Genome-wide detection of human variants that disrupt intronic branchpoints
Pre-mRNA splicing is initiated with the recognition of a single-nucleotide intronic branchpoint (BP) within a BP motif by spliceosome elements. Fifty-six rare variants in 44 human genes have been reported to alter splicing and cause disease by disrupting BP. However, until now, no computational approach has been available to efficiently detect such variants in next-generation sequencing (NGS) data. We established a comprehensive human genome-wide BP database by integrating existing BP data, and by generating new BP data from RNA-seq of lariat debranching enzyme DBR1-mutated patients and from machine-learning predictions. We in-depth characterize multiple features of BP in major and minor introns, and find that BP and BP-2 (two-nucleotides upstream of BP) positions exhibit a lower rate of variation in human populations and higher evolutionary conservation than the intronic background, whilst being comparable to the exonic background. We develop BPHunter as a genome-wide computational approach to systematically and efficiently detect intronic variants that may disrupt BP recognition in NGS data. BPHunter retrospectively identifies 48 of the 56 known pathogenic BP mutations in which we summarize a strategy for prioritizing BP mutation candidates, and the remaining 8 all create AG dinucleotides between BP and acceptor site which is probably the reason for mis-splicing. We demonstrate the utility of BPHunter prospectively by using it to identify a novel germline heterozygous BP variant of STAT2 in a patient with critical COVID-19 pneumonia, and a novel somatic intronic 59-nucleotide deletion of ITPKB in a lymphoma patient, both of which we validate experimentally. BPHunter is publicly available from https://hgidsoft.rockefeller.edu/BPHunter and https://github.com/casanova-lab/BPHunter.