Summary/Abstract Asthma is a common chronic respiratory disease and is considered to be an umbrella diagnosis for several sub-diseases with distinct biological pathways and clinical presentations. Moreover, asthma has a strong genetic predisposition and is associated with early-life factors and environmental exposures. Previous studies identified several associations between asthma and omics factors, but the exact mechanisms underlying most findings remain unclear. Multi-omics approaches were proposed as a tool to dissect asthma endotypes and heterogeneity. In this application, we aim to utilize multi-omics data to disentangle asthma heterogeneity, identify endotypes, and investigate biological mechanisms underlying established risk factors. The analyses will take advantage of three cohorts of (asthmatic) children with high-quality multi-omics data and extensive phenotype information. In Aim 1, we will analyze the interplay between genetic, epigenetic, and metabolomic factors in the development of asthma. In Aim 2, we will identify asthma endotypes based on genetically predicted multi- omics levels, directly inferring the genetic basis of these distinct mechanisms. Furthermore, we will investigate omics signatures of parental asthma history at birth and during childhood (Aim 3). We will characterize the clinical and molecular features associated with significant results throughout the aims. The aims require innovative methodological advances derived as part of the project to unfold the full potential of the data resources and utilize the unique advantages of the family-based designs. Overall, achieving the research objectives demands skills covering respiratory biology, statistics, multi-omics expertise, and data science/causal inference. Julian Hecker, Ph.D., MS, is a statistical geneticist whose long-term career goal is to establish himself as an independent research scientist in the study of asthma and respiratory diseases. The detailed career development plan will support Dr. Hecker to achieve the following training goals and close knowledge gaps: 1.) gain expertise and experience in integrative omics analyses and the processing of multi-omics data, 2.) deepen understanding of asthma pathophysiology, and 3.) improve apprehension of causal inference techniques in epidemiological analyses. Dr. Hecker’s strong theoretical background, in combination with his experience with family-based approaches and analyses, will position him to accomplish these training goals and the aims of this proposal, as well as prepare him for the transition to an independent research role. He will take advantage of the unique research environment in the Channing Division of Network Medicine and Harvard Medical School. The support from his experienced mentoring team and the advisory committee will ensure the proposed project's success.