Welcome to the FinnGen Handbook! ❤️
Welcome to the FinnGen Handbook! ❤️
Green and red data access process
FinnGen Sandbox architecture
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\log \left(\frac{\text{Pr}(Y = 1 | X = x)}{\text{Pr}(Y=0 | X=x)}\right)= \mu + \mathbf{x}\beta + \mathbf{\epsilon}
where $$\log$$ is the[ natural logarithm function](https://en.wikipedia.org/wiki/Natural_logarithm), and $$\text{Pr}(Y=1|X=x)$$ and $$\text{Pr}(Y=0|X=x)$$ represent the respective probabilities that an individual is a case ($$Y=1$$) or control $$(Y=0)$$, given a specific genotype $$x$$. The parameter $$\mu$$ now represents the logarithm of odds ("log-odds") of being a case when carrying no alternate alleles (i.e., genotype $$x=0$$) and $$\beta$$ represents the increase in log-odds of being a case for each copy of the alternate allele. As an example, if we run a GWAS for a disease, and for a specific variant we find a statistically significant (log-odds) estimate of $$\beta=0.1$$, we can calculate the odds ratio as $$e^{\beta} = e^{0.1} \approx 1.11$$ which can be interpreted as each copy of the alternate allele increasing the odds of having the disease by approximately 11%. ### Meta-analysis: Combining estimates from multiple studies The model used in a single GWAS yields an effect estimate $$\beta\_i$$ for a study $$i$$. In a meta-analysis, we combine study-specific effect estimates for $$N$$ studies $$(\beta\_1,\beta\_2,...,\beta\_N)$$ into a single overall effect estimate $$\beta\_{meta}$$ and standard error estimate $$\text{SE}\_{meta}$$ using\beta_{meta} = \frac{\sum_{i=1}^{N} w_i \beta_i}{\sum_{i=1}^{N} w_i} \text{SE}_{meta} = \sqrt{\frac{1}{\sum_{i=1}^{N} w_i}}
where $$w\_i$$ is the weight ([explained below](#weighting-scheme)) assigned to study $$i$$ for that variant. In simple terms, the meta-analysis effect size for a variant is calculated as the sum of each study's effect size (for that variant) multiplied by that study's weight for that variant, divided by the sum of that variant's weights across all included studies. In a similar way to the individual GWAS effect estimates, P-values are found by first calculating the variant's $$Z$$ score as $$Z\_{meta} = \frac{\beta\_{meta}}{\text{SE}\_{meta}}$$ which follows a standard normal distribution. A $$Z$$ test is then performed, which results in a P-value that represents the probability that we would see such an effect estimate (and standard error) by chance, given that the null hypothesis is true of no real phenotype-variant association. Typical genome-wide significance thresholds of P<5x10-8 (or stricter) can then be applied to find the statistically significant variants. For more information on interpretation of association P-values, see the [P-values page](/background-reading/p-values). ### Fixed-effects versus random effects models There are various choices for how the weights $$w\_i$$ are calculated, which will have an effect on contribution of each study's effect size estimate to the overall meta-analysis estimate. One important decision that affects the weighting is the choice between using a fixed-effects or random-effects model when using weights that incorporate the variance of a study's effect size estimate (e.g., weights that are calculated using $$\text{SE}$$). The **fixed-effects (FE) model** assumes there is a single, common true effect size that underlies all studies (i.e., between-study variance, $$\tau^2$$, is negligible). The model assumes that any variation, across the multiple studies, in a variant's effect estimate is due only to random or chance error within each study. The **random-effects (RE) model** assumes that the true effect size varies from study to study (i.e., $$\tau^2$$ is non-zero) and Instead of a single true effect, there is a distribution of true effects. The model asserts that any observed variation of a variant's effect across different studies is due to both random error and statistical heterogeneity (systematic differences) between studies, such as phenotype definition, GWAS model, ancestry, etc. If there is no between-study heterogeneity, fixed- and random-effects models should provide the same results. The core assumption of the FE model is that every study is estimating the exact same underlying true effect, an assumption that is rarely fully satisfied in the context of real-world systematic differences between studies. However, RE models, which acknowledge and incorporate true heterogeneity of effects, typically have significantly less statistical power to detect associations than their FE counterparts. This trade-off between power and realism makes FE models the usual choice for GWAS meta-analysis. ### Weighting scheme When performing meta-analyses, weighting the effect estimates allows individual study estimates to influence the overall estimate based on their precision; studies with more precise estimates have a larger effect on the meta-analysis estimate.The most common weighting schemes for GWAS meta-analysis are inverse-variance weighting and sample size-based weighting. **Inverse-variance weighting** uses the inverse of the effect estimate variance, with weights $$w\_i$$ calculated as:Fixed-effect: w_i = \text{SE}_i^{-2} = \frac{1}{\text{SE}_i^2} Random-effect: w_i = (\text{SE}_i^2 + \hat{\tau}^2)^{-1} = \frac{1}{\text{SE}_i^2 + \hat{\tau}^2}
