Description Usage Arguments Details Value Author(s) References Examples
Fit Generalized Estimation Equation (GEE) model to test associations between a continuous phenotype
and all genotyped SNPs in a genotype file in family data with user specified genetic model. Each pedigree is treated as
a cluster, with independence working correlation matrix used in the robust variance estimator.
The proportion of phenotype variation explained by the tested SNP is not provided.
This function applies the same trait-SNP association test to all genotyped SNPs in the genotype data.
The trait-SNP association test is carried out by using the geese
function from package geepack
.
1 2 | geepack.quant.batch(phenfile,genfile,pedfile,phen,model="a",covars=NULL,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 |
model |
a single character of 'a','d','g', or 'r', with 'a'=additive, 'd'=dominant, 'g'=general and 'r'=recessive models |
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.batch
function first reads in and merges phenotype-covariates, genotype
and pedigree files, then tests the association of phen
against all SNPs in genfile
.
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 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". 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
or analyzed with dominant model. The geepack.quant.batch
function fits GEE model using each pedigree as a cluster
with geese
function from geepack
package.
No value is returned. Instead, results are written to outfile
.
When the genetic model is 'a', 'd' or 'r', the result includes the following columns.
When the genetic model is 'g', beta
and se
are replaced with beta10
,
beta20
, beta21
, se10
, se20
, se21
.
phen |
phenotype name |
snp |
SNP name |
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 |
beta |
regression coefficient of SNP covariate |
se |
standard error of |
chisq |
Chi-square statistic for testing |
df |
degree of freedom of the Chi-square statistic |
model |
model actually used in the analysis |
pval |
p-value of the chi-square statistic |
|
|
beta10 |
regression coefficient of genotype with 1 copy of coded allele vs. that with 0 copy |
beta20 |
regression coefficient of genotype with 2 copy of coded allele vs. that with 0 copy |
beta21 |
regression coefficient of genotype with 2 copy of coded allele vs. that with 1 copy |
se10 |
standard error of |
se20 |
standard error of |
se21 |
standard error of |
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 | ## Not run:
geepack.quant.batch(phenfile="simphen.csv",genfile="simgen.csv",pedfile="simped.csv",
phen="SIMQT",model="a",outfile="simout.csv",sep.ped=",",sep.phe=",",sep.gen=",")
## End(Not run)
|
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