R: Quickest way to create dataframe with an alternative to IFELSE

I have a similar question this the one on this thread: Using R, replace all values in a matrix <0.1 with 0?

But in my case I have hypothetically larger dataset and variable thresholds. I need to create a dataframe with each value retrieved from a condition using the values on the first columns of the same dataframe. These values are different for each line.

Here is an example of the dataframe:

SNP        A1  A2   MAF     
rs3094315  G   A   0.172  
rs7419119  G   T   0.240  
rs13302957 G   A   0.081  
rs6696609  T   C   0.393 

Here is a sample of my code:

seqIndividuals = seq(1:201)
for(i in seqIndividuals) {
  alFrequ[paste("IND",i,"a",sep="")] = ifelse(runif(length(alFrequ$SNP),0.00,1.00) < alFrequ$MAF, alFrequ$A1, alFrequ$A2)
  alFrequ[paste("IND",i,"b",sep="")] = ifelse(runif(length(alFrequ$SNP),0.00,1.00) < alFrequ$MAF, alFrequ$A1, alFrequ$A2)
}

I am creating two new columns for each individual "i" in "seqIndividuals" by retrieving either values from column "A1" if a random value if lower than column "MAF", or "A2" if higher. The code is working great, but as a dataset grows in rows and columns (individuals) the time also grows significantly.

Is there a way to avoid using IFELSE for this situation, as I understand it works as a loop? I tried generating a matrix of random values and then replacing them, but it takes the same time or even longer.

mtxAlFrequ = matrix(runif(length(alFrequ$SNP)*(201)),nrow=length(alFrequ$SNP),ncol=201)
mtxAlFrequ[mtxAlFrequ < alFrequ$MAF] = alFrequ$A1

Thanks!