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Genet Res (Camb). 2017 Aug 30;99:e6. doi: 10.1017/S0016672317000040.
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本文引用的文献

1
Whole-Genome Sequencing in Healthy People.健康人群的全基因组测序
Mayo Clin Proc. 2017 Jan;92(1):159-172. doi: 10.1016/j.mayocp.2016.10.019.
2
Analysis of protein-coding genetic variation in 60,706 humans.对60706名人类的蛋白质编码基因变异进行分析。
Nature. 2016 Aug 18;536(7616):285-91. doi: 10.1038/nature19057.
3
Challenges in exome analysis by LifeScope and its alternative computational pipelines.LifeScope及其替代计算流程在全外显子组分析中的挑战。
BMC Res Notes. 2015 Sep 7;8:421. doi: 10.1186/s13104-015-1385-4.
4
ClinVar: public archive of relationships among sequence variation and human phenotype.ClinVar:序列变异与人类表型之间关系的公共档案。
Nucleic Acids Res. 2014 Jan;42(Database issue):D980-5. doi: 10.1093/nar/gkt1113. Epub 2013 Nov 14.
5
An integrated map of genetic variation from 1,092 human genomes.1092 个人类基因组遗传变异的综合图谱。
Nature. 2012 Nov 1;491(7422):56-65. doi: 10.1038/nature11632.
6
Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration.综合基因组浏览器(IGV):高性能基因组学数据可视化和探索。
Brief Bioinform. 2013 Mar;14(2):178-92. doi: 10.1093/bib/bbs017. Epub 2012 Apr 19.
7
Disease gene identification strategies for exome sequencing.外显子组测序的疾病基因鉴定策略。
Eur J Hum Genet. 2012 May;20(5):490-7. doi: 10.1038/ejhg.2011.258. Epub 2012 Jan 18.
8
A framework for variation discovery and genotyping using next-generation DNA sequencing data.利用下一代 DNA 测序数据进行变异发现和基因分型的框架。
Nat Genet. 2011 May;43(5):491-8. doi: 10.1038/ng.806. Epub 2011 Apr 10.
9
Carrier testing for severe childhood recessive diseases by next-generation sequencing.下一代测序技术在严重儿童隐性疾病中的携带者检测
Sci Transl Med. 2011 Jan 12;3(65):65ra4. doi: 10.1126/scitranslmed.3001756.
10
ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data.ANNOVAR:从高通量测序数据中注释遗传变异的功能。
Nucleic Acids Res. 2010 Sep;38(16):e164. doi: 10.1093/nar/gkq603. Epub 2010 Jul 3.

使用下一代测序技术分析健康人群中来自ClinVar数据库的致病变异。

Analysis of pathogenic variants from the ClinVar database in healthy people using next-generation sequencing.

作者信息

Rančelis Tautvydas, Arasimavičius Justas, Ambrozaitytė Laima, Kavaliauskienė Ingrida, Domarkienė Ingrida, Karčiauskaitė Dovilė, Kučinskienė Zita Aušrelė, Kučinskas Vaidutis

机构信息

Department of Human and Medical Genetics,Faculty of Medicine,Vilnius University,Lithuania.

Department of Physiology, Biochemistry, Microbiology and Laboratory Medicine,Faculty of Medicine,Vilnius University,Lithuania.

出版信息

Genet Res (Camb). 2017 Aug 30;99:e6. doi: 10.1017/S0016672317000040.

DOI:10.1017/S0016672317000040
PMID:28851476
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6865174/
Abstract

Next-generation sequencing (NGS) became an effective approach for finding novel causative genomic variants of genetic disorders and is increasingly used for diagnostic purposes. Public variant databases that gather data of pathogenic variants are being relied upon as a source for clinical diagnosis. However, research of pathogenic variants using public databases data could be carried out not only in patients, but also in healthy people. This could provide insights into the most common recessive disorders in populations. The study aim was to use NGS and data from the ClinVar database for the identification of pathogenic variants in the exomes of healthy individuals from the Lithuanian population. To achieve this, 96 exomes were sequenced. An average of 42 139 single-nucleotide variants (SNVs) and 2306 short INDELs were found in each individual exome. Pooled data of study exomes provided a total of 243 192 unique SNVs and 31 623 unique short INDELs. Three hundred and twenty-one unique SNVs were classified as pathogenic. Comparison of the European data from the 1000 Genomes Project with our data revealed five pathogenic genomic variants that are inherited in an autosomal recessive pattern and that statistically significantly differ from the European population data.

摘要

下一代测序(NGS)成为发现遗传疾病新致病基因组变异的有效方法,并越来越多地用于诊断目的。收集致病变异数据的公共变异数据库正被用作临床诊断的来源。然而,利用公共数据库数据对致病变异的研究不仅可以在患者中进行,也可以在健康人群中开展。这可以为了解人群中最常见的隐性疾病提供线索。本研究的目的是利用NGS和ClinVar数据库的数据,鉴定立陶宛人群健康个体外显子中的致病变异。为实现这一目标,对96个外显子进行了测序。每个个体外显子平均发现42139个单核苷酸变异(SNV)和2306个短插入缺失。研究外显子的汇总数据共提供了243192个独特的SNV和31623个独特的短插入缺失。321个独特的SNV被分类为致病的。将千人基因组计划的欧洲数据与我们的数据进行比较,发现了5个以常染色体隐性模式遗传且在统计学上与欧洲人群数据有显著差异的致病基因组变异。