142T Poster - Evolutionary Genetics
Thursday June 09, 8:30 PM - 9:15 PM

The Genetics and Physiology of Switchgrass Local Adaptation Across North America


Author:
David Lowry

Affiliation: Michigan State University

Keywords:
Other (Local Adaptation)

Local adaptation is a fundamental driver of biodiversity on planet Earth. While recent experiments have begun to dissect the genetic basis of local adaptation, we still have a poor understanding of how individual genetic loci contribute to local adaptation over large-scale environmental gradients. To understand local adaptation at a continental scale, we conducted a long-term 13 field site study, spanning 24 degrees of latitude from central Mexico to the northern United States, in the major bioenergy/bioproducts crop switchgrass (Panicum virgatum). Much of the functional genetic variation in switchgrass is distributed clinally with latitude as well as among upland, lowland, and coastal ecotypes. Southern lowland populations are generally high yielding, tolerant to heat, drought, and pathogens, while northern upland populations are superior in their acclimation to cold and tolerance to freezing tolerant. To understand the genetic basis of local adaptation across central North America, we implemented multi-site quantitative trait locus (QTL) analyses and genome wide association studies (GWAS) across our network of field sites. This work has resulted in the identification of key loci contributing to variation in biomass, flowering time, overwinter survival, microbiome assembly, and resistance to pathogens. The vast majority of these loci have strong genotype x environment interactions, with additive effects varying greatly among field sites. Overall, many of these loci have major positive benefits with minimal fitness trade-offs across field sites. To understand how individual environmental factors contribute to local adaptation, we conducted in-depth laboratory studies of cold acclimation and freezing tolerance. Going forward, we are integrating high-throughput phenotyping, using drones, into our field studies to develop predictive models that can be utilized to increase the productivity of switchgrass.