emp.qtscore {GenABEL} | R Documentation |
Genome-wide significance for a GWA scan. Analysis
function is qtscore
.
emp.qtscore(formula , data, snpsubset, idsubset, strata, trait.type="gaussian", times = 200, quiet=FALSE, bcast = 10)
qtscore
.
See help for this function.
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. |
strata |
Stratification variable. If provieded, scores are computed within strata and then added up. |
trait.type |
"gaussian" or "binomial". If not specified, the procedure quesses the type |
times |
If more then one, the number of replicas to be used in derivation of
empirical genome-wide significance. See emp.qtscore , which
calls qtscore with times>1 for details |
quiet |
do not print warning messages |
bcast |
If the argument times > 1, progress is reported once in bcast replicas |
In the analysis of empirical significance, first time the function
qtscore
is called and result object is
saved. Later, the function qtscore
is called
times
times with replace=FALSE
in order to generate
distribution under the null. Each call, minimal P-value is extracted
and compared with original P-values. For a particular SNP, empirical
P-value is obtained as a proportion of times minimal Ps from resampled data
was less then original P.
The list elements effB, effAB and effBB are the ones obtained from the analysis of the original (not permuted) data set
The function does not yet implement correct analysis for X-linked data.
Object of class scan.gwaa-class
Yurii Aulchenko
qtscore
,
emp.ccfast
,
scan.gwaa-class
data(srdta) a<-qtscore(qt3~age+sex,data=srdta,snps=c(1:200)) plot(a) # this does not make sense, as the whole experiment must be analysed, not a small region! b<-emp.qtscore(qt3~age+sex,data=srdta,snps=c(1:200)) plot(b)