pairwise_env_int_CC.calc {gnonadd}

Pairwise environmental interaction effects for a case control variable

Description

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

Usage

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

Arguments

cc

A numeric vector

g

A matrix, where each colomn represents a variant

env

A matrix, where each row represents an environmental variable

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_term

A boolian variable determining whether a dominance term for the variant should be included as a covariates in the analysis

square_env

A boolian variable determining whether the square of the environmental trait should be included as a covariate 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

env_names

A list of the names of the environmental variables

Value

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

Examples

N_run <- 25000
g_vec <- matrix(0, nrow = N_run, ncol = 3)
freqs <- runif(ncol(g_vec), min = 0, max = 1)
env_vec <- matrix(0, nrow = N_run, ncol = 3)
for(i in 1:ncol(g_vec)){
 g_vec[, i] <- rbinom(N_run, 2, freqs[i])
}
for( i in 1:ncol(env_vec)){
 env_vec[, i] <- round(runif(N_run,min=0,max=6))
}
cc_vec <- rbinom(N_run,1,0.1 * (1.05 ^ g_vec[, 1]) *
          (1.06 ^ env_vec[,1]) * (0.95 ^ g_vec[, 2]) *
          (1.1^(g_vec[, 1] * env_vec[, 1])))
res <- pairwise_env_int_CC.calc(cc_vec, g_vec, env_vec)