Research group of pharmacogenetics

• Hanna Maria Kariis “Genetic studies of the comorbidity between cardiovascular disease and mental disorders: from mechanisms to improved treatment”  
  Supervisors: Lili Milani; Kelli Lehto; Tõnis Org, Maris Alver
   
• Siim Kurvits “Predictive modelling of cardiometabolic diseases in psychiatric patients: towards improved prevention using individual-level genetic data” 
  Supervisors: Kelli Lehto; Lili Milani; Toomas Haller 
   
• Tuuli Jürgenson “Models for risk prediction in complex data structures” 
  Supervisors: Krista Fischer; Lili Milani; Meelis Käärik 
   
• Ida Maria Orula “Haplotype-based pharmacogenomic studies based on electronic health records and genetic data”  
  Supervisors: Sulev Reisberg; Lili Milani 
   
• Martin Meitern “Using Genetic Information as an Input in Changing Health Behaviour” 
  Supervisors: Sten Hansson; Lili Milani; Andero Uusberg
• Ave Põld “Personalised prevention of ischemic heart disease among high polygenic risk individuals” 
  Supervisors: Mikk Jürisson, Lili Milani, Krista Fischer

• Jelisaveta Džigurski “Pharmacogenetic aspects of hormonal contraceptives” 

  Supervisors: Triin Laisk; Lili Milani; Reedik Mägi 

• Laura Birgit Luitva “Models for drug-drug interactions, population pharmacokinetics and pharmacogenetics”

  Supervisors: Krista Fischer; Maris Alver; Lili Milani 

• Merve Nur Güler “Deep learning for predicting quantitative traits from genomic data” 

  Supervisors: Burak Yelmen; Lili Milani; Luca Pagani 

• Kristi Krebs (PhD, 2020) 
  Exploring the genetics of adverse events in pharmacotherapy using Biobanks and Electronic Health Records 
  Supervisors: Lili Milani; Andres Metspalu 
• Epp Kaleviste (PhD, 2020)  
  Genetic variants revealing the role of STAT1/STAT3 signaling cytokines in immune protection and pathology 
  Supervisors: Kai Kisand; Pärt Peterson; Lili Milani 
• Tõnis Tasa (PhD, 2019)
  Bioinformatics Approaches in Personalised Pharmacotherapy 
  Supervisors: Jaak Vilo; Lili Milani; Tuuli Metsvaht 
• Silva Kasela (PhD, 2017) 
  Genetic regulation of gene expression: detection of tissue- and cell type-specific effects
  Supervisors: Lili Milani; Krista Fischer; Andres Metspalu 
• Riin Tamm (PhD, 2017) 
  In-depth analysis of factors affecting variability in thiopurine methyltransferase activity 
  Supervisors: Andres Metspalu; Lili Milani 
• Kaie Lokk (PhD, 2017) 
  Comparative genome-wide DNA methylation studies of healthy human tissues and non-small cell lung cancer tissue 
  Supervisors: Neeme Tõnisson; Lili Milani 
• Laura Birgit Luitva (MA, 2022) 
  Relationship between Depression and Cardiometabolic Diseases Using Multi-State Models Based on Estonian Genome Centre's Data 
  Supervisors: Silva Kasela; Lili Milani 
• Elisabeth Kelner (MA, 2022)  
  The impact of polygenic risk score on the severity and the use of medication on type two diabetes 
  Supervisors: Mikk Jürisson; Heti Pisarev; Vallo Volke; Lili Milani 
• Kadri Maal (MA, 2021)
  Cytochrome P450 2C19 deletions in the Estonian population 
  Supervisors: Lili Milani; Tarmo Puurand 
• Liis Haljasmägi (MA, 2018)  
  Type I IFN neutralizing autoantibodies in patients with systemic lupus erythematosus 
  Supervisors: Kai Kisand; Lili Milani; Pärt Peterson 
• Marili Palover (MA, 2016) 
  Associations between telomere length and DNA methylation levels 
  Supervisors: Lili Milani; Silva Kasela 
• Hanna Rein (MA, 2016) 
  TaqMan technology based methylation pattern analysis to monitore premature immune system aging 
  Supervisors: Kai Kisand; Lili Milani 
• Kelli Grand (MA, 2016) 
  Analysing loss-of-function mutations by pairing 2300 whole genomes with electronic health records 
  Supervisors: Lili Milani; Pärt Peterson 
• Epp Kaleviste (MA, 2015)  
  The impact of STAT1 gain-of-function mutation on JAK-STAT signaling pathway 
  Supervisors: Lili Milani; Kai Kisand 
• Liina Pappa (MA, 2014) 
  Exome sequencing of two patients with unilateral renal hypoplasia 
  Supervisors: Lili Milani; Tiit Nikopensius 
• Mari-Liis Reim (MA, 2013) 
  Factors influencing the activity of thiopurine methyltransferase in human liver 
  Supervisors: Riin Tamm; Lili Milani; Andres Metspalu 
• Silva Kasela (MA; 2013)  
  DNA methylation: normalisation and analysis 
  Supervisors: Krista Fischer; Lili Milani 

