Dr. Kristen Wade’s scientific motivation is to contribute novel, interdisciplinary and evolutionarily-grounded models for understanding complex genetic traits. Her Bachelor of Science degree in Bioinformatics obtained at Virginia Commonwealth University in 2014 has prepared her for the technical challenges of working with large genomics datasets. Her additional bench research training in molecular genetics during that time has provided her with a well-rounded grasp of the phenotypic implications and functional interpretations of disease associated variation. As a predoctoral student in the laboratory of Dr. David Pollock at CU Denver-Anschutz, she pursued rigorous, computational thesis work in evolutionary genomics. Her thesis work focused on understanding evolutionary patterns of the non-coding and repetitive content of the genome, which undergo significantly different selective pressures than coding regions. This experience thoroughly prepared Dr. Wade to engage with non-coding disease variation, which comprise the majority of GWAS signals. In addition to her thesis work, she participated in variety of collaborations across genetic disciplines, which have provided her with a highly interdisciplinary understanding of genomic variation and its functional implications in disease. In 2022 she successfully defended her thesis entitled: “Homology unleashed: non-parametric and alignment-free methods for understanding genome evolution”. She began her postdoctoral work with Dr. Jill Hollenbach at University of California, San Francisco, in 2022. Here, she has continued to expand her boundaries and gained expertise in new bioinformatics methods, implementing de novo genome assembly and linear models of association, as well as receiving training in neurobiology and autoimmune diseases. This diversity of experience, across multiple specialties, makes Dr. Wade uniquely suited to pursue integrative genetic mapping and modelling of complex trait-associated variation. Her background and training have prepared her to participate in the frontier of genetics research and her computational bioinformatics experience makes her well-equipped to contribute novel solutions.