Preprocessing of the heterogenous stock of mice dataset | Analyze hematology traits in mice | Apply mvMAPIT | Analysis Data Availability | All Traits Overview | Two trait pairs HCT & HGB as well as MCV & MCH | Notable SNPs with marginal epistatic effects after applying the mvMAPIT framework to 15 hematology traits | References

Please Cite Us | Getting Started | Running mvMAPIT | True Epistatic SNPs | Running an Informed Exhaustive Search | Visualize Exhaustive Search Results | True epistataic structure

Run SME with PLINK data | Data Requirements and File Formats | Specifying SNPs for Analysis | Understanding the Results | Visualizing Genomic Associations | Understanding Variance Components and Effect Sizes | Narrow Sense Heritability Estimates | SessionInfo

DNAse I hypersensitive sites of erythroid differentiation reveal statistical epistasis in human hematology traits | Mask File Preparation | References | SessionInfo

Genotype Data Size and Number of Blocks | Number of Random Vectors | Number of SNPs Sharing Random Vectors | Genotype Masking for the Gene-by-Gene Interaction Covariance | Explore the Memory Requirements | A Note on the Runtime of SME | SessionInfo

The Sparse Marginal Epistasis Test (SME) | The multivariate Marginal Epistasis Test (mvMAPIT) | The Marginal Epistasis Test (MAPIT)

Mask File Format | Mask Format Requirements | Creating and Using Mask Files | SessionInfo

SessionInfo

Simulate random genotypes | Simulate liabilities | Run MAPIT with Case-Control trait | References

Generate data | Combine $P$-Values with all methods | Plot the result | References

Docker Setup | Build an Image with mvMAPIT | Run the mvMAPIT Image

The Data | Multivariate MAPIT Analysis | Load the data | Apply mvMAPIT | Plot the mvMAPIT results | Plot the regression analysis results | CR6261 | CR9114 | References

Simulate Genotypes | Details on Simulating Pairwise Epistasis