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<p>Olive fruit weight is a crucial trait to consider in olive breeding programs due to its impact on final yield and its relevance for mechanical harvesting and fruit processing. Although environmental conditions influence this trait, fruit weight is primarily determined by genetic factors and exhibits a high degree of heritability in breeding progenies. Despite several studies identifying potential markers associated with fruit weight, these markers have not been validated. In this study, we analyzed 40 genetic markers linked to fruit weight using a dataset comprising 73 cultivars (including 33 newly sequenced varieties) and 10 wild olives with a wide range of phenotypic characteristics, spanning from very light (0.41 g) to very heavy fruits (8.57 g). By examining the phenotype distribution for each genotype of the newly sequenced varieties, we successfully validated 16 genetic markers. Additionally, machine learning tools demonstrated that 9 out of the 16 validated markers have a high predictive ability for fruit weight. As a result, our work provides, for the first time, a set of 9 well-validated genetic markers suitable for use in marker-assisted selection during the early stages of olive breeding programs.</p>

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Genetic markers of olive fruit weight selected to be used in breeding experiments

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月7日

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  • Martín Moret,
  • Alicia Serrano,
  • Angjelina Belaj,
  • Lorenzo León,
  • Raúl de la Rosa,
  • Francisco Luque

摘要

Olive fruit weight is a crucial trait to consider in olive breeding programs due to its impact on final yield and its relevance for mechanical harvesting and fruit processing. Although environmental conditions influence this trait, fruit weight is primarily determined by genetic factors and exhibits a high degree of heritability in breeding progenies. Despite several studies identifying potential markers associated with fruit weight, these markers have not been validated. In this study, we analyzed 40 genetic markers linked to fruit weight using a dataset comprising 73 cultivars (including 33 newly sequenced varieties) and 10 wild olives with a wide range of phenotypic characteristics, spanning from very light (0.41 g) to very heavy fruits (8.57 g). By examining the phenotype distribution for each genotype of the newly sequenced varieties, we successfully validated 16 genetic markers. Additionally, machine learning tools demonstrated that 9 out of the 16 validated markers have a high predictive ability for fruit weight. As a result, our work provides, for the first time, a set of 9 well-validated genetic markers suitable for use in marker-assisted selection during the early stages of olive breeding programs.