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当前和未来用于囊性纤维化诊断的分子方法。

Current and future molecular approaches in the diagnosis of cystic fibrosis.

机构信息

a Laboratoire de Génétique Moléculaire , Centre Hospitalier Universitaire de Montpellier , Montpellier , France.

b EA 7402 , Université de Montpellier , Montpellier , France.

出版信息

Expert Rev Respir Med. 2018 May;12(5):415-426. doi: 10.1080/17476348.2018.1457438. Epub 2018 Apr 19.

Abstract

Cystic Fibrosis is among the first diseases to have general population genetic screening tests and one of the most common indications of prenatal and preimplantation genetic diagnosis for single gene disorders. During the past twenty years, thanks to the evolution of diagnostic techniques, our knowledge of CFTR genetics and pathophysiological mechanisms involved in cystic fibrosis has significantly improved. Areas covered: Sanger sequencing and quantitative methods greatly contributed to the identification of more than 2,000 sequence variations reported worldwide in the CFTR gene. We are now entering a new technological age with the generalization of high throughput approaches such as Next Generation Sequencing and Droplet Digital PCR technologies in diagnostics laboratories. These powerful technologies open up new perspectives for scanning the entire CFTR locus, exploring modifier factors that possibly influence the clinical evolution of patients, and for preimplantation and prenatal diagnosis. Expert commentary: Such breakthroughs would, however, require powerful bioinformatics tools and relevant functional tests of variants for analysis and interpretation of the resulting data. Ultimately, an optimal use of all those resources may improve patient care and therapeutic decision-making.

摘要

囊性纤维化是首批进行人群遗传筛查测试的疾病之一,也是单基因疾病产前和胚胎植入前遗传诊断最常见的指征之一。在过去的二十年中,由于诊断技术的发展,我们对囊性纤维化相关的 CFTR 基因遗传和病理生理学机制的认识有了显著的提高。涵盖领域:桑格测序和定量方法极大地促进了在 CFTR 基因中发现了全世界报道的 2000 多种序列变异。现在我们进入了一个新的技术时代,高通量方法(如下一代测序和液滴数字 PCR 技术)在诊断实验室中得到了广泛应用。这些强大的技术为扫描整个 CFTR 基因座、探索可能影响患者临床演变的修饰因子,以及进行胚胎植入前和产前诊断,开辟了新的前景。专家评论:然而,这些突破需要强大的生物信息学工具和相关的变异功能测试,以分析和解释由此产生的数据。最终,优化利用所有这些资源可能会改善患者的治疗效果和治疗决策。

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