Appendix (Finngen Partner List)

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Appendix (FinnGen partner list): Coordinators: Biobank of Eastern Finland Northern Savo Hospital District University of Helsinki, HiLIFE, University of Eastern Finland Institute for Molecular Medicine Finland FIMM South Savo Social and Health Care Authority Helsinki University Hospital (HUS) Joint Municipal Authority for North Karelia Social The National Institute of Health and Welfare (THL) and Health services (Siun sote) Eastern Savo Hospital District Funders: Central Finland Biobank Central Finland Health Care District Business Finland University of Jyväskylä Abbvie AstraZeneca Northern Finland Biobank Borealis Biogen Northern Ostrobothnia Hospital District Celgene University of Oulu Genentech, a member of the Roche Group Joint Municipal Public Utility for Northern Finland GlaxoSmithKline (GSK) central laboratory Nordlab Merck & Co., Inc., Kenilworth, NJ, USA Central Ostrobothnia Joint Municipal Authority Pfizer for Social and Health Care Sanofi Kainuu Social and Health Care Joint Municipal Authority Finnish Biobanks and their host organisations: Lappi Hospital District Länsi-Pohja Hospital District Auria Biobank Hospital District of Southwest Finland Finnish Clinical Biobank Tampere University of Turku Pirkanmaa Hospital District Satakunta Hospital District University of Tampere Vaasa Hospital District Kanta-Häme Hospital District South Ostrobothnia Hospital District Helsinki Biobank Hospital District of Helsinki and Uusimaa (HUS) THL Biobank University of Helsinki The National Institute of Health and Welfare (THL) Kymenlaakso Social and Health Services (Carea) South Karelia Social and Health Care District Blood Service Biobank (Eksote) Finnish Red Cross Blood Service Finnish Hematology Registry and Clinical Biobank (FHRB) Finnish Red Cross Blood Service Finnish Association of Hematology Institute for Molecular Medicine Finland (FIMM), University of Helsinki .

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1 Contributors of Finngen
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) BMJ Open Resp Res Contributors of FinnGen Steering Committee Aarno Palotie Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland Mark Daly Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland Pharmaceutical companies Howard Jacob Abbvie, Chicago, IL, United States Athena Matakidou Astra Zeneca, Cambridge, United Kingdom Heiko Runz Biogen, Cambridge, MA, United States Sally John Biogen, Cambridge, MA, United States Robert Plenge Celgene, Summit, NJ, United States Mark McCarthy Genentech, San Francisco, CA, United States Julie Hunkapiller Genentech, San Francisco, CA, United States Meg Ehm GlaxoSmithKline, Brentford, United Kingdom Dawn Waterworth GlaxoSmithKline, Brentford, United Kingdom Caroline Fox Merck, Kenilworth, NJ, United States Anders Malarstig Pfizer, New York, NY, United States Kathy Klinger Sanofi, Paris, France Kathy Call Sanofi, Paris, France University of Helsinki & Biobanks Tomi Mäkelä HiLIFE, University of Helsinki, Finland, Finland Jaakko Kaprio Institute for Molecular Medicine Finland, HiLIFE, Helsinki, Finland, Finland Petri Virolainen Auria Biobank / Univ. of Turku / Hospital District of Southwest Finland, Turku, Finland Kari Pulkki Auria Biobank / Univ. of Turku / Hospital District of Southwest Finland, Turku, Finland Terhi Kilpi THL Biobank / Finnish Institute
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Finngen Study Taps Into a Unique Gene Pool in Search of the Next Breakthroughs in Disease Prevention, Diagnosis and Treatment
International Press Release Free for publication 12/19/2017 at 9:00 am ETC/ 7:00 UTC FinnGen study taps into a unique gene pool in search of the next breakthroughs in disease prevention, diagnosis and treatment A unique study that combines genome information with digital health care data has been launched in Finland. The FinnGen study plans to analyse up to 500 000 unique blood samples collected by a nation-wide network of Finnish biobanks. The goal is to deepen our understanding about the origins of diseases and their treatment. The project is expected to continue for six years, with a current budget of €59M. Located at the outskirts of Northern Europe, Finland is one of today’s global hotspots for medical research. Finland tops global listings in healthcare, education, innovation, digitalization and good governance. In addition, country’s 5,5 million population share a unique genetic heritage, which provides researchers with enormous opportunities to innovate. Due to the unique heritage of the Finnish population, genomic data can be analysed faster and more effectively than in populations of more heterogenous origins – significantly improving the chances of breakthrough findings. ”This makes Finland a great testbed for genomic research”, says Kimmo Pitkänen, Director of Helsinki Biobank. The FinnGen study is an unprecedented global research project representing one of the largest studies of this type. Project aims to improve human health through genetic research, and ultimately identify new therapeutic targets and diagnostics for treating numerous diseases. The collaborative nature of the project is exceptional compared to many ongoing studies, and all the partners are working closely together to ensure appropriate transparency, data security and ownership.
