GenABEL (version 1.8-0)

egscore: Fast score test for association, corrected with PC

Description

Fast score test for association between a trait and genetic polymorphism, adjusted for possible stratification by principal components.

Usage

egscore(formula, data, snpsubset, idsubset, kinship.matrix, naxes = 3, strata, times = 1, quiet = FALSE, bcast = 10, clambda = TRUE, propPs = 1)

Arguments

formula

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

Value

Object of class scan.gwaa-class

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.

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")