JOINT: Joint test of genetic main effects and gene-environment...
In rareGE: Testing Gene-Environment Interaction for Rare Genetic Variants

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/rareGE.R

Perform a joint test of genetic main effects and gene-environment interactions, using a Monte Carlo approach to calculate the p-value.

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JOINT(phenotype, genotypes, covariates, mainweights = wuweights, 
interweights = wuweights, family = "gaussian", binomialimpute = FALSE, 
rho = seq(0, 1, by = 0.1), B = 10000)

`phenotype`	a numeric vector of phenotype values.
`genotypes`	a matrix or a data frame for all SNPs in the test gene or genomic region. The order of rows must match the order in `phenotype`.
`covariates`	a matrix, a data frame or a vector of covariates to adjust for. The interaction between SNPs and the first column of `covariates` is tested. The order of rows must match the order in `phenotype`.
`mainweights`	the weight function or vector of genetic main effects (default = wuweights).
`interweights`	the weight function or vector of gene-environment interaction effects (default = wuweights).
`family`	"gaussian" for quantitative traits and "binomial" for dichotomous traits (default = "gaussian").
`binomialimpute`	impute missing genotypes randomly using a binomial distribution with 2 trials and success probability equal to the minor allele frequency. If FALSE, then impute missing genotypes to 0 (default = FALSE).
`rho`	a numeric vector with values between 0 and 1 defining the searching grid for the nuisance parameter ρ in the joint test.
`B`	number of Monte Carlo simulations to approximate the multi-dimensional integral in calculating the p-value of the joint test (default = 10000).

`pJOINT`	p-value of the joint test.
`pJOINTmin`	minimum raw p-value from the searching grid of the joint test.
`pJOINTrho`	ρ value where the minimum raw p-value is attained in the joint test.
`pJOINTps`	a vector of raw p-values in the joint test.
`pJOINTinfo`	a summary of the distribution of the integrand in each Monte Carlo simulation in the joint test. `pJOINT` is the mean excluding missing values.

Han Chen

Chen H, Meigs JB, Dupuis J. (2014) Incorporating gene-environment interaction in testing for association with rare genetic variants. Hum Hered 78, 81-90.

Lee S, Wu MC, Lin X. (2012) Optimal tests for rare variant effects in sequencing association studies. Biostatistics 13, 762-775.

Wu MC, Lee S, Cai T, Li Y, Boehnke M, Lin X. (2011) Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet 89, 82-93.

rareGE

set.seed(12345)
data(rareGEgeno)
data(rareGEpheno)
# quantitative traits - testing for gene-BMI interactions
JOINT(rareGEpheno$y1, rareGEgeno, rareGEpheno[, c("bmi", "age", "sex")],
	B = 1000)
# dichotomous traits - testing for gene-BMI interactions
JOINT(rareGEpheno$y2, rareGEgeno, rareGEpheno[, c("bmi", "age", "sex")], 
	family = "binomial", B = 1000)