REDESIGNING FORAGE GERMPLASM AND PRODUCTION SYSTEMS FOR EFFICIENCY, PROFIT, AND SUSTAINABILITY OF DAIRY FARMS
Location: Dairy Forage and Aquaculture Research
Title: Comparative study of switchgrass cultivars using RNA sequencing technology
| Nandety, Aruna - |
| Childs, Kevin - |
| Buell, Robin - |
| Kaeppler, Shawn - |
Submitted to: Plant and Animal Genome Conference
Publication Type: Abstract Only
Publication Acceptance Date: December 7, 2011
Publication Date: January 13, 2012
Citation: Nandety, A., Childs, K., Buell, R., Kaeppler, S., Casler, M.D. 2012. Comparative study of switchgrass cultivars using RNA sequencing technology. Plant and Animal Genome Conference. http://pag.confex.com/pag/xx/webprogram/Paper3987.html.
Switchgrass (Panicum virgatum L.) is a C4 perennial grass, identified as a promising bioenergy crop. Switchgrass exists in two ecotypes, upland and lowland, which are heterotic, or genetically complementary to each other. The objectives of this study are to assess the potential of SNP markers as a breeding tool in development of hybrid switchgrass cultivars for higher biomass production, and to identify candidate genes responsible for variation in lignification and flowering time. Higher ploidy levels (e.g. 2n = 8x = 72) and lack of complete sequence information have been a hindrance for developing genetic and genomic resources. High-throughput deep RNA sequencing technology (RNAseq) was used to facilitate the construction of transcriptome without a reference genome. Transcriptome assemblies of four upland and three lowland genotypes were constructed from mRNA collected from whole of the above-crown tissues. De novo assemblies were made with a sequencing depth of 90 million paired reads for each assembly (upland, lowland and upland-lowland combined). The short reads were assembled into contigs with an average size of 817 and an N50 size of the contig being 1092. About 46,000 transcripts were annotated from a combined transcriptome of upland and lowland genotypes using maize as a search engine. Relative expression levels will be quantified by fragments per kilobase transcript per million reads (FPKM) analysis, focusing largely on genes responsible for specific traits associated with biomass accumulation. Phylogenetic analysis will be carried out based on the SNP markers.