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A genomic perspective on HLA evolution

MPG-Autoren
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Bitarello,  Bárbara D.
Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

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Meyer_A-genomic_ImGen_2018.pdf
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Zitation

Meyer, D., Aguiar, V. R. C., Bitarello, B. D., Brandt, D. Y. C., & Nunes, K. (2018). A genomic perspective on HLA evolution. Immunogenetics, 70(1), 5-27. doi:10.1007/s00251-017-1017-3.


Zitierlink: http://hdl.handle.net/21.11116/0000-0000-25D8-3
Zusammenfassung
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.