coxph.ped: function of single SNP analysis and gene-based tests for...
In RVFam: Rare Variants Association Analyses with Family Data

Description Usage Arguments Details Value Author(s) References Examples

View source: R/coxph.ped.R

Fit Cox proportional hazards regression model with shared frailty (random effect) in each pedigree for single SNP analysis that tests associations between a survival phenotype and each genotyped SNP on a chromosome in a genotype file and for gene-based tests in family data. The association test is carried out by 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)

`genfile`	a character string naming the genotype file for reading
`phenfile`	a character string naming the phenotype file for reading
`pedfile`	a character string naming the pedigree file for reading
`phen`	a character string for the phenotype name of a survival trait of interest in `test.dat`
`covars`	a character vector for covariates in `phenfile`
`sep.ped`	the field separator character for pedigree file
`sep.phe`	the field separator character for phenotype file
`sep.gen`	the field separator character for genotype file
`time`	the character string of variable named for survival time
`mafRange`	range of MAF to include SNPs for gene-based burden tests, default is c(0,0.05)
`chr`	chromosome number that can be 1,2,...,22, and 'X'
`snpinfoRdata`	a character string naming the RData containing SNP info to be loaded, this should at least include 'Name' (for SNP name), 'Chr', and aggregateBy (default='SKATgene') columns
`aggregateBy`	the column of SNP info on which single SNPs are to be aggregated for burden tests, default is 'SKATgene'
`maf.file`	a character string naming the comma delimited file containing 'Name' for SNP name and 'maf' for MAF
`snp.cor`	a character string naming the RData containing lists of SNP correlation matrix within each 'SKATgene'
`ssq.beta.wts`	a vector of parameters of beta weights used in proposed sum of squares test, default=c(1,25) as in SKAT
`singleSNP.outfile`	a logical value, TRUE indicating single SNP analysis has been done and result files are available for computing SSQ using a different `mafRange`

The coxph.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.

No value is returned. Instead, tab delimited result files and an RData are generated. A single SNP result file, named with 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.

`gene`	gene name
`Name`	SNP name
`maf`	minor allele frequency based on genotyped sample
`ntotal`	number of individuals with genotype, phenotype and covariates
`nmiss`	number of individuals with missing genotype among `ntotal`
`maf_ntotal`	minor allele frequency based on `ntotal`
`beta`	regression coefficient of single SNP test or burden test
`se`	standard error of `beta`
`Z`	signed likelihood ratio statistic
`remark`	additional information of the analysis
`p`	p-value of single SNP test or burden test by LRT
`camfTotal`	sum of `maf_ntotal` of SNPs in a gene
`cmafUsed`	sum of `maf_ntotal` of SNPs selected with `mafRange` in a gene for burden tests or SSQ test
`nsnpsTotal`	total number of SNPs in a gene
`nsnpsUsed`	number of SNPs selected and used in burden tests and SSQ test
`SSQ`	sum of squares statistic
`df`	degrees of freedom of SSQ
`MAC`	minor allele count
`n0`	the number of individuals with 0 copy of coded alleles
`n1`	the number of individuals with 1 copy of coded alleles
`n2`	the number of individuals with 2 copies of coded alleles
`scores`	`beta`/`se`^2 in output RData, where `beta` and `se` are vectors
`cov`	diag(1/se)LD matrixdiag(1/se) in output RData
`n`	maximum `ntotal` in a gene in output RData
`sey`	1 in output RData

Ming-Huei Chen <mhchen@bu.edu> and Qiong Yang <qyang@bu.edu>

Therneau T (2014). A Package for Survival Analysis in S. R package version 2.37-7, http://CRAN.R-project.org/package=survival.

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)