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 R10 release},
  year = 2023,
  url = {https://finngen.gitbook.io/documentation/},
  urldate = {YYYY-MM-DD}
}

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 10 contains

• GWAS summary association statistics

• Fine-mapping results

• Variant annotation

• HLA region analysis

• pQTL analysis

• LoF variant burden test results

Using FinnGen data for publications

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”

2) Cite our latest publication:

Kurki M.I., et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 2023 Jan;613(7944):508-518. doi: 10.1038/s41586-022-05473-8. Epub 2023 Jan 18.

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 R10 release},
  year = 2023,
  url = {https://finngen.gitbook.io/documentation/},
  urldate = {YYYY-MM-DD}
}

Manifest

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

Timeline for releases:

[1] samples used for PheWAS.

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 or through .

SISu v4.2 consists of 8,554 WGS of Finnish individuals from 5 research cohorts from:

1. METSIM (PIs Markku Laakso and Mike Boehnke)

2. FINRISK (PI Pekka Jousilahti)

3. Corogene (PI Juha Sinisalo)

4. Biobank of Eastern Finland (PI Arto Mannermaa)

5. Finnish EUFAM Dyslipidemia Study (PIs Marja-Riitta Taskinen and Samuli Ripatti)

High-coverage (25x) WGS data used to develop the SISu v4.2 reference panel were generated at the McDonnell Genome Institute at Washington University (PIs Ira Hall and Nathan Stitziel).

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 v4.2 imputation reference panel of 8,554 whole genomes.

Merged imputed genotype data is composed of 116 data sets that include samples from multiple cohorts.

• Total number of individuals: 430,897

• Total number of variants (merged set): 21,311,942

• Reference assembly: GRCh38/hg38

The BCOR files were created using LDstore from the Finnish SISu panel v4.2.

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!

• Hail v0.2

• 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 27Jan18.7e1)

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

Genotype imputation was done with the population-specific .

The reference panel variant call set was produced with the GATK HaplotypeCaller algorithm by following GATK best practices for variant calling.

Genotype-, sample- and variant-wise QC was carried out iteratively by using the and the resulting high-quality WGS data for 8,554 individuals were phased with as described in the previous section.

Genotype imputation was carried out by using the population-specific SISu v4.2 imputation reference panel with (version 27Jan18.7e1) as described in the following protocol: .

Post-imputation quality control involved checking the 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.

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

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

Pre-phasing

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

We used regenie for release 10. 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 2.2.4.

Links:

• regenie preprint

• regenie GitHub repository

• FinnGen regenie GitHub repository

• FinnGen regenie pipeline GitHub repository

We analyzed:

• ​2,408 endpoints

  • 2,405 binary endpoints

  • 3 quantitative endpoints (HEIGHT_IRN, WEIGHT_IRN, BMI_IRN)

• 412,181 samples

  • 230,310 females

  • 181,871 males

• 21,311,942 variants

We included the following covariates in the model: sex, age, 10 PCs, Finngen chip version 1 or 2 , and legacy genotyping batch.

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 10.

Excluded endpoints

The endpoints with fewer than 50 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 also note the R10 specific page https://r10.risteys.finngen.fi/

We used two state-of-the-art methods, FINEMAP (Benner, C. et al., 2016; Benner, C. et al., 2018) and SuSiE (Wang, G. et al., 2020) 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 10MB, we iteratively shrink the window by 10%, until the merged window fits into 10MB or is split into merged windows that each fit into 10MB. 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 LDstore2 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 FINEMAP and SuSiE with the maximum number of causal variants in a locus L = 10.

Notes

Due to too many variants and too large LD regions the following endpoints were run with a refined region selection algorithm that limits the region's size to a set maximum length:

• G6_MUSDYST

• G6_MYONEU

• Q17_CONGEN_MALFO_GALLB_BILE_DUCTS_LIVER

• Q17_OTHER_CONGEN_MALFO_DIGES_SYSTEM1

• Q17_OTHER_CONGEN_MALFO_SKIN

• RX_STATIN

• BMI_IRN

• HEIGHT_IRN

Integration to PheWeb

The "Credible Sets"-table on a phenotype page in the R10 PheWeb 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 (Pearson r^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:

Endpoint

We included 2,408 endpoints in the analysis, which consisted of 2,405 binary endpoints and 3 quantitative endpoints (HEIGHT_IRN, WEIGHT_IRN, BMI_IRN). Endpoints with less than 50 cases among the 412,181 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.

