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通过基于开放获取工具的全基因组测序算法揭示色素性视网膜炎复杂病例:隐藏的隐性遗传和潜在的寡基因变异

Unmasking Retinitis Pigmentosa complex cases by a whole genome sequencing algorithm based on open-access tools: hidden recessive inheritance and potential oligogenic variants.

作者信息

González-Del Pozo María, Fernández-Suárez Elena, Martín-Sánchez Marta, Bravo-Gil Nereida, Méndez-Vidal Cristina, Rodríguez-de la Rúa Enrique, Borrego Salud, Antiñolo Guillermo

机构信息

Department of Maternofetal Medicine, Genetics and Reproduction, Institute of Biomedicine of Seville, University Hospital Virgen del Rocío/CSIC/University of Seville, Avenida Manuel Siurot s/n, 41013, Seville, Spain.

Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Seville, Spain.

出版信息

J Transl Med. 2020 Feb 12;18(1):73. doi: 10.1186/s12967-020-02258-3.

DOI:10.1186/s12967-020-02258-3
PMID:32050993
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7014749/
Abstract

BACKGROUND

Retinitis Pigmentosa (RP) is a clinically and genetically heterogeneous disorder that results in inherited blindness. Despite the large number of genes identified, only ~ 60% of cases receive a genetic diagnosis using targeted-sequencing. The aim of this study was to design a whole genome sequencing (WGS) based approach to increase the diagnostic yield of complex Retinitis Pigmentosa cases.

METHODS

WGS was conducted in three family members, belonging to one large apparent autosomal dominant RP family that remained unsolved by previous studies, using Illumina TruSeq library preparation kit and Illumina HiSeq X platform. Variant annotation, filtering and prioritization were performed using a number of open-access tools and public databases. Sanger sequencing of candidate variants was conducted in the extended family members.

RESULTS

We have developed and optimized an algorithm, based on the combination of different open-access tools, for variant prioritization of WGS data which allowed us to reduce significantly the number of likely causative variants pending to be manually assessed and segregated. Following this algorithm, four heterozygous variants in one autosomal recessive gene (USH2A) were identified, segregating in pairs in the affected members. Additionally, two pathogenic alleles in ADGRV1 and PDZD7 could be contributing to the phenotype in one patient.

CONCLUSIONS

The optimization of a diagnostic algorithm for WGS data analysis, accompanied by a hypothesis-free approach, have allowed us to unmask the genetic cause of the disease in one large RP family, as well as to reassign its inheritance pattern which implies differences in the clinical management of these cases. These results contribute to increasing the number of cases with apparently dominant inheritance that carry causal mutations in recessive genes, as well as the possible involvement of various genes in the pathogenesis of RP in one patient. Moreover, our WGS-analysis approach, based on open-access tools, can easily be implemented by other researchers and clinicians to improve the diagnostic yield of additional patients with inherited retinal dystrophies.

摘要

背景

视网膜色素变性(RP)是一种临床和遗传异质性疾病,可导致遗传性失明。尽管已鉴定出大量基因,但使用靶向测序仅约60%的病例能获得基因诊断。本研究的目的是设计一种基于全基因组测序(WGS)的方法,以提高复杂视网膜色素变性病例的诊断率。

方法

使用Illumina TruSeq文库制备试剂盒和Illumina HiSeq X平台,对来自一个大型显性常染色体显性RP家系的三名家庭成员进行WGS,该家系先前的研究未能解决。使用多种开放获取工具和公共数据库进行变异注释、筛选和优先级排序。对候选变异进行桑格测序,并在扩展家庭成员中进行。

结果

我们基于不同开放获取工具的组合开发并优化了一种算法,用于WGS数据的变异优先级排序,这使我们能够显著减少有待人工评估和分离的可能致病变异的数量。按照该算法,在一个常染色体隐性基因(USH2A)中鉴定出四个杂合变异,在受影响成员中呈成对分离。此外,ADGRV1和PDZD7中的两个致病等位基因可能导致一名患者出现该表型。

结论

WGS数据分析诊断算法的优化,辅以无假设方法,使我们能够揭示一个大型RP家系中该疾病的遗传原因,并重新确定其遗传模式,这意味着这些病例在临床管理上存在差异。这些结果有助于增加在隐性基因中携带致病突变的显性遗传病例的数量,以及多种基因可能参与一名患者RP发病机制的情况。此外,我们基于开放获取工具的WGS分析方法可被其他研究人员和临床医生轻松采用,以提高其他遗传性视网膜营养不良患者的诊断率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/075c/7014749/6493e8e9d048/12967_2020_2258_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/075c/7014749/af059d9c6983/12967_2020_2258_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/075c/7014749/7a0b7c2a327b/12967_2020_2258_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/075c/7014749/6493e8e9d048/12967_2020_2258_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/075c/7014749/af059d9c6983/12967_2020_2258_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/075c/7014749/7a0b7c2a327b/12967_2020_2258_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/075c/7014749/6493e8e9d048/12967_2020_2258_Fig3_HTML.jpg

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