Combining genetic constraint with predictions of alternative splicing to prioritize deleterious splicing in rare disease studies.

ABSTRACT:

Background

Despite numerous molecular and computational advances, roughly half of patients with a rare disease remain undiagnosed after exome or genome sequencing. A particularly challenging barrier to diagnosis is identifying variants that cause deleterious alternative splicing at intronic or exonic loci outside of canonical donor or acceptor splice sites.

Results

Several existing tools predict the likelihood that a genetic variant causes alternative splicing. We sought to extend such methods by developing a new metric that aids in discerning whether a genetic variant leads to deleterious alternative splicing. Our metric combines genetic variation in the Genome Aggregate Database with alternative splicing predictions from SpliceAI to compare observed and expected levels of

SUBMITTER: Cormier MJ 

PROVIDER: S-EPMC9664736 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature