Integrating multiple genomic data to predict disease-causing nonsynonymous single nucleotide variants in exome...

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Exome sequencing is crucial for identifying pathogenic single nucleotide variants (SNVs) in inherited diseases.
• Traditional statistical genetics methods struggle with exome data due to variant numbers, rare/de novo mutations, and small population sizes.
• There is a need for effective computational methods to identify causative SNVs from large exome sequencing datasets.

Purpose of the Study:

• To develop and validate a bioinformatics approach, SPRING (Snv PRioritization via the INtegration of Genomic data), for identifying pathogenic nonsynonymous SNVs.
• To prioritize candidate SNVs by calculating their statistical significance as causative for a given disease.
• To provide a tool applicable to diseases with known or unknown genetic bases and various inheritance patterns.

Main Methods:

• SPRING integrates six functional effect scores (SIFT, PolyPhen2, LRT, MutationTaster, GERP, PhyloP) with five association scores from diverse genomic data.
• Genomic data sources include gene ontology, protein-protein interactions, protein sequences, domain annotations, and pathway annotations.
• The method calculates the statistical significance of an SNV being causative for a query disease.

Main Results:

• SPRING demonstrates validity across diseases with partly known or completely unknown genetic bases and various inheritance styles.
• Comprehensive validation experiments confirm the method's effectiveness.
• SPRING successfully detected causative de novo mutations in real exome sequencing data for autism, epileptic encephalopathies, and intellectual disability.

Conclusions:

• SPRING is an effective bioinformatics approach for prioritizing pathogenic nonsynonymous SNVs from exome sequencing data.
• The tool is valuable for identifying causative variants in a wide range of inherited human diseases.
• SPRING is available as an online service, standalone software, and provides genome-wide predictions for thousands of diseases.