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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Genomic prediction applied to high-biomass sorghum for bioenergy production

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Author(s):
de Oliveira, Amanda Avelar [1] ; Pastina, Maria Marta [2] ; de Souza, Vander Filipe [2] ; da Costa Parrella, Rafael Augusto [2] ; Noda, Roberto Willians [2] ; Ferreira Simeone, Maria Lucia [2] ; Schaffert, Robert Eugene [2] ; de Magalhes, Jurandir Vieira [2] ; Borges Damasceno, Cynthia Maria [2] ; Alves Margarido, Gabriel Rodrigues [1]
Total Authors: 10
Affiliation:
[1] Univ Sao Paulo, Dept Genet, Luiz de Queiroz Coll Agr, BR-13418900 Piracicaba, SP - Brazil
[2] Embrapa Maize & Sorghum, BR-35701970 Sete Lagoas, MG - Brazil
Total Affiliations: 2
Document type: Journal article
Source: MOLECULAR BREEDING; v. 38, n. 4 APR 2018.
Web of Science Citations: 5
Abstract

The increasing cost of energy and finite oil and gas reserves have created a need to develop alternative fuels from renewable sources. Due to its abiotic stress tolerance and annual cultivation, high-biomass sorghum (Sorghum bicolor L. Moench) shows potential as a bioenergy crop. Genomic selection is a useful tool for accelerating genetic gains and could restructure plant breeding programs by enabling early selection and reducing breeding cycle duration. This work aimed at predicting breeding values via genomic selection models for 200 sorghum genotypes comprising landrace accessions and breeding lines from biomass and saccharine groups. These genotypes were divided into two sub-panels, according to breeding purpose. We evaluated the following phenotypic biomass traits: days to flowering, plant height, fresh and dry matter yield, and fiber, cellulose, hemicellulose, and lignin proportions. Genotyping by sequencing yielded more than 258,000 single-nucleotide polymorphism markers, which revealed population structure between subpanels. We then fitted and compared genomic selection models BayesA, BayesB, BayesC pi, BayesLasso, Bayes Ridge Regression and random regression best linear unbiased predictor. The resulting predictive abilities varied little between the different models, but substantially between traits. Different scenarios of prediction showed the potential of using genomic selection results between sub-panels and years, although the genotype by environment interaction negatively affected accuracies. Functional enrichment analyses performed with the marker-predicted effects suggested several interesting associations, with potential for revealing biological processes relevant to the studied quantitative traits. This work shows that genomic selection can be successfully applied in biomass sorghum breeding programs. (AU)

FAPESP's process: 13/25132-7 - Genome wide selection methods applied to high biomass sorghum for the production of second generation ethanol
Grantee:Amanda Avelar de Oliveira
Support type: Scholarships in Brazil - Master
FAPESP's process: 15/22993-7 - Transcriptomics for the identification of commercially important genes in sugarcane
Grantee:Gabriel Rodrigues Alves Margarido
Support type: Program for Research on Bioenergy (BIOEN) - Young Investigators Grants