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XMAP
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# XMAP [](https://zenodo.org/badge/latestdoi/598050665) XMAP is a computationally efficient and statistically accurate method for fine-mapping causal variants using GWAS summary statistics. Check out our manuscript in Nature Communications: - [Nature Communications](https://doi.org/10.1038/s41467-023-42614-7) - [Full text PDF](https://urldefense.com/v3/__https://rdcu.be/dpGfM__;!!KjDnqvtInNPT!gO0tXuDsQM0696a74hDXc9wMM_F1ke7-AswOo6Wj32PUCbvTRjP5nd_lTRKx-GDr66NJcCinlJEa6cn1BzyE9Cz2tasbsgWzSCaT8g$) - [bioRxiv preprint](https://doi.org/10.1101/2023.03.30.534832) Briefly, XMAP leverages different LD structures of genetically diverged populations to better distinguish causal variants from a set of associated variants. By jointly modeling SNPs with putative causal effects and polygenic effects, XMAP allows a linear-time computational cost to identify multiple causal variants, even in the presence of an over-specified number of causal variants. It further corrects confounding bias hidden in the GWAS summary statistics to reduce false positive findings and improve replication rates. The fine-mapping results given by XMAP can be further used for downstream analysis to illuminate the causal mechanisms at different cascades of biological processes, including tissues, cell populations, and individual cells. In particular, XMAP results can be effectively integrated with single-cell datasets to identify disease/trait-relevant cells.  # Installation * Prerequisites: XMAP is developed under R (version >= 3.6.1). * Latest version: The latest developmental version of XMAP can be downloaded from GitHub and installed from source by ``` # Install devtools, if necessary if (!requireNamespace("devtools", quietly = TRUE)) install.packages("devtools") # Install XMAP devtools::install_github("YangLabHKUST/XMAP") # load XMAP library(XMAP) ``` # Manual In the R terminal, please use the command `?XMAP` to access the help documents. Please see the tutorial website https://mxcai.github.io/XMAP-tutorial/index.html for details on using XMAP, which includes several tutorials: - For a quick start, see [here](https://mxcai.github.io/XMAP-tutorial/quick_start.html). - For a full real data example with available LD matrice, see [here](https://mxcai.github.io/XMAP-tutorial/LDL.html). - To construct LD matrices with your own genotype data or publicly available data, see [here](https://mxcai.github.io/XMAP-tutorial/LDL_TGP.html) # Tutorial We provide a [tutorial website](https://mxcai.github.io/XMAP-tutorial/index.html) of XMAP for analyzing cross-population GWAS data. Please see the vignettes in the tutorial for details of installation, usage and visualization. The datasets involved in the tutorial can be downloaded [here](https://hkustconnect-my.sharepoint.com/:f:/g/personal/mcaiad_connect_ust_hk/EhJHXBkK_DNBjLFbIPjMeaoBFlmFwlz0F_uXXU0kvIrVGg?e=sTEh8O). # Reproducibility We provide codes and fine-mapping results presented in the XMAP manuscript [here](https://github.com/YangLabHKUST/XMAP/tree/main/results) # Operating systems tested on: macOS Ventura 13.0 Windows 10 Enterprise Version Ubuntu 18.04.5 LTS (Bionic Beaver) # License XMAP is licensed under the GNU General Public License v3.0. # Reference Mingxuan Cai, Zhiwei Wang, Jiashun Xiao, Xianghong Hu, Gang Chen, Can Yang (2023). XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias. Nature Communications, 14(1),6870. [Link](https://doi.org/10.1038/s41467-023-42614-7) # Contact Improvements and new features of XMAP will be updated on a regular basis. Please feel free to contact Mingxuan Cai (mx.cai@cityu.edu.hk) or Prof. Can Yang (macyang@ust.hk) if any questions.
