AIMRC Seminar: Approaches to Genetic Dissection of Complex Traits Using Fruit Fly Model System

The Arkansas Integrative Metabolic Research Center (AIMRC) will host Dr. Elizabeth King, an associate professor of biological science at the University of Missouri, at 11:50 a.m. on Wednesday, Sept. 30, in BELL 2269. King's research integrates computational methods with large-scale empirical studies to focus on understanding the evolution of complex traits. In her seminar, she will highlight new approaches for identifying the genetic and environmental factors that influence metabolism.

Abstract: Metabolic phenotypes are highly complex and hierarchical, in which a high-level trait (e.g., energy expenditure) is the product of the combined effects of a suite of sub-phenotypes. These trait hierarchies are typically influenced by many genetic variants of small effect that interact with one another and with environmental factors such as diet, making the identification of the causative variants a significant challenge. To address this problem, the King lab combines multiple approaches. First, a large multiparent mapping population has been developed in the fruit fly model system to identify genetic variants contributing to hierarchical metabolic traits. Second, experimental evolution has been used to generate fly populations adapted to different dietary regimes and to track the genomic and phenotypic changes that occur during adaptation. This seminar will highlight the progress achieved through each of these approaches, as well as the challenges that remain.

Biography: King is an associate professor in the Division of Biological Sciences at the University of Missouri, where she has been a faculty member since 2014. King completed her Ph.D. at the University of California, Riverside, and her undergraduate studies at Grinnell College. Her research, which has been funded by NIH and NSF, addresses fundamental questions in evolutionary genomics, seeking to understand how genomes change when phenotypes evolve. Her lab integrates computational methods with large-scale empirical studies, with a primary focus on understanding the evolution of complex traits.

This event is supported by NIGMS of the National Institutes of Health under award number 2P20GM139768. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Pizza and beverages will be served. Please contact Kimberley Fuller, fullerk@uark.edu, for more information.

For those unable to attend in person, this seminar will also be available via Zoom.