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ARS Home » Southeast Area » Griffin, Georgia » Plant Genetic Resources Conservation Unit » Research » Publications at this Location » Publication #302143
Title: Genomic selection of biomass traits in a global collection of 976 sorghum accessions

Author
item YU, XIAOQING - Iowa State University
item LI, XIANRAN - Iowa State University
item WU, YUYE - Kansas State University
item MITCHELL, SHARON - Cornell University
item ROOZEBOOM, KRAIG - Kansas State University
item WANG, DONGHAI - Kansas State University
item BERNARDO, REX - University Of Minnesota
item Wang, Ming
item Pederson, Gary
item TESSO, TESFAYE - Kansas State University
item YU, JIANMING - Iowa State University

Submitted to: Plant and Animal Genome Conference
Publication Type: Abstract Only
Publication Acceptance Date: 11/1/2013
Publication Date: 1/13/2014
Citation: Yu, X., Li, X., Wu, Y., Mitchell, S.F., Roozeboom, K.L., Wang, D., Bernardo, R., Wang, M.L., Pederson, G.A., Tesso, T.T., Yu, J. 2014. Genomic selection of biomass traits in a global collection of 976 sorghum accessions.[abstract] Plant and Animal Genome Conference. January 10-15, 2014. San Diego, California. Poster No. 011.

Interpretive Summary: Substantial genetic diversity exists in sorghum (Sorghum bicolor), a key lignocellulosic biofuel species in the United States. The implementation of genomic, genetic tools to select and enhance current germplasm will greatly accelerate new variety development. Our objectives in this study are to address several key questions: 1) How to tap into the vast plant germplasm collections for biomass crop improvement? 2) How to increase the information contained in genotypic and phenotypic data for the selected germplasm? 3) How to leverage a high-throughput phenotyping method such as Near Infrared (NIR) to facilitate plant biomass composition investigation? 4) How robust are the various genomic prediction models on biomass traits? In this study, genotyping by sequencing with the Illumina HiSeq platform was conducted for 976 sorghum accessions sampled from germplasm bank and generated 0.72 million SNPs. A set of 300 accessions, selected to be most representative from this panel were extensively phenotyped for biomass yield, plant height, stem diameter, stalk number, stalk lodging, and root lodging at Kansas and Taxes in 2012 and 2013. Cellulose and lignin content were investigated through both wet chemical and high throughput NIR methods. Bringing phenotype and genotype data together, genomewide prediction models were established with all common methods. Biomass traits of the 700 untested accessions were predicted by using the optimal genomic selection model and will be validated through phenotyping the accessions with extreme high and low biomass potential.

Technical Abstract: Substantial genetic diversity exists in sorghum (Sorghum bicolor), a key lignocellulosic biofuel species in the United States. The implementation of genomic, genetic tools to select and enhance current germplasm will greatly accelerate new variety development. Our objectives in this study are to address several key questions: 1) How to tap into the vast plant germplasm collections for biomass crop improvement? 2) How to increase the information contained in genotypic and phenotypic data for the selected germplasm? 3) How to leverage a high-throughput phenotyping method such as Near Infrared (NIR) to facilitate plant biomass composition investigation? 4) How robust are the various genomic prediction models on biomass traits? In this study, genotyping by sequencing (GBS) with the Illumina HiSeq platform was conducted for 976 sorghum accessions sampled from germplasm bank and generated 0.72 million SNPs. A set of 300 accessions, selected to be most representative from this panel were extensively phenotyped for biomass yield, plant height, stem diameter, stalk number, stalk lodging, and root lodging at Kansas and Taxes in 2012 and 2013. Cellulose and lignin content were investigated through both wet chemical and high throughput NIR methods. Bringing phenotype and genotype data together, genomewide prediction models were established with all common methods. Biomass traits of the 700 untested accessions were predicted by using the optimal genomic selection model and will be validated through phenotyping the accessions with extreme high and low biomass potential.