IVAS

IVAS identifies splicing quantitative trait loci (sQTLs) by associating genotype variation with exon- and junction-level alternative splicing events derived from RNA-seq data within an R/Bioconductor framework.

Key Features:

• Statistical framework: Provides a complete statistical framework for associating genotype variation with exon- or junction-level splicing events from RNA-seq data.
• Resolution: Operates at exon- and junction-level resolution to capture alternative splicing variation and isoform usage.
• Input data: Accepts matched RNA-seq and genotype profiles from population-scale cohorts.
• Cohort-level mapping: Produces sQTL maps for each dataset or cohort.
• Meta-analysis: Performs downstream meta-analysis across datasets to obtain consensus sQTLs with improved statistical power.
• Significance reporting: Reports high-confidence sQTLs with false discovery rate control (e.g., 2,525 sQTLs at FDR < 0.05).
• GWAS integration: Identifies sQTLs that overlap known genome-wide association study (GWAS) loci.
• Functional impact: Highlights sQTLs affecting alternative exons that encode conserved or functionally essential protein domains.
• Implementation: Implemented as an R/Bioconductor package for reproducible analysis within the Bioconductor ecosystem.

Scientific Applications:

• Population-scale regulatory genomics: Mapping sQTLs across large cohorts, including multiple European-ancestry cohorts, to study genetic regulation of splicing.
• Meta-analysis of sQTLs: Combining cohort-specific sQTL maps to derive consensus associations with increased power.
• Functional interpretation of GWAS loci: Linking trait-associated polymorphisms to splicing changes that may explain disease associations.
• Mechanistic studies: Identifying alternative exons and isoform changes that alter conserved or essential protein domains for downstream functional follow-up.

Methodology:

Associates genotype variation with exon- and junction-level splicing events derived from RNA-seq, generates sQTL maps per dataset, and performs downstream meta-analysis to obtain consensus sQTLs.