glmm.binped: 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/glmm.binped.R

Fit generalized linear mixed effects model (GLMM) with logistic link that treats each pedigree as a cluster for single SNP analysis that tests associations between a binary 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 glmm.EC function. In each test, the glmer function from package lme4 is used.

glmm.binped(phenfile,genfile,pedfile,phen,covars=NULL,
mafRange=c(0,0.05),chr,snpinfoRdata,sep.ped=",",sep.phe=",",
sep.gen=" ",aggregateBy="SKATgene",maf.file,
snp.cor,ssq.beta.wts=c(1,25),singleSNP.outfile=F)

`phenfile`	a character string naming the phenotype file for reading
`genfile`	a character string naming the genotype file for reading
`pedfile`	a character string naming the pedigree file for reading
`phen`	a character string for the phenotype name of a binary trait of interest in `test.dat`
`covars`	a character vector for covariates in `phenfile`
`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
`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
`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 glmm.binped 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 GLMM with logistic link that treats each pedigree as a cluster as implemented in glmer function in lme4 R package and to output an RData that is computed based on single SNP results and that is compatible with seqMeta 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". Wald chi-square test 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, 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
`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 statistics
`df`	degree 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>

Bates D, Maechler M, Bolker B and Walker S (2014). lme4: Linear mixed-effects models using Eigen and S4. R package version 1.1-7, http://CRAN.R-project.org/package=lme4.

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: 
glmm.binped(genfile="EC_chr1.txt",phenfile="trait1.csv",pedfile="ped.csv",
phen="trait1",covars=c("age"),sep.ped=",",sep.phe=",",sep.gen=" ",
mafRange=c(0,0.01),chr=1,snpinfoRdata="SNPinfo_EC.RData",aggregateBy="SKATgene",
maf.file="EC_MAF.csv",snp.cor="EC_SNPcor.RData",ssq.beta.wts=c(1,25))

## End(Not run)