Pleiotropy-informed significance analysis of genome-wide association studies (GWAS) with surrogate functional false discovery rates (sfFDR). The sfFDR framework adapts the fFDR to leverage informative data from multiple sets of GWAS summary statistics to increase power in study while accommodating for linkage disequilibrium. sfFDR provides estimates of key FDR quantities in a significance analysis such as the functional local FDR and q-value, and uses these estimates to derive a functional p-value for type I error rate control and a functional local Bayes' factor for post-GWAS analyses (e.g., fine mapping and colocalization). The sfFDR framework is described in Bass and Wallace (2024) <doi:10.1101/2024.09.24.24314276>.
Version: 1.0.0 Depends: R (≥ 3.5.0) Imports: locfit, splines, dplyr, ggplot2 (≥ 3.5.1), patchwork (≥ 1.3.0), gam, qvalue, tibble, tidyr, Rcpp LinkingTo: Rcpp Suggests: testthat (≥ 3.0.0), knitr, rmarkdown Published: 2024-12-02 DOI: 10.32614/CRAN.package.sffdr Author: Andrew Bass [aut, cre], Chris Wallace [aut] Maintainer: Andrew Bass <ab3105 at cam.ac.uk> License: LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL] URL: https://github.com/ajbass/sffdr NeedsCompilation: yes Materials: README NEWS CRAN checks: sffdr results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=sffdr to link to this page.
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