Complex diseases are heterogeneous in their etiology and clinical manifestation. This heterogeneity hampers understanding of their causes, and requires new precision medicine strategies to apply targeted interventions in homogeneous disease subgroups. While statistical genetics methods have provided key insights into disease etiology in recent years, they are not optimized to expose the mechanisms underlying the heterogeneity observed among patients. Therefore, novel strategies to identify and understand disease heterogeneity are needed. In this K99/R00 proposal, we introduce PATHFINDER: a comprehensive research program to reveal the mechanisms leading to heterogeneity in complex diseases. This research program will be prototyped for schizophrenia, inflammatory bowel disease and coronary artery disease, selected for their high heritability, availability of powerful GWASs, and the rich functional genomic and clinical individual- level data available. These diseases provide, thus, excellent templates for extending this approach to a range of complex diseases. To achieve the program objectives, the following analytical strategy is proposed: - In Aim 1 (K99 phase), the context in which putative causal genes act will be determined. This involves identification of tissues and cell types with the highest gene expression and specificity, and identification of other genes functionally related to the genes of interest (e.g. based on metrics such as gene co-expression). - In Aim 2 (K99 phase), our recently developed PRSet software and tool will be utilized to compute pathway- based polygenic risk scores (PRSs), identifying, for each individual, the biological pathways with the highest genetic risk to disease. This will enable the identification of shared and distinct genetic profiles across patients. - In Aim 3 (R00 phase), Dr. García-González will apply the techniques and knowledge gained from Aims 1 and 2 to develop a precision medicine framework for stratifying patients from large biobanks. In Aim 3.1, PRSs associated with clinical factors and disease symptoms will be identified. In Aim 3.2, statistical and machine learning methods will be used to stratify patients into more genetically and clinically homogeneous subgroups. This research program is expected to have a significant translational impact by identifying more homogeneous disease subgroups that will inform mechanistic hypotheses, ultimately leading to more etiology-specific interventions and treatments. Furthermore, the training during the K99 phase will be crucial for Dr. García- González’s scientific development and to set the foundations of her independent research program. The combination of: (i) Dr. García-González’s background in experimental research and statistical genetics, (ii) the expertise of the mentoring team in PRSs methods development, functional genomics and experimental validation approaches, and (iii) the world-leading research performed on medical genetics and genomics at the...