Sabeti, Pardis CXue, James Rujing2022-06-0720222022-05-182022-05Xue, James Rujing. 2022. High-throughput functional characterization of regulatory variants related to human evolution and disease. Doctoral dissertation, Harvard University Graduate School of Arts and Sciences.29209195https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37372146Evolutionary genetics is at an exciting crossroads due to the growing ability to functionally characterize non-coding regions enabled by high-throughput genomic technologies. Although coding sequence differences between organisms are standard targets for investigating the molecular drives that underlie phenotypic changes, they comprise only a small fraction of the sequence changes between animals. Recent developments have enabled the characterization of non-coding regulatory variants genome-wide, giving unprecedented molecular insights into these genomic regions once known as “junk DNA”. In this dissertation, I discuss my, and my collaborators, contributions towards characterizing non-coding genetic variants, focusing on discovering the functional consequences of these variants through the use of massively parallel reporter assays (MPRAs). In Chapter 1, I review computational and experimental epigenomic advances towards annotating cis-regulatory elements (CREs) related to adaptation. I further discuss how these approaches have coincided with developments in MPRAs, which have aided understanding into adaptive mechanisms by its high-throughput ability to assay candidate CREs. In Chapter 2, I describe one application of the MPRA in characterizing 3’UTR genomic variants related to disease and recent human adaptation, providing a catalog of hundreds of variants with functional effects for future experimental characterization. Finally, in Chapter 3, I describe a MPRA that uncovered the functional consequences of human-specific deletions in conserved genomic regions. In this, we further pinpoint two deletions that may have contributed to shaping the molecular architectures of the human brain.application/pdfenEvolution & developmentGeneticsBioinformaticsThesis or Dissertation2022-06-070000-0002-3332-5747