885B Poster - 14. Neural circuits and behavior
Friday April 08, 2:00 PM - 4:00 PM

Uncovering the Genetic Basis of Variation in Learning and Memory Phenotypes using the Drosophila Synthetic Population Resource


Authors:
Victoria Hamlin; Huda Ansaf ; Patricka Williams-Simon ; Elizabeth King

Affiliation: University of Missouri

Keywords:
f. learning/memory; e. quantitative traits

Learning and memory are complex traits in which many genes and regulatory regions affect the phenotypic output of an individual. For animals, these traits are vital functions necessary for adapting to and surviving in an ever-changing environment. Within a given population, there is variation between individuals in their ability to perform these functions, however, the mechanisms underlying this variability are still largely unknown. In addition, some genotypes may show more variable performance than others. Utilizing variance quantitative trait loci (vQTL) mapping, we can identify regions of genetic variation associated with differences in the variability of learning and memory performance in the Drosophila Synthetic Population Resource (DSPR). This multiparent population consists of about 1800 recombinant inbred lines (RILs) which provides us with ability to perform high resolution genome wide scans to identify quantitative trait loci with significant contribution to the residual variability in learning and memory performance. To test the performance of RILs, flies underwent operant conditioning for place and olfactory learning and memory. In the aversive place learning assay, flies were placed in a heat box that would increase to an intolerable temperature when flies crossed into one side of the chamber followed by testing to see if they remained on the cool associated side after training. In the appetitive olfactory assay, flies are starved for eighteen hours then provided a sugar reward paired with an odor in training followed by a Y-maze choice test to see if they selected the positive conditioned odor. Using a double generalized linear model to detect both mean and variance QTLs, we identified several QTL influencing these key phenotypes.