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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
## Mapgen
<!-- badges: start -->
<!-- badges: end -->
Mapgen is a multi-function software that performs the following tasks:
1. Enrichment analysis of functional annotations for a trait of interest.
2. Functionally-informed genetic fine-mapping.
3. Gene mapping based on fine-mapping result and genomic annotations.
## Installation
You can install the development version of `mapgen` from [GitHub][mapgen-github] with:
```{r install-mapgen-package, eval=FALSE}
install.packages("remotes")
remotes::install_github("xinhe-lab/mapgen")
```
After installing, check that it loads properly:
```{r load-mapgen, eval=FALSE}
library(mapgen)
```
## Tutorials
### [Data preparation][data-preparation-tutorial]
Prepare input data: GWAS summary statistics, LD information, etc.
### [Enrichment analysis][enrichment-tutorial]
Assess the enrichment of genetic signals of a trait of interest in functional
annotations using `TORUS`.
*Please install [TORUS](https://github.com/xqwen/torus) software package,
if you need to run enrichment analysis.
### [Functional fine-mapping][finemapping-tutorial]
Perform Bayesian statistical fine-mapping using `SuSiE` on trait-associated loci,
using a informative prior that favors variants located in enriched annotations.
*Please install [susieR](https://github.com/stephenslab/susieR) package,
if you need to run fine-mapping with GWAS summary statistics.
### [Gene mapping][gene-mapping-tutorial]
Infer causal genes at each locus based on fine-mapping result and genomic
annotations.
### [PIP partitioning by annotation categories][partition-pip-tutorial]
Partitioning finemapping PIPs by annotation categories.
### [Making track plots][track-plot-tutorial]
Making track plots of GWAS, finemapping, and annotation data using `Gviz` package.
## Reference
> Alan Selewa\*, Kaixuan Luo\*, Michael Wasney, Linsin Smith, Xiaotong Sun,
Chenwei Tang, Heather Eckart, Ivan Moskowitz, Anindita Basu, Xin He,
Sebastian Pott. Single-cell genomics improves the discovery of risk variants
and genes of atrial fibrillation. _Nat Commun._ 2023 Aug 17;14(1):4999.
doi: 10.1038/s41467-023-40505-5. PMID: 37591828; PMCID: PMC10435551.
[mapgen-github]: https://github.com/xinhe-lab/mapgen
[tutorials]: https://xinhe-lab.github.io/mapgen/articles/index.html
[data-preparation-tutorial]: https://xinhe-lab.github.io/mapgen/articles/data_preparation_tutorial.html
[enrichment-tutorial]: https://xinhe-lab.github.io/mapgen/articles/enrichment_tutorial.html
[finemapping-tutorial]: https://xinhe-lab.github.io/mapgen/articles/finemapping_tutorial.html
[finemapping-ukbb-ld-diagnosis-tutorial]: https://xinhe-lab.github.io/mapgen/articles/finemapping_ukbb_ld_diagnosis.html
[gene-mapping-tutorial]: https://xinhe-lab.github.io/mapgen/articles/gene_mapping_tutorial.html
[partition-pip-tutorial]: https://xinhe-lab.github.io/mapgen/articles/partition_pip_tutorial.html
[track-plot-tutorial]: https://xinhe-lab.github.io/mapgen/articles/track_plot_tutorial.html