The EstBB is a population-based biobank at the Institute of Genomics, University of Tartu. The current cohort size is 200,000 individuals (aged ≥18 years), reflecting the age, sex and geographical distribution of the adult Estonian population. Overall, 83% of the samples are from Estonian individuals, 14% from Russian people and 3% from other ethnicities. All participants were recruited by general practitioners, physicians in hospitals and during promotional events. After recruitment, all participants completed a questionnaire about their health status, lifestyle and diet. Specifically, the questionnaire included personal data (place of birth, place(s) of living, nationality, among others), genealogical data (family history of medical conditions spanning four generations), educational and occupational history, and lifestyle data (physical activity, dietary habits (food frequency questionnaires), smoking status, alcohol consumption, women’s health and quality of life). The EstBB database is linked with national registries (such as the Cancer Registry and Causes of Death Registry), hospital databases and the database of the national health insurance fund, which holds treatment and procedure service bills. Diseases and health problems are recorded as ICD-10 codes and prescribed medicine according to the ATC classification. These health data are continuously updated through periodical linking to national electronic databases and registries. All participants were genotyped with genome-wide chip arrays and further imputed with a population-specific imputation panel consisting of 2,244 high-coverage (30 times) whole-genome sequence data from individuals and 16,271,975 high-quality variants57 (link). Researchers at the EstBB ran an association analysis of the 15 phenotypes (Supplementary Table 8) used in this study in 136,724 individuals. The association analysis was conducted with SAIGE52 mixed models with age, sex and ten PCs used as covariates. We used the Pan UKBB (https://pan.ukbb.broadinstitute.org/) project European subset association analysis summary statistics in the UKBB replication58 (Supplementary Table 7). As both the EstBB and the UKBB are on human genome build 37, we lifted over the coordinates to build 38 to match FinnGen. Variants were then matched on the basis of chromosome, position, reference and alternative alleles. Inverse variance weighted meta-analysis was used to perform a meta-analysis on the three cohorts (code available at https://github.com/FINNGEN/META_ANALYSIS).
Kurki M.I., Karjalainen J., Palta P., Sipilä T.P., Kristiansson K., Donner K.M., Reeve M.P., Laivuori H., Aavikko M., Kaunisto M.A., Loukola A., Lahtela E., Mattsson H., Laiho P., Della Briotta Parolo P., Lehisto A.A., Kanai M., Mars N., Rämö J., Kiiskinen T., Heyne H.O., Veerapen K., Rüeger S., Lemmelä S., Zhou W., Ruotsalainen S., Pärn K., Hiekkalinna T., Koskelainen S., Paajanen T., Llorens V., Gracia-Tabuenca J., Siirtola H., Reis K., Elnahas A.G., Sun B., Foley C.N., Aalto-Setälä K., Alasoo K., Arvas M., Auro K., Biswas S., Bizaki-Vallaskangas A., Carpen O., Chen C.Y., Dada O.A., Ding Z., Ehm M.G., Eklund K., Färkkilä M., Finucane H., Ganna A., Ghazal A., Graham R.R., Green E.M., Hakanen A., Hautalahti M., Hedman Å.K., Hiltunen M., Hinttala R., Hovatta I., Hu X., Huertas-Vazquez A., Huilaja L., Hunkapiller J., Jacob H., Jensen J.N., Joensuu H., John S., Julkunen V., Jung M., Junttila J., Kaarniranta K., Kähönen M., Kajanne R., Kallio L., Kälviäinen R., Kaprio J., Kerimov N., Kettunen J., Kilpeläinen E., Kilpi T., Klinger K., Kosma V.M., Kuopio T., Kurra V., Laisk T., Laukkanen J., Lawless N., Liu A., Longerich S., Mägi R., Mäkelä J., Mäkitie A., Malarstig A., Mannermaa A., Maranville J., Matakidou A., Meretoja T., Mozaffari S.V., Niemi M.E., Niemi M., Niiranen T., O´Donnell C.J., Obeidat M., Okafo G., Ollila H.M., Palomäki A., Palotie T., Partanen J., Paul D.S., Pelkonen M., Pendergrass R.K., Petrovski S., Pitkäranta A., Platt A., Pulford D., Punkka E., Pussinen P., Raghavan N., Rahimov F., Rajpal D., Renaud N.A., Riley-Gillis B., Rodosthenous R., Saarentaus E., Salminen A., Salminen E., Salomaa V., Schleutker J., Serpi R., Shen H.Y., Siegel R., Silander K., Siltanen S., Soini S., Soininen H., Sul J.H., Tachmazidou I., Tasanen K., Tienari P., Toppila-Salmi S., Tukiainen T., Tuomi T., Turunen J.A., Ulirsch J.C., Vaura F., Virolainen P., Waring J., Waterworth D., Yang R., Nelis M., Reigo A., Metspalu A., Milani L., Esko T., Fox C., Havulinna A.S., Perola M., Ripatti S., Jalanko A., Laitinen T., Mäkelä T.P., Plenge R., McCarthy M., Runz H., Daly M.J, & Palotie A. (2023). FinnGen provides genetic insights from a well-phenotyped isolated population. Nature, 613(7944), 508-518.
Other organizations :
Broad Institute, Massachusetts General Hospital, Institute for Molecular Medicine Finland, University of Helsinki, University of Tartu, Finnish Institute for Health and Welfare, Helsinki University Hospital, Tampere University, Tampere University Hospital, Hospital District of Helsinki and Uusimaa, Harvard University, Hasso Plattner Institute, University of Potsdam, Icahn School of Medicine at Mount Sinai, Biogen (United States), University of Cambridge, Optimat (United Kingdom), MRC Biostatistics Unit, Finnish Red Cross, Glaxosmithkline (Finland), Bristol-Myers Squibb (United States), Boehringer Ingelheim (Germany), GlaxoSmithKline (United States), Invalidisäätiö, Maze (United States), Turku University Hospital, University of Turku, Karolinska Institutet, Pfizer (United States), University of Oulu, Oulu University Hospital, Merck & Co., Inc., Rahway, NJ, USA (United States), AbbVie (United States), University of Eastern Finland, Kuopio University Hospital, Northern Ostrobothnia Hospital District, Borealis (Finland), Finland University, Central Finland Health Care District, AstraZeneca (United Kingdom), Novartis (United States), Age UK, GlaxoSmithKline (United Kingdom), Novartis (Switzerland), Folkhälsans Forskningscentrum, Janssen (United States), Janssen (Belgium)
Etiam vel ipsum. Morbi facilisis vestibulum nisl. Praesent cursus laoreet felis. Integer adipiscing pretium orci. Nulla facilisi. Quisque posuere bibendum purus. Nulla quam mauris, cursus eget, convallis ac, molestie non, enim. Aliquam congue. Quisque sagittis nonummy sapien. Proin molestie sem vitae urna. Maecenas lorem.
As authors may omit details in methods from publication, our AI will look for missing critical information across the 5 most similar protocols.
About PubCompare
Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.
We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.
However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.
Ready to
get started?
Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required
