Description Usage Arguments Value References Examples
Perform highpowered detection of genetic effects on DNA methylation using integrated methylation QTL (methylation quantitativetrait locus) mapping and allelespecific analysis.
1 2 
geno 
a data list containing the genotype data 
data 
a data list containing the methylation data 
K 
a known kinship matrix. This matrix should be a positive semidefinite matrix with dimensions equal to the sample size 
Covariates 
a matrix containing the covariates subject to adjustment (Default = NULL) 
numCore 
a positive integer specifying the number of cores for parallel computing (default = 1) 
fit.maxiter 
a positive integer specifying the maximum number of iterations when fitting the generalized linear mixed model (default = 500) 
fit.tol 
a positive number specifying tolerance, the difference threshold for parameter estimates below which iterations should be stopped (default = 1e5) 
verbose 
include verbose output 
A data.frame
containing the following named elements:
loc:
ordinal number of SNPCpG pair being analyzed
numIDV:
number of observations of SNPCpG pair being analyzed
beta:
the fixed effect parameter estimate for the predictor of interest
se_beta:
the standard deviation of fixed effect
pvalue:
P value for the fixed effect, based on the Wald test
h2:
heritability of the transformed rate
sigma2:
total variance component
converged:
a logical indicator for convergence
time:
time to converge
Fan, Y., Vilgalys, T.P., Sun, S., Peng, Q., Tung, J. and Zhou, X., 2019. Highpowered detection of genetic effects on DNA methylation using integrated methylation QTL mapping and allelespecific analysis. bioRxiv, p.615039.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34  # This example demonstrates IMAGE:
data(ExampleData)
geno < ExampleData$geno
K < ExampleData$K
data < ExampleData$data
res=image(geno,data,K)
# We've saved the results of the example above to show an example of
# the outputs IMAGE produces:
data(example_results)
# Toy example for testing purposes only:
geno < list()
geno$hap1 = matrix(sample(c(0,1),25, replace = TRUE, prob = c(0.6,0.4)),
nrow = 5, ncol = 5)
geno$hap2 = matrix(sample(c(0,1),25, replace = TRUE, prob = c(0.6,0.4)),
nrow = 5, ncol = 5)
data < list()
data$r = matrix(sample(0:50,25, replace = TRUE), nrow = 5, ncol = 5)
data$y = matrix(sample(0:50,25, replace = TRUE), nrow = 5, ncol = 5)
data$r1 = matrix(sample(0:50,25, replace = TRUE), nrow = 5, ncol = 5)
data$r2 = matrix(sample(0:50,25, replace = TRUE), nrow = 5, ncol = 5)
data$y1 = matrix(sample(0:50,25, replace = TRUE), nrow = 5, ncol = 5)
data$y2 = matrix(sample(0:50,25, replace = TRUE), nrow = 5, ncol = 5)
K = matrix(runif(25,0.1,0.1), nrow = 5, ncol = 5)
res=image(geno,data,K)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.