where $$\text{SE}\_i$$ is that variant's effect size standard error for study $$i$$ and $$\hat{\tau}^2$$ is the estimated between-study variance. This method ensures that the studies that are more certain of their effect estimate $$\beta$$ (smaller $$\text{SE}$$) have a larger influence on the combined estimate. **Sample size-based weighting** is much simpler to implement and can be used in cases where the effect size estimate variance ($$\text{SE}^2$$) is not provided, with weights set as the study's sample size $$w\_i = N\_i$$. This weighting scheme, however, makes the assumption that larger sample sizes will lead to more precise results and may give an outsized influence to larger studies on the overall effect estimate, regardless of actual precision. Other weighting schemes existing such as imputation-quality weighting, where a variant's effect size is weighted by its imputation quality in each study so that effect sizes of better-imputed variants have a stronger influence, or allele-frequency based weighting (sometimes used in rare variant analyses) which can give more weight to estimates from studies where the variant is rarer. ### Heterogeneity statistics In addition to calculating a combined effect estimates, GWAS meta-analyses typically also provide heterogeneity statistics, which indicate how much a variants' effect size estimates vary between studies. A common choice is the [Cochran's $$Q$$ statistic](https://en.wikipedia.org/wiki/Cochran's_Q_test), which is calculated (for each variant) asQ = \sum_{i=1}^N w_i (\beta_i - \beta_{meta})^2
where $$w\_i$$ and $$\beta\_i$$ are the variant's weight and effect estimate, respective, from study $$i$$ and $$\beta\_{meta}$$ is the variant's effect estimate from the meta-analysis. In simple terms, the squared difference between each study's effect estimate and the meta-analysis is weighted and summed across all studies; the higher the $$Q$$ for a variant, the more variable (heterogeneous) that variant's effects are across the included studies. The $$Q$$ statistic follows a $$\chi^2$$ distribution with $$N-1$$ degrees of freedom, where $$N$$ is the number of studies, and the associated P-value represents the probability that a $$Q$$ statistic of that size or larger would be seen by chance, given that the null hypothesis that there is no heterogeneity between different studies' effect size estimates. The statistical power of Cochran's $$Q$$ can be limited as it depends on the number of studies, so some software also calculate an alternative $$I^2$$ statistic, calculated asI^2 = \frac{Q - (N-1)}{Q} \times 100\%
The $$I^2$$ statistic attempts to capture the percentage of the total variation of a variant's effect size across studies that is due to true heterogeneity (differences inherent in the studies) rather than random variation. Values of $$I^2$$ can loosely be interpreted as low (0-30%), moderate (30-50%), substantial (50-80%) and considerable heterogeneity (80-100%). GWAS effect size estimates will naturally vary across different studies due to many factors, including differences in GWAS models, population ancestries and structures, cohort ascertainment biases, phenotype definitions, sample sizes and the precision of effect estimates themselves. The $$Q$$ statistic (and its P-value) and the $$I^2$$ statistic are important, as they provide an indication of whether the heterogeneity is higher than expected and so whether a particular variant's effect estimates warrant further investigation to find the source of the heterogeneity. ## Key considerations Meta-analysis relies on the assumption that the studies being combined are homogeneous enough for the results to be comparable. 1. Phenotype definition: The phenotype (e.g., Type 2 Diabetes) must be defined and measured consistently across all studies. Differences in case/control ascertainment can introduce heterogeneity (differences in effect estimates not due to chance). 2. Ancestry: Combining cohorts of different genetic ancestries is common and necessary for generalizability, but it may introduce heterogeneity. Advanced methods can be used to account for this. 3. Statistical models: Most modern GWAS implement more complex linear mixed models (LMMs) than the simple linear regression above, which allows them to better correct for population structure and relatedness and reduce the number of false-positive associations. The above-described meta-analysis approaches are still applicable to effect size estimates produced by these more complex models, but care must be taken to ensure that the effect size $$\beta$$ in each contributing study is estimated using a comparable statistical model. 