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<p>Inter-specific hybridisation between natural populations within the genus <i>Actinidia</i> is a common phenomenon and has been used in breeding programmes. Hybridisation between species increases the diversity of breeding populations, incorporating new desirable traits into potential cultivars. We explored genomic prediction in <i>Actinidia</i> breeding, focusing on the closely related species <i>Actinidia arguta</i> and <i>Actinidia melanandra</i>. We investigated the potential of genomic selection by analysing four quantitative traits across intra-specific <i>A. arguta</i> crosses and inter-specific crosses between <i>A. arguta</i> and <i>A. melanandra</i>. The continuous distributions of the studied traits in both intra-specific and inter-specific crosses indicated a polygenic background. A linear mixed model approach was used, incorporating the factor of year of season and a marker-based relationship matrix instead of pedigree as a random effect. After evaluation, the best model was applied to assess variance components and heritability for each quantitative trait. Expanding beyond intra-specific crosses, predictive ability was calculated to investigate inter-specific cross effect. Considering predictive ability, this study explored the impacts of sample size and population structure. A reduction in sample size correlated with decreased predictive ability, while the influence of population structure was particularly pronounced in inter-specific crosses. Finally, the prediction accuracy of genomic estimated breeding values, for parental genotypes, revealed an inter-species effect on prediction confidence. Considering the imbalance in genotype numbers between intra- and inter-specific cross populations, this research highlights the difficulty of genomic prediction in hybrid populations. Understanding prediction accuracy in inter-species crossing designs provides valuable insights for optimising genomic selection.</p>

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Genomic selection in a kiwiberry breeding programme: integrating intra- and inter-specific crossing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月7日

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  • Daniel Mertten,
  • Catherine M. McKenzie,
  • Samantha Baldwin,
  • Susan Thomson,
  • Edwige J. F. Souleyre,
  • Michael Lenhard,
  • Paul M. Datson

摘要

Inter-specific hybridisation between natural populations within the genus Actinidia is a common phenomenon and has been used in breeding programmes. Hybridisation between species increases the diversity of breeding populations, incorporating new desirable traits into potential cultivars. We explored genomic prediction in Actinidia breeding, focusing on the closely related species Actinidia arguta and Actinidia melanandra. We investigated the potential of genomic selection by analysing four quantitative traits across intra-specific A. arguta crosses and inter-specific crosses between A. arguta and A. melanandra. The continuous distributions of the studied traits in both intra-specific and inter-specific crosses indicated a polygenic background. A linear mixed model approach was used, incorporating the factor of year of season and a marker-based relationship matrix instead of pedigree as a random effect. After evaluation, the best model was applied to assess variance components and heritability for each quantitative trait. Expanding beyond intra-specific crosses, predictive ability was calculated to investigate inter-specific cross effect. Considering predictive ability, this study explored the impacts of sample size and population structure. A reduction in sample size correlated with decreased predictive ability, while the influence of population structure was particularly pronounced in inter-specific crosses. Finally, the prediction accuracy of genomic estimated breeding values, for parental genotypes, revealed an inter-species effect on prediction confidence. Considering the imbalance in genotype numbers between intra- and inter-specific cross populations, this research highlights the difficulty of genomic prediction in hybrid populations. Understanding prediction accuracy in inter-species crossing designs provides valuable insights for optimising genomic selection.