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Optimal Contribution Selection Improves the Rate of Genetic Gain in Grain Yield and Yield Stability in Spring Canola in Australia and Canada.

Wallace A CowlingFelipe A Castro-UrreaKatia T StefanovaLi LiRobert G BanksRenu SaradadeviOlaf SassBrian P KinghornKadambot H M Siddique
Published in: Plants (Basel, Switzerland) (2023)
Crop breeding must achieve higher rates of genetic gain in grain yield (GY) and yield stability to meet future food demands in a changing climate. Optimal contributions selection (OCS) based on an index of key economic traits should increase the rate of genetic gain while minimising population inbreeding. Here we apply OCS in a global spring oilseed rape (canola) breeding program during three cycles of S 0,1 family selection in 2016, 2018, and 2020, with several field trials per cycle in Australia and Canada. Economic weights in the index promoted high GY, seed oil, protein in meal, and Phoma stem canker (blackleg) disease resistance while maintaining plant height, flowering time, oleic acid, and seed size and decreasing glucosinolate content. After factor analytic modelling of the genotype-by-environment interaction for the additive effects, the linear rate of genetic gain in GY across cycles was 0.059 or 0.087 t ha -1 y -1 (2.9% or 4.3% y -1 ) based on genotype scores for the first factor (f 1 ) expressed in trait units or average predicted breeding values across environments, respectively. Both GY and yield stability, defined as the root-mean-square deviation from the regression line associated with f 1 , were predicted to improve in the next cycle with a low achieved mean parental coancestry (0.087). These methods achieved rapid genetic gain in GY and other traits and are predicted to improve yield stability across global spring canola environments.
Keyphrases
  • genome wide
  • copy number
  • dna methylation
  • climate change
  • body mass index
  • gene expression
  • quality improvement
  • amino acid
  • risk assessment
  • current status
  • loop mediated isothermal amplification
  • sensitive detection