Linear mixed effect models (LMMs) have a long history in genetics, going back at least as far as when R. A . Fisher proposed the polygenic model and have become a main stay in statistical modeling.  Because they can be computationally intense, LMMs were dropped in favor of simpler statistical tests in the whole genome era of genetics.  However, quite recently LMMs surged in popularity for -omic studies and in particular for genome wide association studies.  In my talk, I will review what makes these models so popular now in genomics, discuss my groups’ recent work with LMMs to detect maternal gene by offspring gene interactions, and then touch on some open questions.