Title: | MArginal ePIstasis Test for Regions |
---|---|
Description: | A genetic analysis tool and variance component model for identifying marginal epistasis between pathways and the rest of the genome. 'MAPITR' uses as input a matrix of genotypes, a vector of phenotypes, and a list of pathways. 'MAPITR' then iteratively tests each pathway for epistasis between any variants within the pathway versus any variants remaining in the rest of the genome. 'MAPITR' returns results in the form of p-values for every pathway indicating whether the null model of there being no epistatic interactions between a pathway and the rest of the genome can be rejected. |
Authors: | Michael Turchin [aut, cre] , Gregory Darnell [aut, ctb] , Lorin Crawford [aut, ctb] , Sohini Ramachandran [aut] , Peter Carbonetto [ctb] |
Maintainer: | Michael Turchin <[email protected]> |
License: | MIT + file LICENSE |
Version: | 1.1.2 |
Built: | 2024-11-11 06:50:56 UTC |
Source: | CRAN |
MAPITR
)Run MAPITR
for a group of pathways on a single
phenotype and a set of genome-wide SNPs
MAPITR( Genotypes, Phenotype, Pathways, Covariates = NULL, CenterStandardize = TRUE, OpenMP = FALSE, cores = NULL, ... )
MAPITR( Genotypes, Phenotype, Pathways, Covariates = NULL, CenterStandardize = TRUE, OpenMP = FALSE, cores = NULL, ... )
Genotypes |
A n x p matrix containing the genotypes (0/1/2) for all p SNPs across all n individuals. No default value. |
Phenotype |
A vector containing phenotypic values for all individuals being analyzed. No default value. |
Pathways |
A r x 2 matrix containing the pathway names and then
a comma-separated list of the |
Covariates |
A n x q matrix containing any q additional covariates that should be included in the M-projection matrix of the model. See Turchin et al. 2020 for details. Note that these are covariates which are applied to both sides of the model, ie the phenotype as well as the genotypes. A y-intercept term is automatically included and does not need to be part of this n x q matrix. No default value. |
CenterStandardize |
A logical |
OpenMP |
A logical |
cores |
A numeric value providing the expected number of cores
if the OpenMP version of the code is being used.
|
... |
Additional optional arguments. |
A list containing two entries. First, a dataframe (Results
)
containing in the first column the list of pathways that were analyzed,
in the second column the associated MAPITR
p-values for each
pathway, in the third column the associated MAPITR
variance
component estimates for each pathway, and in the fourth column the
associated MAPITR
PVEs for each pathway. Second, a matrix
(Eigenvalues
) containing the n associated MAPITR
eigenvalues for each pathway per column.
data(MAPITR_TestData_Genotypes, MAPITR_TestData_Phenotype, MAPITR_TestData_Pathways) MAPITROutput <- MAPITR(MAPITR_TestData_Genotypes, MAPITR_TestData_Phenotype, MAPITR_TestData_Pathways, OpenMP=FALSE) MAPITROutput$Results
data(MAPITR_TestData_Genotypes, MAPITR_TestData_Phenotype, MAPITR_TestData_Pathways) MAPITROutput <- MAPITR(MAPITR_TestData_Genotypes, MAPITR_TestData_Phenotype, MAPITR_TestData_Pathways, OpenMP=FALSE) MAPITROutput$Results
A simulated dataset of genotypes for use in vignettes
A data frame with 500 rows (individuals) and 750 variables (SNPs)
Manually created
A simulated set of pathways for use in vignettes
A data frame with 5 rows (pathways) and 2 variables (pathway name, comma-separated list of pathway column indices)
Manually created
Top 10 principal components from the simulated genotypes for for use in vignettes
A data frame with 500 rows (individuals) and 10 variables (PCs)
Manually created
A simulated set of phenotypes for use in vignettes
A data frame with 500 rows (individuals) and 1 variables (phenotype)
Manually created
A simulated dataset of genotypes for use in unit tests
A data frame with 500 rows (individuals) and 750 variables (SNPs)
Manually created
A simulated set of pathways for use in unit tests
A data frame with 5 rows (pathways) and 2 variables (pathway name, comma-separated list of pathway column indices)
Manually created
Top 10 principal components from the simulated genotypes for for use in unit tests
A data frame with 500 rows (individuals) and 10 variables (PCs)
Manually created
A simulated set of phenotypes for use in unit tests
A data frame with 500 rows (individuals) and 1 variables (phenotype)
Manually created