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bioRxiv · 10.1101/2025.11.25.690514

SuSiE 2.0: improved methods and implementations for genetic fine-mapping and phenotype prediction

McCreight, A.·Cho, Y.·Li, R.·Nachun, D.·Gan, H.-Y.·Carbonetto, P.·Stephens, M.·Denault, W. R.·Wang, G.
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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.

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McCreight, A., Cho, Y., Li, R., Nachun, D., Gan, H.-Y., Carbonetto, P., Stephens, M., Denault, W. R., Wang, G.. 2025-11-28. SuSiE 2.0: improved methods and implementations for genetic fine-mapping and phenotype prediction. https://doi.org/10.1101/2025.11.25.690514

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