The current implementation is DAP-G!

This repository contains the software implementations for a suite of statistical methods to perform genetic association analysis integrating genomic annotations. These methods are designed to perform rigorous enrichment analysis, QTL discovery and multi-SNP fine-mapping analysis in a highly efficient way. The statistical model and the key algorithm, Deterministic Approximation of Posteriors (DAP), are described in this manuscript and this preprint

The repository includes source code, scripts and necessary data to replicate the results described in the manuscript. A detailed tutorial to guide the users through some specific analysis tasks is also included.

For questions/comments regarding to the software package, please contact Xiaoquan (William) Wen (xwen at umich dot edu).

Software distributed under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. See LICENSE for more details.

• dap_src: C/C++ source code of the adaptive DAP algorithm with new and improved features (now working with summary-level statistics)

• torus_src: C/C++ source code of the EM-DAP1 algorithm (for enrichment analysis and QTL discovery)

• utility: utility scripts for results interpretation, file format conversion etc.

• version 1: legacy code of the DAP implementation from version 1

User manual for DAP-G is available in pdf and in html

We are in the process of updating the tutorial for the new DAP-G. The tutorial for the old version of DAP can be found here.

• Xiaoquan Wen (University of Michigan)
• Roger Pique-Regi (Wayne State University)
• Yeji Lee (University of Michigan)

• Wen, X., Lee, Y., Luca, F., Pique-Regi, R. Efficient Integrative Multi-SNP Association Analysis using Deterministic Approximation of Posteriors. The American Journal of Human Genetics, 98(6), 1114-1129
• Lee, Y, Luca, F, Pique-Regi, R,Wen, X. Bayesian Multi-SNP Genetic Association Analysis: Control of FDR and Use of Summary Statistics bioRxiv:316471