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FinnGen Handbook
  • Introduction
  • Where to begin
    • Quick guides
      • New to FinnGen
      • Green data users
      • Red data users
    • I'm new to FinnGen, where is the best place for me to start?
    • What kind of questions can I ask of FinnGen data?
    • How do I make a custom endpoint?
    • How do I run a GWAS of a phenotype I created myself?
    • I'm interested in FinnGen rare variant phenotypes
  • Background Concepts
    • Basics of Genetics
    • Linkage Disequilibrium (LD)
    • Genotype Imputation
    • Genotype Data Processing and Quality Control (QC)
    • GWAS Analysis
    • P Values
    • Heritability and genetic correlations
    • Finemapping
    • Conditional analysis
    • Colocalization
    • Using Polygenic Risk Scores
    • PheWAS analysis
    • Survival analysis
    • Longitudinal Data Analysis
    • GWAS Association to Biological Function
    • Genetic Data Resources outside FinnGen
    • Getting Started with Unix
    • Getting Started with R
    • Structure of the FinnGen project
    • Finnish gene pool and health register data
  • FinnGen Data Specifics
    • FinnGen Data Freezes and Releases
    • Analysis proposals
      • What is a FinnGen analysis proposal and when do I need to submit one?
      • How do I submit an analysis proposal?
      • How are analysis proposals handled?
      • What is a FinnGen bespoke analysis proposal and when do I need to submit one?
      • How do I submit a bespoke analysis proposal?
      • How are bespoke analysis proposals handled?
      • What is the difference between FinnGen analysis proposals and FinnGen bespoke analyses?
      • Existing analysis proposals
    • Finnish Health Registries and Medical Coding
      • Finnish health registries
      • Register data pre-processing
      • Data Masking/Blurring of Visit Dates
      • International and Finnish Health Code Sets
      • More information on health code sets
      • VNR code mapping to RxNorm
      • Register code translation files
    • Endpoints
      • FinnGen clinical endpoints
      • History of creating the FinnGen endpoints
      • Location of FinnGen Endpoint and Control Description Files
        • What's new in DF13 endpoints
        • What’s new in DF12 endpoints
        • What’s new in DF11 endpoints
        • What’s new in the DF10 endpoints
        • What’s new in DF9 endpoints
        • What’s new in DF8 endpoints
      • Interpretation of Endpoint Definition file
      • Location of Endpoint Quality Control Report
      • Creating a User-defined Endpoint(s)
      • Requesting a User-defined Endpoint to be included in Core Analysis
      • Complete follow-up time of the FinnGen registries – primary endpoint data
        • Survival analysis using the truncated endpoint file – secondary endpoint data
    • Biobanks in Finland
    • Publishing FinnGen results
      • Preparing manuscripts or conference abstracts
      • The 1-year “Exclusivity Period” Policy
      • List of Publications using FinnGen Data
      • How to share GWAS summary statistics with FinnGen community
      • How to publish GWAS summary statistics
      • Public Result Releases
    • Red Library Data (individual level data)
      • Genotype data
        • Genotype Arrays Used
          • Legacy cohorts and chips
        • Imputation Panel
          • Sisu v4 reference panel
          • Sisu v3 reference panel
          • Sisu v4.2 reference panel
            • Variant-wise QC metrics file
        • Genome build used in FinnGen
        • Genotype Data Processing Flow
        • Genotype Files in Sandbox
          • Imputed genotypes in VCF format
          • Imputed genotypes in BGEN format
          • Imputed genotypes in PLINK format
          • Chip data
          • Imputed HLA alleles
          • Principal components analysis (PCA) data
          • Kinship data
          • Analysis covariates
          • Polygenic risk scores (PRS)
          • Genetic Ancestry
          • Genetic relationships (GRM)
          • Mosaic chromosomal alterations (mCA)
          • Prune data (R9)
          • Imputed STR genotypes (R8)
      • Phenotype data
        • Register data
        • Detailed longitudinal data
          • Splitting combination codes in detailed longitudinal data
        • Service sector data
          • Service sector data code translations
        • Endpoint and endpoint longitudinal data
        • Kanta lab values
          • Data
          • FAQ
          • How-to guides
        • Kanta prescriptions
