Genetic gain increases by applying the usefulness criterion with improved variance prediction in selection of crosses

Christina Lehermeier, Simon Teyssèdre, Chris Carolin Schön

Research output: Contribution to journalArticlepeer-review

51 Scopus citations

Abstract

A crucial step in plant breeding is the selection and combination of parents to form new crosses. Genome-based prediction guides the selection of high-performing parental lines in many crop breeding programs which ensures a high mean performance of progeny. To warrant maximum selection progress, a new cross should also provide a large progeny variance. The usefulness concept as measure of the gain that can be obtained from a specific cross accounts for variation in progeny variance. Here, it is shown that genetic gain can be considerably increased when crosses are selected based on their genomic usefulness criterion compared to selection based on mean genomic estimated breeding values. An efficient and improved method to predict the genetic variance of a cross based on Markov chain Monte Carlo samples of marker effects from a whole-genome regression model is suggested. In simulations representing selection procedures in crop breeding programs, the performance of this novel approach is compared with existing methods, like selection based on mean genomic estimated breeding values and optimal haploid values. In all cases, higher genetic gain was obtained compared with previously suggested methods. When 1% of progenies per cross were selected, the genetic gain based on the estimated usefulness criterion increased by 0.14 genetic standard deviation compared to a selection based on mean genomic estimated breeding values. Analytical derivations of the progeny genotypic variance-covariance matrix based on parental genotypes and genetic map information make simulations of progeny dispensable, and allow fast implementation in large-scale breeding programs.

Original languageEnglish
Pages (from-to)1651-1661
Number of pages11
JournalGenetics
Volume207
Issue number4
DOIs
StatePublished - Dec 2017

Keywords

  • Bayesian statistics
  • Genomic selection
  • Plant breeding
  • Progeny variance
  • Usefulness criterion

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