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哮喘的全基因组表达数量性状基因座分析。

Genome-wide expression quantitative trait loci analysis in asthma.

机构信息

Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Laval University, Québec, Canada.

出版信息

Curr Opin Allergy Clin Immunol. 2013 Oct;13(5):487-94. doi: 10.1097/ACI.0b013e328364e951.

Abstract

PURPOSE OF REVIEW

Expression quantitative trait loci (eQTL) mapping studies are the next most important step in genomics to identify susceptibility genes and molecular pathways involved in human diseases following the completion of genome-wide association studies (GWAS). This article reviews the emerging concepts in genetics of gene expression and the empirical value of eQTL mapping to refine GWAS asthma susceptibility loci.

RECENT FINDINGS

eQTL mapping studies were paramount to reveal the cis and trans control of gene expression, the cell type and tissue specificity of eQTLs, and the pleiotropic nature of eQTL single nucleotide polymorphisms. A small number of eQTL studies were recently performed in tissues and cell types that are relevant for asthma and are used to interpret the biology underpinning GWAS loci including the most robust asthma susceptibility locus on 17q21.

SUMMARY

The full potential of eQTL mapping studies is just starting to be revealed. Imminent progress is expected owing to the accelerating advances in sequencing technologies to map genetic variants and transcriptomes as well as the development of bioinformatics and computational algorithms to exploit integrative genomic approaches. A short-term challenge in the field of asthma is the creation of well powered eQTL datasets testing gene expression and other molecular phenotypes in disease-relevant tissues.

摘要

目的综述

表达数量性状基因座(eQTL)图谱研究是继全基因组关联研究(GWAS)之后,在基因组学中确定人类疾病易感性基因和分子途径的下一步重要步骤。本文综述了基因表达遗传学的新兴概念以及 eQTL 图谱在细化 GWAS 哮喘易感性基因座方面的经验价值。

最近的发现

eQTL 图谱研究对于揭示基因表达的顺式和反式调控、eQTL 的细胞类型和组织特异性以及 eQTL 单核苷酸多态性的多效性至关重要。最近在与哮喘相关的组织和细胞类型中进行了少量的 eQTL 研究,用于解释 GWAS 基因座的生物学基础,包括 17q21 上最具稳健性的哮喘易感性基因座。

总结

eQTL 图谱研究的全部潜力才刚刚开始显现。由于测序技术在绘制遗传变异和转录组方面的加速进步,以及生物信息学和计算算法在整合基因组方法方面的发展,预计即将取得进展。哮喘研究领域的一个短期挑战是创建功能强大的 eQTL 数据集,以测试疾病相关组织中的基因表达和其他分子表型。

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