        • Minimum extended phenotype data
          • Extracting minimum phenotype data per biobank
          • DNA isolation protocols per biobank
        • Minimum longitudinal data
        • Minimum phenotype data (before R11)
        • Cohort data (before R11)
        • Other register data files in Sandbox
          • Register of Congenital Malformations
          • Finnish Registry for Kidney Diseases
          • Reproductive history data
          • Finnish Cancer Registry: Cervical cancer screening
          • Finnish Cancer Registry: Breast cancer screening
          • Finnish Cancer Registry: Detailed cancer data
          • Finnish Register of Visual Impairment
          • Parental cause of death data
          • Ejection fraction data
          • Finnish National Infectious Disease Register
          • Finnish National Vaccination Register
          • Covid-19 primary care data
          • Blood donor data from the Finnish Red Cross Blood Service (FRCBS)
          • Dental data
          • Socioeconomic data
          • Hilmo and avohilmo extended data
      • Omics data
        • Proteomics
          • Expansion Area 5 proteomics data
          • FinnGen 3 proteomics data
        • Metabolomics
        • Single-cell transcriptomics and immune profiling
        • High-content cell imaging
        • Full blood counts and clinical chemistry
      • Hospital administered medications
      • Whole exome sequencing (WES) data
    • Green Library Data (aggregate data)
      • What is "Green" Data?
      • Accessing Green Data
      • Other analyses available
        • Colocalizations in FinnGen
        • Autoreporting – information on overlaps
          • Index of Autoreporting variables
        • HLA
        • LoF burden test
        • Meta-analyses
      • Core analysis results files
        • Recessive GWAS results format
        • Variant annotation file format
        • Genotype cluster plots format
        • GWAS results format
        • Finemapping results format
        • Colocalization results format
          • Results format in colocalization before DF13
        • Autoreporting results format
        • Sex-specific GWAS results format
        • UKBB-FinnGen meta-analysis file formats
        • Pairwise endpoint genetic correlation format
        • Heritabilities
        • Coding variant associations format
        • HLA association results
        • Proteomics results
        • Coding variant results including CHIP EWAS (Exome-Wide Association Scan)
        • Kanta lab association results v1
    • Disease specific Task Force data
      • Inflammatory bowel disease (IBD) SNOMED codes data
    • Expansion Area 3 (EA3) studies
      • EA3 study: Fatty liver disease study and data in Sandbox
      • EA3 study: Age-related macular degeneration study and data in Sandbox
      • EA3 study: Women's health studies
        • EA3 study: Women’s health – Endometriosis and data in Sandbox
        • EA3 study: Human papilloma virus-related gynecological lesions, and data in Sandbox
        • EA3 study: Women’s health – PCOS and infertility study, and data in Sandbox
      • EA3 study: Diabetic Kidney Disease and Rare Kidney Disease study and data in Sandbox
      • EA3 study: Oncology studies
        • EA3 study: Oncology – Breast cancer study and data in Sandbox
        • EA3 study: Oncology –Prostate cancer study and data in Sandbox
        • EA3 study: Oncology – Ovarian cancer study and data in Sandbox
      • EA3 study: Pulmonary diseases (IPF, asthma and COPD) study and data in Sandbox
      • EA3 study: Immune-mediated diseases
      • EA3 study: Heart Failure study and data in Sandbox
      • FinnGen EA3 leads
  • Disease Specific Task Forces
    • Inflammatory bowel disease (IBD)
    • Kidney Diseases
    • Eye Diseases
    • Rheumatic Diseases
    • Atopic Dermatitis
    • Pulmonary Diseases
    • Neurological Diseases
    • Heart Failure
    • Fibrotic Diseases
    • Metabolic diseases
    • Parkinson's diseases
  • Working in the Sandbox
    • How to get started with Sandbox
    • What is Sandbox and what can you do there
    • What do we mean by "red" and "green" data?
    • General workflows for the most common analyses
    • Quirks and Features
      • Managing your files in Sandbox
      • Navigating the Sandbox
      • How to save Sandbox window configuration
      • Copying and pasting in and out of your IVM
      • How to report issues from within the Sandbox
      • Sharing individual-level data within the Sandbox
      • How to download results from your IVM
