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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Plant Breeding
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1400000

Sparse Testing Designs for Optimizing Predictive Ability in Sugarcane Populations

Provisionally accepted
  • 1 Polytechnic University of Madrid, Madrid, Madrid, Spain
  • 2 University of Florida, Gainesville, Florida, United States
  • 3 Research Center of Sugar Cane, Cali, Cauca, Colombia

The final, formatted version of the article will be published soon.

    Sugarcane is a crucial crop for sugar and bioenergy production. Saccharose content and total weight are the two main key commercial traits that compose sugarcane's yield. These traits are under complex genetic control and their response patterns are influenced by the genotype-by-environment (G×E) interaction. An efficient breeding of sugarcane demands an accurate assessment of the genotype stability through multi-environment trials (METs), where genotypes are tested/evaluated across different environments. However, phenotyping all genotype-in-environment combinations is often impractical due to cost and limited availability of propagation-materials. This study introduces the sparse testing designs as a viable alternative, leveraging genomic information to predict unobserved combinations through genomic prediction models. This approach was applied to a dataset comprising 186 genotypes across six environments (6 × 186 = 1,116 phenotypes). Our study employed three predictive models, including environment and genotype as main effects, as well as the G×E to predict saccharose accumulation (SA) and tons of cane per hectare (TCH). Calibration sets sizes varying between 72 (6.5%) to 186 (16.7%) of the total number of phenotypes were composed to predict the remaining 930 (83.3%). Additionally, we explored the optimal number of common genotypes across environments for G×E pattern prediction. Results demonstrate that maximum accuracy for SA () and for TCH ( ) was achieved using in training ρ = 0. 611 ρ = 0. 341 sets few (3) to no common (0) genotype across environments maximizing the number of different genotypes that were tested only once. Significantly, we show that reducing phenotypic records for model calibration has minimal impact on predictive ability, with sets of 12 non-overlapped genotypes per environment (72 = 12 × 6) being the most convenient cost-benefit combination.

    Keywords: Genomic Selection GS, Genomic Prediction GP, Sparse Testing Designs, optimization, Sugarcane breeding

    Received: 12 Mar 2024; Accepted: 13 Jun 2024.

    Copyright: © 2024 García-Abadillo Velasco, Adunola, Aguilar, Trujillo-Montenegro, Riascos, PERSA, Isidro Sánchez and Jarquin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Diego Jarquin, University of Florida, Gainesville, 32609, Florida, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.