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A fast and scalable method to detect epistasis in complex traits from biobank-scale studies
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Thesmer
package implements a computationally and statisticallyefficient method for detecting marginal epistasis in genome-wideassociation studies (GWAS). Find the full package documentationincluding examples and articles here:Sparse Marginal Epistasis testDocumentation.
- Hutchinson’s stochastic trace estimator: efficient and scalablecomputation
- Mailman algorithm: fast vector-by-matrix operation
- Linear mixed model: controls for population structure
- Multimodal Input: incorporates additional data from HDF5 files toimprove power in detecting gene-by-gene interactions.
- Optimize for Memory Constraints: Highly configurable block wiseprocessing of the data allows to make the most of available resources.See alsoHow To Optimize the Memory Requirements ofSME.
- Parallelization: Utilizes OpenMP for multi-threaded processing.
You can install the latest release from CRAN
install.packages("smer")
You can install the development version ofsmer
fromGitHub with:
install.packages("devtools")devtools::install_github("lcrawlab/sme")
System requirements of the package:
- GNU make
- R (>= 4.4)
- Rhdf5lib (from BioConductor)
- OpenMP (optional)
To installRhdf5lib
, first install the toolBiocManager
from CRAN,then install the library using this tool.
if (!require("BiocManager",quietly=TRUE)) install.packages("BiocManager")BiocManager::install("Rhdf5lib")
The full list of R dependencies can be found in theDESCRIPTIONfile.
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 flagsSHLIB_OPENMP_CXXFLAGS
andLDFLAGS
in the~/.R/Makevars
file.
System | Required Flags |
---|---|
OS X | -Xclang -fopenmp -lomp |
Linux | -fopenmp -lomp |
If the error isld: library "crypto" not found
, installopenssl
(e.g.brew install openssl
).
Compiling the package requires the compiler to find the libraries forthe dependencies. For unix systems, the libraries are typicallyinstalled at/usr/local/lib
and/usr/local/include
. For users usingOS X and homebrew, the libraries are typically installed at/opt/homebrew/lib
and/opt/homebrew/include
.
Non-standard library paths need to be configured. Thesrc/Makevars
file configures the compiler flags and considers theLDFLAGS
andCPPFLAGS
from the~/.R/Makevars
file.
- Stamp J, Crawford L (2025). smer: The Sparse Marginal Epistasis Test. Rpackage version 0.0.1,https://lcrawlab.github.io/sme/,https://github.com/lcrawlab/sme.
- Stamp J, Smith Pattillo S, Weinreich D, Crawford L (2025). Sparsemodeling of interactions enables fast detection of genome-wideepistasis in biobank-scale studies. biorxiv,https://doi.org/10.1101/2025.01.11.632557
- Stamp J, Crawford L (2024). mvMAPIT: Multivariate Genome Wide MarginalEpistasis 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 forepistasis 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 epistasisreveals genome-wide signals of polygenic epistasis. bioRxiv.
- Crawford et al. (2017): Detecting epistasis with the marginalepistasis test. PLoS Genetics.
- Devresse et al. (2024): HighFive - Header-only C++ HDF5 interface.https://zenodo.org/records/13120799
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A fast and scalable method to detect epistasis in complex traits from biobank-scale studies