        • Sandbox download requests – rules and examples for minimum N
      • Keyboard combinations
      • Running analyses in your IVM vs. Pipelines
      • Timeouts and saving your work (backups, github)
      • How to install a R package into Sandbox?
        • How to install R packages with many dependencies
      • Install R and Python packages from the local Sandbox repository
      • How to install a Python package into Sandbox
      • How to install GNU Debian package
      • How to upload your own files to IVM via /finngen/green
      • How to remove files from /finngen/green
      • Using Sandbox as a Chrome application (full screen mode)
      • How to reset your finngen.fi account password
      • Sandbox IVM tool request handling policy
      • Docker images
        • How to get a new Docker image to Sandbox
        • How to mount data into Docker container image
        • Containers available to Sandbox
        • Containers with user customized tool sets
        • How to write a Docker file
        • Anaconda Python environment in the Sandbox
      • Python Virtual Environment in Sandbox
      • How to shut down your IVM
    • Which tools are available?
      • FinnGen exome query tool
      • Custom GWAS tools
        • Custom GWAS GUI tool
        • Custom GWAS command line (CLI) tool
          • Custom GWAS CLI Binary mode
          • Custom GWAS CLI Quantitative mode
        • How to make your summary stats viewable in a PheWeb-style?
        • Finemapping of Custom GWAS analyses
        • PheWeb Users Input Validator tool
        • Conditional analysis of Custom GWAS analyses
      • Pipelines
      • Pre-installed Linux tools
      • PGS Browser
      • Lmod Linux tools
      • Anaconda Python module with ready set of scientific packages
      • Python packages
      • R packages
      • Atlas
        • Quick guide
          • Introduction to OHDSI, OMOP CDM and Atlas
          • From research question to concepts and cohort building
          • Using Atlas in Sandbox
          • Examples on cohort building with Atlas
        • Detailed guide
          • Atlas data model
          • Standard and non-standard codes
          • How to define a cohort in Atlas
            • Select FinnGen data release in Atlas for Search
            • How to define a simple ICD case-control cohort in Atlas
              • Define a simple ICD Concept Set in Atlas
              • Define a simple ICD case cohort in Atlas
              • Define a simple ICD control cohort in Atlas
            • Concept Sets
              • Create Concept Sets using descendants
              • Exclude and Remove codes from Concept Set
              • Simplify Concept Sets that use standard code descendants
              • Create Concept Sets using equivalent standard and non-standard codes
              • View standard code hierarchy in Atlas
            • Cohort Definitions
              • Using the Death register in Atlas
              • Filtering by clinical registries in Atlas
              • Filtering by demographic criteria in Atlas
              • Defining exit rules for a cohort in Atlas
              • Selecting the correct box in Atlas for events and medical codes
            • How to export FinnGen IDs from Atlas
          • Downstream analyses after the Atlas cohorts are created
          • Data Release Summary Statistics in Atlas
          • Cohort Summary Statistics in Atlas
            • Time-dependent Cohort Summary Statistics in Atlas
            • Event inclusion in Cohort Summary Statistics in Atlas
          • Cohort Pathways
      • BigQuery (relational database)
      • Atlas vs BigQuery cohorts
      • Genotype Browser
      • Cohort Operations tool (CO)
        • Upload cohorts to CO
        • Combine cohorts with CO
        • Operate on Atlas cohorts and data with entries and exit events
        • Explore code and endpoint enrichments with CO (CodeWAS)
        • Explore endpoint overlaps with CO
        • Compare custom endpoint to FinnGen endpoint with CO
        • Launch custom GWAS with CO
        • Export FinnGen IDs using CO
        • Understanding phenotypic overlaps using CO
      • Trajectory Visualization Tool (TVT)
        • Running TVT
          • Filtering timelines with TVT
          • Reordering timelines with TVT
          • Clustering timelines with TVT
          • Viewing TVT results
        • Viewing Atlas, CO, and Genotype cohorts in TVT
        • Exporting cohorts from TVT
        • TVT help page
      • LifeTrack
      • Miscellaneous helper scripts/tools
        • Tool to annotate variants with RSIDs
