pairwise_int.calc {gnonadd}

Pairwise interaction effects

Description

Given a set of variants and a quantitative trait, this function calculates the interaction effect of all possible variant-variant pairs

Usage

pairwise_int.calc(
  qt,
  g,
  round_imputed = FALSE,
  dominance_terms = FALSE,
  variant_names = paste(rep("variant", ncol(g)), as.character(1:ncol(g)), sep = "_"),
  covariates = as.data.frame(matrix(0, nrow = 0, ncol = 0))
)

Arguments

qt

A numeric vector

g

A matrix, where each colomn represents a variant

round_imputed

A boolian variable determining whether imputed genotype values should be rounded to the nearest integer in the analysis.

dominance_terms

A boolian variable determining whether dominance terms for the variants should be included as covariates in the analysis

variant_names

A list of the names of the variants

covariates

A dataframe containing any other covariates that should be used; one column per covariate

Value

A dataframe with all possible variant pairs and their estimated interaction effect

Examples

g_vec <- matrix(0, nrow = 100000, ncol = 5)
freqs <- runif(ncol(g_vec), min = 0, max = 1)
for(i in 1:ncol(g_vec)){
 g_vec[,i] <- rbinom(100000, 2, freqs[i])
}

qt_vec <- rnorm(100000) + 0.1 * g_vec[, 1] + 0.2 *
          g_vec[, 2] -0.1 * g_vec[, 3] + 0.2 *
          g_vec[, 1] * g_vec[, 2]
res <- pairwise_int.calc(qt_vec, g_vec)