mmGEBLUP

A systematic simulation program and a novel genomic prediction strategy based on integrated evaluation of major and minor gene and gene-by-environment effects

In our package, we offer Three key functions in the field of quantatative genetics including Genotype-by-Environment (GE) interaction.

Functions
* Trait simulation
* GWAS analysis
* Genomic prediction (a novel model named mmGEBLUP)

Github Link

Package installation

The current GitHub version of mmGEBLUP can be installed via:

library(devtools)
install_github("qixininin/mmGEBLUP")

Example: Trait simulation

This example simulate 1000 (indNum) individual genotypes with 2000 (snpNum) independent markers and their phenotypes with specified genetic architecture and extended GE interaction structure in 3 (envNum) environments.
The trait simulation can be specified through several parameters:

$h_{A}^{2}$ : General heritability
$h_{A E}^{2}$ : Interaction heritability
$ρ_{A}$ : General polygenicity (or the proportion of genetic variance explained by the large-effect term)
$ρ_{A E}$ : Interaction polygenicity (or the proportion of interaction variance explained by the large-interaction-effect term)


                      # Load packages 
                      library(mmGEBLUP)


                      # Set parameters
                      envNum = 3
                      indNum = 1000
                      snpNum = 2000
                      rho_a = 0.5
                      rho_ae = 0.5
                      h2_a = 0.3
                      h2_ae = 0.3
                      sigma_a = h2_a
                      sigma_ae = h2_ae
                      sigma_error = 1-h2_a-h2_ae

                      # Define large additive effect markers
                      major_a_idx = c(500, 750, 1000, 1250, 1500)
                      snpNum_a_major = length(major_a_idx)
                      snpNum_a_minor = snpNum-snpNum_a_major

                      # Define large interaction effect markers
                      major_ae_idx = c(250, 500, 1000, 1500, 1750)
                      snpNum_ae_major = length(major_ae_idx)
                      snpNum_ae_minor = snpNum-snpNum_ae_major

                      # Transform to marker effect variance
                      sigma_a_major = rho_a * sigma_a/ snpNum_a_major
                      sigma_a_minor = (1-rho_a) * sigma_a / snpNum_a_minor
                      sigma_ae_major = rho_ae * sigma_ae/ snpNum_ae_major
                      sigma_ae_minor = (1-rho_ae) * sigma_ae / snpNum_ae_minor

                      # Effect generation
                      eff_list = snp.effect(snpNum = snpNum, envNum = envNum,
                                            major_a_idx = major_a_idx, major_ae_idx = major_ae_idx,
                                            variance_a_major = sigma_a_major , variance_a_minor = sigma_a_minor,
                                            variance_ae_major = sigma_ae_major, variance_ae_minor = sigma_ae_minor)
                      b = eff_list$effects
                      # Plot marker effects
                      p = snp.effect.plot(effects = b)

                      # Genotype generation
                      geno_data = geno.generate(indNum = indNum, snpNum = snpNum, maf.min = 0.05, maf.max = 0.5,
                                                chr.snpNum = c(500, 500, 500, 500))
                      # Phenotype generation
                      pheno_data = pheno.generate(genotypes = t(geno_data[-c(1:3)]), effects = b,
                                                  envNum = envNum, indNum = indNum, sigma.error = sigma_error)


                      save(geno_data, pheno_data, file = "./inst/Simulation-genphe.Rdata")