Intronic position +9 and -9 are potentially splicing sites boundary from intronic variants analysis of whole exome...

Whole exome sequencing (WES) can also detect some intronic variants, which may affect splicing and gene expression, but how to use these intronic variants, and the characteristics about them has not been reported. This study aims to reveal the characteristics of intronic variant in WES data, to further improve the clinical diagnostic value of WES. A total of 269 WES data was analyzed, 688,778 raw variants were called, among these 367,469 intronic variants were in intronic regions flanking exons which was upstream/downstream region of the exon (default is 200 bps). Contrary to expectation, the number of intronic variants with quality control (QC) passed was the lowest at the +2 and -2 positions but not at the +1 and -1 positions. The plausible explanation was that the former had the worst effect on trans-splicing, whereas the latter did not completely abolish splicing. And surprisingly, the number of intronic variants that passed QC was the highest at the +9 and -9 positions, indicating a potential splicing site boundary. The proportion of variants which could not pass QC filtering (false variants) in the intronic regions flanking exons generally accord with "S"-shaped curve. At +5 and -5 positions, the number of variants predicted damaging by software was most. This was also the position at which many pathogenic variants had been reported in recent years. Our study revealed the characteristics of intronic variant in WES data for the first time, we found the +9 and -9 positions might be a potentially splicing sites boundary and +5 and -5 positions were potentially important sites affecting splicing or gene expression, the +2 and -2 positions seem more important splicing site than +1 and -1 positions, and we found variants in intronic regions flanking exons over ± 50 bps may be unreliable. This result can help researchers find more useful variants and demonstrate that WES data is valuable for intronic variants analysis.