4. Heterogeneity of effect: Most GWAS meta-analysis software will also provide a heterogeneity statistic and P-value for each variant tested. For genetic variants identified as statistically significant in a meta-analysis, the statistical significance of the variant's heterogeneity statistic should also be considered with those that are significant warranting further investigation. 5. Weighting approach: The appropriate weighting methods (such as inverse-variance weighting) and model (random-effects versus fixed effects) should be selected based on the available data and summary statistics, as well as tests of the underlying model assumptions through assessing effect estimate heterogeneity. ## FinnGen meta-analyses Starting from data release 12, the FinnGen core team has been performing and releasing results of GWAS meta-analyses of FinnGen, UK Biobank (UKBB) and the Million Veterans Program (MVP) for phenotypes that can be matched between the cohorts. Both two-way FinnGen-UKBB and three-way FinnGen-UKBB-MVP meta-analyses are performed for each release. Meta-analyses are performed using FinnGen's own [in-house meta-analysis workflow](https://github.com/FINNGEN/META_ANALYSIS), which is designed for Google's cloud-based computing environment, using a fixed-effects inverse-variance weighting scheme. More details on quality control, phenotype matching between studies and locations of results files can be found at the [Meta-analyses page](/finngen-data-specifics/green-library-data-aggregate-data/other-analyses-available/meta-analyses). Results can also be viewed at the relevant PheWeb browsers: [FinnGen-UKBB](https://metaresults-ukbb.finngen.fi/) and [FinnGen-UKBB-MVP](https://mvp-ukbb.finngen.fi/). ## GWAS meta-analysis software There are a number of software packages available to perform meta-analysis. The most common include: * [METAL](http://csg.sph.umich.edu/abecasis/Metal/download/) - old but popular, quick and easy to install and use, limited to fixed effects models but can perform inverse-variance and sample-size weighting, efficient for large datasets but limited features, random effects forked version [also available](https://github.com/explodecomputer/random-metal) * [GWAMA](https://genomics.ut.ee/en/tools) - can run fixed and random effects models, with additional tools for QC and visualisation of results * [PLINK](https://www.cog-genomics.org/plink/1.9/) - less commonly used for meta-analysis but is easy to use, can run fixed and random effects models and has good QC features implemented * [mtag](https://github.com/JonJala/mtag) - typically used for jointly analyzing multiple traits, but can also perform (non trans-ancestry) meta-analysis, the python-based [mama](https://github.com/JonJala/mama) extends the mtag framework to handle trans-ancestry meta-analyses * [rmeta R package](https://cran.r-project.org/web/packages/rmeta/index.html) - convenient if meta-analysis within the R environment is preferred * [FINNGEN/META\_ANALYSIS](https://github.com/FINNGEN/META_ANALYSIS) - FinnGen's own meta-analysis pipeline for use in the Google Cloud environment ## Additional reading * [**Genome-wide Association Studies**, Uffelmann et al. (2021), *Nature Reviews Methods Primers*](https://doi.org/10.1038/s43586-021-00056-9) * [**Meta-Analysis in Genome-Wide Association Studies**, Zeggini & Ioannidis (2009), *Pharmacogenomics*](https://doi.org/10.2217/14622416.10.2.191) * [**Random-Effects Model Aimed at Discovering Associations in Meta-Analysis of Genome-wide Association Studies**, Han & Eskin (2011), *AJHG*](https://doi.org/10.1016/j.ajhg.2011.04.014) * Material from Matti Pirinen's GWAS course, part of the Life Science Informatics MSc programme at the University of Helsinki: * [GWAS 1: What is a GWAS?](https://www.mv.helsinki.fi/home/mjxpirin/GWAS_course/material/GWAS1.pdf) * [GWAS 8: Heritability and mixed models](https://www.mv.helsinki.fi/home/mjxpirin/GWAS_course/material/GWAS8.pdf) * [GWAS 9: Meta-analysis and summary statistics](https://www.mv.helsinki.fi/home/mjxpirin/GWAS_course/material/GWAS9.pdf) # Heritability and genetic correlations ## Heritability [Heritability ](https://www.nature.com/articles/nrg2322)measures the proportion of variance of the phenotype that is due to genetic differences between individuals. Heritability is always a property of a particular population, and its value can vary between different environmental conditions as the relative roles of genes and environment change. #### Phenotypic correlations Heritabilities have been estimated for a long time using phenotypic correlations between relatives of different degrees. For example, the traditional twin