glmm.lgst function where the
the lmer function from package lme4 is used.
glmm.lgst.batch(genfile, phenfile, pedfile, outfile, phen, covars = NULL,
model = "a", col.names = T, sep.ped = ",", sep.phe = ",", sep.gen = ",")phenfile phenfile 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, and se21 .betabeta not equal to zerobeta10beta20beta21glmm.lgst.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 genotyped SNP, the genotype data should be coded as 0, 1, 2 indicating the numbers of the coded alleles. The SNP names 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.
Only phenotypes with two categories are analyzed. A phenotype should be coded as
0 and 1, with 1 denoting affected and 0 unaffected. SNPs with low genotype counts
(especially minor allele homozygote) may be omitted or analyzed with dominant model or
analyzed with logistic regression.
The glmm.lgst.batch function fits GLMM using each pedigree as a cluster
with glmm.lgst function from GWAF package and lmer function from lme4 package.
## Not run:
# glmm.lgst.batch(phenfile="simphen.csv",genfile="simgen.csv",pedfile="simped.csv",
# phen="SIMQT",model="d",outfile="simout.csv",sep.ped=",",sep.phe=",",sep.gen=",")
# ## End(Not run)
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