Simulate residual multivariate Gaussian data from a polygenic model

Simulates residual multivariate Gaussian response data from a pedigree where the additive genetic, dominance genetic, and shared environmental effects are taken into account.

rmvtnorm.pedigree(n = 1, pedigree, h2 = 0, c2 = 0, d2 = 0)

Arguments

n	numeric. The number of simulations to generate
pedigree	a pedigree object
h2	numeric. The heritability
c2	numeric. The environmentability
d2	numeric. The dominance deviance effect

Value

Returns a matrix with the simulated values with n columns (one for each simulation) and each row matches the corresponding individual from the pedigree

Details

The three parameters should have a sum: h2+c2+d2 that is less than 1. The total variance is set to 1, and the mean is zero.

See also

pedigree, kinship,

Examples

library(kinship2)
library(mvtnorm)
mydata <- data.frame(id=1:5,
                     dadid=c(NA, NA, 1, 1, 1),
                     momid=c(NA, NA, 2, 2, 2),
                     sex=c("male", "female", "male", "male", "male"),
                     famid=c(1,1,1,1,1))
relation <- data.frame(id1=c(3), id2=c(4), famid=c(1), code=c(1))
ped <- pedigree(id=mydata$id, dadid=mydata$dadid, momid=mydata$momid,
                sex=mydata$sex, relation=relation)
rmvtnorm.pedigree(2, ped, h2=.25)
#>            [,1]        [,2]
#> [1,] -1.2078338  0.51816582
#> [2,] -1.0422381 -1.07491589
#> [3,] -0.5123884  0.09230299
#> [4,] -1.2033905 -0.50694075
#> [5,] -0.9291085  0.99260520