PROJECT SUMMARY/ABSTRACT Forward genetic studies using animal models represent an important tool for the discovery of physiological and biochemical mechanisms that cause human disease. Genetic studies in model organisms complement direct human studies, with advantages that include the ability to apply experimental perturbations and to control both environmental conditions and genetic makeup of study populations. Access to disease-relevant tissues provides opportunities for large-scale molecular profiling and deep physiological phenotyping. Traditional forward genetic experiments in animal models employed crosses between two inbred strains. New genetic populations, derived from more than two founder strains, are being developed for rodents and other model organisms to serve as community resources for systems genetics studies. These multi-parent populations provide improved mapping precision, and their greater genetic variability enables their use for a broader set of disease phenotypes than any one bi-parental population. Multi-parent populations present new analytical challenges, including haplotype reconstruction, the treatment of multiple founder alleles, the need to account for population structure, the use of the distribution of SNP alleles across founder strains to home in on causal SNPs. High dimensional phenotypes present additional challenges and opportunities, with each new technology requiring special care, but with mediation analysis and causal network modeling providing valuable insights into possible disease mechanisms. Software tools that implement the best analysis methods and make them readily accessible, along with interactive data analysis and visualization tools that enable exploration of these high-dimensional data, empower researchers to explore systems genetics data on multi-parent populations. With these general goals, our specific aims are to (1) Develop statistical methods for the genetic analysis of multi-parent cross designs, including methods for haplotype reconstruction with low-pass genotyping-by- sequencing data, identification of subsets of causal SNPs underlying a QTL, multiple-QTL modeling, and the dissection of contributions to heritability; (2) Develop statistical methods for genetic analysis of high- dimensional phenotypes, including mediation analysis and causal network modeling; and (3) Develop next- generation software for system genetic analysis with high-dimensional data, including multi-parent populations and interactive data visualization tools. These aims will support and facilitate the growing use of genetically diverse multi-parent populations in biomedical research.