estimates compare phenotypic similarity in monozygotic twin pairs to similarity in dizygotic twin pairs. Under some strong assumptions (such as that the environmental contribution to the phenotypic similarity would be the same for a monozygotic as for a dizygotic twin pair, and that the genetic effects act additively over loci) it follows that the difference between the correlation estimates of these two types of twin pairs leads to an estimate of heritability In practice, (narrow sense, that is, variance explained by the additive effects of the variants) heritability $$h^2$$ \_\_ can be estimated by comparing individual phenotypic variation among related individuals in a population, by examining the association between individual phenotype and genotype data, or by modelling summary-level data from genome-wide association studies (GWAS). #### Ldsc software In FinnGen, [ldsc](https://github.com/bulik/ldsc) software has been used to estimate heritabilities for all endpoints from the released summary statistics using both Finnish and European LD panel. These **heritability estimates** can be found from: `/finngen/library-green/finngen_R8_analysis_data/ldsc/finngen_R`*`(data freeze specific)`*`_[FIN/EUR].ldsc.heritability.tsv`, and the documentation for the files can be found [here.](/finngen-data-specifics/green-library-data-aggregate-data/core-analysis-results-files/heritabilities) ## Genetic correlation A genetic correlation ($$r\_g$$) between two traits is the proportion of variance that the two traits share due to genetic causes. A genetic correlation of 0 implies that the genetic effects on one trait are are independent of the other, while genetic correlation of 1 implies that all of the genetic influences on the two traits are identical. In practise, genetic correlations between two traits can be computed from the GWAS summary statistics by computing the correlation of the regression coefficients. In FinnGen, [ldsc](https://github.com/bulik/ldsc) software has been used to estimate genetic correlations for all endpoint pairs from the released summary statistics using both Finnish and European LD panel. All these **pairwise genetic correlations** can be found from: `/finngen/library-green/finngen_R.png?alt=media)
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| Release | Total sample size | Total samples in core GWAS analyses | Total endpoints | Core endpoints | Date release to partners | Results released publicly |
|---|---|---|---|---|---|---|
| DF1/R1 | 52,295 | - | - | - | - | - |
| DF2/R2 | 102,739 | 96,499 | 1,485 | 1,485 | Q4 2018 | Q1 2020 |
| DF3/R3 | 146,630 | 135,638 | 2,737 | 1,801 | Q2 2019 | Q2 2020 |
| DF4/R4 | 183,694 | 176,899 | 3,452 | 2,444 | Q4 2019 | Q4 2020 |
| DF5/R5 | 224,737 | 218,792 | 3,858 | 2,803 | Q2 2020 | Q2 2021 |
| DF6/R6 | 271,341 | 260,405 | 3,995 | 2,861 | Q3 2020 | Q3 2021 |
| DF7/R7 | 321,464 | 309,154 | 4,149 | 3,095 | Q1 2021 | Q1 2022 |
| DF8/R8 | 356,213 | 342,499 | 4,431 | 2,202 | Q3 2021 | Q3 2022 |
| DF9/R9 | 392,649 | 377,277 | 4,526 | 2,272 | Q1 2022 | Q2 2023 |
| DF10/R10 | 430,897 | 412,181 | 4,519 | 2,408 | Q3 2022 | Q4 2023 |
| DF11/R11 | 473,681 | 453,733 | 4,415 | 2,444 | Q1 2023 | Q2 2024 |
| DF12/R12 | 520,210 | 500,348 | 4,421 | 2,469 | Q1 2024 | Q2 2024 |
| DF14/R13 | 519,972 | 500,186 | 4,662 | 2,466 | Q1 2025 | ~Q2 2026 |
| DF14/R14 | TBD | TBD | TBD | TBD | Q1 2026 | ~Q2 2027 |
| DF15/R15 | TBD | TBD | TBD | TBD | Q1 2027 | ~Q2 2028 |
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| Column Name | Column Type | Description | Example |
|---|---|---|---|
| VNR | INT64 | six-digit VNR code. | 518 |
| ATC | STRING | ATC group code | N05AH04 |
| MedicineName | STRING | Commerical Name | SEROQUEL |
| AdministrationRouteSourceTextFI | STRING | Administration route in text format as in source. | Suun kautta |
| AdministrationRoute | STRING | Valid value for administration route. | Oral use |
| DosageFormSourceTextFI | STRING | Dosage form in text format as in source. | tabletti, kalvopäällysteinen |
| DosageForm | STRING | Valid value for dosage form. | film-coated tablet |
| PackageSourceTextFI | STRING | Package info in text format as in source. | 10 FOL |
| PackageSize | FLOAT64 | Size of package in float format. | 10 |
| PackageFactor | INT64 | Factor of package in float format. | 1 |
| PackageUnit | STRING | A valid unit value. | fol |
| SubstanceSourceTextFI | STRING | List of substances as in source. | quetiapine |
| Substance | STRING | Substance name. one row per substance. | quetiapine |
| SubstanceStrengthTextFI | STRING | Substance's strength in text format as in source. | 25+100+200 mg |
| Strength | STRING | Mapped or fixed or split substance strength. If not then source strength used. | 100 mg |
| SubstanceStrengthNumenatorValue | FLOAT64 | Substance's strength value in numerator in float format. | 100 |
