Genomic prediction in contrast to a genome-wide association study in explaining heritable variation of complex growth traits in breeding populations of Eucalyptus
© The Author(s). 2017
Received: 7 October 2016
Accepted: 3 July 2017
Published: 11 July 2017
The 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.
Predictive 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.
This 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.
KeywordsGenomic selection GWAS SNP genotyping Relatedness Tree breeding Eucalyptus benthamii E. pellita
Species of Eucalyptus are the most planted hardwood trees worldwide due to their multipurpose applications (e.g. pulp, paper, solid wood and bioenergy), superior growth, high adaptability and wood quality . Amongst the 800 catalogued species of Eucalyptus L’Hér. (Myrtaceae), the “big nine” species within subgenus Symphyomyrtus account for over 95% of the world’s eucalypt plantations . Within this group, Eucalyptus grandis Hill ex Maiden, E. urophylla S.T. Blake, and E. camaldulensis Dehnh are the most economically prominent ones in tropical regions, whereas E. globulus Labill and E. nitens H. Deane & Maiden are notable in temperate regions . The extensive intra- and interspecific diversity and sexual compatibility across species of Symphyomyrtus has been a major advantage to breeders, as it allows rapid blending of gene pools that evolved separately under contrasting environmental pressures . Nevertheless, there is still ample opportunities for expanding the use of some secondary species of Symphyomyrtus not included among the “big nine”, to develop uniquely adapted genetic material that combine rapid growth, good wood quality and adaptation to environmental stresses such as frost, heat and drought.
Eucalyptus benthamii Maiden & Cambage (Camden white gum), a species of restricted occurrence in its natural range in Australia , has showed great potential to expand eucalypt commercial plantations into subtropical regions subject to periodic frosts . Eucalyptus benthamii planted as pure species or in hybrid combinations has received increasing attention in subtropical regions of southern Brazil and southeastern USA [6, 7]. Another species of marginal importance until recently, Eucalyptus pellita F. Mueller (large-fruited red mahogany), is highly suitable for growth in year-round humid lowland equatorial climates under high temperatures, showing a particularly high resistance to pathogens. Eucalyptus pellita is endemic to tropical regions in two disjoint natural forests, in southern New Guinea and in northern Australia . It has shown fast growth in hybrid combination with E. grandis providing resistance to a number of fungal diseases .
Genomic selection (GS) was proposed by Meuwissen et al. , and has gained increasing interest among forest tree breeders. This predictive methodology provides an alternative approach to using marker-assisted selection (MAS) that relies on previously detected discrete quantitative trait loci (QTL) in bi-parental mapping and association genetics experiments. In forest trees, genomic prediction began to be addressed by simulation studies [11, 12] followed by experimental reports in Pinus  and Eucalyptus  demonstrating the positive prospects of this breeding method. Since then, a number of experimental genomic prediction studies have confirmed the potential of GS in conifer species, including Pinus [15–17] and Picea [18–21]. Recently, genomic prediction models were evaluated across generations in maritime pine (Pinus pinaster), [22, 23] demonstrating even more encouraging perspectives of this novel approach to accelerate breeding of forest trees.
Several parameters were shown to affect GS prediction accuracy in simulation studies, such as the number of QTLs controlling the trait, trait heritability, the size of the training population, number of markers and the effective population size (N e ) of the target population . If an adequate density of markers is provided for a given N e , it is expected that most QTL will be in LD with at least one marker and will be captured in predictive models. Consequently, high-throughput and low-cost genotyping platforms constitute an essential tool to apply GS. The reduction of the effective population size leads to increased relatedness between individuals and more extensive LD in the population. Markers fitted in a GS model will capture not only LD but also relatedness between individuals in the training and validation sets. An increase in prediction ability with enhanced relatedness among the training and validation sets was shown early on from simulation studies , and underscored in all recent reviews on the perspectives GS in plant and domestic animals breeding [25, 26]. Phenotypes of individuals closely related to the training population will be better predicted over distantly related individuals.
