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Genomic selection for growth and wood quality in Eucalyptus: capturing the missing heritability and accelerating breeding for complex traits in forest trees
- Resende, Marcos D. V., Resende, Márcio F. R., Jr, Sansaloni, Carolina P., Petroli, Cesar D., Missiaggia, Alexandre A., Aguiar, Aurelio M., Abad, Jupiter M., Takahashi, Elizabete K., Rosado, Antonio M., Faria, Danielle A., Pappas, Georgios J., Jr., Kilian, Andrzej, Grattapaglia, Dario
- The new phytologist 2012 v.194 no.1 pp. 116-128
- Eucalyptus, forest trees, genotype-environment interaction, heritability, linear models, linkage disequilibrium, population size, pulp, quantitative trait loci, quantitative traits, specific gravity, tree and stand measurements, tree breeding, wood, wood quality
- • Genomic selection (GS) is expected to cause a paradigm shift in tree breeding by improving its speed and efficiency. By fitting all the genome‐wide markers concurrently, GS can capture most of the ‘missing heritability’ of complex traits that quantitative trait locus (QTL) and association mapping classically fail to explain. Experimental support of GS is now required. • The effectiveness of GS was assessed in two unrelated Eucalyptus breeding populations with contrasting effective population sizes (Ne = 11 and 51) genotyped with > 3000 DArT markers. Prediction models were developed for tree circumference and height growth, wood specific gravity and pulp yield using random regression best linear unbiased predictor (BLUP). • Accuracies of GS varied between 0.55 and 0.88, matching the accuracies achieved by conventional phenotypic selection. Substantial proportions (74–97%) of trait heritability were captured by fitting all genome‐wide markers simultaneously. Genomic regions explaining trait variation largely coincided between populations, although GS models predicted poorly across populations, likely as a result of variable patterns of linkage disequilibrium, inconsistent allelic effects and genotype × environment interaction. • GS brings a new perspective to the understanding of quantitative trait variation in forest trees and provides a revolutionary tool for applied tree improvement. Nevertheless population‐specific predictive models will likely drive the initial applications of GS in forest tree breeding.