Description Usage Arguments Details Value Author(s) References Examples
Fit Generalized Estimation Equation (GEE) model to test gene-environment or gene-gene interactions for a continuous phenotype
and all genotyped SNPs in a genotype file in family data under additive genetic model. The interaction term is the product of SNP genotype and a covariate for interaction (cov.int
).
The covariate for interaction (cov.int
) can be SNP genotype (gene-gene interaction) or an environmental factor (gene-environment interaction). Only one
interaction term is allowed. When cov.int
is dichotomous, stratified analyses can be requested by specifying sub
="Y". The covariance between the main
effect (SNP) and the interaction effect is provided in the output when stratified analysis is not requested. Each pedigree is treated as
a cluster, with independence working correlation matrix used in the robust variance estimator.
This function applies the same interaction test to all genotyped SNPs in the genotype data.
In each test for interaction, the geese
function from geepack
package is used.
1 2 | geepack.quant.int.batch(phenfile,genfile,pedfile,phen,covars,cov.int,sub="N",outfile,
col.names=T,sep.ped=",",sep.phe=",",sep.gen=",")
|
genfile |
a character string naming the genotype file for reading (see format requirement in details) |
phenfile |
a character string naming the phenotype file for reading (see format requirement in details) |
pedfile |
a character string naming the pedigree file for reading (see format requirement in details) |
outfile |
a character string naming the result file for writing |
phen |
a character string for a phenotype name in |
covars |
a character vector for covariates in |
cov.int |
a character string naming the covariate for interaction, the covariate has to be included in |
sub |
"N" (default) for no stratified analysis, and "Y" for requesting stratified analyses (only when |
col.names |
a logical value indicating whether the output file should contain column names |
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 |
For a continuous trait, the geepack.quant.int.batch
function first reads in and merges phenotype-covariates, genotype
and pedigree files, then tests gene-environment or gene-gene interaction and the association of phen
against all genotyped SNPs in genfile
.
Only one interaction term is allowed, so is the covariate for interaction (cov.int
). When cov.int
is dichotomous, stratified analyses can be
requested by specifying sub
="Y". The covariance between the main effect (SNP) and the interaction effect is provided in the output when stratified analysis is not requested.
genfile
contains unique individual id and genotype data, 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 covariate 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". In all files, missing value should be an empty space, except missing parental id in pedfile
.
SNPs with low genotype counts (especially minor allele homozygote) may be omitted. The geepack.quant.int.batch
function fits GEE model using
geese
function from geepack
package.
No value is returned. Instead, results are written to outfile
.
If stratified analyses are requested, the result file will include the following columns. Otherwise, cov_beta_snp_beta_int
will be included instead of
the results from stratified analyses, that is, beta_snp_cov0
, se_snp_cov0
, pval_snp_cov0
, beta_snp_cov1
, se_snp_cov1
,
and pval_snp_cov1
.
phen |
phenotype name |
snp |
SNP name |
covar_int |
the covariate for interaction |
n |
sample size used in analysis |
AF |
allele frequency of the coded allele |
model |
genetic model used in analysis, additive model only |
beta_snp |
regression coefficient of SNP covariate |
se_snp |
standard error of |
pval_snp |
p-value of testing |
beta_snp_cov0 |
regression coefficient of SNP covariate in stratified analysis using the subset where |
se_snp_cov0 |
standard error of |
pval_snp_cov0 |
p-value of testing |
beta_snp_cov1 |
regression coefficient of SNP covariate in stratified analysis using the subset where |
se_snp_cov1 |
standard error of |
pval_snp_cov1 |
p-value of testing |
beta_int |
regression coefficient of the interaction term |
se_int |
standard error of |
pval_int |
p-value of testing |
Qiong Yang <qyang@bu.edu> and Ming-Huei Chen <mhchen@bu.edu>
Liang, K.Y. and Zeger, S.L. (1986) Longitudinal data analysis using generalized linear models. Biometrika, 73 13–22.
Zeger, S.L. and Liang, K.Y. (1986) Longitudinal data analysis for discrete and continuous outcomes. Biometrics, 42 121–130.
Yan, J and Fine, J. (2004) Estimating equations for association structures. Stat Med, 23 859–874.
1 2 3 4 5 6 | ## Not run:
geepack.quant.int.batch(phenfile="simphen.csv",genfile="simgen.csv",
pedfile="simped.csv",phen="SIMQT",outfile="simout.csv",col.names=T,covars="age",
cov.int="age",sep.ped=",",sep.phe=",",sep.gen=",")
## End(Not run)
|
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