pairwise_int_CC.calc {gnonadd}

Pairwise interaction effects for a case control variable

Description

Given a set of variants and a case control variable, this function calculates the interaction effect of all possible variant-variant pairs

Usage

pairwise_int_CC.calc(
  cc,
  g,
  yob = rep(-1, length(cc)),
  sex = rep(-1, length(cc)),
  round_imputed = FALSE,
  dominance_terms = FALSE,
  covariates = as.data.frame(matrix(0, nrow = 0, ncol = 0)),
  variant_names = paste(rep("variant", ncol(g)), as.character(1:ncol(g)), sep = "_")
)

Arguments

cc

A numeric vector

g

A matrix, where each colomn represents a variant

yob

A numerical vector containing year of birth. If some are unknown they should be marked as -1

sex

A numerical vector containing sex, coded 0 for males, 1 for females and -1 for unknown

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

covariates

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

variant_names

A list of the names of the variants

Value

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

Examples

N_run <- 25000
g_vec <- matrix(0, nrow = N_run, ncol = 5)
freqs <- runif(ncol(g_vec), min = 0,max = 1)
for(i in 1:ncol(g_vec)){
 g_vec[, i] <- rbinom(N_run, 2, freqs[i])
}
cc_vec <- rbinom(N_run,1,0.1 * (1.05 ^ g_vec[, 1]) *
          (1.06 ^ g_vec[,2]) * (0.95 ^ g_vec[, 3]) *
          (1.5^(g_vec[,1] * g_vec[,2])))
res <- pairwise_int_CC.calc(cc_vec, g_vec)