Formula describing fixed effects to be
used in analysis, e.g. y ~ a + b means that outcome (y)
depends on two covariates, a and b. If no covariates
used in analysis, skip the right-hand side of the
equation.
data
An object of gwaa.data-class
snpsubset
Index, character or logical vector with
subset of SNPs to run analysis on. If missing, all SNPs
from data are used for analysis.
idsubset
Index, character or logical vector with
subset of IDs to run analysis on. If missing, all people
from data/cc are used for analysis.
kinship.matrix
kinship matrix, as returned by
ibs, Use weight="freq" with
ibs and do not forget to repalce the
diagonal with Var returned by hom, as shown
in example!
naxes
Number of axes of variation to be used in
adjustment (should be much smaller than number of
subjects)
strata
Stratification variable. If provieded,
scores are computed within strata and then added up.
times
If more then one, the number of replicas to
be used in derivation of empirical genome-wide
significance.
quiet
do not print warning messages
bcast
If the argument times > 1, progress is
reported once in bcast replicas
clambda
If inflation facot Lambda is estimated as
lower then one, this parameter controls if the original
P1df (clambda=TRUE) to be reported in Pc1df, or the
original 1df statistics is to be multiplied onto this
"deflation" factor (clambda=FALSE). If a numeric value
is provided, it is used as a correction factor.
propPs
proportion of non-corrected P-values used
to estimate the inflation factor Lambda, passed directly
to the estlambda
Details
The idea of this test is to use genomic kinship matrix to
first, derive axes of genetic variation (principal
components), and, second, adjust both trait and genotypes
onto these axes. Note that the diagonal of the kinship
matrix should be replaced (default it is 0.5*(1+F), and
for EIGENSTRAT one needs variance). These variances are
porduced by hom function (see example).
The traits is first analysed using LM and with covariates
as specified with formula and also with axes of variation
as predictors. Corrected genotypes are defined as
residuals from regression of genotypes onto axes (which
are orthogonal). Correlaton between corrected genotypes
and phenotype is computed, and test statistics is defined
as square of this correlation times (N - K - 1), where N
is number of genotyped subjects and K is the number of
axes.
This test is defined only for 1 d.f.
Value
Object of class scan.gwaa-class
Author(s)
Yurii Aulchenko
References
Price A. L. et al, Principal components analysis corrects
for stratification in genome-wide association studies.
Nat Genet 38: 904-909.
See Also
qtscore, mmscore,
ibs, scan.gwaa-class
Examples
require(GenABEL.data)
data(ge03d2c)
#egscore with stratification
gkin <- ibs(ge03d2c[,autosomal(ge03d2c)],w="freq")
#replace the diagonal with right elements
diag(gkin) <- hom(ge03d2c[,autosomal(ge03d2c)])$Var
a <- egscore(dm2~sex+age,data=ge03d2c,kin=gkin)
plot(a,df="Pc1df")
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(GenABEL)
Loading required package: MASS
Loading required package: GenABEL.data
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/GenABEL/egscore.Rd_%03d_medium.png", width=480, height=480)
> ### Name: egscore
> ### Title: Fast score test for association, corrected with PC
> ### Aliases: egscore
> ### Keywords: htest
>
> ### ** Examples
>
> require(GenABEL.data)
> data(ge03d2c)
> #egscore with stratification
> gkin <- ibs(ge03d2c[,autosomal(ge03d2c)],w="freq")
> #replace the diagonal with right elements
> diag(gkin) <- hom(ge03d2c[,autosomal(ge03d2c)])$Var
> a <- egscore(dm2~sex+age,data=ge03d2c,kin=gkin)
> plot(a,df="Pc1df")
>
>
>
>
>
> dev.off()
null device
1
>