        • Proper translations of medical, service sector and provider codes
        • BigQuery Connection – R
          • Case study – All register data for a person
          • Case study – UpSet plot
          • Case study – Tornado plot
          • Case study – defining simple cohorts using medical codes for running case-control GWAS
        • BigQuery Connection - Python
          • BigQuery Python - Downstream analysis - Active Ingredient - Bar plot
          • BigQuery Python - Case Study - Sex different - Tornado plot
          • BigQuery Python - Case Study - Comorbidity - Upset plot
          • BigQuery Python - Case Study - Patient Timeline - Scatter plot
      • Sandbox internal API for software developers
    • Working with Phenotype Data
      • Variant PheWas
      • How to select controls for your cases
      • Using the R libraries to look at Phenotype data
      • How to check case counts from the data
      • Creating your own user-defined endpoint
    • Working with Genotype Data
      • Genotype Browser how to
      • Cluster Plots
      • ClusterPlot viewer V3C
      • Rare Variant Calling in V3C
      • Create map of allele
      • Genotypes from VCF files
      • Variant PheWas
      • Interpreting rare-variant analysis results
      • Tools for geno-pheno explorations
        • Example: transferring data from Genotype Browser to LifeTrack
        • Example: Visualizing Genotype Browser output data with TVT
    • Running analyses in Sandbox
      • How to run survival analyses
      • How to create custom endpoint using bigquery: example
      • How to use the Pipelines tool
      • How to submit a pipeline from the command line (finngen-cli)
      • How to run genome-wide association studies (GWAS)
        • How to run GWAS using REGENIE
        • Running quantitative GWAS with REGENIE
        • Conditional analysis
        • Conditional Analysis with custom regions and loci
        • How to run GWAS using SAIGE
        • Adding new covariates in GWAS using REGENIE and SAIGE
        • How to run GWAS using plink2 (for unrelated individuals only)
        • How to run GWAS using GATE (survival models)
        • How to run trajGWAS
        • How to run GWAS using the Regenie unmodifiable pipeline
        • How to run an interaction GWAS using the Regenie unmodifiable pipeline
        • How to run survival analysis using GATE unmodifiable pipeline
        • How to run GWAS on imputed HLA alleles using Regenie
      • How to run finemapping pipeline
        • Finemapping with custom regions in DF12
        • Unmodifiable Finemapping pipeline
      • How to run colocalization pipeline
      • How to run the LDSC pipeline
      • How to run PRS pipeline
      • How to calculate PRS weights for FinnGen data
      • Sandbox path and pipeline mappings
      • If your pipeline job fails
      • Tips on how to find a pipeline job ID
      • Managing memory in Sandbox and data filtering tips
      • Using Google Life Sciences API in Sandbox
      • Pipelines is based on Cromwell and WDL
    • Billing information and where to find more details
      • Monitoring Sandbox costs by Sandbox billing report
      • Monitoring Sandbox costs directly from your Google billing account
  • Working outside the Sandbox
    • Risteys
    • Endpoint Browser
    • PheWeb
      • Volcano plots with LAVAA
    • Meta-analysis PheWeb(s)
    • Coding variant browser
    • Multiple Manhattan Plot (MMP)
      • How to prepare an input file for MMP
      • How to use MMP
    • LD browser
    • Green library data
  • FAQ
    • FinnGen Spin Offs
    • FinnGen access and accounts
      • How do I apply for data access?
      • What is "red" or "green" data?
      • I already have green data access, how do I apply for red data access?
      • I cannot access the /finngen/red?
      • How do I enable two-factor authentication (2FA)?
      • I cannot access my FinnGen account?
      • How to reset account credentials
      • What to do if you suspect your account has been compromised
      • Can't access your smartphone for 2FA?
      • How do I access the FinnGen members' area?
      • How do I access FinnGen All Sharepoint?
      • How can I view existing analysis proposals?
      • How can I join the FinnGen Slack?
      • How do I join the FinnGen Teams group?
      • How to apply SES sandbox access
      • How to request a FinnGen account?
    • FinnGen data
      • What to do if I think I found a mistake in the data?
      • What are the field/column names in FinnGen?
