ld.design {ldDesign}R Documentation

Functions for design of experiments to detect linkage disequilibrium

Description

Find the sample size required to detect linkage disequilibrium with a given Bayes factor, with a given power, or find the power of experimental designs to detect linkage equilibrium with a given Bayes factor.

Usage

ld.design(p, q, D, h2, phi, Bf, power, nmin = 50, nmax = 1e+05, ninterp = 50, 
          missclass.rate = 0, print.it = FALSE)
ld.power(n, p, q, D, h2, phi, Bf, missclass.rate = 0)

Arguments

n ld.power: vector of sample sizes
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
Bf Bayes factor
power ld.design: Power, or probability of detecting an effect with Bayes factor greater than Bf
nmin ld.design: Lower bound for sample size
nmax ld.design: Upper bound for sample size
ninterp ld.design: Number of sample sizes to try
missclass.rate Proportion of marker values which are missclassified, i.e. incorrect (to allow for genotyping errors)
print.it If TRUE print results for sample sizes tried

Details

These functions implement the method described in Ball (2003) for obtaining the power of designs for detecting linkage disequilibrium with a given Bayes factor. The F values, (and hence significance levels) corresponding to the given Bayes factors, sample sizes, and marker genotype frequecies, are calculated using the method of Spiegelhalter and Smith (1982) (R functions oneway.bf.alpha.required, SS.oneway.bf). The power is obtained using a corrected version of the classical deterministic power calculation from Luo (1988) (R function luo.ld.power).

Value

For ld.power, a matrix with columns:

n Sample sizes
power Power of the design with the given sample sizes

Additionally the return value has attributes indicating the linkage disequilibrium parameters used. For ld.design the sample size is returned.

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

Spiegelhalter, D. and A.F.M. Smith 1982 Bayes factors for linear and log-linear models with vague prior information J. Royal Statist Soc. B 44: 377–387.

See Also

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

Examples

ld.power(n=seq(100,1000,by=100),p=0.5,q=0.5,D=0.1,h2=0.1,phi=0,Bf=20)
ld.design(p=0.5,q=0.5,D=0.1,h2=0.1,phi=0,Bf=20,power=0.9,print.it=TRUE,nmin=600,nmax=4000)
ld.design(p=0.5,q=0.5,D=0.1,h2=0.1,phi=0,Bf=20,power=0.9,print.it=FALSE,nmin=1700,nmax=1900)

[Package ldDesign version 1.1-0 Index]