Modification of the SuSiE model enhances calibration and reduces false positives in genetic fine-mapping, highlighting its relevance in molecular QTL studies.
Abstract 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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Wang et al. (2025) studied this question.
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