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An Expanded Analysis Framework for Multivariate GWAS Connects Inflammatory Biomarkers
bioRxiv preprint doi: https://doi.org/10.1101/867267; this version posted December 6, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 An expanded analysis framework for multivariate GWAS connects inflammatory biomarkers 2 to functional variants and disease 3 Sanni E. Ruotsalainen1*, Juulia J. Partanen1*, Anna Cichonska1,2,3, Jake Lin1, Christian Benner1, Ida Surakka4, 4 FinnGen, Mary Pat Reeve1, Priit Palta1,5, Marko Salmi6,7, Sirpa Jalkanen6,7, Ari Ahola-Olli1,8,9, Aarno 5 Palotie1,8,9, Veikko Salomaa10, Mark J. Daly1,8,9, Matti Pirinen1,11,12, Samuli Ripatti1,8,11** & Jukka Koskela1,8** 6 7 1) Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland 8 2) Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto 9 University, Espoo, Finland 10 3) Department of Future Technologies, University of Turku, Turku, Finland 11 4) Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA 12 5) Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia 13 6) MediCity Research Laboratory, University of Turku, Turku, Finland 14 7) Institute of Biomedicine, University of Turku, Turku, Finland 15 8) The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA 16 9) Analytic and Translational Genetics Unit, Massachusetts General Hospital, Harvard Medical School, 17 Boston, Massachusetts, USA 18 10) Finnish Institute for Health and Welfare, Helsinki, Finland 19 11) Department of Public Health, Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, 20 Finland 21 12) Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland 22 23 *authors contributed equally 24 **Corresponding author 25 Tel: +358 50 374 7924, +358 40 567 0826 26 E-mail: [email protected], [email protected] 27 1 bioRxiv preprint doi: https://doi.org/10.1101/867267; this version posted December 6, 2019.
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Aarno Palotie, M.D., Ph.D
Aarno Palotie, M.D., Ph.D. 1 500,000 individuals 2 10% of the population Existing sample collections: 200,000 Finns 300,000 new sample donors within the coming 6 years FinnGen National Health Register data 3 500 000 individuals 500 000 individuals Combined ~1O % of the genotype and Association analyses population register data Imputation Axiom GWA array Public - Private Partnership 4 +2 new partners 5 331 000 participants 331 000 participants, so far 6 7 8 9 10 August 2019 data freeze 181 000 individuals Sandbox for Data Analysis, version 2 Approved users anywhere finngen.fi 11 in world Google Cloud Industry: Individual and group Abbvie Astra Zeneca workspaces Sandbox Biogen Celgene GSK Summary and count Genentech Merck/MSD level results for export, Pfizer Sanofi download, portals Academic: UH, HUS, THL, Finnish Biobanks, standard or custom Sandbox Hospital Districts, Tools Universities in Finland or US, EU • Secure access to individual level data • Data cannot be copied 12 Success with from analyzing first 181,000 participants Data release 4 in numbers 13 • 181,821 individuals with genotypes and phenotypes • 2,443 endpoints (n>100) • Detailed longitudinal data in the Sandbox, 40 million drug purchases and ICD codes, 72 million health care events 14 Examples: New loci discovered based on the Finnish population structure Novel locus Mitja Kurki Juha Karjalainen15 MHC EMSY RNF186 MST1 UBAC2 FCGR2A NKX2-3 IL23R C1orf106 IL10 PheWas & Very consistent inflammatory association signal Fine Genomewide significant in Ankylosing Spondylitis, strong
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Accurate PGx Testing: The Gateway To IPI Supergraphic 2 Personalized Medicine For use as a graphic overlay on the edge of photos, at the given angle. Should act as a bridge between the photo and adjacent background. Please do not change the color or the angle of the supergraphic. POWERED BY CITELINE & IN VIVO SPONSORED BY Accurate PGx Testing: The Gateway To Personalized Medicine Pharmacogenetic testing is critical to the implementation of personalized medicine. Researchers continue to uncover the multiple genetic influences on drug response, and the considerable variations across populations and regions. Hence the urgent need for accurate, comprehensive and accessible tests, in order to ensure that patients receive the most effective and safest treatments. Personalized trials continue to grow The proportion of clinical trials that incorporate PGx biomarkers to target specific patients continues Pharmacogenomics’ potential to transform drug to increase. Data from Informa’s Trialtrove shows discovery and development is becoming clearer. Over that well over half (55%) of all trials initiated in 2018 half of all trials initiated in 2018 included the use of included PGx biomarkers for patient selection into a selective pharmacogenomic and/or pharmacogenetic trial or cohort, up from 40% in 2014. The trend-line (PGx) biomarkers, according to data from Informa’s looks set to continue rising. (See Exhibit 1) Trialtrove. These trials are more likely to succeed than those without – and that advantage has grown This growth could be explained by the fact that trials several-fold in the last two years. which include a PGx biomarker for patient selection 2 / October 2019 © Informa UK Ltd 2019 (Unauthorized photocopying prohibited.) Exhibit 1.
