Evaluation of Models for Utilization in Genomic Prediction of Agronomic Traits in the Louisiana Sugarcane Breeding Program

Subhrajit Satpathy, Dipendra Shahi, Brayden Blanchard, Michael Pontif, Kenneth Gravois, Collins Kimbeng, Anna Hale, James Todd, Atmakuri Rao and Niranjan Baisakh ()
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Subhrajit Satpathy: School of Plant, Environmental and Soil Sciences, Louisiana State University Agricultural Center, Baton Rouge, LA 70803, USA
Dipendra Shahi: School of Plant, Environmental and Soil Sciences, Louisiana State University Agricultural Center, Baton Rouge, LA 70803, USA
Brayden Blanchard: Sugar Research Station, Louisiana State University Agricultural Center, St. Gabriel, LA 70776, USA
Michael Pontif: Sugar Research Station, Louisiana State University Agricultural Center, St. Gabriel, LA 70776, USA
Kenneth Gravois: Sugar Research Station, Louisiana State University Agricultural Center, St. Gabriel, LA 70776, USA
Collins Kimbeng: Sugar Research Station, Louisiana State University Agricultural Center, St. Gabriel, LA 70776, USA
Anna Hale: Sugar Research Unit, USDA-ARS, Houma, LA 70360, USA
James Todd: Sugar Research Unit, USDA-ARS, Houma, LA 70360, USA
Atmakuri Rao: Indian Agricultural Statistics Research Institute, New Delhi 110012, India
Niranjan Baisakh: School of Plant, Environmental and Soil Sciences, Louisiana State University Agricultural Center, Baton Rouge, LA 70803, USA

Agriculture, 2022, vol. 12, issue 9, 1-14

Abstract: Sugarcane ( Saccharum spp.) is an important perennial grass crop for both sugar and biofuel industries. The Louisiana sugarcane breeding program is focused on improving sugar yield by incrementally increasing genetic gain. With the advancement in genotyping and (highthroughput) phenotyping techniques, genomic selection is a promising marker-assisted breeding tool. In this study, we assessed ridge regression best linear unbiased prediction (rrBLUP) and various Bayesian models to evaluate genomic prediction accuracy using a 10-fold cross validation on 95 commercial and elite parental clones from the Louisiana sugarcane breeding program. Datasets (individual and pooled in various combinations) were constructed based on soil type (light—Commerce silty loam, heavy—Sharkey clay) and crop (plant cane, ratoon). A total of 3906 SNPs were used to predict the genomic estimated breeding values (GEBVs) of the clones for sucrose content and cane and sugar yield. Prediction accuracy was estimated by both Spearman’s rank correlation and Pearson’s correlation between phenotypic breeding values and GEBVs. All traits showed significant variation with moderate (42% for sucrose content) to high (85% for cane and sugar yield) heritability. Prediction accuracy based on rank correlation was high (0.47–0.80 for sucrose content; 0.61–0.69 for cane yield, and 0.56–0.72 for sugar yield) in all cross-effect prediction models where soil and crop types were considered as fixed effects. In general, Bayesian models demonstrated a higher correlation than rrBLUP. The Pearson’s correlation without soil and crop type as fixed effects was lower with no clear pattern among the models. The results demonstrate the potential implementation of genomic prediction in the Louisiana sugarcane variety development program.

Keywords: cane yield; genomic selection; prediction models; sucrose; sugarcane (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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