The aim of this research is to uncover factors that contribute to individual differences in human behaviors, diseases, and complex traits, with a particular focus on mental health. These complex traits and diseases arise from the interplay of many genetic variants, each with small effects, and environmental influences.
We will analyze phenotypic data to investigate correlates and risk factors of mental and physical health traits. This will also include imaging data to extract biologically relevant patterns using machine learning techniques and ECG analysis to investigate the relation between behavioral disorders and cardiovascular problems.
From a behavioral genetics perspective, we will explore the genetic architecture of these traits, aiming to understand disease etiology from a biological perspective, the interaction between disease liability and societal factors, and the evolutionary origins of these processes. This includes using statistical genetics and bioinformatics to combine UKB data with other datasets for a more comprehensive view.
We will identify trait loci and causal pathways, investigating how genetic variation translates into phenotypic differences, and quantify the impact of genetic variants on complex traits. We will explore how these genetic factors have influenced and are influenced by evolutionary processes such as natural selection, mating, and migration. We will use established methods, including GWAS, heritability partitioning, and polygenic scoring, while also developing new approaches.
Our team will focus on several areas simultaneously: identifying health phenotype correlates, extracting evolutionary signatures from genotype data, linking complex traits to demographic and genetic outcomes, and employing machine learning for imaging analysis. We will combine multiple modalities (e.g. imaging, ECG, genotype data, WGS, and the exposome) to study psychiatric traits and treatment success, as well as other complex traits.
