Description Usage Arguments Details Value Author(s) References Examples
mQTL mapping in bisulfite sequencing studies by fitting a binomial mixed model, incorporating allelic-specific methylation pattern.
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 semi-definite matrix with dimensions equal to the samplie 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 = 1e-5). |
verbose |
a logical switch for printing detailed information (parameter estimates in each iteration) for testing and debugging purpose (default = TRUE). |
pmt |
an integer specifying the permutation approach, set pmt equals either 1 or 2 will produce permuted p-values(default pmt=0). |
IMAGE properly accounts for the count nature of bisulfite sequencing data and incorporates allele-specific methylation patterns from heterozygous individuals to enable more powerful mQTL discovery. Binomial mixed models (BMM) are fitted using the penalized quasi-likelihood (PQL) method proposed by Breslow and Clayton (1993).
loc |
ordinal number of SNP-CpG pair being analyzed |
numIDV |
number of observations of SNP-CpG 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. |
Yue Fan, Shiquan Sun, Xiang Zhou
Breslow, N.E. and Clayton, D.G. (1993) Approximate Inference in Generalized Linear Mixed Models. Journal of the American Statistical Association 88, 9-25.
1 2 3 4 5 | data(exampledata)
attach(exampledata)
res=image(geno,data,K)
closeAllConnections()
detach(exampledata)
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