GeneticsDesign (version 1.40.0)

GPC: Genetics power calculator for linear trend association studies

Description

Genetics power calculator for linear trend association studies.

Usage

GPC(pA, pD, RRAa, RRAA, r2, pB,  nCase=500, ratio=1, alpha=0.05, quiet=FALSE) GPC.default(pA, pD, RRAa, RRAA, Dprime, pB,  nCase=500, ratio=1, alpha=0.05, quiet=FALSE)

Arguments

pA

High risk allele frequency (A).

pD

Disease prevalence.

RRAa

Genotype relative risk (Aa) = RR(Aa|aa)=Pr(D|Aa)/Pr(D|aa).

RRAA

Genotype relative risk (AA) = RR(AA|aa)=Pr(D|AA)/Pr(D|aa).

r2

LD measure. Assume that D > 0.

Dprime

LD measure.

pB

Marker allele frequency (B).

nCase

Number of cases.

ratio

Control:case ratio = nControl/nCase.

alpha

User-defined type I error rate.

quiet

Print some intermediate results if quiet=FALSE.

Value

power

The estimated power for the association test.

ncp

Non-centrality parameter.

mat.para

A matrix of case-control parameters, including number of cases, number of controls, high risk allele frequency, prevalence, genotypic relative risk (Aa), genotypic relative risk (AA), genotypic risk for aa (baseline).

mat.B

A matrix of marker locus B parameters, including marker allele frequency, linkage disequilibrium (D'), penetrance at marker genotype bb, penetrance at marker genotype Bb, penetrance at marker genotype BB, genotypic odds ratio Bb, genotypic odds ratio BB.

mat.aFreq

A 2 by 2 matrix of expected allele frequencies Pr(B|D), Pr(b|D), Pr(B|non D), Pr(b|non D).

mat.gFreq

A 3 by 2 matrix of expected genotype frequencies Pr(BB|D), Pr(Bb|D), Pr(bb|D), Pr(BB|non D), Pr(Bb|non D), Pr(bb|non D).

mat.stat

Power estimates for a sequence of Type I errors.

Details

The power is for the test that disease is associated with a marker, given high risk allele frequency (A), disease prevalence, genotype relative risk (Aa), genotype relative risk (AA), LD measure (D' or r^2), marker allele frequency (B), number of cases, control:case ratio, and probability of the Type I error. The linear trend test (Cochran 1954; Armitage 1955) is used.

References

Armitage, P. (1955) Tests for linear trends in proportions and frequencies. Biometrics, 11, 375-386.

Cochran, W.G. (1954) Some methods for strengthening the common chi-squared tests. Biometrics, 10, 417-451.

Gordon D, Finch SJ, Nothnagel M, Ott J (2002) Power and sample size calculations for case-control genetic association tests when errors are present: application to single nucleotide polymorphisms. Hum. Hered., 54:22-33.

Gordon D, Haynes C, Blumenfeld J, Finch SJ (2005) PAWE-3D: visualizing Power for Association With Error in case/control genetic studies of complex traits. Bioinformatics, 21:3935-3937.

Purcell S, Cherny SS, Sham PC. (2003). Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits. Bioinformatics, 19(1):149-150.

Sham P. (1998). Statistics in Human Genetics. Arnold Applications of Statistics.

Examples

  res1<-GPC(pA=0.05, pD=0.1, RRAa=1.414, RRAA=2, r2=0.9, pB=0.06, 
                   nCase=500, ratio=1, alpha=0.05, quiet=FALSE)

  res2<-GPC.default(pA=0.05, pD=0.1, RRAa=1.414, RRAA=2, Dprime=0.9, pB=0.06, 
                   nCase=500, ratio=1, alpha=0.05, quiet=FALSE)