• 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 08Jun17.d8b)

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

The BCOR files were created using LDstore from the Finnish SISU panel v4.0.

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:

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!

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.xbhfij6.

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.

Endpoint

We included 2,202 endpoints in the analysis. Endpoints with less than 80 cases among the 342,499 samples were excluded, as well as endpoints labeled with an OMIT tag in the endpoint definition file.

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

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.

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

Additionally to the biobanks mentioned in the previous releases, the following biobanks and cohorts are part of the R8 release:

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 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.

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

Release 8 contains

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

Chip genotype data were using the population-specific of 8,554 whole genomes.

Merged imputed genotype data is composed of 96 data sets that include samples from multiple cohorts.

• Total number of individuals: 356,213

• Total number of variants (merged set): 20,175,454

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 Eagle 2.3.5 as described in the previous section.

Genotype imputation was carried out by using the population-specific SISu v4.0 imputation reference panel with (version 08Jun17.d8b) 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.

SISu v4.0 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 v4.0 reference panel were generated at the McDonnell Genome Institute at Washington University (PIs Ira Hall and Nathan Stitziel).

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.

Colocalizations in FinnGen

Our colocalization approach uses the probabilistic model for integrating GWAS and eQTL data presented in eCAVIAR (Hormozdiari et al. 2016). Compared to eCAVIAR, we are using SuSiE (Wang et al. 2019) to fine-map our inputs and provide an additional colocalization metric (CLPA).

Our goal is to extract a list of genomic regions that show colocalization between two phenotypes p1 and p2. Further, we assume that the summary statistics of p1 and p2 have been fine-mapped. The fine-mapping output for each phenotype contains three columns: the variant identifier (VAR), posterior inclusion probability (PIP), and the credible set (CS) identifier.

CLPP

The Causal Posterior Probability (CLPP) is computed between two credible sets cs1 and cs2, with cs1 coming from a given phenotype p1 and cs2 coming from phenotype p2. CLPP is defined as follows: For vectors x and y, containing the PIP for variants in cs1 and cs2, respectively, CLPP is calculated by

This CLPP calculation is similar to equation 8 in Hormozdiari et al. 2016.

CLPP is dependent on the credible set size. By definition, any credible set size > 1 will yield a CLPP < 1.

CLPA

We derived another colocalization metric called causal posterior agreement (CLPA) that is independent of credible set size.

The picture below shows how colocalizations are defined.

Example Comparison

This rough example shows why we mostly use CLPA since it is independent of sample size.

Data

The colocalization is performed between FinnGen endpoints as well as between FinnGen endpoints and various QTL resources, as shown in the image below.

These resources are listed below:

FinnGen resources

The SuSiE finemapping results for the release were used as the FinnGen data.

Expression QTL datasets

• GTEx v8: SuSiE fine-mapping, 49 tissues, donors of mixed ancestry, Aguet et al. (2019, BioRxiv) (49 tissues only involve tissues with a sample size of n >= 50). Fine-mapping performed by Hilary Finucane, Jacob Ulirsch, Masahiro Kanai from the . Effect size interpretation: change in normalised gene expression (sd units) per alternate allele. Normalization = inverse normal transformation.

• EMBL-EBI (European Bioinformatics Institute) . eQTL data from 24 tissues/cell types, 16 RNAseq sources, 6 Microarray, SuSiE fine-mapping, donors of 88% European ancestry, Kerimov et al. (2020, BioRxiv). For RNAseq data, four quantification methods (gene expression, exon expression, transcript usage, txrevise event usage). Fine-mapping was performed by . Effect size interpretation: change in normalised gene expression (sd units) per alternate allele. Normalization = inverse normal transformation.

• (RNAseq), muscle and adipose tissue.

Metabolon QTL datasets

• GeneRISK: 186 lipid species QTLs, SuSiE fine-mapping of Widen et al. (2020), 7632 Finnish samples. Effect size interpretation: change in standard deviation of the lipid species per alternate allele.

Biomarkers

• UK Biobank: 36 continuous endpoints, 57 biomarkers from UKBB prepared by , SuSiE fine-mapping. Effect size interpretation for quantitative traits: change in standard deviation of the normalized outcome per alternate allele. Effect size interpretation for binary traits increase in log(odds ratios) per alternate allele.

Post-colocalization QC

Only unique source1-source2-pheno1-pheno2-tissue2-quant2-locus_id1-locus_id2 combinations were included in the results. FinnGen endpoints with _COMORB-definition were left out of the results.

Acknowledgements

We thank the following people for helping us assembling the QTL resources:

• Kaur Alasoo and Nurlan Kerimov provided us the fine-mapped EMBL-EBI eQTL catalogue datasets.

• Hilary Finucane, Jacob Ulirsch, Masahiro Kanai gave us access to their fine-mapped GTEx data.

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:

Timeline for releases:

[1] samples used for PheWAS.

We used regenie for release 8. 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

The Docker image used in the analysis is available in .

We analyzed:

• ​2,202 endpoints

• 342,499 samples

• 20,175,455 variants

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

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

Note: SISu v3 was used to calculate the PCA whereas SISu v4.0 was used elsewhere.

PCA

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

Variant filtering and LD pruning

The SISu v3 imputation panel is pruned iteratively, until a target number of SNPs is reached: 8,822,890 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 the final ld params are --indep-pairwise 500.0 50.0 0.15 and 175,281 snps are returned.

Out of the 175,281 snps, 2,228 were not in SISu 4.0 panel.

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 11,091 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 85 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:

• 225,324 inliers: unrelated samples with Finnish ancestry.

• 117,389 outliers: non duplicate samples with Finnish ancestries, but who are also related to the inliers.

• 13,533 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 342,713 samples.

Sample filtering based on phenotype data

Of the 342,713 non-duplicate population inlier samples from PCA, we excluded 211 samples from analysis because of missing minimum phenotype data, and 3 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 342,499 samples was used for core analysis. There are 190,879 females and 151,620 males among these samples.

Further info

Bayesian outlier detection

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

Registries

The disease endpoints were defined using nationwide registries:

• Drug purchase and Drug Reimbursement

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.

For DF8 the number of endpoints was reduced. This was done for a number of reasons - partly to reduce computational costs and also because nearly redundant endpoints could be confusing. Endpoints were reduced through careful calculation of the case overlaps and consultation with the FinnGen clinical groups.

A full list of FinnGen endpoints is for release 8.

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 = 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.