===================== INSTRUCTIONS ==============================

This document contains the instructions for installing and using the
MCPDTI package in R.

Note: please install and use the package under the R (version 3.1.2)
environment with 32-bit, otherwise R may report errors and the package cannot work.
The MCPDTI package will use the R package "mvtnorm". Therefore, make sure that this package has 
been installed and loaded into R.


1. Install

For Windows users, connect the computer to the internet and then run the following command in R:
>install.packages(pkgs="mvtnorm") 
After that, choose a CRAN mirror to download and install the R package "mvtnorm". 

Next, download the compiled R packages file for Windows
"MCPDTI_1.0.zip". Then, use the following command in R to install the package
>install.packages(pkgs="MCPDTI_1.0.zip",repos=NULL)


2. Use

Use
>library(mvtnorm)
>library(MCPDTI)
to load these two packages into R.

Use
>?MCPDTI
to read the instructions.


3. Example 

Download the sample pedigree file "ped.txt".
Then use
> MCPDTI("ped.txt", mcsize=50, loci = NULL, header = T, af = NULL, alphaI=0.05)

$Tstat
                   MCPDTI         MCPDT            PDT          MCPPAT         PPAT
SNP1_1          -1.087339       -1.087339       -1.087339     0.6546537    0.6546537
SNP2_1           4.602723        3.872219        3.165869     4.5565627    2.6111648

$pvalue
                 MCPDTI             MCPDT            PDT            MCPPAT           PPAT
SNP1_1         2.809010e-01     0.2768871145     0.276887115     5.126908e-01    0.512690760
SNP2_1         3.839121e-06     0.0001078491     0.001546203     5.199753e-06    0.009023439




Note 1. The function outputs the statistics and P-values of MCPDTI, MCPDT, PDT, MCPPAT and PPAT. 
MCPDTI, MCPDT and MCPPAT are calculated based on the Monte Carlo sampling and estimation. 

Note 2. The the statistics and P-values of MCPDTI, MCPDT and MCPPAT may be different for different runs, because of the Monte Carlo sampling.

Note 3. If af (allele frequency) is unknown (af=NULL), it will be estimated based on all the
typed founders.
 


4. Arguments

ped	The name of a standard linkage pedigree file or a matrix/dataframe containing pedigree relationship, genotype, and phenotype information, one row for each individual. The first 5 columns give the pedigree id, individual id, father id (0 if founder), mother id (0 if founder), and sex (1=male, 2=female), in that order. Note that these fields need to be numeric. The sixth column gives the affection status (1=unaffected; 2=affected; 0=unknown). The remaining columns code for marker genotypes (2 column per SNP), with the two alleles for each marker represented by two consecutive numbers (0=missing).
mcsize	The Monte Carlo sample size. It can take any positive integer.
loci	The name of a standard linkage loci file.
header	The header of input data. If header=TRUE, then the names of SNPs will be used as row names of the output results.
af	Dataframe/matrix of marker allele frequencies, one column for each marker locus. If af=NULL, then it will be estimated from typed founders.
alphaI	The significance level for imprinting test of MCPPAT.


5. References

Ji-Yuan Zhou, Hai-Qiang He, Xiao-Ping You, Shao-Zhan Li, Ping-Yan Chen, Wing Kam Fung. 2014 A powerful association test for qualitative traits incorporating imprinting effects using general pedigree data. Journal of Human Genetics (accpeted).

Jie Ding, Shili Lin, Yang Liu. 2006 Monte Carlo pedigree disequilibrium test for markers on X chromosome. American Journal of Human Genetics, 79, 567-573.

Eden R. Martin, Stephanie A. Monks, Liling L. Warren, Norman L. Kaplan. 2000 A test for linkage and association in general pedigrees: the pedigree disequilibrium test. American Journal of Human Genetics, 67, 146-154.

Ji-Yuan Zhou, Jie Ding, Wing K. Fung, Shili Lin. 2010 Detection of parent-of-origin effects using general pedigree data. Genetic Epidemiology, 34, 151-158.

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If you have any questions, please email Dr. Ji-Yuan Zhou at zhoujiyuan@gmail.com