Deep Intronic Splicing Variants in Rare Mendelian Diseases: RNA-Seq Splicing Outlier Detection Identifies 28.4% Additional Diagnostic Yield in Undiagnosed Families

This study investigates RNA-seq splicing outlier analysis to detect pathogenic deep intronic and exonic splicing variants missed by exome sequencing in undiagnosed rare Mendelian disease families within the context of molecular genetics and RNA biology, an area of growing scientific importance given its implications for rare disease diagnostic workflow integration of RNA-seq, deep intronic variant reporting standards, and tissue selection guidelines for splicing outlier analysis. Using FRASER and LeafCutter splicing outlier detection on 284 patient RNA-seq samples against 424 tissue-matched controls, with candidate splicing outlier validation by RT-PCR and Sanger sequencing, and causal variant identification by long-read genome sequencing, we examine deep intronic and synonymous exonic variants creating or destroying splicing regulatory elements (ESE, ESS, 5/3 splice sites) that activate cryptic exons or cause exon skipping missed by exome-based variant filtering focused on coding-region variants in 284 undiagnosed rare disease patients from 228 families with prior non-diagnostic exome (median depth 120x), 68% blood/24% fibroblast/8% muscle RNA-seq, median 14-gene disease panel prior investigated drawn from Westridge Genome Diagnostic Center with RNA extraction from patient-banked samples, Illumina NovaSeq 150bp RNA-seq at 60M reads, and 424-control tissue expression reference dataset. Results indicate that splicing outlier analysis identifies diagnostic or strongly candidate splicing variants in 64/228 families (28.1% additional diagnostic yield); deep intronic variants account for 58.4% of new diagnoses; fibroblast RNA-seq yields 2.4x more diagnoses per sample than blood for connective tissue disorders (p < 0.001), with 28.1% additional diagnostic yield; 58.4% deep intronic; fibroblast 2.4x more diagnoses than blood as the primary quantitative benchmark. Concordance between primary and confirmatory measurement approaches exceeded 93%, validating the analytical framework. These findings contribute empirically to molecular genetics and RNA biology and carry actionable implications for the design of programs and policies targeting rare disease diagnostic workflow integration of RNA-seq, deep intronic variant reporting standards, and tissue selection guidelines for splicing outlier analysis.