Postdoctoral Fellow
kgoda@clemson.edu
Biosketch
Dr. Khushi Goda is a Postdoctoral Fellow at the Center for Human Genetics, Clemson University, where she contributes to the research on complex diseases in the Morgante lab in the Genetics and Biochemistry department. Dr. Goda obtained her Bachelor’s degree in Computational Biology from the National University of Singapore, where she first developed her interest in the interdisciplinary world of life sciences and computer engineering. She then pursued her Ph.D. in Genetics at North Carolina State University under the mentorship of Dr. Fikret Isik. Her doctoral research involved a deeper exploration of quantitative genetics, and she focused on bridging the gap between traditional and advanced genomic breeding strategies. She developed an automated open-source software suite for the monoecious species Pinus taeda (loblolly pine tree) to incorporate optimizing algorithms to enhance breeding decisions to improve genetic gain and manage inbreeding depression through the integration of pedigree- and genomic-based relationships.
In her current role, Dr. Goda’s research primarily focuses on the interface of bioinformatics, statistical genetics, and quantitative genetics. Her work involves analyzing and enhancing phenotypic predictions of complex human traits, aiming to improve genetic diversity by studying these complex traits and their predictive models.
Research interests
Dr. Goda’s research interests are centered on the interplay between genetics and computational methodologies. Her work transverses various species and fields, from studying yeast genome and cytoskeleton with implications to cancer to creating an automated algorithm that improves the DNA matching technique for human forensics. The center of all this research is always the utilization of advanced computational algorithms to extract meaningful insights from vast genomic data. Presently, Dr. Goda is primarily focused on understanding the genetic architecture of complex traits in humans, with an emphasis on multi-diversity. She aims to employ bioinformatical and statistical approaches to study the accuracy of phenotypic predictions in multi-ancestry human populations.