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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
<!-- You'll still need to render `README.Rmd` regularly, to keep `README.md`
up-to-date.
`devtools::build_readme()` is handy for this. -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# The Sparse Marginal Epistasis test <img src="man/figures/logo.png" align="right" height="200" alt="" />
<!-- badges: start -->
[](https://github.com/lcrawlab/sme/actions/workflows/r-cmd-check.yml)
[](https://cranlogs.r-pkg.org/badges/grand-total/smer)
[](https://cran.r-project.org/package=smer)
<!-- badges: end -->
The `smer` package implements a computationally and statistically efficient method
for detecting marginal epistasis in genome-wide association studies (GWAS).
Find the full package documentation including examples and articles here:
[Sparse Marginal Epistasis test Documentation](https://lcrawlab.github.io/sme/).
## Key Features
- Hutchinson's stochastic trace estimator: efficient and scalable computation
- Mailman algorithm: fast vector-by-matrix operation
- Linear mixed model: controls for population structure
- Multimodal Input: incorporates additional data from HDF5 files to improve power in
detecting gene-by-gene interactions.
- Optimize for Memory Constraints: Highly configurable block wise processing of the
data allows to make the most of available resources. See also
[How To Optimize the Memory Requirements of SME](https://lcrawlab.github.io/sme/articles/tutorial-memory-optimization.html).
- Parallelization: Utilizes OpenMP for multi-threaded processing.
## Installation
You can install the development version of `smer` from [GitHub](https://github.com/)
with:
``` r
install.packages("devtools")
devtools::install_github("lcrawlab/sme")
```
## Dependencies
System requirements of the package:
- GNU make
- R (>= 4.4)
- Rhdf5lib (from BioConductor)
- OpenMP (optional)
To install `Rhdf5lib`, first install the tool `BiocManager` from CRAN, then install
the library using this tool.
```{r, eval = FALSE}
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("Rhdf5lib")
```
The full list of R dependencies can be found in the
[DESCRIPTION file](https://github.com/lcrawlab/sme/blob/main/DESCRIPTION).
### OpenMP
For OS X and Linux, the OpenMP library can be installed via one of the (shell)
commands specified below:
| System | Command |
|:------------------------------------------|:----------------------------------|
| **OS X (using Homebrew)** | `brew install libomp` |
|**Debian-based systems (including Ubuntu)**| `sudo apt-get install libomp-dev` |
To enable openMP, it may be necessary to configure the compiler flags
`SHLIB_OPENMP_CXXFLAGS` and `LDFLAGS` in the `~/.R/Makevars` file.
| System | Required Flags |
|---------|-------------------------|
| OS X | `-Xclang -fopenmp -lomp`|
| Linux | `-fopenmp -lomp` |
## Known Issues
Compiling the package requires the compiler to find the libraries for the
dependencies. For unix systems, the libraries are typically installed at
`/usr/local/lib` and `/usr/local/include`. For users using OS X and homebrew, the
libraries are typically installed at `/opt/homebrew/lib` and `/opt/homebrew/include`.
Non-standard library paths need to be configured. The `src/Makevars` file
configures the compiler flags and considers the `LDFLAGS` and `CPPFLAGS` from the
`~/.R/Makevars` file.
## References
- Stamp J, Crawford L (2025). SME: The Sparse Marginal Epistasis Test. R package
version 0.0.1, https://lcrawlab.github.io/sme/, https://github.com/lcrawlab/sme.
- Stamp J, Smith Pattillo S, Weinreich D, Crawford L (2025). Sparse modeling of
interactions enables fast detection of genome-wide epistasis in biobank-scale
studies. biorxiv, https://doi.org/10.1101/2025.01.11.632557
- Stamp J, Crawford L (2024). mvMAPIT: Multivariate Genome Wide Marginal Epistasis
Test. R package version 2.0.3, https://lcrawlab.github.io/mvMAPIT/,
https://github.com/lcrawlab/mvMAPIT.
- Stamp et al. (2023): Leveraging genetic correlation between traits for epistasis
detection in GWAS. G3: Genes, Genomes, Genetics.
- Fu, B., Pazokitoroudi, A., Xue, A., Anand, A., Anand, P., Zaitlen, N., &
Sankararaman, S. (2023). A biobank-scale test of marginal epistasis reveals
genome-wide signals of polygenic epistasis. bioRxiv.
- Crawford et al. (2017): Detecting epistasis with the marginal epistasis test.
PLoS Genetics.
- Devresse et al. (2024): HighFive - Header-only C++ HDF5 interface.
https://zenodo.org/records/13120799