Functions for some genetic epidemiology methods.

Description

These functions provide code for genetic epidemiology methods developed at MSKCC. They currently include estimating haplotype disease risk and two stage designs for GWAS.

Details

There are two functions haplotypeOddsRatio and twoStagePower in this package.

Package will be archived and functions added to clinfun package

Author(s)

Venkatraman E. Seshan

Maintainer: Venkatraman E. Seshan <seshanv@mskcc.org>

References

Satagopan JM, Venkatraman ES, Begg CB. (2004) Two-stage designs for gene-disease association studies with sample size constraints. Biometrics

Venkatraman ES, Mitra N, Begg CB. (2004) A method for evaluating the impact of individual haplotypes on disease incidence in molecular epidemiology studies. Stat Appl Genet Mol Biol. v3:Article27.

Internal Functions for haplotypeOddsRatio

Description

Internal functions needed for haplotypeOddsRatio.

Usage

genhaplopairs(n)
hwehaplofreq(n, two2k, g1idx, g2idx, g1tbl, hpair, tol=1e-8)
probonecopy(n, hf0, g1idx, g1tbl, hpair)

Details

These functions are not to be called by the user.

Calculate haplotype disease risk.

Description

Haplotype disease risk is calculated resolving haplotype ambiguity and adjusting for covariates and population stratification.

Usage

haplotypeOddsRatio(formula, gtypevar, data, stratvar=NULL, nsim=100, tol=1e-8)
## S3 method for class 'haploOR'
print(x, ...)

Arguments

Details

This implements the method in the reference below.

Value

It is a list of class haploOR

The "print" method formats the results into a user-friendly table.

Author(s)

Venkatraman E. Seshan

References

Venkatraman ES, Mitra N, Begg CB. (2004) A method for evaluating the impact of individual haplotypes on disease incidence in molecular epidemiology studies. Stat Appl Genet Mol Biol. v3:Article27.

Examples

# simulated data with 2 loci haplotypes 1=00, 2=01, 3=10, 4=11
# control haplotype probabilities p[i]  i=1,2,3,4
# haplotype pairs (i<=j) i=j: probs = p[i]^2 ; i<j: p[i]*p[j]
p <- c(0.25, 0.2, 0.2, 0.35)
p0 <- rep(0, 10)
l <- 0
for(i in 1:4) {for(j in i:4) {l <- l+1; p0[l] <- 2*p[i]*p[j]/(1+1*(i==j))}}
controls <- as.numeric(cut(runif(1000), cumsum(c(0,p0)), labels=1:10))
# case probabilities disease haplotype is 11
or <- c(2, 5)
p1 <- p0*c(1,1,1,2,1,1,2,1,2,8); p1 <- p1/sum(p1)
cases <- as.numeric(cut(runif(1000), cumsum(c(0,p1)), labels=1:10))
# now pool them together and set up the data frame
dat <- data.frame(status=rep(0:1, c(1000, 1000)))
# number of copies of mutant variant for locus 1
dat$gtype1 <- c(0,0,1,1,0,1,1,2,2,2)[c(controls, cases)]
# number of copies of mutant variant for locus 2
dat$gtype2 <- c(0,1,0,1,2,1,2,0,1,2)[c(controls, cases)]
# true number of copies of disease haplotype
dat$hcn <- c(0,0,0,1,0,0,1,0,1,2)[c(controls, cases)]
# model with genotypes only
haplotypeOddsRatio(status ~ 1, c("gtype1","gtype2"), dat)
# model from the logistic fit using the number of copies of disease haplotype
glm(status ~ factor(hcn), dat, family=binomial)

Calculate the power of a two stage design for GWAS

Description

Calculate the power of a two stage design for GWAS under sample size or cost constraints. Implements methods in the refereces below.

Usage

twoStagePower(n=NULL, Cost=NULL, m=5000, mu=0.045, mu.loc=0.5, p=0.10,
            f=NULL, relcost=1, true.needed=1, rho=0, rho0=0, nsim=2000)

Arguments

Details

This implements the method in the reference below.

Value

It returns the power as a single numeric value

Author(s)

Jaya M. Satagopan & Venkatraman E. Seshan

References

Satagopan JM, Venkatraman ES, Begg CB. (2004) Two-stage designs for gene-disease association studies with sample size constraints. Biometrics

Examples

twoStagePower(n=1000)
twoStagePower(Cost=1000)