FinnGen research project is a public-private partnership 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 the end of 2023.

FinnGen results are subjected to one year embargo and, after that, available to the larger scientific community via the Pheweb browser or through data download.

Additionally to the biobanks mentioned in the previous releases, the following biobanks and cohorts are part of the R6 release:

• Auria Biobank

• Biobank Borealis of Northern Finland

• Biobank of Eastern Finland

• Central Finland Biobank

• Finnish Red Cross Blood Service Biobank

• Finnish Clinical Biobank Tampere

• Helsinki Biobank

• Terveystalo Biobank

• THL Biobank

FinnGen individuals were genotyped with Illumina and Affymetrix chip arrays (Illumina Inc., San Diego, and Thermo Fisher Scientific, Santa Clara, CA, USA).

Chip genotype data were imputed using the population-specific SISu v3 imputation reference panel of 3,775 whole genomes.

Merged imputed genotype data is composed of 75 data sets that include samples from multiple cohorts.

• Total number of individuals: 321,464

• 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.

Chip genotyping data produced with previous chip platforms and reference genome builds were lifted over to build version 38 (GRCh38/hg38) following the protocol described here: dx.doi.org/10.17504/protocols.io.nqtddwn.

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

Prior imputation, chip genotyped samples were pre-phased with Eagle 2.3.5 with the default parameters, except the number of conditioning haplotypes was set to 20,000.

The BCOR files were created using LDstore from the Finnish SISU panel v3.

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

LDstore v1.1 can be downloaded via:

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!

• Cromwell-42

• 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)

Endpoint

We included ​​3,095​ endpoints in the analysis. Endpoints with less than 80 cases or less than 1,000 controls among the 309,154 samples were excluded, as well as endpoints labeled with an OMIT tag in the endpoint definition file.

Null models

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.

The null model didn’t initially converge for three phenotypes: L12_ALOPECANDRO, DRY_AMD and N14_ENDOMETRIOSIS_FALLOPIAN_TUBE. For the first two, we excluded legacy batches with less than 10 cases from covariates and for N14_ENDOMETRIOSIS_FALLOPIAN_TUBE, we excluded all legacy batches from covariates, and the nulls converged successfully.

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 1Mb window and r2 threshold of 0.1. This resulted in a set of 55,139 well-imputed not rare variants for relatedness calculation.

We used a genotype block size of 1,000 in regenie step 1.

Association tests

We ran association tests with regenie for each of the 3,095 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).

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

for the latest updates on the project as well as additional background information please consider visiting the study website https://www.finngen.fi/en or follow FinnGen on twitter @FinnGen_FI.

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

SISu v3 consists of 3,775 WGS of Finnish individuals from six research cohorts:

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.

Whereas in previous FinnGen releases we used SAIGE for association analysis, we now switched to regenie for release 7. 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.

We used regenie version 2.0.2 which we modified to include dosage-based calculation of allele frequencies in cases and controls.

Links:

• regenie preprint

• regenie GitHub repository

• FinnGen regenie GitHub repository

• FinnGen regenie pipeline GitHub repository

The Docker image used in the analysis is available in Docker Hub.

We analyzed:

• ​3,095 endpoints

• 309,154 samples

• 16,962,023 variants

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

To download FinnGen summary statistics you will need to fill the online form at this link. You will then receive an email containing the detailed instructions for downloading the data.

Release 7 contains

• GWAS summary association statistics

• Fine-mapping results

• LD estimation from SISu v3

• Variant annotation

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:

We used two state-of-the-art methods, FINEMAP (; ) and SuSiE () to fine-map genome-wide significant loci in FinnGen endpoints.

Briefly, there are three main steps:

1. Preprocessing

For each genome-wide significant locus (default configuration: P < 5e-8), we define a fine-mapping region by taking a 3 Mb window around a lead variant (and merge regions if they overlap). If a merged window exceeds 6MB, we iteratively shrink the window by 10%, until the merged window fits into 6MB or is split into merged windows that each fit into 6MB. We preprocess an input GWAS summary statistics into separate files per region for the following steps.

2. LD computation

We compute in-sample dosage LD using for each fine-mapping region.

3. Fine-mapping

With the inputs of summary statistics and in-sample LD from the steps 1-2, we conduct fine-mapping using and with the maximum number of causal variants in a locus L = 10.

Integration to PheWeb

The "Credible Sets"-table on a phenotype page in the browser shows the SuSiE-fine-mapped credible sets of that phenotype. The variant shown per credible set is the maximum PIP (posterior inclusion probability) variant of that credible set. In addition to the causal variants, variants that were in sufficient LD (pearsonr^2 > 0.05), had a small enough p-value (pval < 0.01), and were close enough to the lead variant (distance to lead variant < 1.5 megabases) were clumped together with the credible set. Variants have been compared against GWAS Catalog and annotated. The LD grouping, annotation and GWAS Catalog comparison were done using the autoreporting pipeline.

