Genomic prediction in contrast to a genome-wide association study in explaining heritable variation of complex growth traits in breeding populations of Eucalyptus

Abstract

BackgroundThe advent of high-throughput genotyping technologies coupled to genomic prediction methods established a new paradigm to integrate genomics and breeding. We carried out whole-genome prediction and contrasted it to a genome-wide association study (GWAS) for growth traits in breeding populations of Eucalyptus benthamii (n =505) and Eucalyptus pellita (n =732). Both species are of increasing commercial interest for the development of germplasm adapted to environmental stresses.ResultsPredictive ability reached 0.16 in E. benthamii and 0.44 in E. pellita for diameter growth. Predictive abilities using either Genomic BLUP or different Bayesian methods were similar, suggesting that growth adequately fits the infinitesimal model. Genomic prediction models using ~5000–10,000 SNPs provided predictive abilities equivalent to using all 13,787 and 19,506 SNPs genotyped in the E. benthamii and E. pellita populations, respectively. No difference was detected in predictive ability when different sets of SNPs were utilized, based on position (equidistantly genome-wide, inside genes, linkage disequilibrium pruned or on single chromosomes), as long as the total number of SNPs used was above ~5000. Predictive abilities obtained by removing relatedness between training and validation sets fell near zero for E. benthamii and were halved for E. pellita. These results corroborate the current view that relatedness is the main driver of genomic prediction, although some short-range historical linkage disequilibrium (LD) was likely captured for E. pellita. A GWAS identified only one significant association for volume growth in E. pellita, illustrating the fact that while genome-wide regression is able to account for large proportions of the heritability, very little or none of it is captured into significant associations using GWAS in breeding populations of the size evaluated in this study.ConclusionsThis study provides further experimental data supporting positive prospects of using genome-wide data to capture large proportions of trait heritability and predict growth traits in trees with accuracies equal or better than those attainable by phenotypic selection. Additionally, our results document the superiority of the whole-genome regression approach in accounting for large proportions of the heritability of complex traits such as growth in contrast to the limited value of the local GWAS approach toward breeding applications in forest trees.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-3920-2) contains supplementary material, which is available to authorized users.

Independent and Joint‐GWASfor growth traits inEucalyptusby assembling genome‐wide data for 3373 individuals across four breeding populations

“…, blue line) is located inside gene model Eucgr.H03281, a gene encoding for an armadillo/beta‐catenin‐like repeats‐containing protein‐related, whose function is involved in the cellulose biosynthetic process. In a recent GWAS study in another Eucalyptus species, E. pellita , we also found a significant association for growth inside Eucgr.F03806, a gene that codes for another armadillo/beta‐catenin‐like repeat positioned on a different chromosome (6) (Müller et al ., ). This gene in Arabidopsis thaliana (AT1G77460) transcribes the protein cellulose synthase interactive 3 (CSI3), which regulates primary cell wall biosynthesis and cellulose microfibrils organization (Lei et al ., ).…”

“…Various studies attempted GWAS for growth traits in forest trees, namely in Populus (Porth et al ., ; Allwright et al ., ; Du et al ., ; Fahrenkrog et al ., ), Pinus (Bartholomé et al ., ; Lu et al ., ) and Eucalyptus (Cappa et al ., ; Müller et al ., ; Resende et al ., ). Despite the considerably large number of individuals used in our study for each population and for the combined dataset, our results suggested that much larger numbers will be necessary to identify discrete regions capturing larger fractions of the genetic variance of complex traits as indicated by simulations (Spencer et al ., ; Visscher et al ., ).…”

“…Although less genetic variation is in principle available in such structured populations, the associations detected in genetically improved material should be considerably more relevant to further breeding, as their effect would be relevant in an already elite background. Following an equivalent rationale, three studies have reported GWAS for growth and wood quality traits in breeding populations of Eucalyptus (Cappa et al ., ; Müller et al ., ; Resende et al ., ). Interestingly, the results were on par with those described earlier in natural populations, in which few associations explaining small fractions of the genetic variation were detected.…”

Genomic basis of European ash tree resistance to ash dieback fungus

“…The levels of accuracy which our GP reached are high, and comparable to those that are used to inform selections in crop 46–50 , tree 12,51 and livestock breeding programmes 52,53 . Thus, our results have the potential to increase the speed at which we can successfully breed ash dieback resistant trees.…”

Genomic and transcriptomic approaches toward plant selection

“…Mixing different approaches is somehow a good way to get more accurate and reliable results while they are based on genomic data and validated by different methods at the same time. In a study on Eucalyptus, Müller et al (2017) showed that how genomic data can be useful for discovering heritable variation and how the genome wide analysis validate the results of genomic prediction (Müller et al, 2017).…”