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Impact of Genomic and Transcriptomic Resources on Apiaceae Crop Breeding Strategies.

Fabio PalumboAlessandro VannozziGianni Barcaccia
Published in: International journal of molecular sciences (2021)
The Apiaceae taxon is one of the most important families of flowering plants and includes thousands of species used for food, flavoring, fragrance, medical and industrial purposes. This study had the specific intent of reviewing the main genomics and transcriptomic data available for this family and their use for the constitution of new varieties. This was achieved starting from the description of the main reproductive systems and barriers, with particular reference to cytoplasmic (CMS) and nuclear (NMS) male sterility. We found that CMS and NMS systems have been discovered and successfully exploited for the development of varieties only in Foeniculum vulgare, Daucus carota, Apium graveolens and Pastinaca sativa; whereas, strategies to limit self-pollination have been poorly considered. Since the constitution of new varieties benefits from the synergistic use of marker-assisted breeding in combination with conventional breeding schemes, we also analyzed and discussed the available SNP and SSR marker datasets (20 species) and genomes (8 species). Furthermore, the RNA-seq studies aimed at elucidating key pathways in stress tolerance or biosynthesis of the metabolites of interest were limited and proportional to the economic weight of each species. Finally, by aligning 53 plastid genomes from as many species as possible, we demonstrated the precision offered by the super barcoding approach to reconstruct the phylogenetic relationships of Apiaceae species. Overall, despite the impressive size of this family, we documented an evident lack of molecular data, especially because genomic and transcriptomic resources are circumscribed to a small number of species. We believe that our contribution can help future studies aimed at developing molecular tools for boosting breeding programs in crop plants of the Apiaceae family.
Keyphrases
  • rna seq
  • single cell
  • genetic diversity
  • climate change
  • gene expression
  • physical activity
  • ms ms
  • body mass index
  • machine learning
  • big data
  • copy number
  • current status
  • cancer therapy
  • artificial intelligence