Power calculation for two-stage case-control design
This function gives power estimates for two-stage case-control design for genetic association.
The false positive rates are calculated as follows,
P(|z1|>C1)P(|z2|>C2,sign(z1)=sign(z2))
and
P(|z1|>C1)P(|zj|>Cj||z1|>C1)
for replication-based and joint analyses, respectively; where C1, C2, and Cj are threshoulds at stages 1, 2 replication and joint analysis,
z1 = z(p1,p2,n1,n2,pi.samples)
z2 = z(p1,p2,n1,n2,1-pi.samples)
zj = sqrt(pi.samples)*z1+sqrt(1-pi.samples)*z2
tscc(model,GRR,p1,n1,n2,M,alpha.genome,pi.samples,pi.markers,K)
model |
any in c("multiplicative","additive","dominant","recessive") |
GRR |
genotype relative risk |
p1 |
the estimated risk allele frequency in cases |
n1 |
total number of cases |
n2 |
total number of controls |
M |
total number of markers |
alpha.genome |
false positive rate at genome level |
pi.samples |
sample% to be genotyped at stage 1 |
pi.markers |
markers% to be selected (also used as the false positive rate at stage 1) |
K |
the population prevalence |
The returned value is a list containing a copy of the input plus output as follows,
model |
any in c("multiplicative","additive","dominant","recessive") |
GRR |
genotype relative risk |
p1 |
the estimated risk allele frequency in cases |
pprime |
expected risk allele frequency in cases |
p |
expected risk allele frequency in controls |
n1 |
total number of cases |
n2 |
total number of controls |
M |
total number of markers |
alpha.genome |
false positive rate at genome level |
pi.samples |
sample% to be genotyped at stage 1 |
pi.markers |
markers% to be selected (also used as the false positive rate at stage 1) |
K |
the population prevalence |
C |
threshoulds for no stage, stage 1, stage 2, joint analysis |
power |
power corresponding to C |
Skol AD, Scott LJ, Abecasis GR, Boehkne M (2006). Joint analysis in more efficient than replication-based aalysis for two-stage genome-wide association studies. Nature Genetics 38:209-213
solve.skol is adapted from CaTS
Jing Hua Zhao
K <- 0.1
p1 <- 0.4
n1 <- 1000
n2 <- 1000
M <- 300000
alpha.genome <- 0.05
GRR <- 1.4
p1 <- 0.4
pi.samples <- 0.2
pi.markers <- 0.1
options(echo=FALSE)
cat("sample%,marker%,GRR,(thresholds x 4)(power estimates x 4)\n")
for(GRR in c(1.3,1.35,1.40)) {
cat("\n")
for(pi.samples in c(1.0,0.5,0.4,0.3,0.2)) {
if(pi.samples==1.0) s <- 1.0
else s <- c(0.1,0.05,0.01)
for(pi.markers in s)
{
x <- tscc("multiplicative",GRR,p1,n1,n2,M,alpha.genome,
pi.samples,pi.markers,K)
l <- c(pi.samples,pi.markers,GRR,x$C,x$power)
l <- sprintf("%.2f %.2f %.2f, %.2f %.2f %.2f %.2f, %.2f %.2f %.2f %.2f",
l[1],l[2],l[3],l[4],l[5],l[6],l[7],l[8],l[9],l[10],l[11])
cat(l,"\n")
}
cat("\n")
}
}
options(echo=TRUE)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.