The HLA-G molecule presents immunomodulatory properties that might inhibit immune responses when interacting with specific Natural Killer and T cell receptors, such as KIR2DL4, ILT2 and ILT4. Thus, HLA-G might influence the outcome of situations in which fine immune system modulation is required, such as autoimmune diseases, transplants, cancer and pregnancy. The majority of the studies regarding the HLA-G gene variability so far was restricted to a specific gene segment (i.e., promoter, coding or 3' untranslated region), and was performed by using Sanger sequencing and probabilistic models to infer haplotypes. Here we propose a massively parallel sequencing (NGS) with a bioinformatics strategy to evaluate the entire HLA-G regulatory and coding segments, with haplotypes inferred relying more on the straightforward haplotyping capabilities of NGS, and less on probabilistic models. Then, HLA-G variability was surveyed in two admixed population samples of distinct geographical regions and demographic backgrounds, Cyprus and Brazil. Most haplotypes (promoters, coding, 3'UTR and extended ones) were detected both in Brazil and Cyprus and were identical to the ones already described by probabilistic models, indicating that these haplotypes are quite old and may be present worldwide.

A challenging task when more than one HLA gene is evaluated together by second-generation sequencing is to achieve a reliable read mapping. The polymorphic and repetitive nature of HLA genes might bias the read mapping process, usually underestimating variability at very polymorphic segments, or overestimating variability at some segments. To overcome this issue we developed hla-mapper, which takes into account HLA sequences derived from the IPD-IMGT/HLA database and unpublished HLA sequences to apply a scoring system. This comprehends the evaluation of each read pair, addressing them to the most likely HLA gene they were derived from. Hla-mapper provides a reliable map of HLA sequences, allowing accurate downstream analysis such as variant calling, haplotype inference, and allele typing. Moreover, hla-mapper supports whole genome, exome, and targeted sequencing data. To assess the software performance in comparison with traditional mapping algorithms, we used three different simulated datasets to compare the results obtained with hla-mapper, BWA MEM, and Bowtie2. Overall, hla-mapper presented a superior performance, mainly for the classical HLA class I genes, minimizing wrong mapping and cross-mapping that are typically observed when using BWA MEM or Bowtie2 with a single reference genome.

Human leukocyte antigen‐C (HLA‐C) is a classical HLA class I molecule that binds and presents peptides to cytotoxic T lymphocytes in the cell surface. HLA‐C has a dual function because it also interacts with Killer‐cell immunoglobulin‐like receptors (KIR) receptors expressed in natural killer and T cells, modulating their activity. The structure and diversity of the HLA‐C regulatory regions, as well as the relationship among variants along the HLA‐C locus, are poorly addressed, and few population‐based studies explored the HLA‐C variability in the entire gene in different population samples. Here we present a molecular and bioinformatics method to evaluate the entire HLA‐C diversity, including regulatory sequences. Then, we applied this method to survey the HLA‐C diversity in two population samples with different demographic histories, one highly admixed from Brazil with major European contribution, and one from Benin with major African contribution. The HLA‐C promoter and 3′UTR were very polymorphic with the presence of few, but highly divergent haplotypes. These segments also present conserved sequences that are shared among different primate species. Nucleotide diversity was higher in other segments rather than exons 2 and 3, particularly around exon 5 and the second half of the 3′UTR region. We detected evidence of balancing selection on the entire HLA‐C locus and positive selection in the HLA‐C leader peptide, for both populations. HLA‐C motifs previously associated with KIR interaction and expression regulation are similar between both populations. Each allele group is associated with specific regulatory sequences, reflecting the high linkage disequilibrium along the entire HLA‐C locus in both populations.