Custom GWAS tools
Trade-offs of different methods
We provide a number of methods that enable you to run GWAS for your custom endpoint(s). This table describes the trade-offs of different methods. All methods use REGENIE. There are also instructions to use SAIGE available, but since DF7 we have moved to use REGENIE, and SAIGE has not been supported similarly since.
The fastest way to get results calculated
The easiest way to get results calculated and out of the sandbox is to use the methods in the first 2 rows - these methods enable the summary statistics to be automatically put into the green bucket and user.results pheweb browser.
If you want to browse your results outside of sandbox, the 2 lower methods require that you request to have results downloaded, which can take more time especially off-hours Helsinki time. These methods also require a bit more coding skills. Using these methods, however, you are able to modify some parameters in your job, e.g. covariates.
In the subsections here we describe the two methods you can use to have your methods visible in userresults.finngen.fi:
And in the subsections here we describe the two other methods you can use to run GWAS for your custom endpoint:
However you can learn more about other additional methods under "How to create a user-defined endpoint".
Upstream analysis
Before entering GWAS consider investigating your phenotype and genotype cohorts. For further instructions see:
General workflows for the most common analyses researchers are conducting with FinnGen data in FinnGen Sandbox
Downstream analysis
After custom GWAS is conducted you may consider further downstream analyses like
How to conduct Finemapping to assign a probability that each significant variant at a locus is the causal variant. More background reading about Finemapping.
Multiple Manhattan Plot to compare several GWAS summary statistics.
Last updated