      • What covariates are used in FinnGen's core GWAS analyses?
      • Does FinnGen have lab results available?
      • Does FinnGen have family and relatedness information available?
      • Where can I find a list of unrelated individuals in FinnGen?
      • When moving from BCOR to .txt files, what does the column called "correlation" mean?
      • Is there really no participant birth year data?
      • How do I calculate time between events?
      • Can I select only the columns needed for my analysis to import into RStudio?
      • What is the difference is between LD-clumping and the Saige conditional analysis?
      • Can I download all pairwise LD data across the genome at once?
      • How to find latest data releases?
      • Why are there differences in the GWAS results between Data Freezes/Releases?
    • Where can I find
      • COVID association results?
      • Users' Meeting materials?
      • A list of what coding variants are enriched in Finland?
      • A comprehensive list of key file locations in FinnGen?
      • Medical code translations?
    • PheWeb
      • What are QQ and Manhattan plots?
      • How can I access PheWeb?
      • Are fine-mapping results that available in PheWeb also available as flat files?
      • Do the autoreports report the 95% or 99% credible set?
    • Registries
      • What do KELA reimbursement codes map to?
      • What's the cutoff date for FinnGen data?
    • Sandbox
      • What is the FinnGen Sandbox?
      • Why does my IVM freeze while loading data into R/Rstudio
      • Where can I find tutorials and documentation on Sandbox?
      • How do I get my own analysis code into Sandbox?
      • Where to ask for software you'd like to see in Sandbox
      • Can I share individual level data between different Sandbox users?
      • Is there a sun grid engine for running long scripts?
      • How to clear browser cache after sandbox update
      • How do I increase the window resolution on my IVM?
      • How can I view pdf, jpg and HTML files?
      • My Sandbox job was killed - why?
      • How to unzip files in the command line
      • Why aren't my keyboard/shortcuts working in Sandbox like they do in my local computer?
      • How to know if my pipeline job was failed due preemption of worker VM
    • Risteys
      • Why is the case number dropping after the "Check pre-conditions, main-only, mode, ICD version" step?
    • Endpoints
      • Where do I find the most recent list of FinnGen endpoints?
      • What does it mean when an endpoint has “mode” at the end?
      • What scenario would cause an NA (missing data) entry rather than a zero?
      • Does it mean anything when a value is written as $!$ instead of NA?
      • Why is there an inconsistency between ICD10 code J84.1 (IPF) and J84.112?
      • How are control endpoints calculated?
      • Can I get a list of FinnGen IDs by control group for my endpoint?
      • What does Level C mean in the endpoints data table?
      • What does the SUBSET_COV field show?
      • Why is there a "K." prefix on some endpoints?
      • Why there are fewer endpoints going from R5 (N = 2,925) to R8 (N = 2,202)?
      • Should I include primary care registry (PRIM_OUT) codes in my cohort definitions?
      • I found BL_AGE after FU_END_AGE in the endpoint data, how is it possible?
      • Why do individuals who are not dead have death age in endpoint data?
      • I found EVENT_AGE after FU_END_AGE in endpoint data, how is it possible?
    • Pipelines
      • Are there example SAIGE pipelines?
      • How do I apply finemapping to my SAIGE results?
      • Why Pipelines is claiming that my files or folders are not in /finngen/red?
    • Citing
      • How do I cite analysis using publicly available FinnGen results?
      • How do I cite FinnGen results that use individual level data?
    • For biobanks
      • How to apply for data return
    • Data Security and Protection
      • How do I report a data breach?
  • Release Notes
    • Data Releases 2025
    • Data Releases 2024
    • Data Releases 2023
    • Data Releases 2022
    • Data Releases 2021
  • Tool Catalog
  • Glossary
  • User Support
  • Data Protection & Security
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On this page
  • Data location
  • Sandbox
  • BigQuery
  • Data columns
  • Overview
  • TEST_OUTCOME
  • TEST_OUTCOME_IMPUTED
  • MEASUREMENT_STATUS
  • Pipeline
  • Additional dataset: Values extraction analysis
  • Summary
  • Location
  • Extraction Summary
  • Other reference tables
  • Test name abbreviations
  • Reference range terms

