Experimental evaluation of genomic selection prediction for rust resistance in sugarcane

Authors: Islam, Md; MCCORD, PER - Washington State University; OLATOYE, MERCUS - University Of Illinois; QIN, LIFANG - Guangxi University; Sood, Sushma; LIPKA, ALEXANDER - University Of Illinois; Todd, James

Submitted to: The Plant Genome
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/22/2021
Publication Date: 9/12/2021
Citation: Islam, M.S., Mccord, P.H., Olatoye, M.O., Qin, L., Sood, S.G., Lipka, A.E., Todd, J.R. 2021. Experimental evaluation of genomic selection prediction for rust resistance in sugarcane. The Plant Genome. 14(3). Article e20148. https://doi.org/10.1002/tpg2.20148.
DOI: https://doi.org/10.1002/tpg2.20148

Interpretive Summary: The total sugarcane production has increased worldwide however, the rate of growth is lower compared to other major crops, mainly due to a plateauing of genetic gain. Genomic selection (GS) has proven to substantially increase the rate of genetic gain in many crops. In order to investigate the utility of GS in future sugarcane breeding, a field trial was conducted using 432 sugarcane clones utilizing an augmented design with two replications. Two major diseases in sugarcane, brown and orange rust (BR and OR), were screened artificially using whorl inoculation method in the field over two crop cycles. The genotypic data were generated through target enrichment sequencing technologies. After filtering, a set of 8,825 single nucleotide polymorphic markers were employed to assess the prediction accuracy of multiple GS models. Using five-fold cross validation, we observed GS prediction accuracies for BR and OR that ranged from 0.28 to 0.43 and 0.13 to 0.29, respectively, across two crop cycles and combined cycles. The prediction ability further improved by including a known major gene for resistance to BR as a fixed effect in the GS model. It also substantially reduced the minimum number of markers and training population size required for GS. The nonparametric GS models outperformed the parametric GS suggesting that non-additive genetic effects could contribute genomic sources underlying BR and OR. This study demonstrated that GS could potentially predict the genomic estimated breeding value for selecting the desired germplasm for sugarcane breeding for disease resistance.

Technical Abstract: The total sugarcane (Saccharum L.) production has increased worldwide; however, the rate of growth is lower compared with other major crops, mainly due to a plateauing of genetic gain. Genomic selection (GS) has proven to substantially increase the rate of genetic gain in many crops. To investigate the utility of GS in future sugarcane breeding, a field trial was conducted using 432 sugarcane clones using an augmented design with two replications. Two major diseases in sugarcane, brown and orange rust (BR and OR), were screened artificially using whorl inoculation method in the field over two crop cycles. The genotypic data were generated through target enrichment sequencing technologies. After filtering, a set of 8,825 single nucleotide polymorphic markers were used to assess the prediction accuracy of multiple GS models. Using five fold cross-validation, we observed GS prediction accuracies for BR and OR that ranged from 0.28 to 0.43 and 0.13 to 0.29, respectively, across two crop cycles and combined cycles. The prediction ability further improved by including a known major gene for resistance to BR as a fixed effect in the GS model. It also substantially reduced the minimum number of markers and training population size required for GS. The nonparametric GS models outperformed the parametric GS suggesting that nonadditive genetic effects could contribute genomic sources underlying BR and OR. This study demonstrated that GS could potentially predict the genomic estimated breeding value for selecting the desired germplasm for sugarcane breeding for disease resistance.