Thank you both for your advice. I ended up implementing both solutions and
testing them on a real dataset of 10,000 rows and 50 inds. The results are
very, very interesting.
For some context, the original two approaches, nested lapply and nested for
loops, performed at 1.501529
and 1.458963 mi
My data looks like this:
> data
name G_hat_0_0 G_hat_1_0 G_hat_2_0 G_0 G_hat_0_1 G_hat_1_1 G_hat_2_1 G_1
1 rs0 0.488000 0.448625 0.063375 1 0.480875 0.454500 0.064625 1
2 rs1 0.002375 0.955375 0.042250 1 0.00 0.062875 0.937125 2
3 rs2 0.050375 0.835875 0.113750
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