416
  46. 46. Cao K, Moormann AM, Lyke KE, Masaberg C, Sumba OP, et al. (2004) Differentiation between African populations is evidenced by the diversity of alleles and haplotypes of HLA class I loci. Tissue Antigens 63: 293–325.K. CaoAM MoormannKE LykeC. MasabergOP Sumba2004Differentiation between African populations is evidenced by the diversity of alleles and haplotypes of HLA class I loci.Tissue Antigens63293325
  47. 47. Sanchez-Mazas A (2007) An apportionment of human HLA diversity. Tissue Antigens 69: Suppl 1198–202.A. Sanchez-Mazas2007An apportionment of human HLA diversity.Tissue Antigens69Suppl 1198202
  48. 48. Mack S, Tsai Y, Sanchez-Mazas A, Erlich H (2006) 13th International Histocompatibility Workshop Anthropology/Human Genetic Diversity Joint Report - Chapter 3: Anthropology/human genetic diversity population reports. In: Hansen J, editor. Immunobiology of the Human MHC: Proceedings of the 13th International Histocompatibility Workshop and Conference. Seattle: IHWG Press. pp. 580–652.S. MackY. TsaiA. Sanchez-MazasH. Erlich200613th International Histocompatibility Workshop Anthropology/Human Genetic Diversity Joint Report - Chapter 3: Anthropology/human genetic diversity population reports.J. HansenImmunobiology of the Human MHC: Proceedings of the 13th International Histocompatibility Workshop and ConferenceSeattleIHWG Press580652
  49. 49. Meyer D, Single RM, Mack SJ, Erlich HA, Thomson G (2006) Signatures of demographic history and natural selection in the human major histocompatibility complex Loci. Genetics 173: 2121–2142.D. MeyerRM SingleSJ MackHA ErlichG. Thomson2006Signatures of demographic history and natural selection in the human major histocompatibility complex Loci.Genetics17321212142
  50. 50. Solberg OD, Mack SJ, Lancaster AK, Single RM, Tsai Y, et al. (2008) Balancing selection and heterogeneity across the classical human leukocyte antigen loci: A meta-analytic review of 497 population studies. Hum Immunol 69: 443–464.OD SolbergSJ MackAK LancasterRM SingleY. Tsai2008Balancing selection and heterogeneity across the classical human leukocyte antigen loci: A meta-analytic review of 497 population studies.Hum Immunol69443464
  51. 51. Prugnolle F, Manica A, Charpentier M, Guegan JF, Guernier V, et al. (2005) Pathogen-driven selection and worldwide HLA class I diversity. Curr Biol 15: 1022–1027.F. PrugnolleA. ManicaM. CharpentierJF GueganV. Guernier2005Pathogen-driven selection and worldwide HLA class I diversity.Curr Biol1510221027
  52. 52. Hao L, Nei M (2005) Rapid expansion of killer cell immunoglobulin-like receptor genes in primates and their coevolution with MHC Class I genes. Gene 347: 149–159.L. HaoM. Nei2005Rapid expansion of killer cell immunoglobulin-like receptor genes in primates and their coevolution with MHC Class I genes.Gene347149159
  53. 53. Middleton D, Gonzelez F (2010) The extensive polymorphism of KIR genes. Immunology 129: 8–19.D. MiddletonF. Gonzelez2010The extensive polymorphism of KIR genes.Immunology129819
  54. 54. Hollenbach JA, Meenagh A, Sleator C, Alaez C, Bengoche M, et al. (2010) Report from the killer immunoglobulin-like receptor (KIR) anthropology component of the 15th International Histocompatibility Workshop: worldwide variation in the KIR loci and further evidence for the co-evolution of KIR and HLA. Tissue Antigens 9999: JA HollenbachA. MeenaghC. SleatorC. AlaezM. Bengoche2010Report from the killer immunoglobulin-like receptor (KIR) anthropology component of the 15th International Histocompatibility Workshop: worldwide variation in the KIR loci and further evidence for the co-evolution of KIR and HLA.Tissue Antigens9999
  55. 55. Fukami-Kobayashi K, Shiina T, Anzai T, Sano K, Yamazaki M, et al. (2005) Genomic evolution of MHC class I region in primates. Proc Natl Acad Sci U S A 102: 9230–9234.K. Fukami-KobayashiT. ShiinaT. AnzaiK. SanoM. Yamazaki2005Genomic evolution of MHC class I region in primates.Proc Natl Acad Sci U S A10292309234
  56. 56. Satta Y (1997) Effects of intra-locus recombination of HLA polymorphism. Hereditas 127: 105–112.Y. Satta1997Effects of intra-locus recombination of HLA polymorphism.Hereditas127105112
