Please use the following description when referring to our project:

The FinnGen study is a large-scale genomics initiative that has analyzed over 500,000 Finnish biobank samples and correlated genetic variation with health data to understand disease mechanisms and predispositions. The project is a collaboration between research organisations and biobanks within Finland and international industry partners.

When using these results in publications, please remember to:

1. Acknowledge the FinnGen study. You can use the following text:

“We want to acknowledge the participants and investigators of the FinnGen study”

1. Cite our latest publication:

Kurki, M.I., Karjalainen, J., Palta, P. et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 613, 508–518 (2023). https://doi.org/10.1038/s41586-022-05473-8

Furthermore, if possible, include "FinnGen" as a keyword for your publication.

If you want to cite this website, use the following citation:

@online{finngen,
  author = {FinnGen},
  title = {{FinnGen} Documentation of R5 release},
  year = 2021,
  url = {https://finngen.gitbook.io/documentation/},
  urldate = {YYYY-MM-DD}
}

• Cromwell-29 and 31

• Wdltool-0.14

• Plink 1.9 and 2.0

• BCFtools 1.7 and 1.9

• Eagle 2.3.5

• Beagle 4.1 (version 08Jun17.d8b)

• R 3.4.1 (packages: data.table 1.10.4, sm 2.2-5.4)

FinnGen a public-private partnership project combining genotype data from Finnish biobanks and digital health record data from Finnish health registries. FinnGen provides a unique opportunity to study genetic variation in relation to disease trajectories in an isolated population.

FinnGen is a growing project, aiming at 500,000 individuals in 2023.

Additionally to the biobanks mentioned in the previous releases, the following biobanks and cohorts are part of the R5 release:

Timeline for releases:

[1] samples used for PheWAS.

Summary association statistics

The {endpoint}.gz have the following structure:

*)Note that the results are based on imputed genotype dosages and produced using SAIGE and that is why the data is not presented as integers but might contain digits.

Fine-mapping results

Two fine-mapping methods were used:

Fine-mapping results are tab-delimited and bgzipped.

SuSiE results have the following filename pattern:

• {endpoint}.SUSIE.cred.bgz

• {endpoint}.SUSIE.snp.bgz

FINEMAP results have the following filename pattern:

• {endpoint}.FINEMAP.region.bgz

• {endpoint}.FINEMAP.snp.bgz

• {endpoint}.FINEMAP.config.bgz

SuSiE output files {endpoint}.SUSIE.snp.bgz have the following structure:

LD estimation

ldstore --bcor FG_LD_chr1.bcor --incl-range 20000000-50000000 --table output_file_name.table

Variant annotation

The variant annotation has measures (HWE, INFO, ...) listed per batch.

Merged imputed genotype data is composed of 63 data sets that include samples from multiple cohorts.

• Total number of individuals: 224,737

• Total number of variants (merged set): 16,962,023

• Reference assembly: GRCh38/hg38

Chip genotype data processing and QC Samples were genotyped with Illumina (Illumina Inc., San Diego, CA, USA) and Affymetrix arrays (Thermo Fisher Scientific, Santa Clara, CA, USA).

Genotype calls were made with GenCall and zCall algorithms for Illumina and AxiomGT1 algorithm for Affymetrix data.

Quality control

In sample-wise quality control, individuals with ambiguous gender, high genotype missingness (>5%), excess heterozygosity (+-4SD) and non-Finnish ancestry were excluded. In variant-wise quality control variants with high missingness (>2%), low HWE P-value (<1e-6) and minor allele count, MAC<3 were excluded.

Pre-phasing

Variant call set was produced with GATK HaplotypeCaller algorithm by following GATK best-practices for variant calling.

Post-imputation quality-control involved checking expected conformity of the imputation INFO-value distribution, MAF differences between the target dataset and the imputation reference panel and checking chromosomal continuity of the imputed genotype calls.

