Additive genetic variance components from LMER in R

I've set up some dummy data in R which makes 40 genetically related lines, they are all siblings within a line so are genetically related by a factor of ½ thus additive genetic variance should be twice the variance explained by line. For the lines there are 200 individuals being measured, for three characters/traits. The first trait has low phenotypic variance, the second has high environmental variance, and the third has high genetic variance.

rm(list=ls())
re = 200 # replicate individuals per line
li = 40  # lines

# setup
set.seed(5)
data = data.frame(rep(1:li, each = re))
colnames(data)="Line"
library(nlme)
library(lme4)
par(mfrow=c(1,3))

# trait 1: little variance (Va or Ve)
data$Trait = rnorm(li*re,10,1) boxplot(data$Trait~data$Line, ylim=c(0,20), main = expression("Low V"[A]*"& Low V"[E])) var1 = var(data$Trait); mean1 = mean(data$Trait); m1 = lmer(data$Trait ~ (1|data$Line)) # trait 2: high enivronmental variance, little Va data$Trait = rnorm(li*re,10,1); data$Trait = data$Trait + rnorm(re*li,0,3)
boxplot(data$Trait~data$Line, ylim=c(0,20),main = expression("Low V"[A]*"& High V"[E]))
var2 = var(data$Trait); mean2 = mean(data$Trait); m2 = lmer(data$Trait ~ (1|data$Line))

# trait 3: high additive genetic variance, little Ve