| SubstanceStrengthNumenatorUnit | STRING | A valid unit value. | mg |
| SubstanceStrengthDeominatorValue | FLOAT64 | Substance's strength value in denominator in float format. | 1 |
| SubstanceStrengthDeominatorUnit | STRING | A valid unit value | 1 |
| ValidRange | BOOL | True if VNR is en the valid range (less than 200000 or between 370000 and 599999)). | TRUE |
| Source | STRING | From which table the code was taken. | ltklVNR or "originalVNR" |
| Status | STRING | How well the medicine has been processed | incomplete_dosageForm |
| VNRnew | STRING | A temporary VNR code created for drugs with single substance multiple strength values. Temporary VNR code will have letters a or b or c attached to the end. | 000518a |
| calculateTotalStrength_message | STRING | How well the strength has been processed. | correct or "missmatch" |
| TotalStrength | FLOAT64 | Total Strength of the drug which is PackageSize * PackageFactor * Dosage | 10 * 1 * 100 = 1000 |
| TotalStrengthUnit | STRING | Total Strength valid unit | mg |
| n_codes | INT64 | Frequency of the VNR code | 260 |
| Dosage | FLOAT64 | SubstanceStrengthNumenatorValue/SubstanceStrengthDeominatorValue | 100/1 = 100 |
| DosageUnit | STRING | A valid unit value | mg |
| MedicineNameFull | STRING | Commerical Name, Dosage Form and SubstanceStrength | SEROQUEL 25+100+200 mg |
| Input | Value |
| Total Drugs | 15,928 |
| Drugs with non-missing VNR Codes | 15,902 |
| Unique VNR codes | 14,655 |
| Drugs with non-missing VNR codes + Ingredient Codes | 13,876 |
| Drugs with non-missing VNR codes + Ingredient Codes + dosage form | 12,897 |
| Drugs with non-missing VNR codes + Ingredient Codes + dosage form + dosage value | 12,644 |
| Drugs with non-missing VNR codes + Ingredient Codes + dosage form + dosage + dosage unit | 12,638 |
| SourceUnit | DrugCount | RecordCount | Factor | TargetUnit | Comment |
|---|---|---|---|---|---|
| % | 303 | 1109084 | 0.01 | mg/mg | |
| IU | 309 | 400416 | 1 | [U] | |
| U | 25 | 360 | 1 | [U] | |
| g | 160 | 1614186 | 1000 | mg | |
| g/l | 8 | 1112 | 1 | mg/mL | |
| mg | 20905 | 68649900 | 1 | mg | |
| mg/days | 111 | 54182 | 0,289 | mg/h | |
| mg/h | 5 | 455 | 1 | mg/h | |
| milli.IU | 73 | 478234 | 1000000 | [U] | |
| ml | 5 | 4885 | 1 | mL | |
| ug/puffs | 6 | 212536 | 0.001 | mg/{actuat} |
| DoseForm | DrugCount | Priority | concept_id | concept_name | Comments |
|---|---|---|---|---|---|
| BASIC CREAM | 11 | 19082224 | Topical Cream | ||
| BATH ADDITIVE | 1 | 19082228 | Topical Solution | ||
| BODY LOTION | 1 | ||||
| CAPSULE | 279 | 0 | 19082168 | Oral Capsule | Standard |
| CAPSULE | 279 | 1 | 19021887 | Capsule | Non-Standard |
| CAPSULE, HARD | 664 | 19082168 | Oral Capsule |
| Source drugs mapped to Clinical Drug | 12089 of 14692 (82.283%) |
| Source drugs mapped to Clinical Drug Form | 562 of 14692 (3.825%) |
| Source drugs mapped to Clinical Drug Comp | 354 of 14692 (2.409%) |
| Source drugs mapped to Ingredient | 588 of 14692 (4.002%) |
| Source drugs mapped Splitted | 74 of 14692 (0.504%) |
| Source drugs mapped Splitted Incomplete | 3 of 14692 (0.02%) |
| Source drugs mapped Total | 13670 of 14692 (93.044%) |
| Source drugs mapped to None | 1022 of 14692 (6.956%) |
| Column name | Explanation |
|---|---|
NAME | naming: Reference name in the FinnGen endpoint data |
LONGNAME | naming: Descriptive name |
Latin | naming: Latin name |
TAGS | categorisation: List of categories the endpoint belongs to |
LEVEL | categorisation: Level in the ICD-10 hierarchy |
OMIT | categorisation: Is a core GWAS? (NA: yes, 1 or 2: no) |
PARENT | categorisation: Parent in the ICD-10 hierarchy |
version | changelog: introduced in data freeze |
Modification_date | changelog: date of last modification |
Modified_by | changelog: author of last modification |
Modification_reason | changelog: purpose of modification |
Special | free text notes |
| Tool | Description | GUI tool | coding skills needed |
|---|---|---|---|
| Atlas | Creating user-defined cohorts and visualization | Yes | No |
| Genome Browser | Examine & export variant-level information | Yes | No |
| Cohort Operations | Operate cohorts made with other tools (e.g. Atlas, Genotype Browser, R), export cohorts, visualization | Yes | No |
| TVT | Visualize large numbers of longitudinal cases with glyphs of user-defined conditions, export | Yes | No |
| RStudio | Interactive coding using R language | No | Yes |
| Jupyter (Python) | Interactive coding using Python language | No | Yes |
| Code Snippets | BigQuery connections using R and Python language | No | Yes |
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Code sets in detailed longitudinal data
| Cohort | Biobank | Comment |
|---|---|---|
| Legacy cohort | Biobank | Description /sample size in whole cohort |
| Auria legacy | Auria | Biobank samples collected in Auria in 2016 (before FinnGen started) |
