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哮喘和过敏性疾病的遗传学。

Genetics of Asthma and Allergic Diseases.

机构信息

National Heart and Lung Institute, Imperial College London, London, UK.

University of Manchester, Manchester, UK.

出版信息

Handb Exp Pharmacol. 2022;268:313-329. doi: 10.1007/164_2021_484.

Abstract

Asthma genes have been identified through a range of approaches, from candidate gene association studies and family-based genome-wide linkage analyses to genome-wide association studies (GWAS). The first GWAS of asthma, reported in 2007, identified multiple markers on chromosome 17q21 as associates of the childhood-onset asthma. This remains the best replicated asthma locus to date. However, notwithstanding undeniable successes, genetic studies have produced relatively heterogeneous results with limited replication, and despite considerable promise, genetics of asthma and allergy has, so far, had limited impact on patient care, our understanding of disease mechanisms, and development of novel therapeutic targets. The paucity of precise replication in genetic studies of asthma is partly explained by the existence of numerous gene-environment interactions. Another important issue which is often overlooked is that of time of the assessment of the primary outcome(s) and the relevant environmental exposures. Most large GWASs use the broadest possible definition of asthma to increase the sample size, but the unwanted consequence of this is increased phenotypic heterogeneity, which dilutes effect sizes. One way of addressing this is to precisely define disease subtypes (e.g. by applying novel mathematical approaches to rich phenotypic data) and use these latent subtypes in genetic studies.

摘要

哮喘基因已经通过一系列方法被鉴定出来,包括候选基因关联研究、基于家族的全基因组连锁分析和全基因组关联研究(GWAS)。2007 年报道的第一个哮喘全基因组关联研究鉴定出了染色体 17q21 上的多个标记与儿童期发病的哮喘有关。这仍然是迄今为止复制效果最好的哮喘基因座。然而,尽管遗传研究取得了不可否认的成功,但结果相对多样化,复制有限,尽管有很大的前景,但哮喘和过敏的遗传学迄今对患者护理、我们对疾病机制的理解和新治疗靶点的开发的影响有限。哮喘遗传研究中精确复制的缺乏部分归因于众多基因-环境相互作用的存在。另一个经常被忽视的重要问题是评估主要结局和相关环境暴露的时间。大多数大型全基因组关联研究使用最广泛的哮喘定义来增加样本量,但这样做的不良后果是增加了表型异质性,从而稀释了效应大小。解决这个问题的一种方法是精确定义疾病亚型(例如,通过将新颖的数学方法应用于丰富的表型数据),并在遗传研究中使用这些潜在的亚型。

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