Identification of 19 Pathogenic Variants in a Clinically Heterogeneous Cohort With Suspected Inborn Errors of Metabolism

Inborn errors of metabolism (IEM) are frequently underdiagnosed in low-resource settings due to limited diagnostic infrastructure. We hypothesized that an integrated clinical-genomic approach could improve diagnosis and management of these conditions. Nineteen Pakistani families with clinically suspected IEM underwent systematic clinical assessment, available biochemical testing, and whole-exome sequencing (WES). Variants were classified according to ACMG/AMP guidelines using evidence from population databases, in silico prediction tools, segregation analysis, and genotype-phenotype correlation. Clinical diagnoses and management strategies were reassessed based on molecular findings. WES provided a molecular diagnosis in 90% (17/19) of families and enabled targeted therapeutic interventions in 70% (13/19). However, clinical outcomes were variable due to advanced disease in some cases and limited follow-up. Seven novel variants were identified in CYP27B1, DYM, MTTP, ALDH3A2, USP53, BRAF, and JAG1, while twelve recurrent mutations were detected in PIGN, GCDH, CLCN7, RNASEH2C, ABCB11, MPV17, IDUA, SMPD1, FBP1, SLC37A4, ACADM, and UGT1A1. Integrating genomic findings with clinical reassessment improved diagnostic precision. An integrated clinical-genomic approach enabled accurate diagnosis of pediatric IEM in resource-limited settings, with particular utility in children with metabolic disorders in a consanguineous population. Identification of both novel and recurrent variants expanded the genotypic and phenotypic spectrum of these disorders and highlighted the clinical utility of genomic diagnostics in optimizing patient care.