The increasing demand of food and the rising concerns about climate change and energy security pushes Sorghum bicolor to the top of global agendas. The main attraction is its ability to provide food and animal feed as well as renewable energy products, and industrial commodities. The species comprises grain and sweet sorghum types. Sweet sorghum has major advantages compared to other sugar and biomass crops. Firstly, sorghum is a species showing extensive genetic variation, including drought and heat tolerant genotypes, which enable the usage of marginal land that is not suitable for cultivating other crops. Secondly, competition between the use of land for food or for energy is less because the grains can be used for food or feed while stems can be used for biofuel production. In the current study, field trials and related genetic analysis were carried out to identify major chromosomal regions that are linked to biofuel-related traits. An experimental population of 213 RILs from a cross between grain sorghum (M71) and sweet sorghum (SS79) was planted in 5 environments in Germany in the vegetation periods 2007 and 2008. This population segregates for alleles controlling biomass yield, juice content, sugar content, fibre content, and many other traits. The approach to identify these chromosomal regions was (1) to phenotype the RIL population for the mentioned traits, (2) to construct a genetic map from the RILs, and (3) to correlate the phenotypic data and genetic map informations. The phenotypic data was analysed for individual location and environments (years*location) using mixed model from SAS® 9.1 version. Genotypes were treated as fixed effects while all other components were treated as random effects. Regarding, sugar-related traits, we analysed brix, glucose content, sucrose content, and sugar content, measured stem diameter, stem juice weight, plant height and fresh panicle weight, and counted the days to anthesis and the number of tillers per plant. We partitioned the variance components as genetic, location, year, genetic x location, genetic x year, location x year, genetic x location x year, and harvest date. The results showed that although location contributed highly to the phenotype in terms of percentage, it was not a significant source of variance. The interaction between genotypes and environment was observed in most traits except for stem juice weight, fresh panicle weight and glucose content. Genetic variance contributed small but significant to the phenotypic variance. And thus the heritability estimates were moderate to high in all traits except stem juice weight (H2=0.18). Pearson correlation estimates showed that these traits were significantly correlated with each other. Flowering dates correlated with all the traits while number of tillers only positively correlated with stem juice weight, and negatively correlated with sugar content and flowering dates. In fibre-related traits, we quantified fibre content traits (acidic detergent fibre, acidic detergent lignin, neutral detergent fibre, cellulose, hemicelluloses), and measured related agronomic traits (fresh leaf mass, stripped stalk mass, dry stalk mass, fresh biomass and dry biomass). We partitioned our variance components as genetic, environment, harvest date and genotype x environment. Genetic variance was significant in all traits except for dry biomass while environment was not significant for all traits. There was a significant genotype x environment interaction in all traits except hemicelluloses. Dry stalk mass, dry biomass and hemicelluloses had low heritability estimates of <=0.24. Pearson correlation estimates showed that fibre content traits significantly and positively correlated with each other as well as agronomic traits, while there were negative correlations among agronomic and fibre content traits.A total of 213 RILs were used to construct the genetic linkage map using 157 markers, i.e. simple sequence repeats (SSR), amplified fragment length polymorphism (AFLP) and expressed sequence tag-simple sequence repeats (EST-SSR). The linkage map was compared to other sorghum published maps and linkage groups were assigned according to the recent sorghum nomenclature. The phenotypic data of each trait was averaged to obtain the combined environmental means to detect QTLs. Composite interval mapping (CIM) of the PLABQTL software was used for analysis. The model AA was used to estimate additive x additive QTL interaction, and the command environ was used to estimate significant QTL in each environment and to detect QTL x environmental interaction. CIM detected a few additive QTLs per trait across the 10 sorghum chromosomes. The additive QTLs co-localized and showed clusters on some chromosome, where the most prominent clusters were observed on chromosomes 1, 2, 6, and 7. QTLs showing multiple effects were observed on all chromosomes except chromosome 5 and 10 and most of these pleiotropic QTLs showed the contribution of the positive allele in each trait. For example, a QTL on SBI-06, position 14, showed the allele contribution of M71 (high cellulose and compact panicle parent) for cellulose and fresh panicle weight, along with the allele contribution of SS79 (high sucrose and biomass parent) for sucrose and fresh biomass. However, QTLs that showed pleiotropic effect also exhibited QTL x environment interaction. Most of the QTLs detected for agronomic biomass (i.e. stripped stalk mass, fresh leaf mass and fresh biomass) interacted significantly with the environment. Additive x additive interaction was detected in 14 out of 20 traits studied totalling 41 digenic pairs. This indicates the abundance of digenic epistasis across the genome. However, failure to detect QTL x QTL interaction can never be interpreted as being absent but can be attributed to QTL x environment interaction, sampling error or accuracy of statistic methods. Among other characteristics, stem sugar components and lignocellulosic biomass are the important characteristics of Sorghum bicolor to determine it as one of the major bionergy crops. There is limited literature on sugar and fibre related traits in sorghum at the moment, and this study among other few, serves as one of the base investigations on the topic. The phenotypic variation of the lines, the moderate to high heritability of the desired traits, the significant correlation and pleiotropic QTLs to the positive direction identified, the additive stable QTLs and their interactions detected suggest that it will be possible to develop improved lines and hybrids by breeding and to ultimately enhance the sorghum plant as a bioenergy crop.
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