  57. 57. Hedrick P (1999) Balancing selection and MHC. Genetica 104: 207–214.P. Hedrick1999Balancing selection and MHC.Genetica104207214
  58. 58. Hughes AL, Nei M (1988) Pattern of nucleotide substitution at major histocompatibility complex class I loci reveals overdominant selection. Nature 335: 167–170.AL HughesM. Nei1988Pattern of nucleotide substitution at major histocompatibility complex class I loci reveals overdominant selection.Nature335167170
  59. 59. Hughes AL, Nei M (1989) Nucleotide substitution at major histocompatibility complex class II loci: evidence for overdominant selection. Proc Natl Acad Sci U S A 86: 958–962.AL HughesM. Nei1989Nucleotide substitution at major histocompatibility complex class II loci: evidence for overdominant selection.Proc Natl Acad Sci U S A86958962
  60. 60. Parham P, Arnett K, Adams E, Little A, Tees K, et al. (1997) Episodic evolution and turnover of HLA-B in the indigenous human populations of the Americas. Tissue Antigens 50: 219–232.P. ParhamK. ArnettE. AdamsA. LittleK. Tees1997Episodic evolution and turnover of HLA-B in the indigenous human populations of the Americas.Tissue Antigens50219232
  61. 61. Sanchez-Mazas A (2001) African diversity from the HLA point of view: Influence of genetic drift, geography, linguistics, and natural selection. Hum Immunol 62: 937–948.A. Sanchez-Mazas2001African diversity from the HLA point of view: Influence of genetic drift, geography, linguistics, and natural selection.Hum Immunol62937948
  62. 62. Tsai Y, Thomson G (2006) Selection intensity differences in seven HLA loci in many populations. In: Hansen JA, editor. Immunobiology of the Human MHC: Proceedings of the 13th International Histocompatibility Workshop and Conference. Seattle: IHWG Press. pp. 199–201.Y. TsaiG. Thomson2006Selection intensity differences in seven HLA loci in many populations.JA HansenImmunobiology of the Human MHC: Proceedings of the 13th International Histocompatibility Workshop and ConferenceSeattleIHWG Press199201
  63. 63. van Oosterhout C (2009) Trans-species polymorphism, HLA-disease associations and the evolution of the MHC. Commun Integr Biol 2: 408–410.C. van Oosterhout2009Trans-species polymorphism, HLA-disease associations and the evolution of the MHC.Commun Integr Biol2408410
  64. 64. van Oosterhout C (2009) A new theory of MHC evolution: beyond selection on the immune genes. Proc Biol Sci 276: 657–665.C. van Oosterhout2009A new theory of MHC evolution: beyond selection on the immune genes.Proc Biol Sci276657665
  65. 65. Belich M, Madrigal J, Hildebrand W, Zemmour J, Williams R, et al. (1992) Unusual HLA-B alleles in two tribes of Brazilian Indians. Nature 357: 326–329.M. BelichJ. MadrigalW. HildebrandJ. ZemmourR. Williams1992Unusual HLA-B alleles in two tribes of Brazilian Indians.Nature357326329
  66. 66. Bradshaw D, Gans C, Jones P, Rizzuto G, Steiner N, et al. (2002) Novel HLA-A locus alleles including A*01012, A*0306, A*0308, A*2616, A*2617, A*3009, A*3206, A*3403, A*3602 and A*6604. Tissue Antigens 59: 325–327.D. BradshawC. GansP. JonesG. RizzutoN. Steiner2002Novel HLA-A locus alleles including A*01012, A*0306, A*0308, A*2616, A*2617, A*3009, A*3206, A*3403, A*3602 and A*6604.Tissue Antigens59325327
  67. 67. Little A, Mason A, Marsh S, Parham P (1996) HLA-C typing of eleven Papua New Guineans: identification of an HLA-Cw4/Cw2 hybrid allele. Tissue Antigens 48: 113–117.A. LittleA. MasonS. MarshP. Parham1996HLA-C typing of eleven Papua New Guineans: identification of an HLA-Cw4/Cw2 hybrid allele.Tissue Antigens48113117
  68. 68. Parham P, Adams E, Arnett K (1995) The origins of HLA-A,B,C polymorphism. Immunol Rev 143: 141–180.P. ParhamE. AdamsK. Arnett1995The origins of HLA-A,B,C polymorphism.Immunol Rev143141180
  69. 69. Parham P, Ohta T (1996) Population Biology of Antigen Presentation by MHC Class I Molecules. Science 272: 67–74.P. ParhamT. Ohta1996Population Biology of Antigen Presentation by MHC Class I Molecules.Science2726774
  70. 70. Perrier P, Reveillere C, Andre-Botte C, Schuhmacher A (2000) Identification of a new DQB1 allele that appears to have been generated by an interallelic sequence exchange. Tissue Antigens 56: 556–559.P. PerrierC. ReveillereC. Andre-BotteA. Schuhmacher2000Identification of a new DQB1 allele that appears to have been generated by an interallelic sequence exchange.Tissue Antigens56556559
