Project Summary Self-renewing cell populations accumulate somatic mutations with aging and/or in response to environmental insult and chronic inflammation. Most of these mutations in normal tissues have no functional consequence. In rare cases, these somatic mutations can confer a selective advantage leading to clonal expansion in hematopoietic stem cells, a phenomenon termed 'Clonal Hematopoiesis of Indeterminate Potential' (CHIP). CHIP has been associated with a selective range of non-hematopoietic conditions, including coronary artery disease, stroke, heart failure, venous thromboembolism, chronic obstructive pulmonary disease, chronic liver diseases, and osteoporosis. However, the molecular mechanisms by which CHIP specifically promotes these diseases remain largely unknown. The overarching goal of this proposal is to molecularly connect CHIP with the development of associated diseases through developing and applying statistical and machine learning methods to multi-omics data in the NHLBI Trans-Omics for Precision Medicine (TOPMed), UK Biobank, and Mass General Brigham Biobank (MGBB). Specifically, I propose the following aims. In Aim 1, I will develop a statistical method that combines multiple proteins implicated in the same pathway to improve discovery and facilitate translational insights for proteomics-based association analyses. The combined protein quantitative traits generated by this method will be used in the following aims. In Aim 2, I will examine how the presence of CHIP and each CHIP driver gene specifically promote certain diseases through (1) identifying the proteomic signatures of CHIP and quantifying the mediation effects to associated diseases and (2) evaluating the potential modifications of CHIP-associated diseases by the expression levels of genes implicated in diverse molecular factors. In Aim 3, I will evaluate how the progression of CHIP promotes associated diseases. I will examine associations between longitudinal change in the number and size of CHIP clones and incident disease risks, as well as generate bulk and single-cell RNA sequencing data among CHIP carriers with or without osteoporosis, a disease strongly associated with CHIP but understudied, and test for differential gene expressions between the two groups. This project's successful execution will identify the mechanisms underlying CHIP-associated disease risks to prioritize therapeutic targets and advance precision medicine goals.