epiGWAS (version 1.0.2)

BOOST: Implements BOOST SNP-SNP interaction test

Description

For a pair of SNPs (\(X_1\), \(X_2\)) and a binary phenotype \(Y\), the BOOST function computes the ratio of maximum log-likelihoods for two models: the full model and the main effects model. Mathematically speaking, the full model is a logistic regression model with both main effects and interaction terms \(\left(X_1, X_2, X_1\times X_2\right)\). The main effects model is a logistic regression model with only \(\left(X_1, X_2\right)\) as covariates. Since we are interested in the synergies with a single variant, we do not implement the initial sure screening stage in BOOST which filters out non-significant pairs.

Usage

BOOST(A, X, Y, ncores = 1)

Arguments

A

target variant. The SNP A is encoded as 0, 1, 2.

X

genotype matrix (excluding A). The only accepted SNP values are also 0, 1 and 2.

Y

observed phenotype. Binary or two-level factor.

ncores

number of threads (default 1)

Value

The interaction statistic between each column in X and A

See Also

The webpage http://bioinformatics.ust.hk/BOOST.html provides additional details about the BOOST software

Examples

# NOT RUN {
X <- matrix((runif(500, min = 0, max = 1) < 0.5) +
    (runif(500, min = 0, max = 1) < 0.5), nrow = 50)
A <- (runif(50, min = 0, max = 1) < 0.5) + (runif(50, min = 0, max = 1) < 0.5)
Y <- runif(50, min = 0, max = 1) < 1/(1+exp(-.5 * A * X[, 3] + .25 * A * X[, 7]))
BOOST(A, X, Y)

# }