Identification of Deep-Intronic Splice Mutations in a Large Cohort of Patients With Inherited Retinal Diseases

Abstract

High throughput sequencing technologies have revolutionized the identification of mutations responsible for a diverse set of Mendelian disorders, including inherited retinal disorders (IRDs). However, the causal mutations remain elusive for a significant proportion of patients. This may be partially due to pathogenic mutations located in non-coding regions, which are largely missed by capture sequencing targeting the coding regions. The advent of whole-genome sequencing (WGS) allows us to systematically detect non-coding variations. However, the interpretation of these variations remains a significant bottleneck. In this study, we investigated the contribution of deep-intronic splice variants to IRDs. WGS was performed for a cohort of 571 IRD patients who lack a confident molecular diagnosis, and potential deep intronic variants that affect proper splicing were identified using SpliceAI. A total of six deleterious deep intronic variants were identified in eight patients. An in vitro minigene system was applied to further validate the effect of these variants on the splicing pattern of the associated genes. The prediction scores assigned to splice-site disruption positively correlated with the impact of mutations on splicing, as those with lower prediction scores demonstrated partial splicing. Through this study, we estimated the contribution of deep-intronic splice mutations to unassigned IRD patients and leveraged in silico and in vitro methods to establish a framework for prioritizing deep intronic variant candidates for mechanistic and functional analyses.

Keywords: deep-intronic mutations; inherited retinal dystrophies; minigenes; splicing; whole-genome sequencing.

Figure 1

Clinical data supported the splicing variants’ deleterious effects based on known genotype-phenotype associations. (A) Fundus autofluorescence (AF) and optical coherence tomography (OCT) of MEP129. (B) OCT and fundus images of MEP130. (C) AF and OCT of MEP337; the right panel shows the pedigree of proband MEP337. Circles represent females; squares represent males. Empty shapes are unaffected relatives. The arrow indicates proband MEP337. M1: c.G11864A; M2: c.8682-654C>G. (D) AF and OCT of DGB289. (E) OCT and fundus images of MEP344. (F) AF and OCT of DGB288. (G) AF and OCT of NEI4320. (H) OCT and fundus images of MEP105.

Figure 2

A minigene splicing assay reveals variant-induced aberrant splicing. (A–E) Demonstrate the splicing results of variants identified in patients: (A) MEP337 and DGB289, (B) MEP344, (C) DGB288, (D) NEI4320, and (E) MEP105. The left panels of (A–E) plot the chromosomal positions of mutations as well as those of the cryptic exons and the stop codons generated by each variant; the middle panels show gel electrophoresis of reverse transcription PCR (RT-PCR) products of all tested minigenes. All the gel bands were longer than the control exons and predicted exons as RT-PCR primers were designed to include partial regions (92 bp) of the first and the third exons (the exons before and after the replaceable exon as shown in Supplementary Figure S2) in the minigene vector. The diagrams in the right panel, which are not to scale, are provided as a schematic of the variant-induced changes in transcript configuration. WT, splicing results of wild-type control; Var, splicing results of identified variants.

Figure 3

Cryptic exon sequences of (A) USH2A splice variant (chr1:216041166C>G) of MEP337 and DGB289, (B) OPA1 splice variant (chr3:193362516A>G) of MEP344, (C) ADGRV1 splice variant (chr5:90099416A>G) of DGB288, (D) RPGRIP1 splice variant (chr14:21793624A>G) of NEI4320, and (E) PCDH15 splice variant (chr10:55597057T>G) of MEP105.