Genetic strategies to uncover the background of common diseases have been highly successful in identifying genetic loci modifying risk of a number of diseases.

These discoveries have enhanced the understanding of many systemic diseases (diabetes, cardiovascular diseases, immunological diseases), set a new foundation to study neuropsychiatric and neurodevelopmental disorders, and opened unexpected avenues for understanding the novel disease mechanisms.

Aim 1: The CoECDG aims to create a collaborative network of investigators with broad clinical, epidemiological, computational and genetic expertise that will utilize data from >500,000 well-characterized study subjects (disease-based, family-based and random population samples) across Finland to decipher new molecular mechanisms critically predisposing to common disorders.

The genetic studies will involve three major projects

Discovering novel molecular mechanisms underlying three systemic diseases – coronary artery disease, diabetes and inflammatory bowel disease (Project 1)

These diseases form a coherent group: they have significant morbidity and mortality; increasing prevalence; partly overlapping genetic background; known environmental risk factors; history of translating mechanistic insights into clinical impact; partially related pathology (e.g. metabolic, cardiologic and inflammatory processes); and midlife onset, suggesting that risk alleles are likely to be under mild-to-moderate selection. Our Center has deep expertise and proven track record with genetic studies of all three diseases. The PIs have assembled an excellent disease-based, family-based and population-based collection, with a total of >300,000 (aiming for 500,000) subjects with rich longitudinal information about the diseases, their progression and related biological phenotypes. These data will be used to facilitate genetic discoveries.

Coronary artery disease (CAD): Samuli Ripatti, Elisabeth Widén
Diabetes: Leif Groop, Tiinamaija Tuomi
Imflammatory bowel disease (IBD): Marky Daly, Martti Färkkilä

Genetic discoveries in neuropsychiatric and neurodevelopmental disorders – schizophrenia, intellectual disability and addictions (Project 2)

These disorders present a complementary challenge: they have considerable morbidity and mortality; fairly stable prevalence across time and populations; fewer known environmental risk factors; poor progress in understanding mechanism or in developing mechanism-driven therapies; growing evidence of overlapping genetics; and severe early-onset effects suggesting that risk alleles are under strong selection. Patients with schizophrenia have altered glucose metabolism already before commencement of antipsychotic treatment and they also have an increased risk of cardiovascular diseases. Our Centre has deep expertise with genetic studies of the disorders. The PIs have assembled highly informative case-control, family-based and twin collections with in-depth information on these disorders.

Schizopherenia, intellectual disability: Aarno Palotie, Benjamin Neale
Addictions: Jaakko Kaprio, Aarno Palotie

Characterizing impact of the genetic discoveries (Project 3)

The collections with almost complete sequence level variation together with rich biomarker and other risk factor data and longitudinal follow-up for disease end-points (n>150,000), allow for detailed profiling of individuals with genetic variants identified in Projects 1 and 2, and in large-scale international collaborative projects. Our CoE has the unique possibility to pool the genetic and phenotypic data from population studies and the disease-specific data-sets to study the common genetic origin of diseases. This is partially known for diabetes and CAD, and diabetes and schizophrenia. When feasible, we will 1) assess the impact of the variants on metabolomics, metagenome and epigenomics profiles of the homozygous and heterozygous carriers or the risk/protective variants; 2) utilize the variants associated with risk factors for the diseases of interest to assess the causality of the risk factors for the disease using Mendelian Randomization approaches; 3) study the functional impact on differentiated cells using IPS cells and organoids; 4) recall carriers for detailed further phenotyping; 5) estimate the population level impact of individual SNPs and polygenic risk over the life course using randomly selected prospective population cohorts with long follow-up for the diseases; 6) study the joint effects of the variants and polygenic risk factors with non-genetic lifestyle and environment, and across the specific disease categories.

Samuli Ripatti, Jaakko Kaprio

Aim 2: The CoECDG aims to develop statistical and computational tools for precision medicine. Our Centre includes a uniquely strong team for computational and statistical genetics (Daly, Neale, Pirinen, Ripatti) with proven track record in both methods development and in innovative application of novel study design and analysis tools.

Aim 3: Develop, test and implement genomic precision medicine. As we learn more about the genetic architectures of the diseases, the translation of these findings into clinical use will become central. While still at an early stage, the implementation strategies regarding complex diseases may rapidly evolve during the next few years. Access to well-characterized population cohorts with extensive risk factor profiling and well characterized clinical patient series provides an opportunity to be at the front line of translational research.

The translational studies will focus on two major projects

Genomics in disease prevention (Project 4)

The PIs of our Centre have long been developing strategies for genetic risk prediction, particularly for type 2 diabetes (T2D) and CAD, including methods and tools to communicate disease risk information back to study participants. For example, we have recently shown that polygenic risk scores help identifying high risk individuals over and beyond traditional risk factors in CAD3. A prerequisite for a wider use is that user-friendly disease prediction tools are developed and incorporated into the decision support systems found in the electronic health records. An example of this for CAD prediction is KardioKompassi®. It estimates not only the current level of risk, but also the impact of ageing or life-style changes.

Tiinamaija Tuomi, Samuli Ripatti, Elisabeth Widén

Genomics in disease management (Project 5)

Our major focus will be to escalate the ongoing work to develop tools for differential diagnostics of disease entities, for prediction of disease progression and outcome, and for strategies for personalized treatment options. The testing will be conducted on tree levels 1) small-scale recall-by-genotype studies as a proof-of-concept; 2) medium-scale studies in the registries linked to the CoE, e.g. Finnish DIREVA and Swedish ANDIS for diabetes, and 3) large scale real-life testing in close collaboration with patient organizations (e.g. Finnish Heart Association, Finnish Diabetes Association, Finnish Association for Mental Health) through the nationwide clinical network of our clinical partners and the national government funded Virtual Hospital project, which includes specific entities (“houses”) for Heart diseases (2017), Diabetes (2018), Gastrointestinal diseases (2017), and Mental health (2014).

Tiinamaija Tuomi & all

Disease specific-projects
  • Coronary artery disease (Ripatti/ Widén)
  • Diabetes (Groop/ Tuomi)
  • Inflammatory bowel disease (Daly/ Färkkilä)
  • Neuropsychiatric diseases (Palotie/ Daly/ Neale)
  • Addictions and mental health (Kaprio/ Palotie)