Our understanding of human evolution has improved rapidly over recent decades, facilitated by large‐scale cataloguing of genomic variability amongst both modern and archaic humans. It seems clear that the evolution of the ancestors of chimpanzees and hominins separated 7–9 million years ago with some migration out of Africa by the earlier hominins; Homo sapiens slowly emerged as climate change resulted in drier, less forested African conditions. The African populations expanded and evolved in many different conditions with slow mutation and selection rates in the human genome, but with much more rapid mutation occurring in mitochondrial DNA. We now have evidence stretching back 300 000 years of humans in their current form, but there are clearly four very different large African language groups that correlate with population DNA differences. Then, about 50 000–100 000 years ago a small subset of modern humans also migrated out of Africa resulting in a persistent signature of more limited genetic diversity amongst non‐African populations. Hybridization with archaic hominins occurred around this time such that all non‐African modern humans possess some Neanderthal ancestry and Melanesian populations additionally possess some Denisovan ancestry. Human populations both within and outside Africa also adapted to diverse aspects of their local environment including altitude, climate, UV exposure, diet and pathogens, in some cases leaving clear signatures of patterns of genetic variation. Notable examples include haemoglobin changes conferring resistance to malaria, other immune changes and the skin adaptations favouring the synthesis of vitamin D. As humans migrated across Eurasia, further major mitochondrial changes occurred with some interbreeding with ancient hominins and the development of alcohol intolerance. More recently, an ability to retain lactase persistence into adulthood has evolved rapidly under the environmental stimulus of pastoralism with the ability to husband lactating ruminants. Increased amylase copy numbers seem to relate to the availability of starchy foods, whereas the capacity to desaturase and elongate monounsaturated fatty acids in different societies seems to be influenced by whether there is a lack of supply of readily available dietary sources of long‐chain polyunsaturated fatty acids. The process of human evolution includes genetic drift and adaptation to local environments, in part through changes in mitochondrial and nuclear DNA. These genetic changes may underlie susceptibilities to some modern human pathologies including folate‐responsive neural tube defects, diabetes, other age‐related pathologies and mental health disorders.

Human leucocyte antigens (HLA) typing has been a challenge due to extreme polymorphism of the HLA genes and limitations of the current technologies and protocols used for their characterization. Recently, next-generation sequencing techniques have been shown to be a well-suited technology for the complete characterization of the HLA genes. However, a comprehensive assessment of the different platforms for HLA typing, describing the limitations and advantages of each of them, has not been presented. We have compared the Ion Torrent Personal Genome Machine (PGM) and Illumina MiSeq, currently the two most frequently used platforms for diagnostic applications, for a number of metrics including total output, quality score per position across the reads and error rates after alignment which can all affect the accuracy of HLA genotyping. For this purpose, we have used one homozygous and three heterozygous well-characterized samples, at HLA-A, HLA-B, HLA-C, HLA-DRB1 and HLA-DQB1. The total output of bases produced by the MiSeq was higher, and they have higher quality scores and a lower overall error rate than the PGM. The MiSeq also has a higher fidelity when sequencing through homopolymer regions up to 9 bp in length. The need to set phase between distant polymorphic sites was more readily achieved with MiSeq using paired-end sequencing of fragments that are longer than those obtained with PGM. Additionally, we have assessed the workflows of the different platforms for complexity of sample preparation, sequencer operation and turnaround time. The effects of data quality and quantity can impact the genotyping results; having an adequate amount of good quality data to analyse will be imperative for confident HLA genotyping. The overall turnaround time can be very comparable between the two platforms; however, the complexity of sample preparation is higher with PGM, while the actual sequencing time is longer with MiSeq.

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