For each round of bootstrapping (say n=1000), I would like to select the genotypes (the columns) at random without replacement from the original dataset. With this I would like to form two groups with 10 and 10 genotypes each to match the genotypes in X and Y subsets respectively.

With the original dataset (correlation between X and Y) and the bootstrapped datasets (correlation between X′ and Y′) draw a histogram as follows to see how many percent of the 1000 pairs derived from random permuting exhibit lower correlations than the original dataset.

A sample histogram is shown below.

Maybe for such a sample data

     set.seed(1) 
     x <- matrix(rnorm(1000), nrow=100, ncol=10)
     y <- matrix(rnorm(1000), nrow=100, ncol=10).

I found a useful MATLAB code which I have added here to bootstrap correlation coefficients. Is there something similar in R to obtain a similar plot?