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

Description

This function implements Risch and Merikangas (1996) statistics evaluating power for family-based linkage 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).

Usage

fbsize(gamma,p,debug=0,error=0)

Arguments

gamma

genotype relative risk assuming multiplicative model

frequency of disease allele

debug

verbose output

error

0=use the correct formula,1=the original paper

Value

The returned value is a list containing:
gammainput gamma
pinput p
n1sample size for ASP
n2sample size for TDT
n3sample size for ASP-TDT
lambdaolambda o
lambdaslambda s

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.

Examples

Run this code

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)
    
cat("\nThe family-based result: \n")
cat("\ngamma   p     Y     N_asp   P_A    Het    N_tdt  Het N_asp/tdt  L_o  L_s\n\n")
for(i in 1:12) {
  g <- models[i,1]
  p <- models[i,2]
  fbsize(g,p)
  if(i%%4==0) cat("\n")
}

# 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)