Combined Analysis of Pleiotropy and Epistasis for Diversity Outbred Mice
Mean-center and normalize phenotypes
Bins a single scan curve into peaks automatically
Snap continuous values to a grid
Error propagation
Calculate empirical p-values
Calculate m
Calculate P Values for Interactions Based on Permutations
The CAPE data object
Converts a read_population object to a multi-parent object
This function mean centers and standardizes a vector
Checks for unused markers
Check community assignments
Check to see if the any markers in the supplied genotype matrix is one...
Checks for underscores in marker names
Bins a vector into chunks
Retrieve colors based on numeric values
Removes markers from data_obj that are not present in the geno_obj
Generate a matrix of consecutive elements
Delete underscores from marker names
Calculate the significance of direct influences of variant pairs on ph...
Draw a pie chart
Estimate Errors of Regression Coefficients
Exponential color function
Drop the 3rd dimension of an array using a summary function, e.g., min...
Calculate a genome-wide significance threshold for the single-variant ...
Get DO colors
Get allele assignments for linkage blocks
Generate coordinates for a circle
Find column numbers using column names
get a hex color string
Generate color ramp
Generate list of concentric circles
Get covariate information
Calculate eigentraits
Gets the geno object
Returns which dimensions the individual, locus, and alleles are in in ...
Return the genotype matrix with covariates added.
Get error bars for interaction plot
Get the best layout matrix for a given number of panes per page.
Get line coordinates
Check selected markers for linear independence.
Get chromosome numbers for markers
Get genotype or covariate values
Get original indices for markers
Get marker genomic position
Get marker names
Get numbers for markers
Convert the final results to an adjacency matrix.
Select marker pairs for pairscan
Get the phenotype matrix
Perform linear regression on multi-allele markers.
Plot trait histograms
Plot a heatmap
Impute missing genotype data using k nearest neighbors
Corrects genotypes, phenotypes, and covariates for kinship.
Calculate the kinship matrix
Identify linkage blocks
Loads input and run CAPE
Creates a covariate from a genetic marker
Generate a Heatmap-type image
This is an internal function to run a single pairscan It is used both ...
Performs marker regression
Get all pairs of elements in a vector
This function performs the pairwise scan on all markers.
Run the pairscan with a kinship correction
Perform pairscan without a kinship correction
Generate a null distribution for the pairscan.
Generates a null distribution for the pairscan
pheatmap generate breaks found at this link https://cran.r-project.org...
pheatmap scale colours found at this link https://cran.r-project.org/p...
Create a covariate from a trait
Convert plink2 files to cape format
Plot phenotypic effect for two markers as a bar plot
Plot Interaction Effects
Plot the final epistatic network in a traditional network view.
Plot phenotypic effects for two markers as a heat map
Plot interaction plot for traits and genetic markers
Plots cape results as a circular network
Plot the result of the pairwise scan
Plot trait pairs against each other
Plot phenotypic effect for two markers as points
Plot results of single-locus scans
Plots eigentraits
Plot concentric trait circles
Plot cape coefficients
Plot trait distributions
Convert qtl2 object to cape format
Read the parameter file, add missing entries
Reads in data in the R/qtl csv format
Remove individuals
Removes individuals from the kinship object to match the cape.obj
Removes genetic markers
Removes individuals and/or markers with missing data
Take out markers not used in cape
Report Progress of a Process
Orients a matrix for proper display in a plot
Runs CAPE
Rank Z normalize
Divide a region into equal parts.
Assign selected eigentraits in the Cape object
Select markers for the pairwise scan.
This function selects the phenotypes in a Cape object
Runs marker regression on each individual genetic marker
Sort a table by a list of columns
Save the cross data in R/qtl CSV format
Write significant cape interactions to a csv file
Combined Analysis of Pleiotropy and Epistasis infers predictive networks between genetic variants and phenotypes. It can be used with standard two-parent populations as well as multi-parent populations, such as the Diversity Outbred (DO) mice, Collaborative Cross (CC) mice, or the multi-parent advanced generation intercross (MAGIC) population of Arabidopsis thaliana. It uses complementary information of pleiotropic gene variants across different phenotypes to resolve models of epistatic interactions between alleles. To do this, cape reparametrizes main effect and interaction coefficients from pairwise variant regressions into directed influence parameters. These parameters describe how alleles influence each other, in terms of suppression and enhancement, as well as how gene variants influence phenotypes. All of the final interactions are reported as directed interactions between pairs of parental alleles. For detailed descriptions of the methods used in this package please see the following references. Carter, G. W., Hays, M., Sherman, A. & Galitski, T. (2012) <doi:10.1371/journal.pgen.1003010>. Tyler, A. L., Lu, W., Hendrick, J. J., Philip, V. M. & Carter, G. W. (2013) <doi:10.1371/journal.pcbi.1003270>.