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  1. FinnGen Data Specifics
  2. Red Library Data (individual level data)
  3. Phenotype data
  4. Kanta lab values

Data

Data location

Sandbox

  • /finngen/library-red/finngen_R13/kanta_lab_1.0/data/finngen_R13_kanta_lab_1.0.txt.gz in textual TSV-gzipped format (for use with awk, grep, UNIX piping)

  • /finngen/library-red/finngen_R13/kanta_lab_1.0/data/finngen_R13_kanta_lab_1.0.parquet in binary Parquet format (for use with Python pandas, R data.frame)

BigQuery

Available in this table: finngen-production-library.sandbox_tools_r12.kanta_r12_v1

Data columns

Overview

Column
Description
SB
ETL

FINNGENID

Study ID (Pseudonymised ID given to the FinnGen participant)

✓

✓

SEX

Sex of the individual, female or male

✓

EVENT_AGE

Age (in years) at time of event, e.g. 12.012

✓

✓

APPROX_EVENT_DATETIME

✓

✓

TEST_NAME

Short name of the lab test, e.g. p-alat, s-tsh

✓

✓

TEST_ID

Code of the lab test (national or local lab test ID)

✓

✓

TEST_ID_IS_NATIONAL

Whether or not the TEST_ID is using the national lab test code system

✓

OMOP_CONCEPT_ID

OMOP Concept ID mapped from the TEST_ID and MEASUREMENT_UNIT

✓

✓

MEASUREMENT_VALUE

Value of the test measurement

✓

✓

MEASUREMENT_UNIT

Corresponding unit for the test measurement

✓

✓

MEASUREMENT_VALUE_HARMONIZED

Value of the test measurement, after harmonization across the OMOP Concept ID

✓

✓

MEASUREMENT_UNIT_HARMONIZED

Corresponding unit for the harmonized measurement value

✓

✓

TEST_OUTCOME

✓

✓

TEST_OUTCOME_IMPUTED

✓

MEASUREMENT_STATUS

✓

✓

REFERENCE_RANGE_GROUP

Reference range for this event, as text

✓

REFERENCE_RANGE_LOW_VALUE

Value for the low bound of the reference range

✓

✓

REFERENCE_RANGE_LOW_UNIT

Corresponding unit for the low bound of the reference range

✓

REFERENCE_RANGE_HIGH_VALUE

Value for the high bound of the reference range

✓

✓

REFERENCE_RANGE_HIGH_UNIT

Corresponding unit for the high bound of the reference range

✓

CODING_SYSTEM_ORG

Derived from CODING_SYSTEM_OID

✓

CODING_SYSTEM_OID

Original name: tutkimuskoodistonjarjestelmaid

✓

✓

TEST_ID_SOURCE

Code of the lab test, as it appeared before preprocessing of the data

✓

TEST_NAME_SOURCE

Short name of the lab test, as it appeared before preprocessing of the data

✓

MEASUREMENT_VALUE_SOURCE

Value of the test measurement, as it appeared before data cleaning

✓

MEASUREMENT_UNIT_SOURCE

Unit of the test measurement, as it appeared before data cleaning

✓

TEST_OUTCOME

This column provides a label comparing the measured value against a reference range.

Value
Description

N

Normal

A

Abnormal

AA

Very abnormal

L

Low

LL

Very low

H

High

HH

Very high

TEST_OUTCOME_IMPUTED

Some rows are missing the TEST_OUTCOME, so an imputed one is provided. The TEST_OUTCOME_IMPUTED is derived by looking at the data from the same OMOP Concept ID for which there are MEASUREMENT_VALUE and TEST_OUTCOME.

Value
Description

N

Imputed Normal

L

Imputed Low

L*

Imputed Low. Less confidence in the imputation due to over-representation of L and H from TEST_OUTCOME

H

Imputed High

H*

Imputed High. Less confidence in the imputation due to over-representation of L and H from TEST_OUTCOME

MEASUREMENT_STATUS

Value
Description

C

Corrected result

F

Final result

R

Unverified result

S

Partial result

Pipeline

A quick summary:

  • duplicate entries are removed (based on id,date,lab test name/code/measurement status & value)

  • text is processed to remove spaces and strange characters

  • test national codes are mapped to names based on known mappings

  • units are cleaned/uniformized and mapped to OMOP based on lab test

  • units are harmonized based on OMOP IDs

  • Another duplication removal step takes place post harmonization to intercept duplicate entries from different systems (checking for ID,date,harmonized test name,value and status)

Additional dataset: Values extraction analysis

On top of the core kanta data, another data set is released, meant for analysis. The idea behind this file is to focus more on numerical values and to manipulate/remove entries for downstream analysis, like flagging/removing problematic values. This file misses some columns from the original data but also contains new ones for analysis purposes. In this way we can keep separate the pure data munging/harmonization from the numerical elaboration of the data for analysis purposes

Summary

A quick summary:

  • the MEASUREMENT_FREE_TEXT column is manipulated to extract shareable information

    • Where the original measurement value is missing and the free text is available, we attempt to extract numerical values from it if they match certain patterns. After some string manipulation if we're left with a pure number we cast it from string to float and is merged with the original valuesto the MEASUREMENT_VALUE_EXTRACTED

    • A boolean column with information about where the data was extracted IS_VALUE_EXTRACTED is added

    • The text is scanned for pos/neg substrings and through a manual mapping, values are mapped to 1 (pos) or 0 (neg) in a new OUTCOME_POS_EXTRACTED column

  • QCing takes place to remove extracted values that are formatted as dates

Location

/finngen/library-red/finngen_R13/kanta_analysis_1.0/

Like for the full munged data, there are two files:

  • finngen_R13_kanta_analysis_1.0.parquet finngen_R13_kanta_analysis_1.0.txt.gz

  • finngen_R13_kanta_analysis_1.0.parquet finngen_R13_kanta_analysis_1.0.parquet

Extraction Summary

In the following table one can find a summary of the extraction process.

