Description Usage Arguments Value Author(s) References See Also Examples
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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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 |
covariates |
a matrix, a data frame or a vector of covariates to adjust for. The interaction between SNPs and the first column of |
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. |
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.
1 2 3 4 5 6 7 8 9 | 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)
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