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Finngen Participant Bulletin
Participant bulletin (version 1.0) 13.1.2021 PARTICIPANT BULLETIN What are FinnGen and its new research sections? FinnGen (www.finngen.fi) is a research project covering the whole of Finland, that aims to increase our understanding of the factors affecting illnesses and to provide new information for improving health care and medical care. FinnGen was launched in 2017 and is planned to last until 30th April 2027. The research is led by the University of Helsinki. You have already supported the study through your biobank consent and sample. FinnGen utilises the genome data (genetic data) generated from biobank samples and health-related data from social and healthcare registers. By analysing both genome data and health data together, we can research what genetic factors (variation that occurs in our genes) affects risk of becoming ill, either protecting from or increasing risk of illnesses. Because national health registers collect data about how citizens use health care services throughout their whole life course, we can also investigate what genetic factors affect disease progression. In addition to genetic contributors, health and illnesses are also affected by numerous environmental and lifestyle factors as well as personal features and preferences. It is important to understand how these factors, together with genetics, affect risk of diseases and their progression. However, not all information that is relevant to health research is available from national registers. For this reason, FinnGen now invites its participants, including you, to take part in new research sections. In these research sections, issues related to the different aspects of life are examined in more detail.
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medRxiv preprint doi: https://doi.org/10.1101/2021.09.03.21262975; this version posted September 5, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license . Insights from complex trait fine-mapping across diverse populations Masahiro Kanai1,2,3,4,5,6, Jacob C Ulirsch1,2,3,7, Juha Karjalainen1,2,3,5, Mitja Kurki1,2,3,5, Konrad J Karczewski1,2,3, Eric Fauman8, Qingbo S Wang1,2,3,6,9, Hannah Jacobs1,2,3,10, François Aguet11, Kristin G Ardlie11, Nurlan Kerimov12,13, Kaur Alasoo12,13, Christian Benner5, Kazuyoshi Ishigaki14, Saori Sakaue2,6,15,16, Steven Reilly11,17, The BioBank Japan Project18, FinnGen5, Yoichiro Kamatani19,20, Koichi Matsuda21, Aarno Palotie1,2,5,22, Benjamin M Neale1,2,3, Ryan Tewhey23,24,25, Pardis C Sabeti11,17,26, Yukinori Okada6,9,27, Mark J Daly1,2,3,5,28, Hilary K Finucane1,2,3,28 1Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA, 2Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA, 3Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA, 4Program in Biomedical Informatics, Harvard Medical School, Boston, MA, USA, 5Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland, 6Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan,
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Downloaded from the Journal Website35
medRxiv preprint doi: https://doi.org/10.1101/2020.10.23.20213652; this version posted October 27, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. 1 A global atlas of genetic associations of 220 deep phenotypes 2 3 Saori Sakaue1-5, 46, *, Masahiro Kanai1, 5-9, 46, Yosuke Tanigawa10, Juha Karjalainen5-7,9, Mitja 4 Kurki5-7,9, Seizo Koshiba11,12, Akira Narita11, Takahiro Konuma1, Kenichi Yamamoto1,13, 5 Masato Akiyama2,14, Kazuyoshi Ishigaki2-5, Akari Suzuki15, Ken Suzuki1, Wataru Obara16, 6 Ken Yamaji17, Kazuhisa Takahashi18, Satoshi Asai19,20, Yasuo Takahashi21, Takao 7 Suzuki22, Nobuaki Shinozaki22, Hiroki Yamaguchi23, Shiro Minami24, Shigeo Murayama25, 8 Kozo Yoshimori26, Satoshi Nagayama27, Daisuke Obata28, Masahiko Higashiyama29, 9 Akihide Masumoto30, Yukihiro Koretsune31, FinnGen, Kaoru Ito32, Chikashi Terao2, 10 Toshimasa Yamauchi33, Issei Komuro34, Takashi Kadowaki33, Gen Tamiya11,12,35,36, 11 Masayuki Yamamoto11,12,35, Yusuke Nakamura37,38, Michiaki Kubo39, Yoshinori Murakami40, 12 Kazuhiko Yamamoto15, Yoichiro Kamatani2,41, Aarno Palotie5,9,42, Manuel A. Rivas10, Mark J. 13 Daly5-7,9, Koichi Matsuda43, *, Yukinori Okada1,2,41,44,45, * 14 15 1. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 16 Japan. 17 2. Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative 18 Medical Sciences, Yokohama, Japan 19 3. Center for Data Sciences, Harvard Medical School, Boston, MA, USA 20 4. Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and 21 Women’s Hospital, Harvard Medical School, Boston, MA, USA 22 5.
