Directed evolution experiments to generate multigenic changes to lifespan and development
One of the most difficult, yet important, aspects of biology is identifying the genetic changes that are responsible for differences in biological traits. A major hurdle to their identification is the extremely large number of genetic variants (millions) between two individuals. In order to simplify this process, we are interested in developing directed evolution approaches to reinvigorate forward genetics screens to study biological traits. Instead of screening for animals defective for a phenotype, we are evolving animals to change a phenotype. Due to the power of selection, mutations of small effect size can be fixed. Furthermore, multigenic trait changes can be generated by successively fixing advantageous mutations. Causative mutations can be rapidly and definitively identified using high-throughput mapping and next generation sequencing.
We are now applying this approach to multiple phenotypes to try to understand
- Gene discovery: Can we identify genetic risk factors for human diseases and traits?
- Epistatis: How important and complicated are gene interactions between most genetic variants?
- Evolutionary analysis: How repeatable is evolution of these strains?
Once established, we anticipate this directed evolution approach will be used by ourselves and other research labs as a general tool.
Mechanistic study of neural signal processing to connect genetic variation with phenotypic variation
Neural circuits have the remarkable ability to process information about an organism’s external environment to modify behavior, metabolism, and development. As a framework to understand how genetic variation can impact biological traits, we are now studying how natural genetic variation affects this sensory transduction. A number of useful optogenetics tools have emerged, such as GCaMP and channelrhodopsin, to allow us to monitor and manipulate any neuron of choice. We are using microfluidics-controlled delivery of pheromones and fluorescence-based Ca++ imaging to study signal transduction. How are salient features of the environment encoded by the nervous system? How does this encoding change in different environments or in different genetic backgrounds? We believe that focusing on this layer can identify endophenotypes or nonlinear interactions between neurons that can aid in prediction of genetic variants effect on a phenotype.