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

fbsize {gap} | R Documentation

## Sample size for family-based linkage and association design

### Description

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).

### Usage

    fbsize(gamma,p,alpha=c(1e-4,1e-8,1e-8),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

### 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

### 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.

### Note

extracted from rm.c

### Author(s)

Jing Hua Zhao

### 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

[Package gap version 1.2.3-1 Index]