PDIVAS: Pathogenicity predictor for Deep-Intronic Variants causing Aberrant Splicing

Abstract

Deep-intronic variants often cause genetic diseases by altering RNA splicing. However, these pathogenic variants are overlooked in whole-genome sequencing analyses, because they are quite difficult to segregate from a vast number of benign variants (approximately 1,500,000 deep-intronic variants per individual). Therefore, we developed the Pathogenicity predictor for Deep-Intronic Variants causing Aberrant Splicing (PDIVAS), an ensemble machine-learning model combining multiple splicing features and regional splicing constraint metrics. Using PDIVAS, around 27 pathogenic candidates were identified per individual with 95% sensitivity, and causative variants were more efficiently prioritized than previous predictors in simulated patient genome sequences. PDIVAS is available at https://github.com/shiro-kur/PDIVAS.