Sample QC and PCA

This is a description of the quality control procedures applied before running the GWAS.


The PCA for population structure has been run in the following way:

Variant filtering and LD pruning

The following filters were applied:

  • Exclusion of chromosome 23

  • Exclusion of variants with info score < 0.95

  • Exclusion of variants with missingness > 0.01 (based on the GP; see conversion)

  • Exclusion of variants with MAF < 0.05

  • LD pruning with window 500kb, step 50kb, r^2 filter of 0.1

This filtering step produced 41,678 variants, that were used for the rest of the analysis.

PCA outlier detection

Then, FinnGen data was merged with the 1k genome project (1kgp) data, using the variants mentioned above. A round of PCA was performed and a bayesian algorithm was used to spot outliers. This process got rid of 5,520 outliers, of which 3,138 are from the FinnGen samples. The figure below shows the scatter plots for the first 3 PCs. Outliers, in brown, are separated from the FinnGen yellow cluster.

While the method automatically detected as being outliers the 1kgp samples with non European and southern European ancestries, it did not manage to exclude some samples with Western European origins. Since the signal from these samples would have been too small to allow a second round to be performed without detecting substructures of the Finnish population, another approach was used. The FinnGen samples that survived the first round were used to compute another PCA. The EUR and FIN 1kg samples were then projected onto the space generated by the first 3 PCs. Then, the centroid of each cluster was calculated and used to calculate the squared mahalanobis distance of each FinnGen sample to each of the centroids. Being the squared distance a sum of squared variables (with unitary variance, due to the mahalanobis distance), we could see it as a sum of 3 independent squared variables. This allowed to map the squared distance into a probability (chi squared with 3 degrees of freedom). Therefore, for each cluster, a probability of being part of it was computed. Then, a threshold of 0.95 was used to exclude FinnGen samples whose relative chance of being part of the Finnish cluster was below the level. This method produced another 538 outliers. The figure below shows the first three principal components.

FIN 1kgp samples are in purple, while EUR 1kgp sample are in Blue. Samples in green are FinnGen samples who are flagged as being non Finnish, while red ones are considered Finnish.


In a next step, all pairs of Finngen samples up to second degree were returned. The figure shows the distribution of kinship values.

Then, the previously defined “non Finnish” samples were excluded and 2 algorithms were used to return a unique subset of unrelated samples:

  • one called greedy would continuously remove the highest degree node from the network of relations, until no more links are left in the network.

  • one called native, based on a native implementation of python’s networkx package, performed on each subgraph of the network.

The largest independent set of either algorithm would be used to keep those sample, while flagging the others as “outliers” for the final PCA.

Then, the subset of outliers who also belong to the set of duplicates/twins was identified.

Final PCA

To compute the final step the Finngen samples were ultimately separated in three groups:

  • 156,977 inliers: unrelated samples with Finnish ancestry.

  • 61,980 outliers: non duplicate samples with Finnish ancestries, but who are also related to the inliers.

  • 5,780 rejected samples: either of non Finnish ancestry or are twins/duplicates with relations to other samples.

Finally, the PCA for the inliers was calculated, and then outliers were projected on the same space, allowing to calculate covariates for a total of 218,957 samples.

Sample filtering based on phenotype data

Of the 218,957 non-duplicate population inlier samples from PCA, we excluded 154 samples from analysis because of missing minimum phenotype data, and 11 samples because of mismatch between imputed sex and sex in registry data. ​A total of 218,792 samples was used for core analysis.

Further info

Bayesian outlier detection

Documentation from the original developers of the algorithm can be found here: