interaction_CC.calc {gnonadd}

Variant-Variant interaction effects on a case control variable

Description

This function estimates the interaction effect of a pair of genetic variant on a case-control variable We apply a logistic regression model to estimate interaction effects. We include a linear term for each variant seperately, coded as 0,1 and 2 for non-carriers, heterozygotes and homozygous carriers of the effect allele. We also include an interaction term, coded as the product of the two genotype values. Effect size and significance is based on the interaction term.

Usage

interaction_CC.calc(
  cc,
  g1,
  g2,
  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))
)

Arguments

cc

A numeric vector

g1

A vector with (possibly imputed) genotype values. All entries should be larger than 0 and smaller than 2.

g2

A vector with (possibly imputed) genotype values. All entries should be larger than 0 and smaller than 2.

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

Value

A list with the interaction effect (on log-scale) and corresponding standard error, z statistic and p-value

Examples

g1_vec <- rbinom(100000, 2, 0.9)
g2_vec <- rbinom(100000, 2, 0.1)
cc_vec <- rbinom(100000,1,0.1 * (1.05^g1_vec) *
          (1.05^g2_vec) * (1.3 ^ (g1_vec * g2_vec)))
res <- interaction_CC.calc(cc_vec, g1_vec, g2_vec)