Reliable genetic diagnosis of NCF1 (p47phox)-deficient chronic granulomatous disease using high-throughput sequencing

Amy P Hsu1, Eric Karlins2, Justin Lack3,4

  • 1Immunopathogenesis Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH), Bethesda, MD, United States.

Frontiers in Immunology
|September 11, 2025
PubMed

Insights

A new bioinformatic method enables genetic diagnosis for NCF1-CGD patients, including those with non-deletion mutations. This approach uses existing sequencing data to identify NCF1 gene mutations, improving diagnostic accuracy for chronic granulomatous disease.

Area of Science:

  • Immunology and Genetics
  • Bioinformatics and Computational Biology

Background:

  • Chronic granulomatous disease (CGD) is a primary immunodeficiency caused by mutations in the NADPH oxidase complex.
  • NCF1 (p47phox) gene mutations account for a significant portion of CGD cases, but genetic diagnosis is challenging due to pseudogenes.
  • Current diagnostic methods for NCF1-CGD, such as DHR and immunoblotting, can be insufficient for identifying all mutation types.

Purpose of the Study:

  • To develop and validate a bioinformatic method for the genetic diagnosis of NCF1-CGD.
  • To identify both deletion (ΔGT) and non-deletion mutations in the NCF1 gene using sequencing data.
  • To investigate the role of pseudogene NCF1B and NCF1C in NCF1-CGD pathogenesis.

Main Methods:

  • Development of a bioinformatic pipeline utilizing existing short or long-read sequencing data.
  • Analysis of sequencing data from 48 NCF1-CGD patients and carriers.
  • Comparison of NCF1 sequences from NCF1-CGD patients with healthy controls (1000Genomes cohort).

Main Results:

  • Successfully identified both ΔGT and non-ΔGT NCF1 gene mutations in NCF1-CGD patients.
  • Confirmed that ΔGT mutations result from pseudogene (NCF1B/NCF1C) sequence replacement in the NCF1 locus.
  • Observed reciprocal pseudogene replacement by NCF1 in some healthy individuals, highlighting complex genetic interactions.

Conclusions:

  • The developed bioinformatic method provides a means for genetic diagnosis in NCF1-CGD patients, including those with previously undiagnosed mutations.
  • Reanalysis of existing sequencing data can yield definitive genetic diagnoses, improving patient management.
  • The methodology holds potential for application in diagnosing other genetic disorders involving pseudogenes.
Abstract