coxph.EC function. Likelihood ratio test (LRT) result is reported. In each
test, the coxph function from package survival is used.
coxph.ped(phenfile,phen,covars=NULL,mafRange=c(0,0.05),chr,genfile,
pedfile,snpinfoRdata,sep.ped=",",sep.phe=",",sep.gen=" ",time,
aggregateBy="SKATgene",maf.file,snp.cor,ssq.beta.wts=c(1,25),
singleSNP.outfile=F)test.dat phenfile mafRange phen and singleSNP, contains columns: gene, Name,
maf, ntotal, nmiss, maf_ntotal, beta, se, Z,
remark, p (p-value from LRT), MAC, n0, n1, and n2.
A burden test result file, named with phen and T/MB for Li & Leal 2008/Madsen & Browning
2009 respectively, contains columns: gene, beta, se, Z,
cmafTotal, cmafUsed, nsnpsTotal, nsnpsUsed, nmiss,
remark, and p. A SSQ test result file, named with phen and SSQ, contains
columns: gene, SSQ, cmafTotal, cmafUsed, nsnpsTotal,
nsnpsUsed, nmiss, df, and p. A generated RData that is a list that
contains scores, cov, n, maf and sey for each gene with
gene names being the names of the list. Note maf in RData is MAF based on ntotal.ntotalntotalbetamaf_ntotal of SNPs in a genemaf_ntotal of SNPs selected with mafRange in a gene for
burden tests or SSQ testbeta/se^2 in output RData, where beta and se are vectorsntotal in a gene in output RDatacoxph.ped function reads in and merges phenotype, genotype, and pedigree files to
perform single SNP analysis, two burden tests (weight=1 for Li & Leal 2008; weight=1/(MAF)/(1-MAF)
for Madsen & Browning 2009), and one sum of squares (SSQ) test (Wei 2009) using Cox proportional
hazards regression model with shared frailty (random effect) in each family as implemented
in coxph function in survival R package and to output an RData that is computed
based on single SNP results and that is compatible with seqMeta R package for conducting
meta-analysis. For burden tests and SSQ test, SNPs genotypes/results are aggregated by
aggregateBy (default = "SKATgene") using SNPs selected according to user specified
mafRange within each gene (by default). genfile contains unique individual numerical
id and genotype data on a chromosome, with the column names being "id" and SNP names. For each SNP,
the genotype data should be coded as 0, 1, 2 indicating the numbers of the coded alleles. The SNP
name in genotype file should not have any dash, '-' and other special characters(dots and
underscores are OK). phenfile contains unique individual id, phenotype and covariates data,
with the column names being "id" and phenotype and covaraite names. pedfile contains
pedigree informaion, with the column names being "famid","id","fa","mo","sex". LRT is used
in all genetic association tests.
Terry M. Therneau and Patricia M. Grambsch (2000). Modeling Survival Data: Extending the Cox Model. Springer, New York. ISBN 0-387-98784-3.
Li, B. and Leal, S. M (2008). Methods for Detecting Associations with Rare Variants for Common Diseases: Application to Analysis of Sequence Data. Am J Hum Genet, 83(3), 311-321.
Madsen, B. E. and Browning, S. R (2009). A Groupwise Association Test for Rare Mutations Using a Weighted Sum Statistic. PLoS Genet, 5(2) e1000384.
Wei P (2009). Asymptotic Tests of Association with Multiple SNPs in Linkage Disequilibrium. Genet Epidemiol, 33(6), 497-507.
## Not run:
# coxph.ped(genfile="EC_chr1.txt",phenfile="trait1.csv",pedfile="ped.csv",
# phen="trait1",covars=NULL,sep.ped=",",sep.phe=",",sep.gen=" ",
# mafRange=c(0,0.01),chr=1,snpinfoRdata="SNPinfo_EC.RData",
# aggregateBy="SKATgene",time="survival_time",maf.file="EC_MAF.csv",
# snp.cor="EC_SNPcor.RData")
# ## End(Not run)
Run the code above in your browser using DataLab