This function gives power estimates for two-stage case-control design for genetic association.
tscc(model, GRR, p1, n1, n2, M, alpha.genome, pi.samples, pi.markers, K)The returned value is a list containing a copy of the input plus output as follows,
any in c("multiplicative","additive","dominant","recessive").
genotype relative risk.
the estimated risk allele frequency in cases.
expected risk allele frequency in cases.
expected risk allele frequency in controls.
total number of cases.
total number of controls.
total number of markers.
false positive rate at genome level.
sample% to be genotyped at stage 1.
markers% to be selected (also used as the false positive rate at stage 1).
the population prevalence.
threshoulds for no stage, stage 1, stage 2, joint analysis.
power corresponding to C.
any in c("multiplicative","additive","dominant","recessive").
genotype relative risk.
the estimated risk allele frequency in cases.
total number of cases.
total number of controls.
total number of markers.
false positive rate at genome level.
sample% to be genotyped at stage 1.
markers% to be selected (also used as the false positive rate at stage 1).
the population prevalence.
Jing Hua Zhao
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,
$$z1 = z(p1,p2,n1,n2,pi.samples)$$ $$z2 = z(p1,p2,n1,n2,1-pi.samples)$$ $$zj = sqrt(pi.samples)*z1+sqrt(1-pi.samples)*z2$$
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