Predicting comorbid cardiovascular disease in individuals with mental disorder by decoding disease mechanisms (1.01.2020−30.06.2024); Principal Investigator: Lili Milani; Horizon 2020 project 

Financier: Commission of the European Communities; Financing: 554 000 EUR. (Project coordinator: Ole Andreassen, University of Oslo, total budget 5,9 M EUR) 

The Nordics-centered project is designed to uncover mechanisms that cause higher incidence of cardiovascular disease in people with mental disorders. It considers genetic and lifestyle risks and their impact on mental disorders. During the project, structural brain changes and body fat composition from MRI data will be analyzed in combination with gene expression and functional studies. CoMorMent has a multidisciplinary expert team in clinical science, genetic epidemiology, molecular genetics, and neuroscience combined with experts in machine learning and computation.

Data for analysis: genotyped biobank samples united with large national registries with information about disease trajectories and comorbidity in over 1.8 million people belonging to the biobanks of the Nordics, UK and Estonia. 

Project website: https://www.comorment.uio.no/ 

Using real-world big data from eHealth, biobanks and national registries, integrated with clinical trial data to improve outcome of severe mental disorders (1.06.2021−31.05.2025); Principal Investigator: Lili Milani; Horizon 2020 project 

Financier: Commission of the European Communities; Financing: 444 000 EUR. (Project coordinator: Ole Andreassen, University of Oslo, total budget 6M EUR) 

The REALMENT project aims to develop innovative tools to individualize treatments using available psychiatric medication. Big data from populations, cohorts and eHealth samples and whole genome genotypes will be analyzed in an EU-wide sustainable infrastructure. Artificial intelligence and machine learning methods will be used to develop prediction and stratification tools for precision psychiatry. For implementation, the clinical management platform (4MENT) will be made available to provide decision support to clinicians for optimizing therapeutic effects. 

In addition to the CoMorMent consortium members, REALMENT project includes enterprises that can facilitate the implementation of project results to practice. 

Data for analysis: Big data from populations (Nordic registries), cohorts (European biobanks), and eHealth samples (medical records), whole genome genotypes (n=1.8 million). Validation will be performed in large RCT data (n=10k) cohorts. 

Project website: https://www.realment.uio.no/  

Improve Safety in Polymedication by Managing Drug-Drug-Gene Interactions (1.06.2022−30.11.2025); Principal Investigator: Lili Milani; Horizon Europe project 

Financier: Commission of the European Communities; Financing: 598 000 EUR. (Project coordinator: Thorsten Lehr, University of Saarland, total budget 5,6 M EUR) 

The project will develop a framework to define, assess and manage real-life drug-drug-gene interactions for physicians and individual patients. The main objectives are to use machine learning methods to develop an evidence-based risk scoring system, an electronic tool for patients to manage their therapies, create a mathematical model for effective dose adaptions for clinically relevant compounds and validate it. The vast consortium is largely based in Germany, but includes collaborators from Greece, Estonia and Finland. 

Data for analysis: genotypes, linking to electronic health records (EHRs), self-reported questionnaires’ (health, environmental/life-style) data 

Project website: https://www.safepolymed.eu/ 

(1.01.2022−31.12.2024); Principal Investigator: Lili Milani; Swedish Research Council funded project 

Financier: Swedish Research Council; Financing: 57 000 EUR (Project coordinator: Magnus Ingelman-Sundberg, Karolinska Institutet, total budget 3,6 M SEK) 

The project aims to identify, characterize and quantify unexplained genetically-predicted interindividual variability in drug response which will enable clinicians to perform precise dosing for each individual patient. Three labs from Sweden, Norway and Estonia will analyze the data on plasma concentrations, treatment failure, adverse drug reactions, and genetic sequences.  

Special emphasis will be placed on the identification of novel haplotypes, globally identified mutations of importance for adverse drug reactions response, the role of rare mutations and the development of a novel genetic platform. 

The project aims to deliver a novel diagnostic tool, in compliance with the FAIR principles and GDPR, to be implemented for decision support by integrating AI-derived pharmacogenomics-based algorithms into existing electronic infrastructure.