The columns of the table are explained below:

Summary association statistics

GWAS summary statistics (tab-delimited, bgzipped, genome build 38, tabix index files included) are named as {endpoint}.gz. For example, endpoint I9_CHD has I9_CHD.gz and I9_CHD.gz.tbi.

To learn more about the methods used, see section GWAS.

The {endpoint}.gz have the following structure:

Fine-mapping results

Two fine-mapping methods were used:

• SuSiE

• FINEMAP

Fine-mapping results are tab-delimited and bgzipped.

SuSiE results have the following filename pattern:

• {endpoint}.SUSIE.cred.bgz

• {endpoint}.SUSIE.cred_99.bgz

• {endpoint}.SUSIE.snp.bgz

FINEMAP results have the following filename pattern:

• {endpoint}.FINEMAP.config.bgz

• {endpoint}.FINEMAP.region.bgz

• {endpoint}.FINEMAP.snp.bgz

To learn more about the methods used, see section Fine-mapping.

{endpoint}.SUSIE.cred.bgz contain credible set summaries from SuSiE fine-mapping for all genome-wide significant regions. {endpoint}.SUSIE.cred_99.bgz contain the 99% credible set summaries while the default is 95%. They have the following structure:

{endpoint}.SUSIE.snp.bgz contain variant summaries with credible set information and have the following structure:

{endpoint}.FINEMAP.config.bgz contain summary fine-mapping variant configurations from FINEMAP method and have the following structure:

{endpoint}.FINEMAP.region.bgz contain summary statistics on number of independent signals in each region and have the following structure:

{endpoint}.FINEMAP.snp.bgz has summary statistics of variants and into what credible set they may belong to. Columns:

LD estimation

Linkage disequilibrium (LD) was estimated from SISU v3 for each chromosome. Use the tool LDstore (v1.1) for further usage of the bcor files.

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

To learn more about the methods used, see section LD estimation.

Variant annotation

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

Genotype imputation was done with the population-specific .

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

Genotype-, sample- and variant-wise QC was applied in an iterative manner by using the and the resulting high-quality WGS data for 3,775 individuals were phased with Eagle 2.3.5 as described in the previous section.

Genotype imputation was carried out by using the population-specific SISu v3 imputation reference panel with (version 08Jun17.d8b) as described in the following protocol: .

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.

Registries

The disease endpoints were defined using nationwide registries:

• Drug purchase and Drug Reimbursement

• Digital and Population Data Services Agency

• Statistics Finland

• Register of primary health care visits: AVOHILMO

• Care Register for Health Care: HILMO

• Finnish cancer registry

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.

A full list of FinnGen endpoints is available online for release 7.

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

risteys.finngen.fi (Risteys = intersection in Finnish) allows browsing of the FinnGen data at the phenotype level, including endpoint definitions, statistics about number of individuals, gender distribution, and longitudinal relationships.

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 R7 release},
  year = 2022,
  url = {https://finngen.gitbook.io/documentation/},
  urldate = {YYYY-MM-DD}
}

Timeline for releases:

[1] samples used for PheWAS.

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 imputation panel is pruned iteratively, until a target number of SNPs is reached: 882,004,8 starting variants: only variants with a minimum info score of 0.9 in all batches are kept.

The script starts with [500.0, 50.0, 0.9] params in plink (window,step,r2). It then decreases 0.05 in r2 iteratively pruning the imputation panel until the threshold of 200,000 SNPs is reached. Once the SNP count falls under 200000 the closest pruning is returned.

If the higher r2 is closer, 200000 snps are randomly selected, else the last pruned snps are returned.

Plink flags used: --snps-only --chr 1-22 --max-alleles 2 --maf 0.01 .

For this run the final ld params are --indep-pairwise 500.0 50.0 0.15 and 175411 snps are returned.

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 9,767 FinnGen samples. The figure below shows the scatter plots for the first 3 PCs. Outliers, in green , are separated from the FinnGen red cluster.

While the method automatically detected as being outliers the 1kg 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 77 outliers. The figure below shows the first three principal components.

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

Kinship

Then all pairs of FinnGen samples up to second degree were returned. The figure below 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:

• 207,840 inliers: unrelated samples with Finnish ancestry.

• 101,472 outliers: non duplicate samples with Finnish ancestries, but who are also related to the inliers.

• 12,152 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 PC space, allowing to calculate covariates for a total of 309,312 samples.

Sample filtering based on phenotype data

Of the 309,312 non-duplicate population inlier samples from PCA, we excluded 154 samples from analysis because of missing minimum phenotype data, and 4 samples because of failing sex check with F thresholds of 0.4 and 0.7. Sex matched between genotype data and phenotype data for all individuals! A total of 309,154 samples was used for core analysis. There are 173,746 females and 135,408 males among these samples.

Further info

Bayesian outlier detection