OMOP
N_EXTRACTED
%_EXTRACTED
%_NA_MEASUREMENT
N_POSNEG
%_EXTRACTED
%_NA_OUTCOME
conceptName

OMOP ID

N of extracted numerical values

Percentage of numerical values extracted

Percentage of extracted values that had NA in raw data measurement

N of extracted POS/NEG values

Percentage of POS/NEG extracted values

Percentage of extracted values that had NA in raw data outcome

Concept Name

3026361

2095662

22.6799

100.0000

2

0.0000

100.0000

Erythrocytes [#/volume] in Blood

3018095

118284

22.3749

100.0000

67950

12.8536

6.2384

Leukocytes [#/volume] in Urine

Other reference tables

Test name abbreviations

Test name abbreviations come from different laboratory testing centers around Finland. Some are standardized nationally and some are used only locally in different hospitals and test centers.

We have put a lot of effort into standardizing these to international OHDSI OMOP Concept ID (primarily from LOINC) so we hope that you do not need to interpret them very often! However, in case you have reason to use them, we provide the meaning of most abbreviations here.

Prefixes for lab test name abbreviations

aB

Arterial blood

Af

Puncture fluid

aG

Alveolar gas

Am

Amniotic fluid

As

Ascitic fluid

B

Blood

Bf

Bronchus fluid

Bi

Bile

Bl

Bronchoalveolar lavation

Bm

Bone Marrow

Bo

Bone

Br

Breast

Bu

Bursa

Ca

Cannula/IV port

cB

Capillary blood

Cf

Cervix fluid

Cn

Central nervous system

cU

Collected urine

Cv

Choroid villus

Di

Dialysis fluid

Dj

Duodenal juice

dU

Diurnal urine

E

Erythrocyte

Ex

Sputum

F

Fecal

fB

Fasting blood

Fl

Vaginal fluor

fP

Fasting plasma

fS

Fasting serum

Gi

Gastrointestinal

Gj

Gastric juice

Hb

Hemoglobin

He

Heart

Ki

Kidney

L

Leukocytes

Lf

Lacrimal fluid

Li

Likvor/CSF

Ln

Lymph Node

Lr

Liver

Lu

Lung

Ly

Lymphocytes

M

Muscle

mB

Machine blood

Me

Meconium

Mf

Mammary fluid

Mm

Maternal milk

Mu

Mucosa

Ne

Nerve

Ns

Nasal secretion

nU

Nocturnal urine

P

plasma

Pd

Peritoneal dialysis

Pf

Pleura

Pi

Pituitary gland

Pl

Placenta

Pp

Periodontal pocket

Ps

Pharyngeal secretion

Pt

Patient

Pu

Pus

S

Serum

Sa

Saliva

Se

Secretion

Sk

Skin

Sp

Semen

Sw

Sweat

Sy

Syncytial fluid

T

Thrombocyte

Ts

Tissue

Tu

Tumor

U

Urine

uA

Umbilical arterial blood

Ug

Urogenital

uS

Umbilical serum

uV

Umbilical venous blood

vB

Venous blood

W

Water

Suffixes for lab test name abbreviations

-Ab

Antibody

-AbA

IgA antibody

-AbE

IgE antibody

-AbG

IgG antibody

-AbM

IgM antibody

-Ag

Antigen

-Akt

Activity

-Aktt

Activation products

-Cl

Clearance

-Ct

Control

-D

DNA

-Di

Dialysis

-EVi

Special culture

-EM

Electron Microscopic

-F

Fetal

-Fc

Flow cytometry

-Fr

Fraction

-Gr

Gestational

-IF

Immunofluorescence

-IH

Immunohistochemistry

-Ind

Index

-Ion

Ionized

-Is

Iso enzymes

-ISH

in situ -hybridisation

-Jtk

Follow-up study

-Jvi

Follow-up culture

(jatkoviljely)

