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:

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.

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.

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 3 contains

• GWAS summary association statistics

• Fine-mapping results

• from

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 data is available at:

Timeline for releases:

[1] samples used for PheWAS.

Additionally to the biobanks mentioned in the previous releases, the following biobanks and cohorts are part of the R3 release:

SISu v3 consists of 3,775 high coverage (30x) WGS Finnish individuals from six 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.

Genotype imputation was done with the population-specific SISu v3 reference panel .

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 Hail framework v0.1 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 Beagle 4.1 (version 08Jun17.d8b) as described in the following protocol: dx.doi.org/10.17504/protocols.io.nmndc5e.

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.

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 42 data sets that include samples from multiple cohorts.

• Total number of individuals: 146,630

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

• Reference assembly: GRCh38/hg38

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

can be downloaded via:

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

Summary association statistics

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

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: .

Quality control

• Cromwell-29 and 31

• Wdltool-0.14

• Plink 1.9 and 2.0

• BCFtools 1.5 and 1.7

• 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)

We used the (r3 release) software for running the R3 GWAS.

is a mixed model logistic regression R/C++ package, able to account for related samples.

We analyzed:

• ​ 1,801 endpoints

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

In summary, we removed 10,992 samples who were either of non-Finnish ancestry or twins/duplicates. Finnish ancestry was assessed with a combination of PCA and a Bayesian method for outlier detection.

PCA

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

We estimated the loss of function (LoF) burden of each gene on every endpoint.

First, we calculated per individual and gene whether any loss of function variant(s) was present, yielding a $n \times p$ matrix with 0 and 1 values ( $n$being the number of individuals and $p$ the number of genes).

Then we used the new summarised variables as input in the SAIGE GWAS, replacing the genotype matrix that was used in the regular GWAS.

Endpoint

We included 1,801 endpoints from the phenotype/registry teams’ pipeline in the analysis. Endpoints with OMIT in the endpoint definition file were excluded, as well as endpoints with less than 100 cases among the 135,638 samples. “Smoking: yes” and “Smoking: current or former” were created based on the respective smoking data in the phenotype data file.

Null models

For the null model calculation for each endpoint, we used age, sex, 10 PCs and genotyping batch as covariates.

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 < 5 %. The remaining variants were LD pruned with a 1Mb window and r2 threshold of 0.1. This resulted in a set of 35,557 common, well-imputed variants for GRM calculation.

options for the null computation:

• LOCO = false

• numMarkers = 30

• traceCVcutoff = 0.0025

Association tests

We ran association tests against each of the 1,801 endpoints with for each variant with a minimum allele count of 10 from the imputation pipeline (SAIGE optionminMAC = 10). The alternative allele is always the effect allele.

For matters related to this documentation, click Edit on GitHubor 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 or follow FinnGen on twitter .

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

Registries

The disease endpoints were defined using nationwide registries:

To identify potential causal variants in GWAS signals, we fine-mapped each genome-wide significant (p < 5e-8) region from the 1,801 GWAS endpoints. Each region was fine-mapped with and . We used in-sample LD for fine-mapping.

We used a 3-megabase window (+- 1.5M) around each lead variant, merged overlapping regions into one, and used these regions for fine-mapping.