多样性在精准医学中的重要性:跨祖先群体遗传关联的普遍性,有助于更好地识别疾病易感性变异。

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Lauren A Cruz, Jessica N Cooke Bailey, Dana C Crawford
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引用次数: 0

摘要

全基因组关联研究(GWAS)彻底改变了我们对常见遗传变异及其对常见人类疾病和性状的影响的理解。GWAS于2000年代中期开发并采用,导致可搜索的基因型-表型目录和全基因组数据集,可用于进一步的数据挖掘和分析,最终开发转化应用。GWAS革命迅速而具体,几乎只包括欧洲人后裔,而忽视了世界上大多数的遗传多样性。在这篇叙述性的综述中,我们叙述了早期建立基因型-表型目录的GWAS景观,现在普遍认为该目录不足以完全理解复杂的人类遗传学。然后,我们描述了扩大基因型-表型目录所采取的方法,包括研究群体、合作联盟和旨在推广并最终发现非欧洲血统人群全基因组关联的研究设计方法。随着预算友好型全基因组测序的出现,在多样化基因组发现的努力中建立的合作和数据资源无疑为遗传关联研究的下一章提供了基础。
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 Diversity in Precision Medicine: Generalizability of Genetic Associations Across Ancestry Groups Toward Better Identification of Disease Susceptibility Variants.

Genome-wide association studies (GWAS) revolutionized our understanding of common genetic variation and its impact on common human disease and traits. Developed and adopted in the mid-2000s, GWAS led to searchable genotype-phenotype catalogs and genome-wide datasets available for further data mining and analysis for the eventual development of translational applications. The GWAS revolution was swift and specific, including almost exclusively populations of European descent, to the neglect of the majority of the world's genetic diversity. In this narrative review, we recount the GWAS landscape of the early years that established a genotype-phenotype catalog that is now universally understood to be inadequate for a complete understanding of complex human genetics. We then describe approaches taken to augment the genotype-phenotype catalog, including the study populations, collaborative consortia, and study design approaches aimed to generalize and then ultimately discover genome-wide associations in non-European descent populations. The collaborations and data resources established in the efforts to diversify genomic findings undoubtedly provide the foundations of the next chapters of genetic association studies with the advent of budget-friendly whole-genome sequencing.

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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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