# GWAS

We used regenie for the FinnGen R13 release. Regenie's main advantages are fast leave-one-chromosome-out relatedness calculation which avoids proximal contamination, and use of an approximate Firth test which gives more reliable effect size estimates for rare variants.

Regenie version 3.3 was used for all the endpoints.

Links:

* [regenie preprint](https://www.biorxiv.org/content/10.1101/2020.06.19.162354v2.full.pdf)
* [regenie GitHub repository](https://github.com/rgcgithub/regenie)
* [FinnGen regenie GitHub repository](https://github.com/FINNGEN/regenie)
* [FinnGen regenie pipeline GitHub repository](https://github.com/FINNGEN/regenie-pipelines)

We analyzed:

* ​2,755 endpoints
  * 2,752 binary endpoints
  * 3 quantitative endpoints (HEIGHT\_IRN, WEIGHT\_IRN, BMI\_IRN)
* 500,186 samples
  * 281,909 females
  * 218,277 males
* 21,311,644 variants

We included the following covariates in the model: sex, age, 10 PCs, Finngen chip version 1 or 2 , and legacy genotyping batch.


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