  71. 71. Silvera-Redondo C, Gomez-Casado E, Martinez-Laso J, Egea E, Garavito G, et al. (2000) A new HLA-Cw allele (Cw*0808) found in a Colombian Mestizo individual possibly generated by an intralocus/interloci gene conversion. Immunogenetics 51: 1053–1057.C. Silvera-RedondoE. Gomez-CasadoJ. Martinez-LasoE. EgeaG. Garavito2000A new HLA-Cw allele (Cw*0808) found in a Colombian Mestizo individual possibly generated by an intralocus/interloci gene conversion.Immunogenetics5110531057
  72. 72. Tavoularis S, Ouellet S, Stephens S (2001) Identification of three new DRB3* (DRB3*0106, DRB3*0107 and DRB3*02022) alleles. Tissue Antigens 57: 390–393.S. TavoularisS. OuelletS. Stephens2001Identification of three new DRB3* (DRB3*0106, DRB3*0107 and DRB3*02022) alleles.Tissue Antigens57390393
  73. 73. Watkins D, McAdam S, Liu X, Strang C, Milford E, et al. (1992) New recombinant HLA-B alleles in a tribe of South American Amerindians indicate rapid evolution of MHC class I loci. Nature 357: 329–333.D. WatkinsS. McAdamX. LiuC. StrangE. Milford1992New recombinant HLA-B alleles in a tribe of South American Amerindians indicate rapid evolution of MHC class I loci.Nature357329333
  74. 74. Martinsohn JT, Sousa AB, Guethlein LA, Howard JC (1999) The gene conversion hypothesis of MHC evolution: a review. Immunogenetics 50: 168–200.JT MartinsohnAB SousaLA GuethleinJC Howard1999The gene conversion hypothesis of MHC evolution: a review.Immunogenetics50168200
  75. 75. Kingman J (1982) The coalescent. Stochastic Process Appl 13: 235–248.J. Kingman1982The coalescent.Stochastic Process Appl13235248
  76. 76. Excoffier L, Schneider S (1999) Why hunter-gatherer populations do not show signs of Pleistocene demographic expansions. Proc Natl Acad Sci 96: 10597–10602.L. ExcoffierS. Schneider1999Why hunter-gatherer populations do not show signs of Pleistocene demographic expansions.Proc Natl Acad Sci961059710602
  77. 77. Harpending HC, Batzer MA, Gurven M, Jorde LB, Rogers AR, et al. (1998) Genetic traces of ancient demography. Proc Natl Acad Sci U S A 95: 1961–1967.HC HarpendingMA BatzerM. GurvenLB JordeAR Rogers1998Genetic traces of ancient demography.Proc Natl Acad Sci U S A9519611967
  78. 78. Rogers AR, Harpending H (1992) Population growth makes waves in the distribution of pairwise genetic differences. Mol Biol Evol 9: 552–569.AR RogersH. Harpending1992Population growth makes waves in the distribution of pairwise genetic differences.Mol Biol Evol9552569
  79. 79. Schneider S, Excoffier L (1999) Estimation of past demographic parameters from the distribution of pairwise differences when the mutation rates vary among sites: application to human mitochondrial DNA. Genetics 152: 1079–1089.S. SchneiderL. Excoffier1999Estimation of past demographic parameters from the distribution of pairwise differences when the mutation rates vary among sites: application to human mitochondrial DNA.Genetics15210791089
  80. 80. Schierup MH, Hein J (2000) Consequences of recombination on traditional phylogenetic analysis. Genetics 156: 879–891.MH SchierupJ. Hein2000Consequences of recombination on traditional phylogenetic analysis.Genetics156879891
  81. 81. Wiuf C (2000) A coalescence approach to gene conversion. Theor Popul Biol 57: 357–367.C. Wiuf2000A coalescence approach to gene conversion.Theor Popul Biol57357367
  82. 82. Wiuf C, Hein J (2000) The coalescent with gene conversion. Genetics 155: 451–462.C. WiufJ. Hein2000The coalescent with gene conversion.Genetics155451462
  83. 83. Sawyer S (1989) Statistical tests for detecting gene conversion. Mol Biol Evol 6: 526–538.S. Sawyer1989Statistical tests for detecting gene conversion.Mol Biol Evol6526538
  84. 84. Sawyer S (1999) GENECONV: A computer package for the statistical detection of gene conversion. S. Sawyer1999GENECONV: A computer package for the statistical detection of gene conversion.Department of Mathematics, Washington University in St Louis. Department of Mathematics, Washington University in St Louis.
