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将非编码遗传关联转化为对免疫介导疾病的更好理解。

Translating non-coding genetic associations into a better understanding of immune-mediated disease.

机构信息

Genetic Mechanisms of Disease Laboratory, The Francis Crick Institute, London NW1 1AT, UK.

Department of Immunology and Inflammation, Imperial College London, London W12 0NN, UK.

出版信息

Dis Model Mech. 2023 Mar 1;16(3). doi: 10.1242/dmm.049790. Epub 2023 Mar 7.

Abstract

Genome-wide association studies have identified hundreds of genetic loci that are associated with immune-mediated diseases. Most disease-associated variants are non-coding, and a large proportion of these variants lie within enhancers. As a result, there is a pressing need to understand how common genetic variation might affect enhancer function and thereby contribute to immune-mediated (and other) diseases. In this Review, we first describe statistical and experimental methods to identify causal genetic variants that modulate gene expression, including statistical fine-mapping and massively parallel reporter assays. We then discuss approaches to characterise the mechanisms by which these variants modulate immune function, such as clustered regularly interspaced short palindromic repeats (CRISPR)-based screens. We highlight examples of studies that, by elucidating the effects of disease variants within enhancers, have provided important insights into immune function and uncovered key pathways of disease.

摘要

全基因组关联研究已经确定了数百个与免疫介导疾病相关的遗传位点。大多数与疾病相关的变异是非编码的,其中很大一部分变异位于增强子内。因此,迫切需要了解常见的遗传变异如何影响增强子的功能,从而导致免疫介导的(和其他)疾病。在这篇综述中,我们首先描述了识别调节基因表达的因果遗传变异的统计和实验方法,包括统计精细映射和大规模平行报告基因检测。然后,我们讨论了描述这些变异调节免疫功能的机制的方法,例如基于成簇规律间隔短回文重复序列 (CRISPR) 的筛选。我们强调了一些研究的例子,这些研究通过阐明增强子内疾病变异的影响,提供了对免疫功能的重要见解,并揭示了疾病的关键途径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d822/10040244/ae340a57d32f/dmm-16-049790-g1.jpg

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