| Auria legacy, Direva | Auria | DIREVA is the diabetes register of the Vaasa hospital district, which is carried out through the co-operation with Lund University in Malmö, Botnia Study and University of Helsinki. 7000 participants. Blood/DNA samples available from all participants. |
| Botnia | THL | Ongoing study since 1990 investigating diabetes risk factors To date, >15 000 participants from 1400 families. DNA available from all participants, also serum, plasma, cell and RNA samples, from subset, genotype data available from 12720, WES from 395 participants. Botnia data includes; Botnia DGI affy, Botnia T2DGo and Botnia-regeneon. |
| Corogene | THL | Collected at the Helsinki University hospital during 2006-2008 from patients with coronary artery disease (CAD) and other related heart diseases to understand risk factors of heart diseases. DNA and genotype data available from 4890 participants. |
| Eastern Finland legacy | Eastern Finland | KBCP (breast cancer patients, ~400), Nomod (Nordic modic project, back surgery patients, ~60), NPH (normal pressure hydrocephalus cohort, patients, relatives and contorls ~950), L-MCI (neuro/alzheimer patients, ~705) |
| Eastern Finland legacy (Adgen) | Eastern Finland | A multidisciplinary study, which focuses on identification of novel Alzheimer’s disease (AD)-associated genes and pathways using existing clinical cohorts from Eastern and Northern Finland ~1700 patients fulfilling the NINCDS-ADRDA criteria for probable AD and ~1700 cognitively healthy controls from Eastern and Northern Finland. |
| Eastern Finland legacy (Aneurysm) | Eastern Finland | - |
| Eastern Finland legacy (NOMOD, IIH S100B) | Eatern Finland | NOMOD (Nordic modic project, back surgery patients ~370), IIH patients (~70), S100B biomarker study (acute head injury study, n~50) |
| FinHealth | THL | A population survey study by the Finnish Institute for Health and Welfare (THL), producing up-to-date information on health, health behavior, functional capacity and well-being of adults residing in Finland. 6650 participants with DNA, serum and plasma, genotype data available from 6250 participants. NMR metabolomics from 5290. |
| FinHIT | THL | Fin-HIT is a prospective school-based cohort study including approximately 11,000 Finnish adolescents aged 9–12 years at enrolment across densely populated areas of Finland. The cohort will be followed at least for 25 years. The objectives of the study are to explore non-conventional determinants for weight and weight gain; to investigate etiology of weight-related health outcomes; and to understand underlying molecular mechanisms involved in the origin and development of obesity and related health outcomes. |
| FinIPF | THL | A nationwide registry study of idiopathic pulmonary fibrosis (IPF) patients. The aim is to monitor the prevalence, treatment, pathophysiology and prognosis IPF patients. From 2017, FinnishIPF study has collected biobank samples from 206 participants, which form the FinnIPF subcohort |
| FINRISKI | THL | Cross-sectional population surveys carried out every 5 years by THL, to assess health status, risk factors of chronic diseases (e.g. CVD, diabetes, obesity, cancer) and health behavior among the working age population, in Finland. Total of 41 000 participants; Currently available: DNA 36 300, plasma/ serum 27 200 and genotype data from 31 300 participants. WES 12160, WGS 4460, NMR metabolomics 27 160. PBMC available from 2700 participants. RNA from subsets. Transcriptomics and other omics 500+500.Telomers 4070. |
| GENERISK | THL | A prospective study investigating genetic risk factors of cardiovascular diseases and utilizing genetic information in preventing diseases. Conducted in years 2015-2018 in several different areas of Finland. Participant follow-up is still ongoing. DNA, serum and plasma available from 7300 participants, genotype data available from 7270 participants. |
| Health 2000-2011 | THL | A comprehensive combination of health interview and health examination survey carried out by THL in 2000-2001 with a follow-up in 2011. Investigating public health problems and population’s functional capacity in working-aged and the aged population in Finland. 8 611 individual participants, DNA, serum and plasma available from all participants (baseline and follow-up), genotype data available from 7700, WES 4960 , WGS 205 NMR metabolomics 10290, Telomers 7404 |
| Helsinki Heart Study | Helsinki BB | Five-year trial, collected between 1981-90, testing the efficacy of simultaneously elevating serum levels of HDL cholesterol and lowering levels of non-HDL cholesterol with gemfibrozil in reducing the risk of coronary heart disease in asymptomatic men. Blood/DNA and serum available from 4000 participants, genotype data currently available from 2000 participants. |
| HKI BB Finnaki | Helsinki BB | Studying incidence, risk factors and outcome of acute kidney injury (AKI) in Finnish Intensive Care Units. 2546 participants. DNA/genotype data available from all participants. |