-Kj

Conjugate

-Lm

Species specificity

-MS

Mass spectrometry

-Nh

Nucleic acid

-O

Qualitative

-Oc

Oligoclonal

-Pa

Long term

-Pse

Screening and categorization

-PT

Rapid test

-R

Exercise stress test

-S

Stimulation

-Sc

Sub classes

-Ty

Typing

-V

Free or unconjugated

-Vi

Microbiology culture (e.g. u-Baktvi = bacterial culture from urine, ps-stravi = Strep A culture in pharayngeal secretion, F-sienVi = fungal culture in stool)

-Vit

Vitamine

-Vr

Staining

-Vt

Point of care (vieritesti), often a rapid test

Reference range terms

Test reference ranges are a free text string that can have a lot of Finnish in them. For those who don’t speak Finnish, we provide here translations of some of the common words you will see in reference ranges:

General terms:

  • AIKUISET: Adults

  • ALLE: Under/Below

  • ALK: Abbreviation for "alkaen", meaning "starting from" or "beginning at"

  • ALTISTUMATTOMAT: Unexposed (individuals)

  • AAMUNÄYTE: Morning sample

  • EDELLEEN: Still, continuing

  • FERTIILI-IKÄ: Fertile age

  • HOITOALUE: Treatment range

  • JA: And

  • JÄÄNNÖSPIT: Residual concentration

  • KAIKKI: All, everyone

  • KATSO: See, look at

  • KK: Abbreviation for "kuukausi", meaning month

  • KS: Abbreviation for "katso", meaning "see" or "look at"

  • KTS: Another abbreviation for "katso"

  • KYMENLAAKSONLAB: Kymenlaakso Laboratory (a specific lab in Finland)

  • LAPSET: Children

  • LEUK: Leukocytes (white blood cells)

  • LIER: Likely referring to "lieriöt", meaning casts (in urine analysis)

  • MIEHET: Men

  • NAISET: Women

  • NEGAT: Negative

  • NORMAALI: Normal

  • OHJEKIRJA: Manual, guidebook

  • PAASTO: Fasting

  • POJAT: Boys

  • POSTMENOPAUSSI: Postmenopausal

  • PREMENOPAUSAALISET: Premenopausal

  • PUBERT: Puberty

  • RASKAUS: Pregnancy

  • SUOSITELTAVA: Recommended

  • TAVOITE: Target, goal

  • TAVOITEARVO: Target value

  • TERAP: Therapeutic

  • TOKSINEN: Toxic

  • TULKINTA: Interpretation

  • TUPAKOIMATTOMAT: Non-smokers

  • TYTÖT: Girls

  • V: Abbreviation for "vuosi", meaning year

  • VASTASYNT: Newborn

  • VIITEARVO: Reference value

  • VKO: Abbreviation for "viikko", meaning week

  • VRK: Abbreviation for "vuorokausi", meaning day (24-hour period)

  • YLI: Over, above

Age-related terms:

  • 0-6PV: 0-6 days

  • 1KK-1V: 1 month to 1 year

  • 1V-: 1 year and older

  • 2-4V: 2-4 years

  • 5-10V: 5-10 years

  • 11-15V: 11-15 years

  • 16V-: 16 years and older

  • 18V-: 18 years and older

Medical terms:

  • ERYT: Erythrocytes (red blood cells)

  • EPIT.SOLUT: Epithelial cells

  • FOLLIKK.VAIHE: Follicular phase (of menstrual cycle)

  • MAKUU: Lying down (usually referring to blood pressure measurement)

  • MENARKEA: Menarche (first menstrual period)

  • PYSTY: Standing (usually referring to blood pressure measurement)

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Date (randomized) and time (not randomized) of event, e.g. 2020-01-02T07:30 ()

Label given for the outcome of the test to indicate how it falls against the reference range ()

Imputed test outcome ()

Code indicating the status of the lab test measurement ()

The pipeline is available in github () where technical information on how the raw data was processed can be found.

The pipeline is available in github () where technical information on how the raw data was processed can be found.

https://github.com/FINNGEN/kanta_lab_preprocessing/
https://github.com/FINNGEN/kanta_lab_preprocessing/
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see value table
see value table
see value table
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extraction_summary_names.txt
On top of the core kanta data, another data set is released, meant for analysis. The idea behind this file is to focus more on numerical values and to manipulate/remove entries for downstream analysis, like flagging/removing problematic values. This file misses some columns from the original data but also contains new ones for analysis purposes. In this way we can keep separate the pure data munging/harmonization from the numerical elaboration of the data for analysis purposes