Description Usage Arguments Details Value Author(s) References Examples
Fit linear mixed effects (LME) model for single SNP analysis that
tests associations between a continuous 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 lme.EC
function. In each test, the lmekin
function from package
coxme
is used.
1 2 3 |
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 continuous
trait of interest in |
covars |
a character vector for covariates in |
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 |
The lme.ped
function reads in and merges phenotype, genotype,
and pedigree files, and creates a relationship coefficient matrix using
pedfile
and kinship2
package to perform single SNP analysis,
two burden tests (weight=1 for Li & Leal 2008; weight=1/(MAF)/(1-MAF) for
Madsen & Browning 2009), one sum of squares (SSQ) test (Wei 2009) using
a LME model as implemented in lmekin
function in coxme
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
(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 |
maf_ntotal |
minor allele frequency based on |
beta |
regression coefficient of single SNP test or burden test |
se |
standard error of |
Z |
Wald Z statistic |
remark |
additional information of the analysis |
p |
p-value of single SNP test or burden test |
camfTotal |
sum of |
cmafUsed |
sum of |
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 |
|
cov |
diag(1/se)*LD matrix*diag(1/se) in output RData |
n |
maximum |
sey |
residual standard error in output RData |
Ming-Huei Chen <mhchen@bu.edu> and Qiong Yang <qyang@bu.edu>
coxme package: mixed-effects Cox models, sparse matrices, and modeling data from large pedigrees. Beth Atkinson (atkinson@mayo.edu) for pedigree functions.Terry Therneau (therneau@mayo.edu) for all other functions. 2007. Ref Type: Computer Program. http://cran.r-project.org/web/packages/coxme/.
Abecasis, G. R., Cardon, L. R., Cookson, W. O., Sham, P. C., & Cherny, S. S (2001). Association analysis in a variance components framework. Genet Epidemiol, 21 Suppl 1, S341-S346.
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.
1 2 3 4 5 6 7 | ## Not run:
lme.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",maf.file="EC_MAF.csv",
snp.cor="EC_SNPcor.RData",ssq.beta.wts=c(1,25))
## End(Not run)
|
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