Functional Characterization of Genetic Variant Effects on Expression.

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Annual Review of Biomedical Data Science Pub Date : 2022-04-28 DOI:10.1146/annurev-biodatasci-122120-010010

Elise D. Flynn, T. Lappalainen

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引用次数: 5

Abstract

Thousands of common genetic variants in the human population have been associated with disease risk and phenotypic variation by genome-wide association studies (GWAS). However, the majority of GWAS variants fall into noncoding regions of the genome, complicating our understanding of their regulatory functions, and few molecular mechanisms of GWAS variant effects have been clearly elucidated. Here, we set out to review genetic variant effects, focusing on expression quantitative trait loci (eQTLs), including their utility in interpreting GWAS variant mechanisms. We discuss the interrelated challenges and opportunities for eQTL analysis, covering determining causal variants, elucidating molecular mechanisms of action, and understanding context variability. Addressing these questions can enable better functional characterization of disease-associated loci and provide insights into fundamental biological questions of the noncoding genetic regulatory code and its control of gene expression. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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基因变异对表达影响的功能表征。

通过全基因组关联研究（GWAS），人类中数千种常见的遗传变异与疾病风险和表型变异有关。然而，大多数GWAS变体属于基因组的非编码区，这使我们对其调控功能的理解变得复杂，而且很少有GWAS变体效应的分子机制得到明确阐明。在这里，我们开始综述遗传变异效应，重点关注表达数量性状基因座（eQTL），包括它们在解释GWAS变异机制中的作用。我们讨论了eQTL分析的相关挑战和机遇，包括确定因果变异、阐明分子作用机制和理解上下文变异。解决这些问题可以更好地表征疾病相关基因座的功能，并深入了解非编码遗传调控密码及其对基因表达的控制的基本生物学问题。《生物医学数据科学年度评论》第5卷预计最终在线出版日期为2022年8月。请参阅http://www.annualreviews.org/page/journal/pubdates用于修订估算。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Annual Review of Biomedical Data Science

CiteScore

11.10

自引率

1.70%

发文量

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.