| Kuusamo 2011 | THL | |
| Migraine | THL | The Migraine Family Study by FIMM/UH is focused on identifying variation in genes that are associated with migraine, headache-related symptoms and chronic disease risk factors. Collected since 1992 and ongoing. DNA available from 9450 participants, genotype data available from 8470 participants, WES 490, WGS 625. |
| Northern Finland Birth Cohort 1935, 1945, 1966, 1986 | Arctic Biobank | The University of Oulu has collected several extensive population survey data sets, such as the Northern Finland Birth Cohorts NFBC1966 and NFBC1986. Blood/DNA samples available from 14 400 participants, genotype data from 10 000 participants. |
| SETTI (ATBC) | THL | The Alpha-Tocopherol, Beta-Carotene (ATBC) Lung Cancer Prevention Study (1984-1993) of male smokers was a randomized, double-blinded, placebo-controlled trial testing whether alpha-tocopherol and/or beta-carotene supplements can reduce the incidence of lung and other cancers. Serum 29 000, DNA samples available from 20 000 participants, genotype data from 4000. |
| SUPER | THL | The Study is a part of the International Stanley Global Neuropsychiatric Genomics Initiative, seeking to expand knowledge of genetic diversity and biological background in schizophrenia, bipolar disorder, and autism. DNA, serum and plasma available from 9350 and genotype data from 9025 participants. |
| THL Diabetes studies | THL | THL has collected samples and data from diabetics and their family members in several sub-studies between 1986 and 2013. The focus has been on the genetic background and environmental triggers of diabetes in Finland. Size: DNA from 13 600 participants, plasma, serum and cell samples from subsets; genotype data available from 9080 participants. |
| THL Psychiatric Family Collections (FSZ and BPD) | THL | The THL Psychiatric Family Collections include families ascertained for schizophrenia, schizoaffective disorder and bipolar spectrum disorder. The objective of the study is to identify the genetic risk factors for serious psychiatric disorders in Finnish families. |
| Twin study | THL | Population-based study of UH collected from Finnish twins and their family members. Based on three longitudinal questionnaire data-sets from >14 000 participants. The collection has been expanded with follow-up sub-studies involving samples and data. DNA available from 14200 participants, genotype data available from 14100 participants. |
| Dataset | Chip type |
|---|---|
| DS1_BOTNIA_Dgi | Affymetrix_250KNsp_250KSty |
| DS2_BOTNIA_T2dgo | lllumina_HumanOmni2.5-4v1_B_SNV_array |
| DS3_COROGENE_Sanger | Illumina Human610-Quadv1_B |
| DS4_FINRISK_Corogene | Human610-Quadv1_B |
| DS5_FINRISK_Engage | HumanCoreExome-12v1-0_A |
| DS6_FINRISK_FR02_Broad | GSAMD-24v1-0_20011747_A1 |
| DS7_FINRISK_FR12 | Illumina_HumanCoreExome-24-v1.1 |
| DS8_FINRISK_Finpcga | Illumina_HumanCoreExome-12v1.1a |
| DS9_FINRISK_Mrpred | Illumina_HumanCoreExome-24_v1.0 |
| DS10_FINRISK_Palotie | HumanCoreExome-12v1-1_A |
| DS11_FINRISK_PredictCVD_COROGENE_Tarto | HumanOmniExpress-12v1_A |
| DS12_FINRISK_Summit | HumanOmniExpress-12v1_H |
| DS13_FINRISK_Bf | Illumina_HumanCoreExome-24_v1.0 |
| DS14_GENERISK | lllumina_HumanCoreExome-24v1-0_A |
| DS15_H2000_Broad | Broad_GWAS_supplemental_15061359_A1 |
| DS16_H2000_Fimm | Illumina_HumanCoreExome-24v1-1_A |
| DS17_H2000_Genmets | Human610-Quadv1_B |
| DS18_MIGRAINE_1 | PsychChip_15048346_B-_24 |
| DS19_MIGRAINE_2 | HumanCoreExome-12v1-0_A |
| DS20_SUPER_1 | GSAMD-24v1-0_20011747_A1 |
| DS21_SUPER_2 | GSAMD-24v1-0_20011747_A1 |
| DS22_TWINS_1 | Illumina Human670/Human610 |
| DS23_TWINS_2 | IlluminaCoreExome |
| DS24_SUPER_3 | GSAMD-24v1-0_20011747_A1 |
| DS25_BOTNIA_Regeneron | GSA-24v1-0_A1 |
| DS26_DIREVA | InfiniumCoreExome-24v1-1 |
| DS27_NFBC66 | Illumina HumanCNV370DUO Analysis BeadChip |
| DS28_NFBC86 | HumanOmniExpressExome v 1.2 |
| Column | Description |
| FID | Family ID (same as IID for FG) |
| IID | Sample ID |
| PCn | Value of the nth principal component |
| Column | Description |
| #CHROM | Chromosome number |
| ID | Variant name |
| MAJ | Major allele |
| NONMAJ | Non-major allele |
| PCn | Loading of the nth principal component |
| Abbreviation *1 | Abbreviation *1 | Register | Source |
| PURCH | LAAKE_ATC | Kela drug purchase register (since 1995) | Social Insurance Institution of Finland (Kela) |
| REIMB | KELAEK | Kela drug reimbursement register (since 1964) | Social Insurance Institution of Finland (Kela) |
| INPAT | HILMO | Inpatient Hilmo register (Health-Hilmo) (since 1969) | Finnish Institute for Health and Welfare (THL) Care Register for Health Care |
| OPER_IN | OPER | Inpatient Hilmo register (Health-Hilmo) operations (since 1969) | Finnish Institute for Health and Welfare (THL) Care Register for Health Care |
