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南非人类群体基因组学和疾病定位的前瞻性途径。

Prospective avenues for human population genomics and disease mapping in southern Africa.

机构信息

DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.

出版信息

Mol Genet Genomics. 2020 Sep;295(5):1079-1089. doi: 10.1007/s00438-020-01684-8. Epub 2020 May 21.

Abstract

Population substructure within human populations is globally evident and a well-known confounding factor in many genetic studies. In contrast, admixture mapping exploits population stratification to detect genotype-phenotype correlations in admixed populations. Southern Africa has untapped potential for disease mapping of ancestry-specific disease risk alleles due to the distinct genetic diversity in its populations compared to other populations worldwide. This diversity contributes to a number of phenotypes, including ancestry-specific disease risk and response to pathogens. Although the 1000 Genomes Project significantly improved our understanding of genetic variation globally, southern African populations are still severely underrepresented in biomedical and human genetic studies due to insufficient large-scale publicly available data. In addition to a lack of genetic data in public repositories, existing software, algorithms and resources used for imputation and phasing of genotypic data (amongst others) are largely ineffective for populations with a complex genetic architecture such as that seen in southern Africa. This review article, therefore, aims to summarise the current limitations of conducting genetic studies on populations with a complex genetic architecture to identify potential areas for further research and development.

摘要

人群中的群体结构在全球范围内是显而易见的,是许多遗传研究中的一个众所周知的混杂因素。相比之下,混合映射利用群体分层来检测混合人群中的基因型-表型相关性。由于与世界其他人群相比,南非人群具有独特的遗传多样性,因此在针对特定祖先疾病风险等位基因的疾病图谱绘制方面具有巨大潜力。这种多样性导致了许多表型,包括特定祖先的疾病风险和对病原体的反应。尽管 1000 基因组计划极大地提高了我们对全球遗传变异的理解,但由于缺乏大规模的公开可用数据,南非人群在生物医学和人类遗传研究中仍然严重代表性不足。除了公共存储库中缺乏遗传数据外,用于基因型数据的推断和相位的现有软件、算法和资源(除其他外)在复杂遗传结构的人群中(如在南非看到的)效果不佳。因此,本文旨在总结在具有复杂遗传结构的人群中进行遗传研究的当前局限性,以确定进一步研究和开发的潜在领域。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7515/7240165/cb3072382f70/438_2020_1684_Fig1_HTML.jpg

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