Scholarlink

Introduction <p>Inborn errors of immunity (IEIs) constitute a diverse group of more than 500 disorders resulting from pathogenic variants in over 500 causative genes, with most being monogenic diseases. The use of exome sequencing based on next-generation sequencing technologies has significantly advanced the discovery of causative variants underlying IEIs and has achieved diagnostic yields of up to 40%. Despite these advances, a substantial proportion of patients still remain genetically undiagnosed due to limitations in detecting deep intronic or structural variants. Accordingly, we applied whole-genome sequencing to a cohort of patients suspected of IEIs in order to evaluate its diagnostic yield and capacity to identify novel structural genomic alterations.</p> Methods <p>We analyzed data from 25 probands presenting with suspected IEIs based on clinical features, who were enrolled through the National Bio-Big Data Program's whole genome sequencing (WGS) project at Samsung Medical Center, spanning July 2020 to February 2022. The study utilized a stepwise analytical protocol involving initial candidate gene panel analysis for detecting small variants, subsequent investigation of structural variants, and then a genotype-driven approach utilizing in-house bioinformatics pipelines. All identified variants were assessed for pathogenicity in accordance with the 2015 ACMG/AMP guidelines for the interpretation of sequence variants.</p> Results <p>Causative variants were detected in 10 (40%) probands using candidate gene panel analysis, which included <i>BTK, CYBB, DKC1, DNAH11, DNAH5, IL2RG, NFKB2, PIK3CD</i> and <i>SH2D1A</i>. Genotype-driven analysis identified pathogenic variants in two (8%) probands involving <i>NF1</i> and <i>PTPN11</i>, while an additional five (20%) probands were found to have structural variants, including <i>BTK</i>, <i>LRBA and SH2D1A</i>. In total, genetic analysis revealed causative variants in 60% of patients. Variants of uncertain significance were identified in four cases among three probands (12%).</p> Conclusion <p>WGS facilitated the robust identification of causative genetic variants, including complex structural changes. These results suggest that employing WGS in patients suspected of IEIs could provide additional diagnostic yield. </p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Whole Genome Sequencing in 25 Families with Suspected Inborn Errors of Immunity: Diagnostic Yield and Clinical Relevance of Genome-wide Analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月23日

查看全文
  • Jong Kwon Lee,
  • Ja-Hyun Jang,
  • Doo Ri Kim,
  • Areum Shin,
  • Jong-Ho Park,
  • Young-Gon Kim,
  • Jong-Won Kim,
  • Yae-Jean Kim,
  • Eun-Suk Kang

摘要

Introduction

Inborn errors of immunity (IEIs) constitute a diverse group of more than 500 disorders resulting from pathogenic variants in over 500 causative genes, with most being monogenic diseases. The use of exome sequencing based on next-generation sequencing technologies has significantly advanced the discovery of causative variants underlying IEIs and has achieved diagnostic yields of up to 40%. Despite these advances, a substantial proportion of patients still remain genetically undiagnosed due to limitations in detecting deep intronic or structural variants. Accordingly, we applied whole-genome sequencing to a cohort of patients suspected of IEIs in order to evaluate its diagnostic yield and capacity to identify novel structural genomic alterations.

Methods

We analyzed data from 25 probands presenting with suspected IEIs based on clinical features, who were enrolled through the National Bio-Big Data Program's whole genome sequencing (WGS) project at Samsung Medical Center, spanning July 2020 to February 2022. The study utilized a stepwise analytical protocol involving initial candidate gene panel analysis for detecting small variants, subsequent investigation of structural variants, and then a genotype-driven approach utilizing in-house bioinformatics pipelines. All identified variants were assessed for pathogenicity in accordance with the 2015 ACMG/AMP guidelines for the interpretation of sequence variants.

Results

Causative variants were detected in 10 (40%) probands using candidate gene panel analysis, which included BTK, CYBB, DKC1, DNAH11, DNAH5, IL2RG, NFKB2, PIK3CD and SH2D1A. Genotype-driven analysis identified pathogenic variants in two (8%) probands involving NF1 and PTPN11, while an additional five (20%) probands were found to have structural variants, including BTK, LRBA and SH2D1A. In total, genetic analysis revealed causative variants in 60% of patients. Variants of uncertain significance were identified in four cases among three probands (12%).

Conclusion

WGS facilitated the robust identification of causative genetic variants, including complex structural changes. These results suggest that employing WGS in patients suspected of IEIs could provide additional diagnostic yield.