在大型人类基因研究中纳入非欧洲人群对加强精准医疗的重要性。

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Dan Ju, Daniel Hui, Dorothy A Hammond, Ambroise Wonkam, Sarah A Tishkoff
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引用次数: 0

摘要

基因组医学的目标之一是揭示个体的遗传疾病风险,这通常需要将基因型与表型联系起来的数据,就像全基因组关联研究(GWAS)所做的那样。虽然采用多基因风险评分(PRS)等预测工具可能有临床前景,但目前的情况是,非欧洲血统的个体可能无法从基因组医学中获益,因为他们在大规模遗传学研究中的代表性不足。在此,我们将讨论为什么这种不公平会给基因组医学带来问题,以及 PRS 在不同人群中可移植性低的原因。我们还调查了已发表的 GWAS 中的祖先代表性,并研究了对 GWAS 参与者祖先多样性的估计可能存在的偏差。非洲是人类基因组学研究中代表性最弱的地区之一,我们强调了在非洲扩大基因研究的重要性,并讨论了伦理、资源和技术问题,以促进基因组医学的公平发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Importance of Including Non-European Populations in Large Human Genetic Studies to Enhance Precision Medicine.

One goal of genomic medicine is to uncover an individual's genetic risk for disease, which generally requires data connecting genotype to phenotype, as done in genome-wide association studies (GWAS). While there may be clinical promise to employing prediction tools such as polygenic risk scores (PRS), it currently stands that individuals of non-European ancestry may not reap the benefits of genomic medicine because of underrepresentation in large-scale genetics studies. Here, we discuss why this inequity poses a problem for genomic medicine and the reasons for the low transferability of PRS across populations. We also survey the ancestry representation of published GWAS and investigate how estimates of ancestry diversity in GWASparticipants might be biased. We highlight the importance of expanding genetic research in Africa, one of the most underrepresented regions in human genomics research, and discuss issues of ethics, resources, and technology for equitable advancement of genomic medicine.

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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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