In this study, we report the development of genomic prediction models for growth traits in two breeding populations of E. benthamii (n =505) and E. pellita (n =732) using SNP data generated with the multi-species Eucalyptus EUChip60k SNP chip. Using a genomic relationship matrix (GRM) we compared the pedigree and genome-estimated breeding values and narrow-sense heritabilities in the two populations. Different Bayesian methods for predicting growth traits were compared. The impact of variable numbers of SNPs, different SNP sampling methods based on their position in the genome, and the impact of relatedness on genomic prediction were also evaluated. Finally, a genome-wide association analysis was carried out on the same datasets to evaluate what would be the ability to capture heritability and detect discrete associations for complex growth traits in an operational breeding population under selection.
Populations and phenotypic data
General attributes of the breeding populations and trials studied
Total number of trees in trial
Total number of open pollinated (OP) families
Number of blocks
Number of individuals/OP family
Number of trees measured
Number of trees used in the analyses
Effective population size (N e ) estimated from LD data
Age at phenotyping (yr)
Rio Verde, GO
Number of traits
Genotyping and filtering
A total of 552 E. benthamii trees and 771 E. pellita trees were genotyped using the Eucalyptus Illumina Infinium EUChip60K . The genotypic data were filtered to remove SNPs with call rate (CR) ≤ 90% and monomorphic SNPs, therefore keeping all SNPs with Minimum Allele Frequency (MAF) > 0 in the analysis. Because trees were genotyped before the final field measurements, some genotyped trees died, so that ultimately 505 individuals of E. benthamii and 732 of E. pellita had full genotypic and phenotypic data for further analyses. An alternative SNP dataset was also generated by keeping only SNPs MAF ≥0.05. With the objective of evaluating the effect of LD-pruning on predictions, polymorphic SNPs (CR ≥ 90% and MAF > 0 or MAF ≥ 0.05) were pruned based on pairwise linkage disequilibrium (LD) estimates using PLINK v1.9 , to generate a pruned subset of SNPs that are in approximate linkage equilibrium (LE). The LD based SNP pruning method was applied with a window size of 100 Kbp, shifting the window by one SNP at the end of each step and removing one SNP from a pair of SNPs if LD was greater than 0.2 (plink command: --indep-pairwise 100 kb 1 0.2).
Effective population size estimation, population structure and LD analyses
Effective population size (N e ) was estimated based on the linkage disequilibrium (LDN e ) method implemented in NeEstimator v2.01  for each species. A random mating model and MAF < 0.05 was used for excluding rare alleles in LDN e. Confidence intervals for these estimates were obtained using the parametric method in NeEstimator, where the number of independent alleles is used as the degree of freedom in a chi-square distribution. The genetic structure for both eucalypt populations estimated based on a Bayesian clustering method was determined with STRUCTURE v2.2.4  using only the LD-pruned SNPs set. The individual structures were classified in K clusters according to genetic similarity. The admixture model was applied, with correlated allelic frequencies, using no previous population information. The number of tested clusters (K) ranged from 1 to 10, and each K was replicated 10 times. The burn-in period and the number of Markov Chain Monte Carlo (MCMC) replications were 100,000 and 200,000, respectively. The number of genetic groups was determined based on the criteria proposed by Evanno et al.  using the program STRUCTURE HARVESTER v0.6.93 . The software CLUMPP v1.1.2  was used to find consensus among the 10 most probable K interactions. Principal component analysis (PCA) was performed using SNPRelate R package , with only the LD-pruned SNPs set. Analyses of linkage disequilibrium were performed using LDcorSV . Pairwise estimates of LD were calculated by the classical measure of the squared correlation of allele frequencies at diallelic loci (r 2 ), as well as correcting for bias due to relatedness and population structure (r 2 VS), and adjusting it independently for relatedness (r 2 V) and for population structure (r 2 S). To estimate the adjusted LD, the genomic relationship matrix (GRM) was computed using the Powell method  implemented in R. The population structure results were based on the most probable value of K (K = 2). The LD decay of r 2 with distance in Kbp was fitted by a nonlinear regression model between adjacent sites using the R script by Marroni et al. . To visualize patterns of LD decay in the two eucalypts species, all the LD estimates (r 2 , r 2 V, r 2 S, r 2 VS) were plotted up to a 100 Kbp distance.
Genomic and pedigree-based breeding value predictions
The assumptions of the m vector depend on the prior adopted. The respective priors used in the linear regression coefficients for each model are described in Additional file 1. To estimate the parameters of the models a total 200,000 iterations of MCMC were used with a burn-in period of 50,000 cycles and every fifth sample was kept. For all these models, a 10-fold cross-validation approach was applied as described previously.
