Figure Legends
Figure 1. Results from in-vitro minigene assays
demonstrating multiple consequences as a result of variants proximal to
the canonical splice site. Left , gel electrophoresis snapshots
of cDNA products amplified from primers designed for control exons
within the minigene (exon 1 & exon 2 ). All prominent
bands were cut out and Sanger sequenced. Right , solid red blocks
illustrate alignment of sequenced cDNA transcripts to features within
the minigene vector: control exons (grey boxes ) and inserted
exons (purple boxes ). (a) SCN2A
c.2919+3A>G , showing complete exon exclusion and exon
truncation in minigene vectors containing the c.2919+3A>G
variant (top two alignments) and normal splicing in minigene
vectors containing the WT sequence (bottom alignment). The first
resulted in a transcript with a truncated exon,
NM_001040142.1:r.2563_2710del, and the second resulted in a complete
exon skip, NM_001040142.1:r.2563_2919del. While we interpreted both
events as ‘likely pathogenic’ it is noteworthy that these events were
considered differently using ACMG criteria; the exon truncation event
resulted in a frameshift and introduction of a premature stop codon
(PVS1 ), whereas the complete exon skipping event resulted in the
inframe removal of 119 amino acids from the transcript (PM4 ).(b) MERTK c.2486+6T>A , showing a shifting of the
exon included in the reading frame in minigene vectors containing the
c.2486+6T>A variant (top alignment) and normal
splicing in minigene vectors containing the WT sequence (bottomalignment). This novel variant is present in two individuals with severe
rod-cone dystrophy, and resulted in the simultaneous usage of a cryptic
exonic splice acceptor site and a cryptic intronic splice donor site
creating a novel exon (chr2: 112,779,939-112,780,082, GRCh37 ),
and a premature stop codon in the penultimate exon, p.(Trp784Valfs*10).
Figure 2. Comparison of in silico strategies to
prioritize 250 variants of uncertain significance with functional
investigations performed. (a) Receiver operating characteristics area
under the curve (AUC) comparisons for nine in silicoprioritization strategies demonstrating that SpliceAI (AUC=0.95,
95%CI=0.93-0.98) and a consensus approach (AUC=0.94, 95%CI=0.91-0.96)
outperform other strategies for prioritization. (b) AUC comparisons
between SpliceAI, a consensus approach and a novel metric, demonstrates
that a weighted approach slightly increases accuracy of prioritization
over single approaches alone (AUC=0.96, 95%CI=0.94-0.98). (c-d)
Accuracy comparisons of each insilico prioritization approach
across 2000 bootstraps utilizing region-specific pre-defined thresholds:
(c) violin plot demonstrating the calculated accuracy of eachin-silico prioritization approach; (d) frequency that each
strategy is the best or joint best performing.
Figure 3. Summary of the overlap and correlations observed
between the scores from in silico splicing prediction algorithms
for 18,013 unique rare variants identified in a large cohort of 2783
individuals with rare disease undergoing genetic testing, specifically
for syndromic and non-syndromic inherited retinal disorders. (a) Bar
chart showing overall count of unique variants prioritized using
pre-defined thresholds for each in silico prediction algorithm.
(b) Overlap between the unique variants prioritized by the five most
correlated in silico prediction tools. (c) Grouped bar chart
demonstrating the overlap of variants prioritized by each tool
segregated by the region of the genome that the variant impacts, as
defined by Jagadeesh et al . (d) Correlation between SpliceAI
score and the number of additional tools also prioritizing the variant
for the 528 unique rare variants prioritized by SpliceAI.