A genomic perspective on HLA evolution.

A genomic perspective on HLA evolution.
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DOI:
10.1007/s00251-017-1017-3
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发表时间:
2018-01
期刊:
影响因子:
3.2
通讯作者:
Nunes K
Nunes K
中科院分区:
医学4区
文献类型:
--
作者:
Meyer D;C Aguiar VR;Bitarello BD;C Brandt DY;Nunes K

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几十年的研究已经令人信服地表明,经典的人类白细胞抗原(HLA)基因座具有自然选择的特征。尽管有这样的结论,许多问题仍然存在的类型的选择制度对这些基因座,选择的时间框架,以及遗传变异和自然选择之间的功能联系。在这篇综述中,我们认为,基因组数据集,特别是下一代测序(NGS)在人口规模产生的数据集,正在改变我们对HLA进化的理解。我们表明,全基因组数据可用于执行强大的选择测试,能够识别HLA基因的阳性和平衡选择。重要的是,这些测试表明,自然选择可以在近代和古代的时间尺度上被识别出来。我们讨论了全基因组关联研究的结果如何影响HLA基因的进化研究,以及基因组数据如何用于调查涉及多个位点相互作用的适应性变化。我们讨论了正确解释涉及HLA区域的基因组分析所必需的方法学发展。这些发展包括调整NGS分析框架,以便处理高度多态性的HLA数据,以及开发工具和理论来搜索选择的签名,量化分化,并测量HLA区域内的混合物。最后,我们表明,HLA基因的分子表型的高通量分析,即转录水平,现在是一种可行的方法,可以增加另一个层面的遗传变异的研究。
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.
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