| OUTPAT | ERIK_AVO | Specialist outpatient Hilmo register (Health-Hilmo) (since 1998) | Finnish Institute for Health and Welfare (THL) Care Register for Health Care |
| OPER_OUT | ERIK_OPER | Specialist outpatient Hilmo register (Health-Hilmo) operations (since 1998) | Finnish Institute for Health and Welfare (THL) Care Register for Health Care |
| PRIM_OUT | AVO | Primary health care outpatient visits register (Avohilmo) (since 2011) | Finnish Institute for Health and Welfare (THL) Register of Primary Heath Care visits |
| CANC | CANCER | Cancer register (since 1953) | Finnish Cancer Registry |
| DEATH | DEATH | Cause of death register (since 1969) | Statistics Finland |
| Phenotype data set | Start year or time period | Standardized codes used | In the Detailed longitudinal and Service sector data file (abbreviation) | and OMOP CDM availability | Other data file |
| Finnish Cancer Registry | 1953 | ICD-O-3 | Yes (CANC) | Yes | Yes (Click first column link to see data location) |
| Drug Reimbursement (KELA) | 1964 | ICD for indication, Finnish specific reimbursement codes | Yes (REIMB) | Yes | |
| Drug Purchase (KELA) | 1995 | ICD for indication, Finnish specific reimbursement code | Yes (PURCH) | Yes | |
| Causes of death (STAT) | 1969 | ICD-10,9,8 | Yes (DEATH) | Yes | |
| Care Register for Health Care, inpatient visits (THL Hilmo) | 1969 | ICD-10,9,8 and ATC | Yes (INPAT) | Yes | |
| Care Register for Health care, inpatient operations (THL HIlmo) | 1986 | Finnish specific NOMESCO and Finnish Hospital League operation codes | Yes (OPER_IN) | Yes | |
| Primary Health Care specialist outpatient visits incl. operations (THL Hilmo) | 1998 | NOMESCO and Finnish Hospital League operation codes | Yes (OUTPAT/OPER_OUT) | Yes | |
| Primary Health Care outpatient visits (Avohilmo) | 2011 | ICD10, nurse procedures( SPAT codes) , nurse provided cause of visit (ICPC) | Yes (PRIM_OUT) | Yes | |
| Population Register (DVV) | 1964 | Birth data as of DF12 | Minimum extended phenotype data file | ||
| Finnish Registry for Kidney Diseases | 1964 | Click first column link to see data description | Yes | Click first column link to see data file location | |
| Finnish Register of Visual Impairment (THL) | 1983 | Click first column link to see data description | Yes | Click first column link to see data file location | |
| Medical Birth Register (THL) | 1987 | Click first column link to see data description | Yes | Click first column link to see data file location | |
| Register of Congenital Malformations (THL) | 1980 | Click first column link to see data description | Click first column link to see data file location | ||
| Sosioeconomic data (STAT) (limited access to Finnish researchers) | 1970 | Click first column link to see data description | Click first column link to see data file location | ||
| Finnish National Infectious Disease Register (THL) | 1989 | Click first column link to see data description | Click first column link to see data file location | ||
| Finnish Cancer Registry: Cervical cancer screening (THL) | 1991 | Click first column link to see data description | Click first column link to see data file location) | ||
| Finnish Cancer Registry: Breast cancer screening (THL) | 1992 | Click first column link to see data description | Click first column link to see data file location | ||
| Finnish National Vaccination Register (THL) | 2011 | Click first column link to see data description | Click first column link to see data file location | ||
| EA3: Fatty liver disease | 31.5.2012-31.5.2022 | Click first column link to see data description | Click first column link to see data file location | ||
| EA3: Age-related macular degeneration | Click first column link to see data description | Click first column link to see data file location | |||
| EA3: Women's health PCOS | 1.1.1990-1.2.2023 | Click first column link to see data description | Click first column link to see data file location | ||
| EA3: Women's health Endometriosis | until 23.3.2023 | Click first column link to see data description | Click first column link to see data file location | ||
| EA3: Women's health | until 22.2.2023 | Click first column link to see data description | Click first column link to see data file location | ||
| EA3: Pulmonology | 1.1.2000-23.11.2022 | Click first column link to see data description | Click first column link to see data file location | ||
| EA3: Diabetes and rate kidney diseases | until 31.10.2022 | Click first column link to see data description | Click first column link to see data file location | ||
| EA3: Heart Failure | Click first column link to see data description | Click first column link to see data file location | |||
| EA3: Immune-mediated diseases (Rheumatic and IBD) | 14.3.2013-14.3.2023 | Click first column link to see data description | Click first column link to see data file location |