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Significant progress has been made in forest genetics research and tree breeding in the last decades. Tree  breeding  is  the  selection  of  trees  with  superior  characteristics  of  economic  interest (growth  rate,  stem  straightness,  disease  resistance, wood quality),  multiplying  these  selections  or bringing copies of them together  to intermate, and then deploying the resultant seed or propagules in the forest as the new planting stock.  The level of genetic gain tends to be related to the level of input (time, labor, money) in selecting the best trees or deploying the best material.  Field  trials  help  identify  the  best  selection  in  terms  of  genetic  quality  but  are costly and take 20 or so years. Vegetative propagation may speed up the rate with which the best trees are deployed but can increase costs.  Many breeding programs in Europe and in the world have achieved substantial genetic gains in productivity, pest resistance and wood quality. Genetically improved plantations from breeding programs have had and continue to make significant impacts on forest productivity, wood supplies, and sustainability of forest resources (Li and McKeand, 2005). Currently, more than 40 % of global supply in industrial wood comes from planted forests (Payn et al., 2015), most of them using improved planting stock.

A new era of breeding has appeared in late 1980s with the first efforts to develop genetic marker based approaches in forest trees. The first DNA-based markers, RFLPs, allowed obtaining dense genetic maps to scan the genome and map quantitative trait loci (QTLs). This approach was quite effective toward mapping QTLs in many forest tree species but the approach could not be brought to application in tree breeding due to low levels of linkage disequilibrium (LD) in forest tree breeding populations and recombination between flanking markers and QTLs with each generation. The next generation of DNA markers based of the polymerase chain reaction (PCR), RAPD, AFLP and SSR, did not solve the LD and recombination problem, even though more markers were available and throughput increased.

The situation began to change in the early 2000s with the availability of automated DNA sequencing technology and single nucleotide polymorphism (SNP) genetic markers. The updated version of the Populus genome, the recently released Eucalyptus grandis genome and the concerted efforts towards the generation of genome sequences for spruces (Picea sp.) and pines (Pinus sp.) by several groups worldwide, are fueling a multitude of inter-disciplinary studies and applications in sustainable forest production and conservation. (Grattapaglia 2011). Now association studies could be performed where SNPs within candidate genes controlling complex traits could be identified and thus “solving” or minimizing the LD and recombination limitation. This approach to complex trait dissection has been widely applied in forest trees and the early approach of QTL mapping in segregating populations has been mostly abandoned. The association genetic approach has been used to find candidate gene SNPs associated to a broad array of quantitative traits of interest (wood properties, growth, abiotic stresses and disease resistance) (Neale 2011). However, important questions remain still unsolved :

• Why individual SNP x trait associations (such as QTL mapping before) only account for a small proportion of the variation (generally less than 2-3% of the total phenotypic variance) and the total variation explained by all markers is generally less than 50% ?
• What is the importance of DNA epiterogenesis phenomena in breeding of forest species?
• How climate changes can be taken in consideration in genomic assisted breeding ?
• Are there ways to speed up the breeding of wood species growing in northern hemisphere to levels of those practiced in southern hemisphere ?

Grattapalia, D., BMC Proceedings, 2011 5(Suppl 5):A1

Li & McKeand, For Gen, 2005 12(2) 141-143

Neale, D., BMC Proceedings, 2011 5(Suppl 7):I4

Payn, T, et al, 2015. Changes in planted forests and future global implications. Forest Ecology and Management 352 (2015) 57-67.