Enhanced Disease Susceptible Variant Identification via Short Identity by Descent Segments

Rare diseases affect millions of individuals worldwide, yet diagnostic yields for them still remain low. Among variant identification approaches, identity by descent (IBD) mapping is used to identify disease susceptible variants originating from a recent common ancestor among affected individuals, but existing IBD detection models struggle to identify these variants in short IBD segments. Here, we introduce SILO, a novel model to detect disease susceptible variants in both short and long IBD segments. SILO employs a two-stage procedure to detect IBD segments. In the first stage, SILO identifies long IBD segments based on common variants. In the second stage, SILO utilizes rare variants to detect short IBD segments using a seed-and-extend algorithm. We evaluated SILO in simulated data and real data from the 1000 Genomes Project. Our results demonstrate that SILO outperforms existing models in detecting disease susceptible variants within short IBD segments, and show comparable performance in detecting these variants within longer IBD segments. These findings highlight the potential of SILO to increase diagnostic yields for rare diseases by enhancing the identification of previously overlooked disease susceptible variants in short IBD segments. Nonetheless, we note that the detection of short IBD segments remains challenging due to limited precision, leaving room for future improvement.