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Unicorn Startup Trawls Databases for Protective Genetic Modifiers
news IN this section Tools to visualize Pediatric hospital What’s happened gene expression spinout lands in biotech around in situ p490 $4.8 billion deal the world p494 p492 Unicorn startup trawls databases for protective genetic modifers Startup aims to identify disease modifying genes in people with milder than expected phenotypes as starting points for drug discovery programs. fter two-and-a-half years in stealth mode, Maze Therapeutics unveiled a A$191 million series A funding round and a stellar line-up of scientists who aim to discover new biology and new drug targets by systematically mining human genetic databases for genetic modifiers that reduce the impact of disease-causing mutations. The company is, at the same time, establishing a functional genomics platform to study the phenotypic consequences of disrupting the genome. Combining these two types of data will, it hopes, lead to new insights into disease biology that can be translated into starting points for drug discovery programs. The general concept is not new, but Maze’s founding team and its backers hold that this is an opportune time to pursue such a strategy at scale because of the simultaneous availability of large volumes of human genomic and associated clinical data and the tools and technologies for conducting detailed phenotypic analyses. The hunt for such genetic exceptions At DeCode in Reykjavik, blood samples from Icelanders are stored for genetic testing. Credit: dpa picture came to prominence in 2016 when a group alliance / Alamy Stock Photo of researchers—headed by Stephen Friend, founder of Sage Bionetworks, and Eric Schadt of Mount Sinai under the collective the other 3.199 billion base pairs in your forms the basis of gene editing programs umbrella of the Resilience Project— genome are irrelevant to your disease,” underway at CRISPR Therapeutics and announced they had identified 13 people says Nicholas Katsanis, professor of cell Sangamo Biosciences.
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An Efficient and Accurate Frailty Model Approach for Genome-Wide Survival
bioRxiv preprint doi: https://doi.org/10.1101/2020.10.31.358234; this version posted November 1, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. An efficient and accurate frailty model approach for genome-wide survival association analysis controlling for population structure and relatedness in large-scale biobanks Rounak Dey1‡ ,Wei Zhou2,3,4‡, Tuomo Kiiskinen5,6, Aki Havulinna5,6, Amanda Elliott1,2,3, Juha Karjalainen2, 3,4,5, Mitja Kurki2,3,4,5, Ashley Qin1, FinnGen, Seunggeun Lee7, Aarno Palotie2,3,4,5, Benjamin Neale2,3,4*, Mark Daly2,3,4,5*, Xihong Lin1,3,8* 1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA 2Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA; 3Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA; 4Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA; 5Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki, Finland 6 Finnish Institute for Health and Welfare, Helsinki, Finland. 7Graduate School of Data Science, Seoul National University, Seoul, Korea 8Department of Statistics, Harvard University, Cambridge, Massachusetts, USA ‡These authors contributed equally to this work *These authors jointly supervised this work bioRxiv preprint doi: https://doi.org/10.1101/2020.10.31.358234; this version posted November 1, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
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The Finnish Genomic Research Landscape and Finngen
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