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Sparse testing designs for optimizing predictive ability in sugarcane populations.

Julian Garcia-Abadillo1,2, Paul Adunola3, Fernando Silva Aguilar4

  • 1Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid, Madrid, Spain.

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|August 7, 2024
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Summary
This summary is machine-generated.

Sugarcane breeding can be more efficient using sparse testing designs. Genomic prediction models accurately predict yield traits with fewer phenotyped genotypes, optimizing multi-environment trials (METs).

Keywords:
genomic prediction GPgenomic selection GSoptimizationsparse testing designssugarcane breeding

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Area of Science:

  • Plant Breeding and Genetics
  • Agricultural Science
  • Bioenergy Crop Research

Background:

  • Sugarcane yield is determined by saccharose content and total weight, crucial for sugar and bioenergy.
  • Genotype-by-environment (G×E) interactions significantly influence these complex genetic traits.
  • Accurate genotype stability assessment via multi-environment trials (METs) is vital but often impractical due to cost and material limitations.

Purpose of the Study:

  • To introduce and evaluate sparse testing designs for sugarcane breeding.
  • To leverage genomic prediction models for predicting unobserved genotype-environment combinations.
  • To optimize the cost-benefit of phenotyping strategies in METs.

Main Methods:

  • Applied genomic prediction models incorporating environment, genotype, genomic markers, and G×E interactions.
  • Utilized a dataset of 186 sugarcane genotypes across six environments (1,116 total phenotypes).
  • Tested calibration set sizes from 6.5% to 16.7% of total phenotypes to predict unobserved combinations.

Main Results:

  • Maximum prediction accuracy for saccharose accumulation (SA) and tons of cane per hectare (TCH) was achieved with minimal or no common genotypes across environments in training sets.
  • Sparse designs, with few (3) to no common genotypes, maximized the number of unique genotypes tested.
  • Reducing phenotypic records for calibration had minimal impact on predictive ability; 12 non-overlapped genotypes per environment (72 total) offered the best cost-benefit.

Conclusions:

  • Sparse testing designs combined with genomic prediction offer a viable and cost-effective alternative to traditional METs in sugarcane.
  • Optimizing genotype allocation across environments is key to maximizing prediction accuracy for yield traits.
  • This approach significantly reduces the phenotyping burden without compromising breeding efficiency.