luo.ld.power {ldDesign}R Documentation

Classical deterministic power calculation for association studies to detect linkage disequilibrium

Description

Classical deterministic power calculation for power to detect linkage disequilibrium between a bi-allelic QTL and a bi-allelic marker, at a given significance level in a population level association study.

Usage

luo.ld.power(n, p, q, D, h2, phi, Vp = 100, alpha, print.it = TRUE, 
             missclass.rate = 0)

Arguments

n The sample size, i.e. number of individuals genotyped and tested for the trait of interest
p Bi-allelic marker allele frequency
q Bi-allelic QTL allele frequency
D Linkage disequilibrium coefficient
h2 QTL `heritability', i.e. proportion of total or phenotypic variance explained by the QTL
phi Dominance ratio: phi = 0 denotes purely additive, phi = 1 denotes purely dominant allele effects
Vp Total or phenotypic variance: and arbitrary value may be used
alpha Significance level for hypothesis tests
print.it If TRUE print summary of results
missclass.rate Proportion of marker values which are missclassified, i.e. incorrect

Details

This is based on the 'fixed model' power calculation from Luo (1998, Heredity 80, 198–208), with corrections described in Ball (2003). This function, combined with oneway.bf.alpha, oneway.bf.alpha.required, is used in Ball (2003) to design experiments for detecting linkage disequilibrium with a given power to achieve a given Bayes factor.

Value

Returns the power, or probability of detecting an effect, with the given parameters, at the given significance level.

Author(s)

Rod Ball rod.ball@forestresearch.co.nz www.forestresearch.co.nz

References

Ball, R.D. 2003 Experimental designs for reliable detection of linkage disequilibrium in unstructured random population association studies.

Luo, Z.W. 1988 Detecting linkage disequilibrium between a polymorphic marker locus and a trait locus in natural populations. Heredity 80, 198–208

See Also

ld.sim,oneway.bf.alpha,oneway.bf.alpha.required, SS.oneway.bf

Examples

data(luo.ld.populations)
options(digits=3)
powers <- numeric(nrow(luo.ld.populations))
for(ii in 1:nrow(luo.ld.populations)){
   cat("ii=",ii,"\n")
   powers[ii] <- luo.ld.power(n=luo.ld.populations[ii,"n"],
                              p=luo.ld.populations[ii,"p"],
                              q=luo.ld.populations[ii,"q"],
                              D=luo.ld.populations[ii,"D"],
                              h2=luo.ld.populations[ii,"h2"],
                              phi=luo.ld.populations[ii,"phi"],
                              Vp=100,
                              alpha=0.05)
}
cbind(luo.ld.populations,power=powers)

[Package ldDesign version 1.1-0 Index]