Data for analysis: host genotypes, linking to electronic health records (EHRs), self-reported questionnaires’ (health, environmental/life-style) data from > 20,000 patients. 

(1.01.2018−30.06.2023); Principal Investigator: Lili Milani; national research project  

Financier: Estonian Research Council; Financing: 835 000 EUR. 

Interindividual variability in drug metabolism and sensitivity for drug toxicity persists as a major problem for drug treatment. Recent research has highlighted the large extent of rare variants in genes with importance for drug metabolism. The aim of this study is to create a catalog of common and rare genetic variants related to suboptimal drug metabolism and adverse drug reactions (ADRs) in the Estonian and Swedish populations. This will be achieved using genome sequences of 5000 and genotype data of 50,000 individuals combined with extensive health records regarding drug prescriptions and ADR diagnoses. The identified gene-ADR associations will be tested using functional studies. The results will serve as a basis for variants that can be included for preemptive pharmacogenomic testing, which could ultimately reduce the health and economic burden of low drug efficacy and unnecessary side effects caused by genetic variants. 

Data for analysis: Estonian biobank and UK biobank genotype and electronic health records, additional data on drug metabolism pharmacokinetics gathered during a clinical trial 

Preventing non-communicable diseases caused by the post-acute phase of cOvid-19 INfecTion (1.01.2024−31.12.2027); Principal Investigator: Pärt Peterson, Senior Researcher: Lili Milani; Horizon Europe project 

Financier: Commission of the European Communities; Financing:  96 000 EUR (Project coordinator: Claus Desler Madsen, University of Copenhagen, total budget 9,0 M EUR, total budget for University of Tartu 771 250 EUR). 

The project will develop knowledge-based biomarkers for the prevention and management of non-communicable diseases with a virtual twin model that offers clinical decision support, and clinical guidelines and recommendations for the healthcare. The goal is to diminish the effects of the COVID-19  post-acute phase.  

Data for analysis: genotyped biobank samples united with large national registries with information about disease trajectories and comorbidity, more than 6 million Europeans, and cross-sectional biobanks from more than 6000 Europeans. 

Project website: will be updated. 

Centre for Data Enriched Medicine (TeamPerMed) (1.09.2023−31.08.2029); Principal Investigator: Mait Metspalu, Senior Researcher: Lili Milani; HorizonEurope project 

Financier: Commission of the European Communities, Estonian Ministry of Education and Research; Financing: 200 000 EUR (Total budget 6,3 M EUR, thereof 3,3 M EUR from EC). 

Centre for Personalised Medicine (TeamPerMed) will be a multi-disciplinary centre that integrates expertise in Genomics, IT, Clinical Medicine and Socio-Economic Analysis to create a scalable framework for developing clinical guidelines and clinical decision support tools that can be effectively integrated into healthcare systems. 

Lili Milani will coordinate the clinical trial of pharmacogenomics. 

Partners: Erasmus Meical Centre, the Netherlands, and the University of Helsinki, Finland. 

Data for analysis: genotyped biobank samples united with large national registries with information about disease trajectories and comorbidity. 

Project website: will be updated. 

Personalised Medicine by Predictive Modeling in Stroke for better Quality of Life (1.05.2018−30.04.2023); Principal Investigator: Lili Milani; Horizon 2020 project 

Financier: Commission of the European Communities; Financing: 366 000 EUR. (Project coordinator: Dietmar Frey, CHARITE Berlin, total budget 6,0 M EUR) 

The project integrated various data from different sources: genomics, microbiomics, biochemical; imaging, social, lifestyle, gender; economic and worklife. 
Novel decision support models to address the needs of the patient in all stages of the disease (1. Prevention, 2. Acute treatment, 3. Rehabilitation, 4. Reintegration) were created. PRECISE4Q was designed to have a clinically measurable and sustainable impact, leading to better understanding of risk, health and resilience factors and support patients throughout their life-long journey by personalised strategies for their specific needs. 

Data for analysis: genotyped biobank samples united with large national registries with information about disease trajectories and comorbidity. 

Project website: https://precise4q.eu/  

(1.10.2021−30.06.2023); Project Investigator: Lili Milani; national research project 

Financier: Ministry of Education and Research; Financing: 164 000 EUR (Project coordinator: Marje Oona, University of Tartu, total budget 555 000 EUR). 

The objective of the study is to assess the need for booster doses of COVID-19 vaccines. The specific aims are: to assess the persistence of immunity after COVID-19 vaccination and the factors influencing it among the adult population in Estonia, and to assess the persistence of immunity after SARS-CoV-2 infection and the factors influencing it in the Estonian adult population.