Identifying pathogenic variants in rare pediatric neurological diseases using exome sequencing.

Area of Science:

• Genetics
• Bioinformatics
• Medical Genomics

Background:

• Accurate identification of pathogenic variants is essential for diagnosing genetic disorders.
• Current variant annotation tools face challenges in detecting all disease-causing mutations.

Purpose of the Study:

• To evaluate the effectiveness of four variant annotation tools (allele frequency, ClinVar, SpliceAI, Phenomatcher) in identifying pathogenic single nucleotide and small insertion/deletion variants (SNVs/small indels).
• To assess the utility of combining these tools for improved rare disease diagnosis.

Main Methods:

• Retrospective analysis of 271 pathogenic SNVs/small indels using allele frequency data (gnomADv4.0, 54KJPN), ClinVar database, SpliceAI, and Phenomatcher.
• Evaluation of variant pathogenicity based on allele frequency, clinical significance in ClinVar, splice site impact, and phenotype correlation.

Main Results:

• 13 de novo pathogenic variants were found with allele frequency <0.001% in large cohort data.
• 38.4% of candidate SNVs/small indels were registered as pathogenic or likely pathogenic in ClinVar.
• SpliceAI identified four variants affecting RNA splicing, located 11-50 bp from exon-intron boundaries.
• PhenoMatcher prioritized candidate genes with a maximum score ≥0.6 for approximately 95% of cases, demonstrating phenotype-based utility.

Conclusions:

• A combination of multiple variant annotation tools and appropriate evaluation significantly enhances the diagnostic yield for rare diseases.
• Allele frequency, ClinVar, SpliceAI, and Phenomatcher collectively improve the identification of pathogenic variants, including very rare de novo mutations and splice site alterations.