Dario Grattapaglia

Publications (2)8.98 Total impact

• Article: Genomic selection in forest tree breeding

  Dario Grattapaglia, Marcos D. V. Resende
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  ABSTRACT: Genomic selection (GS) involves selection decisions based on genomic breeding values estimated as the sum of the effects of genome-wide markers capturing most quantitative trait loci (QTL) for the target trait(s). GS is revolutionizing breeding practice in domestic animals. The same approach and concepts can be readily applied to forest tree breeding where long generation times and late expressing complex traits are also a challenge. GS in forest trees would have additional advantages: large training populations can be easily assembled and accurately phenotyped for several traits, and the extent of linkage disequilibrium (LD) can be high in elite populations with small effective population size (N e) frequently used in advanced forest tree breeding programs. Deterministic equations were used to assess the impact of LD (modeled by N e and intermarker distance), the size of the training set, trait heritability, and the number of QTL on the predicted accuracy of GS. Results indicate that GS has the potential to radically improve the efficiency of tree breeding. The benchmark accuracy of conventional BLUP selection is reached by GS even at a marker density ~2 markers/cM when N e ≤ 30, while up to 20 markers/cM are necessary for larger N e. Shortening the breeding cycle by 50% with GS provides an increase ≥100% in selection efficiency. With the rapid technological advances and declining costs of genotyping, our cautiously optimistic outlook is that GS has great potential to accelerate tree breeding. However, further simulation studies and proof-of-concept experiments of GS are needed before recommending it for operational implementation. KeywordsGenome-wide selection–Effective population size–Linkage disequilibrium–Marker-assisted selection–MAS
  Tree Genetics & Genomes 04/2012; 7(2):241-255. · 2.34 Impact Factor
• Article: Genomic selection for growth and wood quality in Eucalyptus: capturing the missing heritability and accelerating breeding for complex traits in forest trees.

  Marcos D V Resende, Márcio F R Resende, Carolina P Sansaloni, Cesar D Petroli, Alexandre A Missiaggia, Aurelio M Aguiar, Jupiter M Abad, Elizabete K Takahashi, Antonio M Rosado, Danielle A Faria, Georgios J Pappas, Andrzej Kilian, Dario Grattapaglia
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  ABSTRACT: • 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 (N(e) = 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.
  New Phytologist 04/2012; 194(1):116-28. · 6.64 Impact Factor