SUMMARY The recent availability of large-scale whole genome datasets has revealed the startling scale of rare genetic variation present in human populations. There is increasing evidence that rare genetic variants can have profound effects on multiple complex disease phenotypes; however, the systematic characterization of these variants is limited by current cohort sizes and approaches to interpretation. One powerful emerging approach for rare variant interpretation is in the integration of functional data to enable in vivo assay of rare variant-driven molecular dysregulation, enabled by large-scale data integration of genomic, functional, and phenotypic resources. In this proposal, we outline computational and statistical approaches to systematically annotate and isolate – on a genome-wide scale – rare variants linked with extreme effects on multiple molecular phenotypes (rare molecular outlier variants) and, through integrating biobank-scale phenotypic data, their downstream effects on diverse complex disease risk. We recently applied this approach in GTEx and TOPMed to show that utilizing outlier gene expression provides a powerful framework for identifying large phenotypic-effect rare variants in genes with known impact on complex diseases. Specifically, in this proposal we will combine large-scale genomic and diverse multi-omics data to develop and extend novel computational and statistical methods to provide the first systematic characterization of personalized complex disease risk contributed by rare genetic variants. Our methods are readily applicable to research in any complex disease area, including anthropometric, neurological and cancer research. Our efforts will increase our understanding of how rare variants interact with polygenic disease risk predictions derived from polygenic risk scores, currently limited to relatively small-effect common variant GWAS hits, and show how rare molecular outlier variants provide a framework for systematically characterizing both cis- and trans-regulatory disease networks impacting core disease genes as theorized in the omnigenic model. Furthermore, we outline preliminary results suggesting that rare molecular outlier variants substantially increase power for uncovering large-effect rare variants over genome annotation methods (namely, protein truncating variants – limited to coding regions only), and outline an approach for quantifying these effects in disease prediction and drug targeting applications. Overall, these activities will increase our understanding of complex disease genetics. The use of genetic-only methods such as GWAS would require cohort sizes within the millions, illustrating the importance of functional genomic data to our approach. We have a strong track record of releasing software and pipelines to implement prior methods, and will make any new work rapidly available on public repositories. Our efforts will provide important contributions to understanding the rapidly growing d...