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Last data update: 2014.03.03

R: thresholds and power for two-stage gwas

twoStageGwasPower

R Documentation

thresholds and power for two-stage gwas

Description

Computes the critical values for a two-stage gwas association study and compute power for replication and joint analyses.

Usage

twoStageGwasPower(pD, pG, grr, inheritance = "multiplicative", pi.samples, pi.markers, alpha.marker, n.cases, n.controls)

Arguments

`pD`	Probability of disease in the population (prevalence)
`pG`	Frequency of disease allele in the population
`grr`	Genotypic relative risk
`inheritance`	Inheritance type ("dominant", "recessive", "multiplicative", or "additive"
`pi.samples`	Proportion of samples genotyped in Stage 1
`pi.markers`	Proportion of markers genotyped in Stage 2
`alpha.marker`	Significance level used for each marker, accounting for multiple comparisons among a large number of markers
`n.cases`	Number of cases
`n.controls`	Number of controls

Details

This function computes the critical values and powers of the replication and joint methods of analyzing a two-stage GWAS design. Details may be found in Skol AD, Scott, LJ, Abecasis GR, Boehnke M (2006)

Value

A list containing:

`power.SingleStage`	Power of a one stage design
`power.joint`	Power of a joint analysis
`power.rep`	Power of a replication analysis (based only on the second stage markers)
`c1`	Stage one threshold
`c2`	Replication (stage two) threshold
`c.joint`	Joint analysis threshold
`c.singleStage`	Single stage design threshold
`penetrance.GG`	Penetrance of the GG genotype (homozygous for disease allele)
`penetrance.Gg`	Penetrance of the Gg genotype
`penetrance.gg`	Penetrance of the gg genotype
`p0`	disease allele frequency in controls
`p1`	disease allele frequency in cases
`p.stageOne`	probability that associated markers will be followed up in Stage 2
`savings`	reduction in genotyping using two-stage design as compared to the single-stage design

Author(s)

Dirk F Moore <dirkfmoore@gmail.com>

References

Skol AD, Scott, LJ, Abecasis GR, Boehnke M (2006) Nature Genetics doi:10.1038/ng1706

http://www.sph.umich.edu/csg/abecasis/CaTS/

Examples

# prevalence of disease is 0.10, the allele frequency is 0.40,
#  a multiplicative model with 0.40 samples in the first stage and
#  10% of the markers selected for Stage 2. There are 1000 cases
#  and 1000 controls, 300,000 markers, with a genome-wide alpha of 0.05

power.gwas.out <- twoStageGwasPower(pD=0.10, pG=0.40, grr=1.40, 
                 inheritance="multiplicative", pi.samples=0.40, pi.markers=0.10, 
                 alpha.marker=0.05/300000, n.cases=1000, n.controls=1000)
power.gwas.out

# Same, but with 1% of markers selected for Stage 2
power.gwas.out2 <- twoStageGwasPower(pD=0.10, pG=0.40, grr=1.40, 
             inheritance="multiplicative", pi.samples=0.40, pi.markers=0.010, 
             alpha.marker=0.05/300000, n.cases=1000, n.controls=1000)
power.gwas.out2

# Same, but a dominant model with 4000 controls and 2000 cases
power.gwas.out3 <- twoStageGwasPower(pD=0.10, pG=0.40, grr=1.40, 
           inheritance="dominant", pi.samples=0.40, pi.markers=0.10, 
           alpha.marker=0.05/300000, n.cases=2000, n.controls=4000)
power.gwas.out3