Accounting for uncertainty in residual variances improves calibration of the Sum of Single Effects model for small sample sizes
Denault, W. R. P.; Carbonetto, P.; Li, R.; Consortium, T. A. s. D. F. G.; Wang, G.; Stephens, M.
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The Sum of Single Effects (SuSiE) model is a widely adopted method for genetic fine-mapping. We show that, in small-sample studies, the original SuSiE fitting procedure produces substantially higher rates of false positive findings. We show that a simple modification to SuSiE improves performance in small-sample studies. This modification is particularly important for emerging molecular QTL applications in rare cell types and primary tissues where sample sizes are inherently limited.
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