Another genomics PhD here. It's a complex topic. We know that combinatorial effects (epistasis in genetics lingo) matter, from population genetics studies in model organisms. This is despite the fact that simple linear models perform well in the human population - provided they are against some reasonably constant genetic background, low allele frequencies mean that the combinatorial effects are well captured by linear ones.

The problem is that even if you only care about pairwise combinations, there are far too many of them, given a uniform prior. Even if we sequence everyone on earth we wouldn't have anywhere near enough info, sequencing additional individuals has diminishing returns because there's only so much genetic variation in the human population (and ~23000^2 possible pairwise combinations).

What we need are good priors over combinations of mutations. To do that we'll need detailed info about which genes function together to produce which phenotypes. Such models exist already and are seeing moderate success, but we need new ideas and more data than any one startup could provide. Which is exactly what molecular biologists are working on.