eric-czech
commented
4 years ago Along these lines, G2P (from https://pubmed.ncbi.nlm.nih.gov/30848784/) is probably the most comprehensive tool I've seen so far for generating both genotypes and phenotypes with a good bit of configurability.
PhenotypeSimulator (Mayer & Birney 2018) is another decent one for layering in relationships between given genotypes and generated phenotypes. What it supports is a good outline for what a simulator for association testing should do:
- multiple phenotypes
- independent and infinitesimal SNP effects (important for LMM testing)
- non-genetic effects
- effects from population structure and relatedness
- pleiotropy
- it can simulate genotypes to an extent but these are generally imported from elsewhere (resampling methods seem like a promising way to do this)
- No binary/categorical phenotypes though unfortunately
The examples are pretty good too, e.g.:
A few other phenotype-only tools:
- https://github.com/bcm-uga/NaturalGWAS
- https://github.com/spiros/tofu
- https://github.com/chr1swallace/simGWAS - Directly simulates summary stats for phenotypes rather than ever needing genotypes
We should start thinking about how to simulate data as part of a public API. PLINK and Hail support this and I think we should think about it now because it will be an important part of improving unit testing. I was chatting with @ravwojdyla and we're both at about the same place in our testing -- we have more simplistic test cases now but would both benefit from synthetic data representing a single dimension of genetic structure, likely with some tunable level of complexity. Essentially we need a better version of Hypothesis and while we're at it, why not make it part of the API?
Some examples:
make_betasandsimulate_phenotypeswould be useful for validation LMM and multi-trait models we work onIt may be that most users don't care about simulators that aren't representative of comprehensive genetic structure (e.g. hapgen), but I think being explicit about our simulations would improve understanding of the methods and that this should be something we coordinate on regardless, rather than making private versions for test cases on a per-method basis. This would also make it much easier to demonstrate what a method does without always having to appeal real datasets.