Bope Christian Domilongo, Chimusa Emile R, Nembaware Victoria, Mazandu Gaston K, de Vries Jantina, Wonkam Ambroise
Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.
Departments of Mathematics and Computer Sciences, Faculty of Sciences, University of Kinshasa, Kinshasa, Democratic Republic of Congo.
Front Genet. 2019 Jun 25;10:601. doi: 10.3389/fgene.2019.00601. eCollection 2019.
Genomic medicine is set to drastically improve clinical care globally due to high throughput technologies which enable speedy detection and analysis of clinically relevant mutations. However, the variability in the prediction methods and categorization of functionally relevant genetic variants can pose specific challenges in some populations. mutation prediction tools could lead to high rates of false positive/negative results, particularly in African genomes that harbor the highest genetic diversity and that are disproportionately underrepresented in public databases and reference panels. These issues are particularly relevant with the recent increase in initiatives, such as the Human Heredity and Health (H3Africa), that are generating huge amounts of genomic sequence data in the absence of policies to guide genomic researchers to return results of variants in so-called actionable genes to research participants. This report (i) provides an inventory of publicly available Whole Exome/Genome data from Africa which could help improve reference panels and explore the frequency of pathogenic variants in actionable genes and related challenges, (ii) reviews available prediction mutation tools and the criteria for categorization of pathogenicity of novel variants, and (iii) proposes recommendations for analyzing pathogenic variants in African genomes for their use in research and clinical practice. In conclusion, this work proposes criteria to define mutation pathogenicity and actionability in human genetic research and clinical practice in Africa and recommends setting up an African expert panel to oversee the proposed criteria.
由于高通量技术能够快速检测和分析临床相关突变，基因组医学有望在全球范围内大幅改善临床护理。然而，预测方法的变异性以及功能相关基因变异的分类在某些人群中可能带来特定挑战。突变预测工具可能导致高比例的假阳性/阴性结果，尤其是在非洲基因组中，非洲基因组具有最高的遗传多样性，但在公共数据库和参考面板中的代表性却严重不足。随着近期一些倡议（如人类遗传与健康（H3Africa））的增加，这些问题变得尤为突出，这些倡议在没有政策指导基因组研究人员将所谓可操作基因中的变异结果反馈给研究参与者的情况下，正在生成大量基因组序列数据。本报告（i）提供了一份来自非洲的公开可用全外显子组/基因组数据清单，这有助于改进参考面板，并探索可操作基因中致病变异的频率及相关挑战，（ii）回顾了可用的预测突变工具以及新变异致病性分类标准，（iii）提出了分析非洲基因组中致病变异以用于研究和临床实践的建议。总之，这项工作提出了在非洲人类遗传研究和临床实践中定义突变致病性和可操作性的标准，并建议设立一个非洲专家小组来监督所提议的标准。
