Leveraging cell-type specificity and similarity improves single-cell eQTL fine-mapping.

3b ), SuSiE predominantly captured cell-type-specific patterns but missed numerous shared effects, whereas mvSuSiE excessively weighted on both cell-type-specific and shared-by-all patterns, resulting in an unexpected trend where the number of eGenes increased with the number of shared cell types.

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Let \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{\mathcal{H}}}}$$\end{document} H denote a set of marginally insignificant SNPs.

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This is expected, as fine-mapping methods are designed to resolve strong signals within LD blocks, and typically work poorly for small genetic effects that are not even marginally significant.

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