Although the KIR gene content polymorphism has been studied worldwide, only a few isolated or Amerindian populations have been analyzed. This extremely diverse gene family codifies receptors that are expressed mainly in NK cells and bind HLA class I molecules. KIR-HLA combinations have been associated to several diseases and population studies are important to comprehend their evolution and their role in immunity. Here we analyzed, by PCR-SSP (specific sequencing priming), 327 individuals from four isolated groups of two of the most important Brazilian Amerindian populations: Kaingang and Guarani. The pattern of KIR diversity among these and other ten Amerindian populations disclosed a wide range of variation for both KIR haplotypes and gene frequencies, indicating that demographic factors, such as bottleneck and founder effects, were the most important evolutionary factors in shaping the KIR polymorphism in these populations.

Methods to impute HLA alleles based on dense single nucleotide polymorphism (SNP) data provide a valuable resource to association studies and evolutionary investigation of the MHC region. The availability of appropriate training sets is critical to the accuracy of HLA imputation, and the inclusion of samples with various ancestries is an important pre-requisite in studies of admixed populations. We assess the accuracy of HLA imputation using 1000 Genomes Project data as a training set, applying it to a highly admixed Brazilian population, the Quilombos from the state of São Paulo. To assess accuracy, we compared imputed and experimentally determined genotypes for 146 samples at 4 HLA classical loci. We found imputation accuracies of 82.9%, 81.8%, 94.8% and 86.6% for HLA-A, -B, -C and -DRB1 respectively (two-field resolution). Accuracies were improved when we included a subset of Quilombo individuals in the training set. We conclude that the 1000 Genomes data is a valuable resource for construction of training sets due to the diversity of ancestries and the potential for a large overlap of SNPs with the target population. We also show that tailoring training sets to features of the target population substantially enhances imputation accuracy.

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