Genetic investigation of idiopathic liver injury in children by whole exome sequencing

Abstract

Childhood liver diseases, caused by multiple etiologies, pose a significant burden globally. Liver injury of unknown causes remains a challenge in pediatric hepatology. For instance, the etiology remains unexplained in nearly half of the pediatric cases with acute liver failure. This ambiguity impedes early diagnosis and the timely consideration of treatment options. Recent studies, utilized by genome-wide approaches such as whole-exome sequencing (WES), reveal that idiopathic liver injury can be due to hitherto silent monogenic liver diseases. In our study, we aimed to investigate the monogenic determinants of idiopathic liver injury in children. We performed WES on 20 pediatric patients presenting with either recurrent elevated liver transaminases (rELT) of unknown etiology or indeterminate acute liver failure (ALF). We searched for potential disease-causing variants in a manually curated panel of 380 genes associated with inherited monogenic diseases with hepatobiliary phenotypes. We identified rare nonsynonymous variants in nine genes in total 6 patients, five rELT patients and one ALF patient. Then, we evaluated the causal concordance between the gene mutated and the clinical phenotype observed in each patient through an in-depth case-level assessment. Overall, we established a genetic diagnosis in four out of 10 rELT patients. We identified two novel mutations in ACOX2 and PYGL, expanding the spectrum of genetic mutations implicated in monogenic liver diseases. Additionally, we discovered two previously-reported morbid mutations in ABCB4 and PHKA2. Moreover, we identified five variants of uncertain significance (VUS) in CDAN1, JAG1, PCK2, SLC27A5, or VPS33B in rELT or ALF patients. This study further supports the utility of WES in clinical settings to enhance our understanding and management of idiopathic liver diseases in children, providing early diagnosis and precise treatment. By identifying the genetic variants contributing to liver injury, clinicians can predict disease progression more accurately, provide more personalized treatment strategies, and make decisions on liver transplantation when necessary.