Example of the Finngen Project

How to Utilize Genomic Information in Medicine: 1 Example of the FinnGen Project

Anu Jalanko Drug development

Identifying Preclinical Clinical the molecule research trials Review Approval TO USE

Universities, Pharmaceutical Laboratories, biobanks companies, hospitals, doctors, universities pharmaceutical companies 10 YEAR JOURNEY Genomic Information

• Identification of single mutations causing rare 3 diseases or pharmacogenomic variants • Now Routine diagnostics

• Obtaining more information on genetics of complex diseases • Polygenic Risk Scores already utilized for prevention

• Looking for genetic means to personalized medicine • Top priority of research Personalized medicine

Personalized drug development can be up to 3-4 years faster. → reduced cost and faster health benefits AC C E S S TO G E N O M I C DATA & BIOBANKS

GOVERNMENT BACKING

AC C E S S TO THE PEOPLE DIGITAL HEALTH DATA

W E L L- F U N C T I O N I N G HEALTHCARE SYSTEM Finland Government backing

6 • National Genome Strategy • The Biobank act • Secondary usage of register data • National Genome Center The Finnish Biobank Act of 2013

10 Biobanks 7 • Registration of biobanks, broad consent and protection of participants • Transfer of existing sample and data collections to biobanks • Possibility to recalling • Possibility to collect samples and data from the health care Terveystalo • Collaboration with industry Biobank Finland

7 + + + 8

POPULATION HEALTH BIOBANKS GENOME ISOLATE REGISTERS DATA

INNOVATIVE STUDY DESIGNS National registers

Hospital discharge

Hospital procedure 9 Register data Outpatient visit for administration Outpatient procedure

Primary care All data harmonized Register data Primary care procedure for Interconnected by the 11 digit person number administration Cancer register

Cause of death Register data for Drug purchase administration

Drug reimbursement Moving from single time point case collections to a comprehensive view of health and disease

1234 10 - Cross-sectional investigation in a diabetes study: diabetes & short stature 120360

Linkage with person numbers to:

Medical Birth Register

Register of Prescription Medicines ↑ National Health Register - kidney chart apnea Diabetes Stroke myo diagnoses / procedures (ICD codes) pressure of Kidney stage infarction birthweight Sleep -creatinine - S insufficiency Growth Blood Causes of Death Register End Low medication Death disease cardial Cancer Register

Clinical Laboratory databases via Biobanks

The Nationwide electronic registers provide a unique possibility for data mining Reconstruction of major life-time events instead of a single-point snapshot EARLY SETTLEMENT • 2000-10 000 years ago 11 • South and Coast

LATE SETTLEMENT • 16th century • multiple bottle necks LATE SETTLEMENT

EXPANSION • 18th century – population 250 000 • Today – population 5.4 million

EARLY SETTLEMENT Finnish bottleneck boosts the frequency of a POPULATION ISOLATE subset of high impact variants

• Risk effects 12 • Tens of Mendelian diseases • AKT2 knockouts ↑insulin level and ↑T2D risk • TOP3B and SETD1A in schizophrenia and learning disabilities • Protective effects • Knockouts of SLC30A8 ® T2D risk ↓ by 65% • Knockouts of LPA ® CAD risk ↓ by 20% • RNF186 truncating variant with ulcerative colitis risk ↓ by 70% FinnGen National Health Register data

500 000 individuals 500 000 individuals ~1O % of the Combined genotype and Association analyses population register data

Abbvie Astra Zeneca Biogen Celgene Imputation Genentech GSK Merck/MSD Pfizer Sanofi UH, HUS, THL Axiom GWA array Hospital Districts, Universities FinnGen partners

14 FinnGen will highlight Finnish Strengths

• Pharma companies reach for new developments in drug design 15 ØFinnGen shows: Finland was selected as an important partner due to exceptionally good prerequisities for excellent science ØFinnGen will lead Finland to the forefront of global biobank and genome research

• FinnGen was not created by chance – goverment heavily supports growth in health sector ØHealth Sector Growth Stragegy and Genome Strategy

• Important to continue development work – FinnGen is the first level in a long journey Biobanks as FinnGen Partners

