Investigation of cryptic JAG1 splice variants as a cause of Alagille syndrome and performance evaluation of splice predictor tools

Haploinsufficiency of JAG1 is the primary cause of Alagille syndrome (ALGS), a rare, multisystem disorder. The identification of JAG1 intronic variants outside of the canonical splice region as well as missense variants, both of which lead to uncertain associations with disease, confuses diagnostics...

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Bibliographic Details
Published in:HGG advances 2024-10, Vol.5 (4), p.100351, Article 100351
Main Authors: Keefer-Jacques, Ernest, Valente, Nicolette, Jacko, Anastasia M., Matwijec, Grace, Reese, Apsara, Tekriwal, Aarna, Loomes, Kathleen M., Spinner, Nancy B., Gilbert, Melissa A.
Format: Article
Language:English
Subjects:
Alagille syndrome
Alagille Syndrome - diagnosis
Alagille Syndrome - genetics
ALGS
Alternative Splicing - genetics
Computational Biology - methods
cryptic splicing variants
genomic diagnostics
Humans
JAG1
Jagged-1 Protein - genetics
Pangolin
RNA Splicing - genetics
SpliceAI
splicing
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Summary:Haploinsufficiency of JAG1 is the primary cause of Alagille syndrome (ALGS), a rare, multisystem disorder. The identification of JAG1 intronic variants outside of the canonical splice region as well as missense variants, both of which lead to uncertain associations with disease, confuses diagnostics. Strategies to determine whether these variants affect splicing include the study of patient RNA or minigene constructs, which are not always available or can be laborious to design, as well as the utilization of computational splice prediction tools. These tools, including SpliceAI and Pangolin, use algorithms to calculate the probability that a variant results in a splice alteration, expressed as a Δ score, with higher Δ scores (>0.2 on a 0–1 scale) positively correlated with aberrant splicing. We studied the consequence of 10 putative splice variants in ALGS patient samples through RNA analysis and compared this to SpliceAI and Pangolin predictions. We identified eight variants with aberrant splicing, seven of which had not been previously validated. Combining these data with non-canonical and missense splice variants reported in the literature, we identified a predictive threshold for SpliceAI and Pangolin with high sensitivity (Δ score >0.6). Moreover, we showed reduced specificity for variants with low Δ scores (
ISSN:2666-2477
2666-2477
DOI:10.1016/j.xhgg.2024.100351