JOINT: Joint test of genetic main effects and gene-environment interactions

Description

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

Usage

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

Arguments

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 $\rho$ in the joint test.

number of Monte Carlo simulations to approximate the multi-dimensional integral in calculating the p-value of the joint test (default = 10000).

Value

pJOINT: p-value of the joint test.
pJOINTmin: minimum raw p-value from the searching grid of the joint test.
pJOINTrho: $\rho$ 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.

References

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.

Examples

Run this code

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)