Abstract
Sum of Single Effects regression (SuSiE) has become widely adopted for genetic fine-mapping, yet its original implementation faces architectural limitations that hinder extensibility and performance. We present SuSiE 2.0, featuring a modular redesign for extensibility, up to 5x speed improvements for summary statistics applications, and several useful extensions including SuSiE-ash, a new method that improves calibration when strong signals coexist with moderate effects. Simulations and real data benchmarks demonstrate performance across diverse genetic architectures, highlighting improved calibration of SuSiE-ash for fine-mapping under complex polygenic backgrounds with 1.5–3x FDR reduction while maintaining power, and revealing SuSiE-based methods as effective yet underappreciated tools for TWAS prediction.
Full Text Availability
The license terms selected by the author(s) for this preprint version do not permit archiving in PMC. The full text is available from the preprint server.
