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Several decades of research have convincingly shown that classical human leukocyte antigen (HLA) loci bear signatures of natural selection. Despite this conclusion, many questions remain regarding the type of selective regime acting on these loci, the time frame at which selection acts, and the functional connections between genetic variability and natural selection. In this review, we argue that genomic datasets, in particular those generated by next-generation sequencing (NGS) at the population scale, are transforming our understanding of HLA evolution. We show that genomewide data can be used to perform robust and powerful tests for selection, capable of identifying both positive and balancing selection at HLA genes. Importantly, these tests have shown that natural selection can be identified at both recent and ancient timescales. We discuss how findings from genomewide association studies impact the evolutionary study of HLA genes, and how genomic data can be used to survey adaptive change involving interaction at multiple loci. We discuss the methodological developments which are necessary to correctly interpret genomic analyses involving the HLA region. These developments include adapting the NGS analysis framework so as to deal with the highly polymorphic HLA data, as well as developing tools and theory to search for signatures of selection, quantify differentiation, and measure admixture within the HLA region. Finally, we show that high throughput analysis of molecular phenotypes for HLA genes—namely transcription levels—is now a feasible approach and can add another dimension to the study of genetic variation.

The presence of TP53 gene mutations in breast cancer has been associated with worse prognosis. These mutations interfere with the ability of the p53 protein, a transcription factor, to regulate the expression of target genes. Unlike the wild-type protein, which is rapidly degraded in cells, mutated forms have increased half-life and accumulate in tumor cells. Immunohistochemistry (IHC) is widely used in Brazil in the determination of breast cancer patients' prognosis. However, this technique is not able to detect many altered forms of the p53 protein (false-negative results) and readily detects the accumulation of wild-type p53 (false-positive results) that is associated with non-tumoral processes. For these reasons, we have set out to compare the efficiency of IHC with a molecular technique that detects gene variations at the DNA level in the evaluation of Brazilian patients with sporadic breast cancer. We have used denaturing gradient gel electrophoresis (DGGE) to study the TP53 status in 45 tumors, finding 26 allelic variants, most of them located in exon 4. Comparing the two techniques, IHC showed a false-negative rate of 64% and a false-positive rate of 50%. These results confirm the inability of IHC to correctly detect TP53 status, reason because it should not be routinely used to establish prognosis of breast cancer patients in Brazilian Pathology Laboratories. We recommend the utilization of a screening method, such as DGGE, followed by sequencing of altered exonic fragments to correctly detect TP53 gene variants and establish the prognosis of breast cancer patients.