15.08.2017 - 31.07.2022
We focus on deciphering the genetic basis of obesity and of adult height. Obesity is an enormous public health problem with no safe and effective therapies that foretells a future epidemic of diabetes, cardiovascular disease, cancer, and early death. Understanding the specific genetic and biological factors that control susceptibility or resistance to obesity will provide crucial clues that can guide the design of new, critically-needed interventions and therapies. Adult height is the endpoint of childhood growth, a fundamental developmental process and marker of childhood health, but is also the classic model polygenic trait because it is highly heritable and easily measured. As such, studying height has facilitated the development (by us and others) of genetic and computational methods that have been applied broadly to other polygenic traits and diseases.
Aim 1. Perform the largest GWAS to date, using >1.5 million deeply imputed samples from multiple ancestries, focused on measures of obesity and height. Within the GIANT framework, we will coordinate the generation of and perform the meta-analysis of deeply imputed GWAS data, to find associated variants not discoverable in previous GWAS. We will provide infrastructure for imputation to deep reference panels (including the haplotype reference consortium panel), and perform QC and meta-analysis of association data.
Aim 2. Assemble and analyze data from >100,000 exome and whole genome sequences to discover low frequency and rare variants, coding and noncoding, that influence measures of obesity and height. We will make available infrastructure/software for sequence processing, variant calling and analysis. We will QC and perform meta-analysis of single variant associations and gene-based tests of rarer coding variants.
Aim 3. Integrate the association results from common and rare/low-frequency variants, and use complementary data sets to implicate causal genes and biological processes. We will use the GWAS results to guide association tests of aggregations of rare noncoding variants from sequence data. We will also use integrative computational methods – developed, tested and/or refined using height – to interpret the association results for obesity. We will use expression, metabolite, epigenetic, and other genomic data to prioritize genes, gene sets, regulatory elements and metabolites as likely causal contributors to obesity
ASTRA - Tartu Ülikooli ASTRA projekt PER ASPERA
November 2016 – August 2022
Andres Metspalu, Maris Väli-Täht, Allen Kaasik
The project focusses on strengthening the core laboratories of University of Tartu through further development and modernization of equipment base and furnishing possible laboratory workspaces for cooperation opportunities with businesses.
The wider aim of the project is to develop uniform competence centre on the basis of existing core laboratories, which would ensure the sustainable development of existing technologies. The action therefore unites upgrading of infrastructure, databases and development of operational competence. The ultimate goal is to provide the highest level of know-how for businesses and health care sectors.
DocuMental - digital support system for mental health
01.03.2017 – 30.11.2017
Connected Health Cluster
Andres Metspalu, Eduard Maron
Estonian Genome Center is a partner of Connected Health Cluster (CH) - a permanent, mutually beneficial partnership between stakeholders in Estonia, who are committed to accelerating the adoption of connected health solutions, at scale on commercial terms. The aim of the Connected Health Cluster is:
• to impact healthier life and improve patient care
• to provide economic growth through the adoption of connected health solutions
• to accelerate global access for the Estonian cluster members
Our vision is to transform healthcare services through the implementation of connected health solutions. By encouraging innovation through entrepreneurship and providing a collective forum for connected health advocates, CH brokers solutions for these challenges, thereby creating financial efficiencies for Estonian government and ultimately helping healthcare providers to deliver quality care for people, patients and families in Estonia and internationally. Connected Health Cluster has got 60+ partners, which includes 43 companies (startups, health IT, biotech), R&D partners and need partners, i.e. hospitals, GPs, big Pharma, spas, sportsmen, etc.
Within Connected Health cluster, Estonian Genome Center is a partner in a joint development project „DocuMental“. The activities of the project are carried out in cooperation with North Estonia Medical Center, lead partner Documental and Tallinn University of Technology. The Project is co-financed by the European Regional Development Fund in total amount of 29 900€.
The project will develop a documental electronic support system, which is a decision-making support system for mental health diagnosis and treatment services. The support system will help reduce medical errors, improve mental health management, reduce the time spent by the specialist for initial assessment of condition and to improve the correct implementation of clinical guidelines in clinical practice. The support system also allows to raise the quality of the patient consultation and the time spent. The system will help to improve the patient's involvement in the treatment process, enabling a better exchange of information between the patient and the medical team and allowing the patient to monitor their treatment plan, and psychiatric status.
Eesti Genoomikakeskus II /Estonian Center of Genomics/Roadmap II (project No. 2014-2020.4.01.16-0125)
Andres Metspalu, Maris Alver
Dilated cardiomyopathies (DCM) represent a major cause for heart failure (HF), especially in the young. Although it is estimated that 30-50% of all DCM cases are caused by a genetic predisposition, the precise mechanisms that underlie the variations in disease susceptibility and phenotype presentation including risk for HF development or sudden cardiac death (SCD) are virtually unknown.
in collaboration with Janssen Research & Development LLC
The feasibility study will provide a report on data availability and technical requirements for three main objectives: 1) characterize genetic variations associated with Diabetes Mellitus Type 1 (T1D) in the Estonian GeneBank population 2) identify and characterize individuals with or at -risk of T1D; 3) evaluate feasibility of recruiting their siblings and children in potential observatonal or interventional study.