Abstract
Neurofibromatosis type 1, characterized by neurofibromas and café-au-lait macules, is one of the most common genetic disorders caused by pathogenic NF1 variants. Because of the high proportion of splicing mutations in NF1, identifying variants that alter splicing may be an essential issue for laboratories. Here, we investigated the sensitivity and specificity of SpliceAI, a recently introduced in silico splicing prediction algorithm in conjunction with other in silico tools. We evaluated 285 NF1 variants identified from 653 patients. The effect on variants on splicing alteration was confirmed by complementary DNA sequencing followed by genomic DNA sequencing. For in silico prediction of splicing effects, we used SpliceAI, MaxEntScan (MES), and Splice Site Finder-like (SSF). The sensitivity and specificity of SpliceAI were 94.5% and 94.3%, respectively, with a cut-off value of Δ Score > 0.22. The area under the curve of SpliceAI was 0.975 (p < 0.0001). Combined analysis of MES/SSF showed a sensitivity of 83.6% and specificity of 82.5%. The concordance rate between SpliceAI and MES/SSF was 84.2%. SpliceAI showed better performance for the prediction of splicing alteration for NF1 variants compared with MES/SSF. As a convenient web-based tool, SpliceAI may be helpful in clinical laboratories conducting DNA-based NF1 sequencing.
Highlights
Neurofibromatosis type 1 (NF1; OMIM # 162200) is an autosomal dominant inherited disease and one of the most common human genetic disorders, with an incidence of ~1 in 3000 [1]
NF1 is caused by loss-of-function variants in the tumor suppressor gene, neurofibromin 1 (NF1; MIM * 613113) [1,2], which is located at chromosome 17q11.2 and contains 60 translated exons spanning over 280 kb of genomic DNA [3,4]
Confirmed splicing variants were mostly located in canonical splice sites; type I splice variants causing exon skipping [8] were the most common consequence of the splicing effects
Summary
Neurofibromatosis type 1 (NF1; OMIM # 162200) is an autosomal dominant inherited disease and one of the most common human genetic disorders, with an incidence of ~1 in 3000 [1]. High proportions of the reported NF1 disease-causing variants are single nucleotide variants (SNVs), small insertions and/or deletions of nucleotides (INDELs) (see Human Gene Mutation Database: http://www.hgmd.cf.ac.uk/, accessed on 1 July 2021) [5], which are predicted to result in a premature termination codon. To achieve a sufficient detection rate of pathogenic variants, a multistep sequence analysis procedure for both NF1 gDNA and complementary DNA (cDNA) has been recommended [3,6,9]. Since mRNA is vulnerable to decay [12,13], the yield, purity, and integrity of extracted mRNA may not be sufficient for cDNA sequence analysis. These obstacles may lead to challenges in identifying splicing variants of NF1. To compensate for the relatively low detection rate of sequencing of only gDNA, predicting the splice effect of NF1 using in silico tools would be beneficial
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