|POUDEL, HARI - University Of Wisconsin|
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 5/23/2017
Publication Date: N/A
Technical Abstract: Switchgrass (Panicum virgatum L.) is a North American native perennial warm season grass and a promising cellulosic bioenergy feedstock. It has two ecotypes – lowland and upland. The lowland ecotype has generated considerable interest because of its higher biomass compared to the upland ecotype. However, lowland ecotypes planted in northern latitudes exhibit very low winter survival. Winter survival can be improved by selectively saving surviving plants, but this approach requires several years for the completion of one generation, and its success is highly dependent on the presence of winter conditions that generate the appropriate selection pressure. Genomic selection (GS) can potentially enhance switchgrass breeding by reducing selection time and eliminating the dependence on weather. In this study, genomic prediction was assessed on a total of 264 lowland half sib (HS) families and 121 lowland × upland HS families representing multiple geographic regions. The phenotype was the winter survival score based on visual assessment of the percentage of living shoots after the initial spring growth on the scale of 0-20. The maternal parent of each HS family was genotyped using the exome capture platform and was aligned to Version 1.1 of AP13 switchgrass reference genome. Prediction accuracies using all 385 HS families as a single training population were 0.57 and 0.53, using field data from 2014 and 2015, respectively. This model was tested for validation on a different dataset consisting of 1146 HS families representing 132 lowland and upland populations. The genomic estimated breeding values (GEBV) for winter survival decreased with decrease in 30-year normal minimum temperature at the site-of-origin of the source population (p < 0.05). Our results suggested that winter survival of lowland switchgrass in Wisconsin can be predicted using genomic DNA markers. This GS model will be useful in both breeding lowland switchgrass for increased winter survivorship and in identifying target sites for additional lowland switchgrass germplasm collection.