Integrative Computational and Statistical Analysis of BARD1 Variants: Insights into Pathogenicity and Cancer Risk Prediction

Abstract

Background:Variants in the BRCA1-associated RING domain protein 1 (BARD1) have been implicated in multiple cancers, including pediatric malignancies. Despite this, the pathogenicity of many BARD1 variants remains unclear.
Objective:This study employs integrative computational and statistical approaches to analyze BARD1 variants, aiming to predict their pathogenicity and assess their role in cancer risk among pediatric patients.
Methods:Publicly available datasets, including ClinVar and COSMIC, were mined for BARD1 variants. Bioinformatic tools such as PolyPhen-2, SIFT, and MutationTaster were used for in silico predictions of pathogenicity. Protein structure and function analyses were conducted using I-TASSER and Swiss-Model, while population frequency data were obtained from gnomAD. Statistical analyses were performed to correlate BARD1 variants with clinical outcomes.
Results:Out of 102 BARD1 variants analyzed, 38 were classified as pathogenic based on in silico predictions. Structural modeling revealed significant alterations in the RING domain for several pathogenic variants, correlating with impaired protein-protein interactions. Statistical analysis demonstrated a strong association between specific BARD1 variants and poor clinical outcomes (p < 0.01). Population frequency analysis indicated that certain pathogenic variants are more prevalent in pediatric cancer cohorts compared to general populations.
Conclusion:This study highlights the critical role of BARD1 variants in pediatric oncology, providing a comprehensive framework for future genetic and functional studies. The integration of computational and statistical methods offers a robust approach to predict pathogenicity and assess cancer risk.