Accounting for uncertainty in residual variances improves calibration for fine-mapping with small sample sizes Article Swipe

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Gao Wang , William R. P. Denault , Peter Carbonetto , Ruixi Li , Matthew Stephens ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.21203/rs.3.rs-7490160/v1

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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Type: article
Landing Page: https://doi.org/10.21203/rs.3.rs-7490160/v1
PDF: https://www.researchsquare.com/article/rs-7490160/latest.pdf
OA Status: gold
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DOI: https://doi.org/10.21203/rs.3.rs-7490160/v1

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Title: Accounting for uncertainty in residual variances improves calibration for fine-mapping with small sample sizes

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Publication year: 2025

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Publication date: 2025-10-16

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Authors: Gao Wang, William R. P. Denault, Peter Carbonetto, Ruixi Li, Matthew Stephens

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Landing page: https://doi.org/10.21203/rs.3.rs-7490160/v1

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PDF URL: https://www.researchsquare.com/article/rs-7490160/latest.pdf

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