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. 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 228,119 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 2,408 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).

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.

To learn more about the methods used, see section .

The {endpoint}.gz have the following structure:

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.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

{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:

pQTL summary statistics

The {probeName}.gz 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.

The PheWeb portal can be used to browse results from FinnGen's predetermined endpoints (or 'phenotypes') a.k.a. core analysis results. FinnGen PheWeb tutorial is available .

These were analysed for genetic associations, which allows for disproportionate case-control numbers and corrects for relatedness between samples with a sparse genetic relatedness matrix.

The results from each association run are uploaded onto the PheWeb portal, which can be accessed by clicking this link:

Home Page

The figure below shows the a table of the first few endpoints ('phenotypes') in FinnGen with the highest numbers of GWAS significant loci, along with the summary of case-control analyses and the number of hits.

You can reorder the table by clicking on the appropriate header value (in the figure above, we clicked on GWAS significant loci to order the table based on the number of GWAS loci).

From home page in PheWeb, you can also go directly to coding variant browser by clicking the icon '' in the top right corner.

Endpoint Page

Upon clicking an endpoint ('phenotype'), you will then be directed to the endpoint's page which will contain information such as case-control numbers and results from the association scan of the endpoint. In the following screenshot, we show the endpoint results for “Type 2 diabetes, wide definition”.

On the endpoint page, you will find a similar Manhattan plot from the association scan which summarizes the association results for your endpoint.

Scrolling further, you will also be able to see the Manhattan plot in a tabular format, distinguished by either the traditional GWAS hits or based on a.

Variant Page

You can also browse based on a variant of your choice and see a PheWas plot:

The Manhattan plot shown in the figure above also shows p-values from the association scans for FinnGen endpoints. Scrolling down, you will again be able to see the association scan results for the FinnGen endpoints in this variant in a tabular format.

All results (endpoint and variant-wise) can be downloaded in a tabular format by clicking Download table.

Gene Page

Gene pQTL and disease colocalizations

• source - pQTL platform source (i.e. FinnGen Olink, FinnGen Somascan, UKB-PPP)

• region - region for which the fine-mapping was run

• CS - running number for independent credible sets in a region

• variant - top variant associated with the credible set

• CS bayes factor (log10)

• CS min r2 - minimum R2 correlation between variants in the credible set

• beta - top variant effect size

• p-value - top variant p-value

• CS PIP - overall Posterior Inclusion Probability (PIP) of the variant

• consequence - most severe consequence of the variant

• gene most severe - gene corresponding to most severe consequence of the variant

The nested sub-table for a single gene pQTL contains a list of disease colocalizations between the FinnGen endpoints and the pQTL in question colocalizing with the lead variant of the pQTL (read more about colocalizations in FinnGen). The sub-table includes the following columns:

• phenotype - FinnGen endpoint (by clicking to the phenotype you will be navigated to the PheWeb region page corresponding to the phenotype in question)

• description - FinnGen endpoint description

• clpp - causal posterior probability calculated for a colocalization

• clpa - causal posterior agreement calculated for a colocalization

• len intersect - CS intersect

• len cs1 - FinnGen endpoint credible set size

• len cs2 - pQTL credible set size

All results can be downloaded in a tabular format by clicking Download table.

PheWeb for previous data releases

The PheWeb pages for previous data releases are available at

Note: PheWeb is continuously being developed, and some features available in newer DFs may not be available in PheWeb versions for earlier DFs.

FINNGEN PROTEOMICS SUMMARY STATISTICS

General description

Compiled summary stats for proteomics QTL in imputed SNPs on FINNGEN. Two platforms are included, Olink (619 samples across 2925 proteins) and SomaScan (828 samples across 7596 proteins).

• pQTL: The association results are from PLINK2 and in unrelated samples only (Relatedness cutoff based on genotype correlation r > 0.0625 for HapMap3 SNPs). Due to different sample size, the SNP set are not same between those two dataset.

• Finemap: see Fine-mapping page, we input the summary data from pQTL

• autoreporting: annotation of independent hits lead variants with e.g. gwas catalog, putative causal coding variants in credible set, gnomad allele frequencies

See the "Software" section for the software or pipeline we used.

