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Generate gene expression data for trans-QTL analysis

Source: R/simulate_transQTL_null.R
gene_data.Rd

This function simulates genotype and gene expression data for trans-QTL analysis, including both cis and trans effects with specified network structures.

Usage

gene_data(
  n,
  p,
  A_snp_cis,
  A_cis_trans,
  A_trans,
  MAF = NULL,
  beta = 1,
  gamma_cis_trans = 0.5,
  gamma_trans_trans = 0.5,
  maf_setting = 1
)

Arguments

n

Sample size

p

Number of SNPs

A_snp_cis

Binary adjacency matrix indicating SNP-cis gene relationships

A_cis_trans

Binary adjacency matrix indicating cis-trans gene relationships

A_trans

Binary adjacency matrix indicating trans-trans gene relationships

MAF

Optional vector of minor allele frequencies. If NULL, MAFs will be generated randomly.

beta

Effect size for SNP to cis gene effects

gamma_cis_trans

Effect size for cis to trans gene effects

gamma_trans_trans

Effect size for trans to trans gene effects

maf_setting

Integer flag for MAF generation method (1=uniform random, 2=sample from provided MAF)

Value

A list containing:

  • maf: Vector of minor allele frequencies

  • G_unscaled: Unscaled genotype matrix

  • G: Scaled genotype matrix

  • G_centered: Column-centered genotype matrix

  • E_cis_unscaled: Unscaled cis gene expression matrix

  • E_cis: Scaled cis gene expression matrix

  • E_cis_centered: Column-centered cis gene expression matrix

  • E_trans_unscaled: Unscaled trans gene expression matrix

  • E_trans: Scaled trans gene expression matrix

  • E_trans_centered: Column-centered trans gene expression matrix

Examples

# Define adjacency matrices
A_snp_cis <- as.matrix(Matrix::bdiag(lapply(1:8, function(x) rep(1,5))))
A_snp_cis <- rbind(A_snp_cis, matrix(0, nrow = 20, ncol = 8))

A_cis_trans <- cbind(c(1,1,0,0,0,0,0,0),
                    c(0,0,1,1,0,0,0,0),
                    c(0,0,0,0,1,1,0,0),
                    c(0,0,0,0,0,0,1,1),0,0,0,0,0)

A_trans <- cbind(0,0,0,0,c(1,rep(0,8)),
                c(0,1,1,rep(0,6)),
                c(0,0,0,1,rep(0,5)),
                c(0,0,0,0,1,1,0,0,0),
                c(rep(0,5),1,1,0,0))

# Simulate data
data <- gene_data(n = 1000, p = 60, A_snp_cis, A_cis_trans, A_trans,
                  beta = 0.5, gamma_cis_trans = 0.5, gamma_trans_trans = 0.5)