Human leukocyte antigen G (HLA-G) belongs to the family of non-classical HLA class I genes, located within the major histocompatibility complex (MHC). HLA-G has been the target of most recent research regarding the function of class I non-classical genes. The main features that distinguish HLA-G from classical class I genes are (a) limited protein variability, (b) alternative splicing generating several membrane bound and soluble isoforms, (c) short cytoplasmic tail, (d) modulation of immune response (immune tolerance), and (e) restricted expression to certain tissues. In the present work, we describe the HLA-G gene structure and address the HLA-G variability and haplotype diversity among several populations around the world, considering each of its major segments [promoter, coding, and 3′ untranslated region (UTR)]. For this purpose, we developed a pipeline to reevaluate the 1000Genomes data and recover miscalled or missing genotypes and haplotypes. It became clear that the overall structure of the HLA-G molecule has been maintained during the evolutionary process and that most of the variation sites found in the HLA-G coding region are either coding synonymous or intronic mutations. In addition, only a few frequent and divergent extended haplotypes are found when the promoter, coding, and 3′UTRs are evaluated together. The divergence is particularly evident for the regulatory regions. The population comparisons confirmed that most of the HLA-G variability has originated before human dispersion from Africa and that the allele and haplotype frequencies have probably been shaped by strong selective pressures.

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.