Data

Folder structure

/Somascan: from Somascan V4.1 assay, 828 unrelated samples from R12 imputed genotype

/Olink: from Olink Explore 3072 library, 619 unrelated samples from R12 imputed genotype

/pQTL:  proteomics QTL via PLINK2 with pre-adjusted age, sex, batch variables and 5PCs, then rank-based inverse normal transformed.

    Files:  ${PROBE_NAME}.txt.gz, bgzipped summary statistics.

            ${PROBE_NAME}.txt.gz.tbi, tabix index without the header line.


/Finemap:  Finemap of the pQTL results above for SNPs with MAC > 8
    FINEMAP: finemapping results from FINEMAP
    - all: all results
    - filter: filtered results

    Susie: finemapping results from Susie
    - all: all results
    - filter: filtered results

/autoreporting:
    Filter and group genome-wide significant variants from FinnGen summary statistics
    Annotate the genome-wide significant variants using gnomAD and FinnGen annotations.
    - group_report: grouped results by phenotype
    - variant_report: results for each variant

Column descriptions for pQTL results

Map the probes's name to gene symbol

File: all_gene_info.txt

• Olink: Other annotations from Olink as indicated by the column name.

• Somascan: Other annotations from SomaScan as indicated by the column name. Note: NA means no matching, but keep the probe in the data to be complete.

Software

• Assocation: PLINK2 v2.00a3.3LM AVX2 Intel (3 Jun 2022)

• Finemap: FINNGEN/finemapping-pipeline, revised on R10, hash e0792ea

• Coloc: FINNGEN/colocalization, revised on R10, hash 2e0632c

• Autoreporting: FINNGEN/autoreporting, revised on hash 9dbea66

HLA imputation

The HLA data was imputed from R10 genotype data, using HIBAG models created by Jarmo Ritari from the Finnish Blood Bank. More information can be found in the repository:

https://github.com/FRCBS/HLA-imputation

as well as in the publication:

Ritari J, Hyvä rinen K, Clancy J, FinnGen, Partanen J, Koskela S. Increasing accuracy of HLA imputation by a population-specific reference panel in a Finngen biobank cohort. NAR Genomics and Bioinformatics, Volume 2, Issue 2, June 2020, lqaa030, https://doi.org/10.1093/nargab/lqaa030

Genotype data was constructed from the dosage data using PLINK 2.

Variant summary

A snp-stats report was generated with qctool

Association testing

Association testing was performed using Regenie 2.2.4. Same settings were used as in the core GWAS analysis. See the Association tests page for more information.

Association summary

A summary was created from the regenie summary statistic outputs. This summary contains the most significant variant (by p-value) for each phenotype. Pheweb links to phenotype and gene pages have been added as additional columns.

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.

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 sisu version 4.2 imputation panel is pruned iteratively, until a target number of SNPs is reached:

9,641,808 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 200,000 the closest pruning is returned.

If the higher r2 is closer, 200,000 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 180,037 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 14,547 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 us 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 43 outliers. The figure below shows the first three principal components.

FIN 1kg 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:

• 259,801 inliers: unrelated samples with Finnish ancestry.

• 153,927 outliers: non duplicate samples with Finnish ancestries, but who are also related to the inliers.

• 17,169 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 413,728 samples.

Sample filtering based on phenotype data

Of the 413,728 non-duplicate population inlier samples from PCA, we excluded 1,543 samples from analysis because of missing minimum phenotype data, and 5 samples because of failing sex check with F thresholds of 0.4 and 0.7. A total of 412,181 samples were used for core analysis. There are 230,310 females and 181,871 males among these samples.

Further info

Bayesian outlier detection

Documentation from the original developers of the algorithm can be found here: http://www.well.ox.ac.uk/~spencer/Aberrant/aberrant-manu.

Variant Selection

Loss of function (LoF) variants were generated from vcf files with VEP (https://github.com/Ensembl/ensembl-vep). LoF variants are defined as having consequences in the list [frameshift_variant,splice_donor_variant,stop_gained,splice_acceptor_variant]. Also, a max_maf (0.01) and minimum info score (0.8) filters are applied. This left us with 579 genes with 2+ LoF variants that can be used for the association tests.

Endpoint

We used 2,405 binary phenotypes in the analyses.

Null Models

We used as inputs the nulls already calculated for GWAS

Association tests

Tests are performed with regenie --step2 in burden mode using a max mask (i.e. using the maximum number of ALT alleles across sites)