INVESTIGATOR: Yu, J., Tesso, T., K. Roozeboom, Wang, D., Bernardo, R., Wang, M., Pederson, G.
NON-TECHNICAL SUMMARY: Research in plant feedstock genomics is well positioned to contribute to bioenergy production by combining the merits of established plant breeding and germplasm improvement approaches with cutting-edge genomics strategies. Two essential components of accelerated biomass crop improvement are the understanding and exploitation of genetic diversity and of the genotype-phenotype relationship. Assessment of genetic diversity provides germplasm and information on an array of trait characteristics for systematic integration and exploitation. A robust genotype-phenotype relationship allows predictive approaches for efficient and cost-effective breeding, superior allele mining, and introgression on a gene/genomic region basis. The proposed research integrates several key genomics-assisted strategies into biomass sorghum research (i.e., selective phenotyping, genomic selection, and association mapping) and combines these genetic strategies with a high-throughput phenotyping method (i.e., near infrared spectroscopy for biomass composition) and traditional field-based phenotyping experiments.
OBJECTIVES: 1) genotypically characterize a diverse set of sorghum germplasm and selectively phenotype a representative set of germplasm for their biomass potential; 2) develop a standardized method for high-throughput and cost-effective phenotyping for sorghum biomass composition through near-infrared reflectance (NIR) spectroscopy; and 3) discover additional useful germplasm by genomewide prediction and useful genes by association mapping for biomass yield and composition.
APPROACH: We will genotype a collection of 1000 sorghum accessions with at least 50,000 SNP markers and identify a sample of 300 that captures the maximum amount of diversity in the collection. These 300 accessions will be evaluated in multi-environment experiments for biomass yield and compositional traits, the latter measured with new NIR calibrations we will develop. The best of the remaining 700 accessions will be identified by genomewide selection and evaluated in subsequent field experiments. We will also identify and evaluate accessions that carry haplotypes associated with biomass traits but with a low frequency in the sample of 300 and accessions carry novel haplotypes.
Project Progress:
Data access through CyVerse: https://doi.org/10.25739/qv3s-gk89
1. 1009 sorghum accessions were selected from the GRIN database as the initial material (Please contact us if you are interested to see the full list).
2. Genotyping by sequencing (GBS) with the Illumina HiSeq platform was conducted for 976 sorghum accessions (out of the 1009 accessions) and generated 1,849,026 SNPs (Please contact us if you are interest in having access to the GBS data). Further filtering the data resulted in a set of 340,496 SNPs.
3. 300 sorghum accessions were selected based on GBS SNP data and have been field tested at Lubbock, TX (2012 and 2013), and Manhattan, KS (2013). (Please contact us if you are interested to see the list).
- Note 1: This set of 300 biomass sorghum accessions is being studied in an independently funded project by the DOE/USDA Plant Feedstock Genomics for Bioenergy Program, led by Dr. Pat Schnable. "Yu Panel" is the term used in that project.
- Note 2: Please contact us if you want to see a list of trait descriptors we used for biomass trait measurement.
- Note 3: NIR calibration equations are being developed for different sorghum biomass component analysis by Dr. Donghai Wang.
4. Genomic Estimated Breeding Values (GEBVs) were obtained for 709 accessions.
5. A set of 200 sorghum accessions were selected based on their GEBVs for empirical validation at Manhattan, KS (2014).
6. Please visit the webpage of the workshop series at the Plant and Animal Genomes Conference,Genomic Selection and Genome-Wide Association Studies (GS+GWAS): 2016, 2015, 2014, and 2013.
Highlighted Publication:
Yu, X., X. Li, T. Guo, C. Zhu, Y. Wu, S.E. Mitchell, K.L. Roozeboom, D. Wang, M.L. Wang, G.A. Pederson, T.T. Tesso, P.S. Schnable, R. Bernardo, and J. Yu*. 2016. Genomic prediction contributing to a promising global strategy to turbocharge gene banks. Nature Plants 2:16150.
Xu, F., J. Yu, T. Tesso, F. Dowell, and D. Wang. 2013. Qualitative and quantitative analysis of lignocellulosic biomass using infrared techniques: A mini-review. Applied Energy 104:801–809.