  85. 85. Jakobsen IB, Wilson SR, Easteal S (1998) Patterns of reticulate evolution for the classical class I and II HLA loci. Immunogenetics 48: 312–323.IB JakobsenSR WilsonS. Easteal1998Patterns of reticulate evolution for the classical class I and II HLA loci.Immunogenetics48312323
  86. 86. Takahata N, Satta Y (1998) Selection, convergence, and intragenic recombination in HLA diversity. Genetica 102–103: 157–169.N. TakahataY. Satta1998Selection, convergence, and intragenic recombination in HLA diversity.Genetica102–103157169
  87. 87. Schierup MH, Mikkelsen AM, Hein J (2001) Recombination, balancing selection and phylogenies in MHC and self-incompatibility genes. Genetics 159: 1833–1844.MH SchierupAM MikkelsenJ. Hein2001Recombination, balancing selection and phylogenies in MHC and self-incompatibility genes.Genetics15918331844
  88. 88. Kwok WW, Kovats S, Thurtle P, Nepom GT (1993) HLA-DQ allelic polymorphisms constrain patterns of class II heterodimer formation. J Immunol 150: 2263–2272.WW KwokS. KovatsP. ThurtleGT Nepom1993HLA-DQ allelic polymorphisms constrain patterns of class II heterodimer formation.J Immunol15022632272
  89. 89. Kwok WW, Nepom GT, Raymond FC (1995) HLA-DQ polymorphisms are highly selective for peptide binding interactions. J Immunol 155: 2468–2476.WW KwokGT NepomFC Raymond1995HLA-DQ polymorphisms are highly selective for peptide binding interactions.J Immunol15524682476
  90. 90. Satta Y, O'HUigin C, Takahata N, Klein J (1994) Intensity of natural selection at the major histocompatibility complex loci. Proc Natl Acad Sci U S A 91: 7184–7188.Y. SattaC. O'HUiginN. TakahataJ. Klein1994Intensity of natural selection at the major histocompatibility complex loci.Proc Natl Acad Sci U S A9171847188
  91. 91. Slatkin M, Muirhead CA (2000) A method for estimating the intensity of overdominant selection from the distribution of allele frequencies. Genetics 156: 2119–2126.M. SlatkinCA Muirhead2000A method for estimating the intensity of overdominant selection from the distribution of allele frequencies.Genetics15621192126
  92. 92. Salamon H, Klitz W, Easteal S, Gao X, Erlich HA, et al. (1999) Evolution of HLA class II molecules: Allelic and amino acid site variability across populations. Genetics 152: 393–400.H. SalamonW. KlitzS. EastealX. GaoHA Erlich1999Evolution of HLA class II molecules: Allelic and amino acid site variability across populations.Genetics152393400
  93. 93. Valdes A, McWeeney S, Meyer D, Nelson M, Thomson G (1999) Locus and population specific evolution in HLA class II genes. Annals of Human Genetics 63: 27–43.A. ValdesS. McWeeneyD. MeyerM. NelsonG. Thomson1999Locus and population specific evolution in HLA class II genes.Annals of Human Genetics632743
  94. 94. Garrigan D, Hedrick P (2003) Perspective: detecting adaptive molecular polymorphism: lessons from the MHC. Evolution Int J Org Evolution 57: 1707–1722.D. GarriganP. Hedrick2003Perspective: detecting adaptive molecular polymorphism: lessons from the MHC.Evolution Int J Org Evolution5717071722
  95. 95. Tiercy J-M, Sanchez-Mazas A, Excoffier L, Shi X, Jeannet M, et al. (1992) HLA-DR Polymorphism in a Senegalese Mandenka Population: DNA Oligotyping and Population Genetics of DRB1 Specificities. Am J Hum Genet 51: 592–608.J-M TiercyA. Sanchez-MazasL. ExcoffierX. ShiM. Jeannet1992HLA-DR Polymorphism in a Senegalese Mandenka Population: DNA Oligotyping and Population Genetics of DRB1 Specificities.Am J Hum Genet51592608
