fbsize: Sample size for family-based linkage and association design

In gap: Genetic Analysis Package

Sample size for family-based linkage and association design

Description

Sample size for family-based linkage and association design

Usage

fbsize(
  gamma,
  p,
  alpha = c(1e-04, 1e-08, 1e-08),
  beta = 0.2,
  debug = 0,
  error = 0
)

Arguments

gamma	genotype relative risk assuming multiplicative model.
p	frequency of disease allele.
alpha	Type I error rates for ASP linkage, TDT and ASP-TDT.
beta	Type II error rate.
debug	verbose output.
error	0=use the correct formula,1=the original paper.

Details

This function implements Risch and Merikangas (1996) statistics evaluating power for family-based linkage (affected sib pairs, ASP) and association design. They are potentially useful in the prospect of genome-wide association studies.

The function calls auxiliary functions sn() and strlen; sn() contains the necessary thresholds for power calculation while strlen() evaluates length of a string (generic).

Value

The returned value is a list containing:

• gamma input gamma.

• p input p.

• n1 sample size for ASP.

• n2 sample size for TDT.

• n3 sample size for ASP-TDT.

• lambdao lambda o.

• lambdas lambda s.

Note

extracted from rm.c.

Author(s)

Jing Hua Zhao

References

Risch, N. and K. Merikangas (1996). The future of genetic studies of complex human diseases. Science 273(September): 1516-1517.

Risch, N. and K. Merikangas (1997). Reply to Scott el al. Science 275(February): 1329-1330.

Scott, W. K., M. A. Pericak-Vance, et al. (1997). Genetic analysis of complex diseases. Science 275: 1327.

See Also

pbsize

Examples

models <- matrix(c(
   4.0, 0.01,
   4.0, 0.10,
   4.0, 0.50, 
   4.0, 0.80,
   2.0, 0.01,
   2.0, 0.10,
   2.0, 0.50,
   2.0, 0.80,
   1.5, 0.01,    
   1.5, 0.10,
   1.5, 0.50,
   1.5, 0.80), ncol=2, byrow=TRUE)
outfile <- "fbsize.txt"
cat("gamma","p","Y","N_asp","P_A","H1","N_tdt","H2","N_asp/tdt","L_o","L_s\n",
    file=outfile,sep="\t")
for(i in 1:12) {
  g <- models[i,1]
  p <- models[i,2]
  z <- fbsize(g,p)
  cat(z$gamma,z$p,z$y,z$n1,z$pA,z$h1,z$n2,z$h2,z$n3,z$lambdao,z$lambdas,file=outfile,
      append=TRUE,sep="\t")
  cat("\n",file=outfile,append=TRUE)
}
table1 <- read.table(outfile,header=TRUE,sep="\t")
nc <- c(4,7,9)
table1[,nc] <- ceiling(table1[,nc])
dc <- c(3,5,6,8,10,11)
table1[,dc] <- round(table1[,dc],2)
unlink(outfile)
# APOE-4, Scott WK, Pericak-Vance, MA & Haines JL
# Genetic analysis of complex diseases 1327
g <- 4.5
p <- 0.15
cat("\nAlzheimer's:\n\n")
fbsize(g,p)
# note to replicate the Table we need set alpha=9.961139e-05,4.910638e-08 and
# beta=0.2004542 or reset the quantiles in fbsize.R

gap documentation built on Aug. 26, 2023, 5:07 p.m.