sme

A fast and scalable method to detect epistasis in complex traits from biobank-scale studies

https://github.com/lcrawlab/sme

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A fast and scalable method to detect epistasis in complex traits from biobank-scale studies

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# The Sparse Marginal Epistasis test 


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[![CRAN downloads](https://cranlogs.r-pkg.org/badges/grand-total/smer)](https://cranlogs.r-pkg.org/badges/grand-total/smer)
[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/smer)](https://cran.r-project.org/package=smer)


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

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