1. METSIM (PIs Markku Laakso and Mike Boehnke)

2. FINRISK (PI Pekka Jousilahti)

3. Health2000 (PI Seppo Koskinen)

4. Finnish Migraine Family Study (PI Aarno Palotie)

5. Merck/Tienari samples (PI Pentti Tienari)

6. MESTA samples (PI Jaana Suvisaari)

High-coverage (25-30x) WGS data used to develop the SISu v3 reference panel were generated at the Broad Institute of MIT and Harvard and at the McDonnell Genome Institute at Washington University; and jointly processed at the Broad Institute.

Release 5 contains

Using FinnGen data for publications

Please remember to acknowledge the FinnGen study when using these results in publications.

You can use the following text:

We want to acknowledge the participants and investigators of FinnGen study.

Manifest

The manifest file with the link to all the downloadable summary stats is available at:

Endpoint

We included ​​2,803​ endpoints from the phenotype/registry teams’ pipeline in the analysis. Endpoints with less than 100 cases among the 218,792 samples were excluded.

Null models

For the null model computation for each endpoint, we used age, sex, 10 PCs and genotyping batch as covariates. Each genotyping batch was included as a covariate for an endpoint if there were at least 10 cases and 10 controls in that batch to avoid convergence issues. One genotyping batch need be excluded from covariates to not have them saturated. We excluded Thermo Fisher batch 16 as it was not enriched for any particular endpoints.

For calculating the genetic relationship matrix, we used the genotype dataset where genotypes with GP < 0.95 have been set missing. Only variants imputed with an INFO score > 0.95 in all batches were used. Variants with > 3 % missing genotypes were excluded as well as variants with MAF < 1 %. The remaining variants were LD pruned with a 1Mb window and r2 threshold of 0.1. This resulted in a set of 58,702 well-imputed not rare variants for GRM calculation.

• LOCO = false

• numMarkers = 30

• traceCVcutoff = 0.0025

• ratioCVcutoff = 0.001

Association tests

• Null model .rda objects were trimmed to reduce RAM consumption

• Het and alt hom counts in cases and controls are included in the output

We analyzed:

• ​2,803 endpoints

• 218,792 samples

• 16,962,023 variants

We included the following covariates in the model: sex, age, 10 PCs, genotyping batch.

We used a 3-megabase window (+- 1.5M) around each lead variant and merged overlapping regions into one. After merging, a handful of regions became too large to computationally handle with SuSiE. For such regions, we only merged two overlapping pieces when the LD between the two lead variants was r2 > 0.2. When LD was less than that, we made each of the two overlapping regions non-overlapping by splitting the overlap in half.

Integration to PheWeb

The columns of the table are explained below:

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

PCA

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.

Kinship

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

Registries

The disease endpoints were defined using nationwide registries:

We harmonized over the International Classification of Diseases (ICD) revisions 8, 9 and 10, cancer-specific ICD-O-3, (NOMESCO) procedure codes, Finnish-specific Social Insurance Institute (KELA) drug reimbursement codes and ATC-codes.

These registries spanning decades were electronically linked to the cohort baseline data using the unique national personal identification numbers assigned to all Finnish citizens and residents.

Excluded endpoints

The endpoints with fewer than 80 cases, and developmental “helper” endpoints were excluded from the final PheWas (“OMIT” tag in the endpoint definition file).

Risteys

For matters related to this documentation, click Edit on GitHubor send us an email to finngen-info@helsinki.fi.

If you want to host FinnGen summary statistics on your website, please get in contact with us at: humgen-servicedesk@helsinki.fi.

The panel has been divided per chromosome. For example, to use the LD information in the first chromosome, FG_LD_chr1.bcor would be the file to use.

Settings used

• number of samples: 3775

• window size: 1500 kb

• accuracy: low

• number of threads: 96

• LD threshold to include correlations: 0.05

Example usage

wget http://www.christianbenner.com/ldstore_v1.1_x86_64.tgz

And an example to extract variant range 20 Mb - 50 Mb from chromosome 7 is as follows:

ldstore --bcor FG_LD_chr7.bcor --incl-range 20000000-50000000 --table output_file_name.table

Note

It is not preferred to use these LD estimate files for e.g. fine-mapping, since many of the fine-mapping methods (e.g. SuSiE) require in-sample LD information for good results!