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
data$Trait = rnorm(li*re,10,1); data$Trait = data$Trait+rep(rnorm(li,0,3),each=re)
boxplot(data$Trait~data$Line, ylim=c(0,20), main = expression("High V"[A]*"& Low V"[E]))
var3 = var(data$Trait); mean3 = mean(data$Trait); m3 = lmer(data$Trait ~ (1|data$Line))

Plots of the three traits,

Then to estimate additive variance ($V_A$) I extract the line variance ($V_L$) from the lmer model and double it,

# line variances (variance in additive effect of each haploid genome)
m1_line = unlist(VarCorr(m1))[[1]]; 
m2_line = unlist(VarCorr(m2))[[1]]; 
m3_line = unlist(VarCorr(m3))[[1]]

# additive variance (double the line variance because it is a hemiclone)
m1_add = 2*m1_line; 
m2_add = 2*m2_line; 
m3_add = 2*m3_line

Residual variances should be (assuming perfect experimental design, no measurement error etc.) the estimate of environmental variance ($V_E$) and phenotypic variance ($V_P$) should be the sum of $V_L$ and $V_E$,

# residual variance
m1_res = attr(VarCorr(m1), "sc")^2
m2_res = attr(VarCorr(m2), "sc")^2
m3_res = attr(VarCorr(m3), "sc")^2

# phenotypic variance
m1_phe = m1_line + m1_res
m2_phe = m2_line + m2_res
m3_phe = m3_line + m3_res

Heritability is additive variance divided by the phenotypic variance

$h^2 = V_A / V_P$

But I think it is correct in this case to use line variance rather than additive variance (if someone could explain in an answer that would be useful), so I’ve done,

# heritability (line/ (line+ residual))
m1_h2 = m1_line/ m1_phe
m2_h2 = m2_line/ m2_phe
m3_h2 = m3_line/ m3_phe
m1_h2; m2_h2; m3_h2

My question(s):

Is it appropriate to use the lmer function in R to extract variance components in this manner?

Have I calculated $V_A$, $V_E$, $V_P$, $h^2$ correctly? I think $V_A$ and $V_E$ are correct, $V_P$ could perhaps be the sum of $V_A$ and $V_E$ rather than $V_L$, and subsequently $h^2$ may be $V_A/V_P$.