PhD positions 2026

Supervisor: Mari Sepp

Autism spectrum disorders (ASD) rank among the most common neurodevelopmental disorders globally. Hundreds of ASD risk gene have been identified, but interpreting their functional significance remains challenging. Cumulative evidence implicates deficits in the cerebellum in ASD aetiology, but a systematic characterisation of the disease mechanisms in the cerebellum is missing. In the planned PhD thesis projects, we will build a comprehensive map of cell-level ASD phenotypes in the developing cerebellum using single-cell genomics, spatial mapping, and sparse labelling of cellular morphologies. We will characterise the molecular, cellular, anatomical and morphological phenotypes in the cerebella of ASD mouse models, and map the phenotypes elicited by perturbations of ASD risk genes in human induced pluripotent stem cell-derived cerebellar organoids. These approaches will enable the identification of points of convergence and phenotype-driven ASD subgroups, and improve our understanding of disease mechanisms.

Contact: [email protected]

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Environmental risk factors and genetic confounding in adult attention deficit and hyperactivity disorder features

Supervisor(s): Kelli Lehto, Reedik Mägi, Jaanika Kronberg

This PhD project is part of a larger interdisciplinary ERC-funded programme to understand the causal mechanisms underlying adult attention deficit and hyperactivity disorder (ADHD) mechanisms. While ADHD is a childhood-onset, highly heritable neurodevelopmental disorder, recent years have seen a marked increase in adult diagnoses, especially following the COVID-19 pandemic. Notably, adult ADHD features — such as inattention, impulsivity, and emotional dysregulation — may also be influenced by environmental factors like stress, sleep deprivation, substance use, screen time, and post-viral effects. The current understanding of the environmental influences on adult ADHD remains limited, particularly due to challenges in disentangling causality from confounding.

This project will systematically assess the role of environmental exposures (e.g. childhood adversity, life stress, substance use, screen use, COVID-19 severity) on adult ADHD features, leveraging innovative genomics-informed approaches. The research will employ polygenic scores and longitudinal analyses, as well as parent-child trio designs, to disentangle genetic and familial confounding and true causal effects. The PhD candidate will have access to the extensive registry-linked Estonian Biobank (over 211,000 participants, including 86,000 with detailed mental health data and 11,500 parent-child trios), the UK Biobank (500,000 participants) and opportunities for collaboration with major international cohorts and consortia. The project involves three core themes: mapping environmental risks for adult ADHD across age and gender; quantifying genetic confounding and mediation effects; and investigating familial confounding using advanced genetic epidemiological methods. This work aims to clarify the interplay between genes and environment in adult ADHD representation, with broad implications for research and practice.

Contact: [email protected]

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An interpretable framework for complex trait genomics using deep generative models

Supervisors: Burak Yelmen, Flora Jay

Despite major advancements in genome-wide association studies and the increasing availability of diverse datasets, the underlying genetic mechanisms of complex traits remain largely elusive. Recent deep learning applications enable predictive and generative modeling of high-dimensional genomic data, yet the black-box nature of these models limits their biological utility. In this project, we propose to bridge the gap between interpretability and generative neural networks by introducing a comprehensive framework for modeling the interactive genomic landscape of complex traits. By developing domain-specific architectures and interpretability methodologies, we aim to capture multi-locus and multi-phenotype structure, generate realistic synthetic cohorts, and identify key genomic positions. This framework will provide a holistic approach to complex trait genetics, offering novel insights and ultimately advancing precision medicine.

Contact: [email protected]

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Host genetics and virome links to chronic diseases in the Estonian Biobank

Supervisors: Erik Abner, Elin Org

The aim of this doctoral project is to investigate how exposure to infectious diseases, immune system function, and the human microbiome interact with host genetics to influence the risk of chronic diseases. Using genetic data from the Estonian Biobank, microbiome DNA sequencing data, serological profiles from biobank participants, and electronic health records, the project seeks to identify biological links between virome composition, antibody profiles in blood, and a wide range of chronic health traits. The project will apply bioinformatic and statistical genetics approaches to characterise the gut virome, building on existing microbiome DNA sequencing data. Virome features, including their presence, abundance, and diversity, will be quantified and analysed in relation to bacterial community structure, host genetic variation, and chronic disease phenotypes derived from electronic health records. This will enable the identification of virome patterns associated with long term health outcomes, as well as shared genetic factors influencing host-microbiome interactions.

In parallel, phage-based immunoprecipitation sequencing (PhIP-seq) will be established to detect antibodies against a broad range of infectious agents in plasma samples from the Estonian Biobank. These data will allow population level assessment of past infectious exposure and immune responses and enable analyses of seroprevalence patterns and their associations with chronic health traits. Integrating serological data with microbiome and virome features in overlapping samples will make it possible to assess how immune history, microbial ecosystems, and host genetics jointly shape the risk of complex diseases.

Combining these different layers of health-related data will provide a population-based framework for studying disease mechanisms linked to infections. The results of this project will improve our understanding of the biological links between infectious exposure and chronic diseases, and support future research in risk assessment, prevention, and the development of personalized medicine approaches.

Contact: [email protected]

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Integrating ancient and modern genomes to study the evolution of complex traits

Supervisors: Alena Kushniarevich, Lehti Saag, Kristiina Tambets, Georgi Hudjashov

Recent advances in genomics and bioinformatics have enabled the integration of large-scale modern biobank data with time-stratified ancient human genomes, providing powerful tools for studying human evolutionary history and the genetics of complex traits. Improvements in genotype imputation for ancient genomes have substantially increased analytical power, facilitating more robust investigation of the evolutionary origins of modern genetic variation and disease susceptibility. By combining ancient genomic data with modern genome-wide association studies and polygenic scores, this doctoral project examines how the genetic architecture of complex traits has changed over time and aims to characterise the evolution of complex traits in the Eastern Baltic region over the past two thousand years through joint analysis of ancient and contemporary human genomes.

Contact: [email protected]

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Predicting the risk and progression of complex diseases by utilising genomics, omics, and electronic health records data

Supervisors: Urmo Võsa, Priit Palta

The Estonian Biobank (EstBB) links genomic, metabolomic, electronic health records (EHR), and drug-usage data for over 200,000 participants, yet the full potential of these data modalities for disease prediction remains underused. This PhD project will improve trait and disease prediction by integrating machine learning and modern AI with complementary data layers - molecular QTL resources, multi-omics measurements, and longitudinal clinical histories. The work will also combine large-scale QTL datasets (eQTL, pQTL, metabolite and splicing QTL) with high-powered GWAS summary statistics to prioritise trait-relevant molecular features via colocalisation, Mendelian randomisation, and genetic correlation analyses. These functional signals will inform both refined PRS and extended models that incorporate QTL-derived genome-wide annotations and QTL-based polygenic scores for intermediate molecular traits. Finally, the project will develop a multimodal generative AI model that integrates PRS and omics with longitudinal EHR event sequences to forecast future health states as trajectories rather than static risk estimates. These models will be trained in EstBB and externally validated in UK Biobank data, enabling more personalised, context-aware prediction of disease onset, progression milestones, readmissions, and projected healthcare costs.

Contact: [email protected]

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