We included 2,447 endpoints in the analysis, which consisted of 2,444 binary endpoints and 3 quantitative endpoints (HEIGHT_IRN, WEIGHT_IRN, BMI_IRN). Endpoints with less than 50 cases among the 453,733 samples were excluded, as well as endpoints labeled with an OMIT tag in the endpoint definition file.
The quantitative endpoints HEIGHT and WEIGHT were acquired from minimum phenotype data. After that, phenotype BMI was formed from them, and all of them were inverse normal transformed.
7 endpoints did not progress past step1 in regenie pipeline due to convergence issues, and were discarded. The endpoints are:
For regenie step 1 LOCO prediction computation for each endpoint, we used age, sex, 10 PCs, Finngen 1 or 2 chip or legacy genotyping batch as covariates. For sex-specific phenotypes, sample sex was left out from the covariates. We excluded covariates that had less than 10 cases.
For calculating genetic relatedness in regenie step 1, we included variants 1) imputed with an INFO score > 0.95 in all batches and 2) > 97 % non-missing genotypes and 3) MAF > 1 %. The remaining variants were LD pruned with a 1.5Mb window and r2 threshold of 0.2. This resulted in a set of 215,152 well-imputed not rare variants for relatedness calculation.
We used a genotype block size of 1,000 in regenie step 1.
We ran association tests with regenie for each of the 2,440 endpoints for each variant with a minimum allele count of 5 among each phenotype’s cases and controls. We used the approximate Firth test for variants with an initial p-value of less than 0.01 and computed the standard error based on effect size and likelihood ratio test p-value (regenie options --firth --approx --pThresh 0.01 --firth-se).