Back

The FORGENIUS genomic resources: new genotyping tools and genomic data for 23 forest tree species and their Genetic Conservation Units

Pinosio, S.; Bagnoli, F.; Avanzi, C.; Castellani, M. B.; Frascella, A.; McEvoy, S. L.; Olsson, S.; Spanu, I.; Vajana, E.; FORGENIUS CONSORTIUM, ; Gonzalez-Martinez, S. C.; Pyhäjärvi, T.; Scotti, I.; Vendramin, G. G.; Piotti, A.

2025-08-10 genomics
10.1101/2025.08.08.669074 bioRxiv
Show abstract

Genetic diversity is a critical but often overlooked component of biological diversity. The European H2020 FORGENIUS project is precisely aimed at increasing the quality and quantity of genetic data to start monitoring the European network of forest Genetic Conservation Units (GCUs). A first step in this direction was developing standardized genomic resources for 23 forest tree species, spanning from rare and scattered (e.g., Abies nebrodensis and Torminalis glaberrima) to widespread, economically relevant ones (e.g., Fagus sylvatica, Picea abies and Pinus sylvestris). Here, we describe the development and application of targeted genotyping tools, primarily based on Single Primer Enrichment Technology (SPET), along with existing SNP arrays for the selected species. The SPET panels developed in FORGENIUS were designed to capture {square}10,000 loci per species, balancing species-specific and randomly distributed regions to ensure broad genome coverage and minimize ascertainment bias. Across 7,192 genotyped trees, we identified over 1.8 million single nucleotide polymorphisms (SNPs) covering approximately 50 Mb of DNA sequence. SPET panels demonstrated high genotyping efficiency and cross-species transferability, especially within genera such as Quercus and Abies. They represent a cost-effective, flexible, and scalable solution for population-level genetic assessments across diverse taxa, enabling standardized, genome-wide characterization of the GCU network. These resources not only promote the establishment of genetic monitoring, support genetically informed conservation strategies and improve our understanding of adaptive responses in European forests, but also enhance species delimitation and hybrid detection, and enable the characterization of phylogenetically related but previously underexplored species.

Published in Molecular Ecology Resources (predicted rank #1) · training set

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.