Genetic Analysis of Stem Composition Variation in Sorghum Bicolor

• Main Author: Evans, Joseph
• Other Authors: Mullet, John
• Format: Others
• Language: en_US
• Published: 2012
• Subjects: Sorghum; Genomics; Composition; Biofuels; Bioinformatics; QTL Mapping
• Online Access: http://hdl.handle.net/1969.1/ETD-TAMU-2012-08-11625

Sorghum (Sorghum bicolor [L.] Moench) is the world's fifth most economically important cereal crop, grown worldwide as a source of food for both humans and livestock. Sorghum is a C4 grass that is well adapted to hot and arid climes and is popular for cultivation on lands of marginal quality. Recent interest in development of biofuels from lignocellulosic biomass has drawn attention to sorghum, which can be cultivated in areas not suitable for more traditional crops, and is capable of generating plant biomass in excess of 40 tons per acre. While the quantity of biomass and low water consumption make sorghum a viable candidate for biofuels growth, the biomass composition is enriched in lignin, which is problematic for enzymatic and chemical conversion techniques. The genetic basis for stem composition was analyzed in sorghum populations using a combination of genetic, genomic, and bioinformatics techniques. Utilizing acetyl bromide extraction, the variation in stem lignin content was quantified across several sorghum cultivars, confirming that lignin content varied considerably among sorghum cultivars. Previous work identifying sorghum reduced-lignin lines has involved the monolignol biosynthetic pathway; all steps in the pathway were putatively identified in the sorghum genome using sequence analysis. A bioinformatics toolkit was constructed to allow for the development of genetic markers in sorghum populations, and a database and web portal were generated to allow users to access previously developed genetic markers. Recombinant inbred lines were analyzed for stem composition using near infrared reflectance spectroscopy (NIR) and genetic maps constructed using restriction site-linked polymorphisms, revealing 34 quantitative trail loci (QTL) for stem composition variation in a BTx642 x RTx7000 population, and six QTL for stem composition variation in an SC56 x RTx7000 population. Sequencing the genome of BTx642 and RTx7000 to a depth of ~11x using Illumina sequencing revealed approximately 1.4 million single nucleotide polymorphisms (SNPs) and 1 million SNPs, respectively. These polymorphisms can be used to identify putative amino acid changes in genes within these genotypes, and can also be used for fine mapping. Plotting the density of these SNPs revealed patterns of genetic inheritance from shared ancestral lines both between the newly sequenced genotypes and relative to the reference genotype BTx623.