Article
Application of a next-generation sequencing (NGS) panel in 128 patients with idiopathic cholestasis: preliminary results
Search Medline for
Authors
Published: | May 30, 2025 |
---|
Outline
Text
Background: The use of next generation sequencing (NGS) to find genetic variants underlying acute, chronic or episodic cholestatic liver disease (CLD) is increasingly common (Zheng et al., Hepatology, 2022). While monogenic genotype-phenotype correlations have been described in paediatric intrahepatic cholestasis (van Wessel et al., J Hepatol, 2020), disease aetiology is less clear in later-onset cases of CLD. Here we present data from eight years of NGS analysis of patients with idiopathic CLD using a “cholestasis NGS panel” covering 24, later 32 transcripts.
Patients & methods: Between 2013 and 2021, 128 patients with cholestatic liver disease of unknown aetiology were analysed using 24 (2013–2016) and 32 (2016–2021) gene panel NGS. The generated variant call files were analysed for GnomAD frequency, amino acid alterations, pathogenicity score and biochemical consequences using the GeneTalk online DNA analysis tool. Variants that were rare or absent in GnomAD were analysed using SIFT, Polyphen and the ABCB4-specific modelling tool VASOR (Behrendt et al., Hepatol Commun, 2022).
Results: NGS using 24/32 gene panels revealed potentially disease-associated variants in around a third (n = 41) of altogether 128 patients with idiopathic cholestatic liver disease. Less than 10% (4/41) of variants in PFIC-associated genes were private, i.e. not present in GnomAD. On the other hand, the “common European PFIC2” variants p.E297G and p.D482G (Strautnieks et al., Gastroenterology, 2008), could be detected in only 3 individuals in our cohort. In contrast, NOTCH2 variants indicative of an Alagille-like aetiology were found in 12 patients. Finally, 31 did not have any genetic variants that could be associated with the cholestatic phenotype.
Conclusions: NGS in patients with unclear cholestatic phenotypes reveals a wide variety of potentially disease-associated and -modifying variants. Compilation of these data and follow-up in larger registries is required to estimate their impact on liver health. Analysis of genome data from large populations will help to understand the long-term effects of these variants on health outcomes.