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If you use this software in your research, please cite the references listed below.
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 (2025). smer: Sparse Marginal Epistasis Test. https://github.com/lcrawlab/sme, https://lcrawlab.github.io/sme/
Stamp J, DenAdel A, Weinreich D, Crawford, L (2023). Leveraging the Genetic Correlation between Traits Improves the Detection of Epistasis in Genome-wide Association Studies. G3 Genes|Genomes|Genetics 13(8), jkad118; doi: https://doi.org/10.1093/g3journal/jkad118
Stamp J, Crawford L (2022). mvMAPIT: Multivariate Genome Wide Marginal Epistasis Test. https://github.com/lcrawlab/mvMAPIT, https://lcrawlab.github.io/mvMAPIT/
Crawford L, Zeng P, Mukherjee S, & Zhou X (2017). Detecting epistasis with the marginal epistasis test in genetic mapping studies of quantitative traits. PLoS genetics, 13(7), e1006869. https://doi.org/10.1371/journal.pgen.1006869
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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