===================== INSTRUCTIONS ============================== This document contains the instructions for installing and using the MCGDTI package in R. Note: please install and use the package under the R (version 3.4.1) environment with 32-bit, otherwise R may report errors and the package cannot work. 1. Install For Windows users, download the compiled R package file for Windows "MCGDTI_1.0.zip". Then, use the following command in R to install the package >install.packages(pkgs="MCGDTI_1.0.zip",repos=NULL) For Linux and Unix users, download the compiled R package file for Linux and Unix "MCGDTI_1.0.tar.gz". Then, use the following command in R to install the package >install.packages(pkgs="MCGDTI_1.0.tar.gz",repos=NULL) 2. Use Use >library(MCGDTI) to load the package into R. Use >?MCGDTI to read the instructions. 3. Example First, use >data(ped) to load the sample pedigree data file in the "MCGDTI" package. Then use >MCGDTI(ped, mcsize = 50, header = T, loci = NULL, status_missing = 9, af = NULL) $Tstat MCGDTI MCGDT-ME MCPPAT SNP1_1 0.00704976 -0.069907 -0.4685213 SNP2_1 41.35675093 3.873221 3.4822411 $pvalue MCGDTI MCGDT-ME MCPPAT SNP1_1 9.964813e-01 0.9442676830 0.6394118528 SNP2_1 1.045913e-09 0.0001074062 0.0004972358 Note 1. If the ped data set was saved in the text file, then use >MCGDTI("ped.txt", mcsize = 50, header = T, loci = NULL, status_missing = 9, af = NULL) Note 2. For incomplete pedigree data (with missing genotypes), the function outputs the statistics and P-values of MCGDTI, MCGDT-ME and MCPPAT. These methods are calculated based on the Monte Carlo sampling and estimation. Note 3. For complete pedigree data (without missing genotypes), the function outputs the statistics and P-values of GDTI, GDT-ME and PPAT. Note 4. The statistics and P-values of MCGDTI, MCGDT-ME and MCPPAT may be different for different runs, because of the Monte Carlo sampling. Note 5. 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, 0 = unknown), in that order. Note that these fields need to be numeric. The 6th column gives the affection status (1 = unaffected, 2 = affected, 9 = unknown). The remaining columns code for marker genotypes (2 columns 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. header The header of input data. If ped contains variable names, header is taken as TRUE (or T); otherwise, header is taken as FALSE (or F). The default value is FALSE (or F). loci The name of a standard linkage loci file. status_missing The input variable "status_missing" is the missing value for the affection status in the data file, and the default value is 9. It can take NA, but cannot take 1, 2, or any other string values. af Dataframe/matrix of marker allele frequencies, one column for each marker locus. If af=NULL, then it will be estimated from the typed founders. 5. References Chen, W. M., Manichaikul, A., Rich, S. S. (2009). A generalized family-based association test for dichotomous traits. The American Journal of Human Genetics, 85(3):364-376. Ding, J., Lin, S., Liu, Y. (2006). Monte Carlo pedigree disequilibrium test for markers on the X chromosome. The American Journal of Human Genetics, 79(3):567-573. Ott, J., Lathrop, G. M. (1990). SLINK: a general simulation program for linkage analysis. The American Journal of Human Genetics, 47:A204. Li, J. L., Wang, P., Fung, W. K., Zhou, J. Y. (2017). Generalized disequilibrium test for association in qualitative traits incorporating imprinting effects based on extended pedigrees. (submitted) Zhou, J. Y., Ding, J., Fung, W. K., Lin, S. (2010). Detection of parent-of-origin effects using general pedigree data. Genetic Epidemiology, 34(2):151-158. ----------------------------------------------------------------- If you have any questions, please email Dr. Ji-Yuan Zhou at zhoujiyuan@gmail.com