HE vs REML estimator in GWAS


Are there some good references or is there someone who knows the scale of the difference in computation between REML and HE to estimate the variance components in a mixed effects model, say in GCTA fastGWA?

I have seen some papers showing HE having wider CIs so I am quite hesitant to use it, but I have a longitudinal model which I know takes a lot longer to run than a cross-sectional GWAS. I am trying to balance speed vs point estimate accuracy but currently have no idea of how big the difference is in terms of run time between these 2 estimators.

Thanks in advance!!

(Note: I am considering to do a GWAS on the delta of pheno at t1, t2 and t3 as a last resort.)

Hi Ralph,
Here is an article I really like about mixed models, Advantages and pitfalls in the application of mixed-model association methods | Nature Genetics, it gives an idea of the computational cost for GCTA/REML.
Computationally, HE is a linear regression [of the phenotype covariance on the genotype covariance - across all pairs of individuals in your sample]. So the number of observations is N*(N-1)/2, which quickly gets large (N is the number of individuals).
This paper by Valentin Hivert et al., uses both methods
Estimation of non-additive genetic variance in human complex traits from a large sample of unrelated individuals - PMC and they discuss the differences of statistical power, and also include considerations about computation.
I am aware of a couple GWAS that use longitudinal data, e.g., Genetic variants associated with longitudinal changes in brain structure across the lifespan | Nature Neuroscience, they can be very interesting per se.
I hope this helps.