HLA supertype variation across populations: new insights into the role of natural selection in the evolution of HLA-A and HLA-B polymorphisms

Abstract

Supertypes are groups of human leukocyte antigen (HLA) alleles which bind overlapping sets of peptides due to sharing specific residues at the anchor positions-the B and F pockets-of the peptide-binding region (PBR). HLA alleles within the same supertype are expected to be functionally similar, while those from different supertypes are expected to be functionally distinct, presenting different sets of peptides. In this study, we applied the supertype classification to the HLA-A and HLA-B data of 55 worldwide populations in order to investigate the effect of natural selection on supertype rather than allelic variation at these loci. We compared the nucleotide diversity of the B and F pockets with that of the other PBR regions through a resampling procedure and compared the patterns of within-population heterozygosity (He) and between-population differentiation (G ST) observed when using the supertype definition to those estimated when using randomized groups of alleles. At HLA-A, low levels of variation are observed at B and F pockets and randomized He and G ST do not differ from the observed data. By contrast, HLA-B concentrates most of the differences between supertypes, the B pocket showing a particularly high level of variation. Moreover, at HLA-B, the reassignment of alleles into random groups does not reproduce the patterns of population differentiation observed with supertypes. We thus conclude that differently from HLA-A, for which supertype and allelic variation show similar patterns of nucleotide diversity within and between populations, HLA-B has likely evolved through specific adaptations of its B pocket to local pathogens.

Keywords: Adaptation; HLA; Human populations; Natural selection; Pathogens; Supertypes.

Fig. 1

Supertype variation, a boxes represent the frequency distributions of the four HLA-A and the five HLA-B supertypes and the “non-classified alleles” NCA and NCB, respectively; b each box represents the distribution of the number of distinct alleles of each supertype per population; and c the dark gray section of the bars represents the number of populations showing only one allele for the referred supertype (referred to as “monomorphic populations”). The light gray section of the bars represents the number of populations where the referred supertype was not detected

Fig. 2

HLA-A supertype frequencies. Heat map summarizing the frequencies of the four HLA-A supertypes and the non-classified alleles (NCAs). Population names are shown on the right

Fig. 3

HLA-B supertype frequencies. Heat map summarizing the frequencies of the five HLA-B supertypes and the non-classified alleles (NCBs). Population names are shown on the right

Fig. 4

Plots of G _ST values between populations based on allele (Y axis) and supertype (X axis) frequencies. The correlation (Rxy) and significance were obtained using a Mantel test. Complete dataset, all populations and reduced dataset, excluding Pacific, Australian, Taiwanese, and Native American populations

Fig. 5

Total nucleotide diversity (π _total) at HLA-A and HLA-B PBR pockets. Each box represents the distribution of the total nucleotide diversity per pocket for the populations of the complete dataset

Fig. 6

Nucleotide diversity between supertypes (π _st) at HLA-A and HLA-B PBR pockets. Each box represents the distribution of the nucleotide diversity between supertypes per pocket for the populations of the complete dataset

Fig. 7

P value distributions obtained through simulations for the expected heterozygosity (He). The p value is defined as the proportion of simulated datasets with He larger than the observed He. The results obtained with the complete (top) and reduced (bottom) dataset are shown

Fig. 8

Simulation results for G _ST. The red line represents the average observed G _ST. We calculated the average G _ST value for each simulated step and then determined the significance as the proportion of simulated values smaller than the observed one. The results with the complete (top) and reduced (bottom) datasets are shown