• Biobanks collect samples for research use and access by FinnGen 16 project is according to standard pocesses • Biobank host organizations – universities, hospital districts and research institutions participate in the FinnGen research project • Genome data produced by FinnGen will be returned to the ownership of biobanks Motivation:

• FinnGen is a research project with global dimension 17 • FinnGen support has boosted biobanking activities throughout Finland • Collaboration with industry is a prerequisite for eveloping new therapies • Combining Finnish biobank activities with scientific excellence will bring Finland to global forefront 500 000 18 “Legacy collections”: ~1O % of the Prospective 200 000 population collections:

Legacy samples 300 000 and New samples collected by biobanks New sample collections supported by FinnGen Existing population cohorts, e.g. Genetic feedback study Health survey Population survey 8000 individuals 10 000 cases 40 000 cases

Examples

Psychosis Botnia Migraine Existing disease 10 000 cases 15000 cases >8000 cases cohorts, e.g.

19 Prospective collections

Hospital Biobanks: 20 Five University Hospital Districts 742 000

Finnish blood service: 50 000 samples mostly to be used as controls 816 000

1 111 000

869 000 1 904 000 Accumulation of new samples at Finnish biobanks

2421 Finngen Disease Groups of Interest

Neurology Other N=2489 N=18009 Hospital biobanks Oncology Ophtalmology N=22327 N=13854 Dermatology Pulmonology N=4952 N=10292 Gastroenterology Rheumatology Cardiometabolic N=35969 N=6769 N=6797

22 FinnGen

Step 1: Producing 23 genome data Step 2: 1O % of the Combining genome data population with digital health registry data FINNGEN1 Chip

Imputation grid 540,008 24

Partner content Finnish rare coding 57,008 116,402

FINNGEN1 736,145

Pharmacogenetic Clinvar pathogenic 4,600 14,900 HLA + KIR Thermo Fisher, Axiom 10,800 Constructing disease endpoints

Coronary heart disease Alzheimer’s 25 Hospital or outpatient Hospital or outpatient discharge registry discharge registry

ICD: I21, I22 ICD: G30, F00

Procedure code: Case Drug reimbursement Coronary bypass surgery ATC: NO6D

Cause of death Cause of death

ICD: I20-I25 ICD: G30, F00 contributing 26

FINNGEN DATA FREEZES EVERY 6 MONTHS 500000 460000 390000 390000 320000 320000 300000 250000 210000 180000 140000 100000 50000 0

-17 -18 -18 -19 -19 -20 -20 -21 -21 -22 -22 -23 -23 FINAL

AUGUST AUGUST AUGUST AUGUST AUGUST AUGUST AUGUST FEBRUARY FEBRUARY FEBRUARY FEBRUARY FEBRUARY FEBRUARY February 2019 data freeze 140 000 individuals FinnGen Sandbox for Data Analysis

Abbvie Astra Zeneca Biogen Celgene Sandbox Genentech GSK Googl Merck/MSD Pfizer e Sanofi Cloud UH, HUS, THL Finnish Biobanks, Sandbox Health & Hospital Districts, Genome Data Universities Tools • Secure access to individual level data • Data cannot be copied 27 FinnGen analysis strategies and scientific aims

Utilize genetic strategies to understand disease mechanisms

28 GWAS PheWAS Genome-wide association analysis Wide phenotypic mining

Better understanding of genetic Common risk factors between Identification of causative genetic background of diseases different diseases variants

Identification of disease Identification of risk groups Identification of protective subgroups (prevention) variants (stratification) FinnGen January 2019

• Project has access to 200 000 biobank samples 29 • 150 000 genotyped • GWAS and PheWAS analyses have been performed to 102 000 study participants • First analysis results implicate new significant associations within all eight disease categories analysed Combining results from other large biobanks

Biobank Japan 30

GWAS/PheWAS GWAS/PheWAS GWAS/PheWAS

Meta-analysis

BBJ collaborators: Masa Kanai, Yuki Okada, Yoichiro Kamatani Acknowledgements

Contact: FinnGen Scientific Director [email protected]