  96. 96. Bodmer J, Cambon-Thomsen A, Hors J, Piazza A, Sanchez-Mazas A (1997) Anthroplogy report. Introduction. In: Charron D, editor. Proc 12th Int Histocompatibility Workshop and Conference. Paris: EDK. pp. 269–284.J. BodmerA. Cambon-ThomsenJ. HorsA. PiazzaA. Sanchez-Mazas1997Anthroplogy report. Introduction.D. CharronProc 12th Int Histocompatibility Workshop and ConferenceParisEDK269284
  97. 97. Robinson J, Waller MJ, Parham P, de Groot N, Bontrop R, et al. (2003) IMGT/HLA and IMGT/MHC: sequence databases for the study of the major histocompatibility complex. Nucleic Acids Res 31: 311–314.J. RobinsonMJ WallerP. ParhamN. de GrootR. Bontrop2003IMGT/HLA and IMGT/MHC: sequence databases for the study of the major histocompatibility complex.Nucleic Acids Res31311314
  98. 98. Tajima F (1989) Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123: 585–595.F. Tajima1989Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.Genetics123585595
  99. 99. Ewens W (1972) The sampling theory of selectively neutral alleles. Theor Popul Biol 3: 87–112.W. Ewens1972The sampling theory of selectively neutral alleles.Theor Popul Biol387112
  100. 100. Watterson G (1978) The homozygosity test of neutrality. Genetics 88: 405–417.G. Watterson1978The homozygosity test of neutrality.Genetics88405417
  101. 101. Tajima F (1983) Evolutionary relationship of DNA sequences in finite populations. Genetics 105: 437–460.F. Tajima1983Evolutionary relationship of DNA sequences in finite populations.Genetics105437460
  102. 102. Excoffier L, Smouse P, Quattro J (1992) Analysis of Molecular Variance Inferred From Metric Distances Among DNA Haplotypes: Application to Human Mitochondrial DNA Restriction Data. Genetics 131: 479–491.L. ExcoffierP. SmouseJ. Quattro1992Analysis of Molecular Variance Inferred From Metric Distances Among DNA Haplotypes: Application to Human Mitochondrial DNA Restriction Data.Genetics131479491
  103. 103. Nei M (1987) Molecular evolutionary genetics. New York: Columbia Univ. Press. M. Nei1987Molecular evolutionary geneticsNew YorkColumbia Univ. Press
  104. 104. Wright S (1951) The genetical structure of populations. Ann Eugen 15: 323–354.S. Wright1951The genetical structure of populations.Ann Eugen15323354
  105. 105. Excoffier L, Laval G, Schneider S (2005) Arlequin ver. 3.0: An integrated software package for population genetics data analysis. Evolutionary Bioinformatics Online 1: 47–50.L. ExcoffierG. LavalS. Schneider2005Arlequin ver. 3.0: An integrated software package for population genetics data analysis.Evolutionary Bioinformatics Online14750
  106. 106. Reynolds J, Weir B, Cockerham C (1983) Estimation for the coancestry coefficient: basis for a short-term genetic distance. Genetics 105: 767–779.J. ReynoldsB. WeirC. Cockerham1983Estimation for the coancestry coefficient: basis for a short-term genetic distance.Genetics105767779
  107. 107. Ray N (2002) Geodist. N. Ray2002Geodist.Laboratory of Genetics and Biometry, University of Geneva. Laboratory of Genetics and Biometry, University of Geneva.
  108. 108. Smouse P, Long J, Sokal R (1986) Multiple Regression and Correlation Extensions of the Mantel Test of Matrix Correspondence. Syst Zool 35: 627–632.P. SmouseJ. LongR. Sokal1986Multiple Regression and Correlation Extensions of the Mantel Test of Matrix Correspondence.Syst Zool35627632
  109. 109. Rohlf F (2000) NTSYSpc: Numerical taxonomy and multivariate analysis system. Applied Biostatistics, Inc. F. Rohlf2000NTSYSpc: Numerical taxonomy and multivariate analysis systemApplied Biostatistics, Inc