**Genetic Improvement of Sorghum for Biomass Traits Using Genomics Approaches Using Genomics Approaches**

**Genetic Improvement of Sorghum for Biomass Traits** 

DOI: 10.5772/intechopen.73010

Bushra Sadia, Faisal Saeed Awan, Fozia Saleem, Hafeez Ahmad Sadaqat, Sarmad Frogh Arshad and Haseeb Shaukat Hafeez Ahmad Sadaqat, Sarmad Frogh Arshad and Haseeb Shaukat

Bushra Sadia, Faisal Saeed Awan, Fozia Saleem,

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.73010

#### **Abstract**

Nonrenewable energy resources deplete with the passage of time due to rapid increase in industrialization and population. Hence, countries worldwide are investing dearly in substitute energy resources like biofuel from miscellaneous set of feedstocks. Among the energy crops, sorghum serves as a model crop due to its drought tolerance, small genome size (730 Mb), high biomass, dry matter contents, quick growth, wide adaptability to diverse climatic and soil conditions and C4 photosynthesis. Sweet sorghum with high sugar content in stalk is an efficient feedstock for advanced biofuels and other bio-based products from sugars. However, high biomass sorghum has the utility as a feedstock for cellulosic biofuels. The enhanced yield of monomeric carbohydrates is a key to cheap and efficient biofuel production. The efficiency of lignocellulosic biofuels is compromised by recalcitrance to cell wall digestion, a trait that cannot be efficiently improved by traditional breeding. Therefore, scientists are looking for solutions to such problems in biomass crop genomes. Sorghum genome has been completely sequenced and hence this crop qualifies for functional genomics analysis by fast forward genetic approaches. This chapter documents the latest efforts on advancement of sorghum for biomass potential at morphological and molecular level by exploiting genomics approaches.

**Keywords:** biofuel, sorghum, association mapping, lignocellulosic feedstock, genomics, microRNAs, marker-assisted selection
