Whole Exome Sequencing Analysis in Fetal Skeletal Dysplasia Detected by Ultrasonography: An Analysis of 38 Cases.

Whole Exome Sequencing Analysis in Fetal Skeletal Dysplasia Detected by Ultrasonography: An Analysis of 38 Cases.
复制标题

超声检测胎儿骨骼发育不良的全外显子组测序分析:附38例分析

DOI:
10.3389/fgene.2021.728544
复制
发表时间:
2021
影响因子:
3.7
通讯作者:
Wang H
Wang H
中科院分区:
生物学3区
文献类型:
--
作者:
Peng Y;Yang S;Huang X;Pang J;Liu J;Hu J;Shen X;Tang C;Wang H

文献摘要

参考文献

被引文献

相似文献

背景:骨骼发育不良(SD)是一组异质性遗传性疾病,主要影响骨骼和软骨。本研究旨在确定胎儿 SD 的遗传原因,并评估产前全外显子组测序 (WES) 对这种疾病的诊断率。方法:对 38 名经超声鉴定 SD 且核型和单核苷酸多态性 (SNP) 分析结果正常的胎儿进行 WES。通过生物信息学分析选择候选变体,并通过桑格测序进行验证。结果:WES 揭示了 65.79% (25/38) 胎儿中与 SD 相关的致病性或可能致病性变异,10.53% (4/38) 胎儿中 SD 相关基因的不确定意义变异 (VUS),以及 31.58% (12/38) 胎儿中的偶然发现。本研究中发现的 SD 相关变异影响了 10 个基因,其中 35.71% (10/28) 的变异是新的。结论:WES对产前SD的诊断率较高,可改善妊娠管理、产前咨询和未来妊娠复发风险评估。新发现的变异扩大了这种疾病的突变谱。
Background: Skeletal dysplasias (SDs) are a heterogeneous group of genetic disorders that primarily affect bone and cartilage. This study aims to identify the genetic causes for fetal SDs, and evaluates the diagnostic yield of prenatal whole-exome sequencing (WES) for this disorder. Methods: WES was performed on 38 fetuses with sonographically identified SDs and normal results of karyotype and single nucleotide polymorphism (SNP) analysis. Candidate variants were selected by bioinformatics analysis, and verified by Sanger sequencing. Results: WES revealed pathogenic or likely pathogenic variants associated with SDs in 65.79% (25/38) of fetuses, variants of uncertain significance (VUS) in SDs-related genes in 10.53% (4/38) cases, and incidental findings in 31.58% (12/38) fetuses. The SDs-associated variants identified in the present study affected 10 genes, and 35.71% (10/28) of the variants were novel. Conclusion: WES has a high diagnostic rate for prenatal SDs, which improves pregnancy management, prenatal counseling and recurrence risk assessment for future pregnancies. The newly identified variants expanded mutation spectrum of this disorder.
DOI: 10.1016/s0140-6736(18)32042-7
发表时间: 2019-02-23
期刊: LANCET
影响因子: 168.9
作者:
Petrovski, Slave;Aggarwal, Vimla;Wapner, Ronald J.
通讯作者: Wapner, Ronald J.
DOI: 10.1002/mgg3.1312
发表时间: 2020-06-04
影响因子: 2
作者:
Peng, Ying;Pang, Jialun;Wang, Hua
通讯作者: Wang, Hua
DOI: 10.1016/j.metabol.2017.06.001
发表时间: 2018-03-01
影响因子: 9.8
作者:
Tournis, Symeon;Dede, Anastasia D.
通讯作者: Dede, Anastasia D.
DOI: 10.1172/jci28163
发表时间: 2006-08-01
影响因子: 15.9
作者:
Hafner, Christian;van Oers, Johanna M. M.;Hartmann, Arndt
通讯作者: Hartmann, Arndt
DOI: 10.1002/ajmg.a.61366
发表时间: 2019-10-21
影响因子: 2
作者:
Mortier, Geert R.;Cohn, Daniel H.;Warman, Matthew L.
通讯作者: Warman, Matthew L.