W541
Genetic and Genomics-Based Approaches to Elucidate Crop Timing and Quality Traits in Petunia

Date: Sunday, January 12, 2014
Time: 1:55 PM
Room: Royal Palm Salon 5-6
Ryan Warner , Michigan State University, East Lansing, MI
Krystle Wiegert-Rininger , Michigan State University, East Lansing, MI
Yufang Guo , Michigan State University, East Lansing, MI
Wei-Kuang Lin , Michigan State University, East Lansing, MI
Veronica Vallejo , Michigan State University, East Lansing, MI
Cornelius S. Barry , Michigan State University, East Lansing, MI
The cultivated petunia, Petunia hybrida, is derived from the progenitor species P. axillaris and P. integrifolia.  These species and others, including P. exserta, exhibit considerable variability and unique traits compared to commercial P. hybrida germplasm.  We are utilizing these wild relatives of the cultivated petunia, through both mapping-based and candidate gene approaches, to understand the molecular genetics underlying development rate in plants, among other crop timing and quality traits.  Petunia axillaris, P. exserta and P. integrifolia de novo transcriptome assemblies comprised of 100 bp paired-end RNA-seq reads from five tissue libraries (callus, 3-week old seedling, shoot apical meristem, mixed floral developmental stages, and trichome) were developed and utilized for single nucleotide polymorphism detection between the species.  Two interspecific hybrid Petunia F7 recombinant inbred line populations, P. integrifolia × P. axillaris, and P. axillaris × P. exserta were developed and phenotyped for development rate across a range of temperatures, data which will be used for QTL identification.  Development rate, quantified as new nodes d-1, was highly variable within a population and across temperature.  For example, at 14°C, the average development rate of the ten fastest and ten slowest P. integrifolia × P. axillaris RILs was 0.42 and 0.13 nodes d-1, respectively.  RILs exhibiting the fastest and slowest development rates across the temperature range were identified and will be utilized for RNA-seq expression profiling to identify differentially expressed genes between the fast and slow lines.