Applied Genomics in Poplar: Innovative Breeding

Poplar species (Populus spp.) are appreciated by diverse industries such as pulp and paper, lumber, veneer, plywood, and composite panels production, partly due to their versatile juvenile productivity. Populus species also have well-developed genomic resources since the early twenty-first century. However, the factual utilization of these resources in operational poplar breeding still faces major bottlenecks due to long generation times and the complexity of the adaptive traits. Therefore, in this chapter, we aim to discuss major improvements in the fields of landscape genomics, genetic mapping, genomic prediction, and gene editing that may speed up poplar’s pre- and breeding efforts. We first discuss how landscape genomics may help to unveil hidden natural adaptive variation. As a second step we explore how traditional genetic mapping and genomic prediction efforts may leverage polygenic adaptation via genomic estimated adaptive values. We close by prospecting the promises of machine learning, multi-omics, and machine learning approaches. We envision that implementing more robust genome-wide association mapping and genomic prediction models, capable to better account for subtle and non-additive effects, together with modern CRISPR/Cas9 technology, is critical to improve the efficiency of poplar tree improvement.