Genomic predictions using selected SNPs subsets
The Bayesian Ridge-Regression (BRR) model was fitted using different subsets of SNPs of various sizes and selected using different criteria as described below. Initially a random sampling of SNPs stratified by chromosome was tested using (i) a cumulative approach, such that from the smallest subset of SNPs tested, additional ones were added to the previous set and (ii) a non-cumulative fashion, where different final sets of SNPs were randomly selected from all available SNPs. Next, variable positions of SNPs were tested, including: (iii) evenly spaced SNPs across the genome; (iv) only SNPs within gene models annotated in the Eucalyptus reference genome ; (v) SNPs based on LD-pruning and (vi) SNPs from individual chromosomes. For each subset we estimated the predictive ability and genomic heritability. First, we evaluated models using different SNP subsets (from all 13,787 and 19,506 SNPs available for E. benthamii and E. pellita respectively, down to 2000 in smaller increments of 1000 SNPs, 1500, 1250, 1000, 750, 500, 300, 250, 200, 150 and 100 SNPs) with either a cumulative (i) or non-cumulative (ii) sampling of SNPs. For each number of SNPs and sampling strategy, ten replicates were performed. The evenly spaced SNPs subsets (iii) were created using different target windows sizes, with 1 SNPs every 10, 50, 100, 250, 500 Kbp and 1 Mbp, resulting in variable average distances between SNPs (Additional file 2: Table S1). For the within-gene SNP subset (iv), all SNPs located within annotated gene models (genic regions) and SNPs located outside of annotated gene models (intergenic regions) in the Eucalyptus genome were evaluated. To create the subsets of SNPs selected based on LD pruning (v), SNPs in approximate LE (r 2 ≤ 0.2) with each other were chosen using PLINK v1.9 . Finally, in the chromosome-specific SNP subsets (vi) the prediction models were fitted independently using only SNPs on each chromosome separately.
Genomic prediction controlling for relatedness between training and validation sets
To assess the relative impact of relatedness versus historical LD on the predictive ability, BRR prediction models were fitted minimizing relatedness between training and validation populations. Individuals were split into training and validation sets based on a Principal Component Analysis (PCA) or STRUCTURE analysis (K = 2). In E. benthamii, 21 outlier individuals were removed and the remaining individuals were split into two subpopulations based on maximum genetic distance, one with 310 trees and the other with 174. For E. pellita, 26 outliers were excluded and the remaining 706 individuals were split into two subpopulations with 192 and 514 trees. As a control, a 10-fold cross-validation in each direction, with the same numbers of individuals used in the split populations, was carried out by random allocation of the individuals to training and validation sets.
Genome-wide association analysis
Of the 60,904 SNPs in the EUChip60K, 50,303 (82.6%) and 49,518 (81.3%) were genotyped for E. benthamii and E. pellita respectively (Additional file 2: Figure S1A), by using the phylogenetically appropriate SNP clustering file for SNP calling , and filtering for SNPs with CR ≥ 90%. After selecting polymorphic SNPs (MAF > 0) 13,787 and 19,506 SNPs were retained for further analyses with a final rate of missing data of 1.4% and 0.8% for E. benthamii and E. pellita, respectively. An alternative SNP dataset was also used by filtering out SNPs with MAF < 0.05 to investigate whether removing lower frequency SNPs had an impact on genomic predictions. A total of 7563 SNPs for E. benthamii and 12,483 SNPs for E. pellita were retained for this alternative set.
Linkage disequilibrium and estimated effective population sizes
Genomic and pedigree-estimated heritabilities
Estimates of narrow-sense heritabilities (h 2 ) and predictive abilities (r gy ), obtained using pedigree data (ABLUP) and genomic data (several methods), for the E. benthamii and E. pellita breeding populations
r gy (SE)
r gy (SE)
r gy (SE)
r gy (SE)
r gy (SE)
r gy (SE)
- 0.030 (0.028)
- 0.009 (0.026)
MAF > 0
MAF ≥ 0.05
Consistent with expectations, predictive abilities (r gy ) followed the same trend as the estimated genomic heritabilities (Table 2). Predictive abilities estimated using different Bayesian methods produced equivalent estimates to those obtained using GBLUP and pedigree-based. For the E. benthamii population both pedigree and genomic predictive abilities were generally low, averaging 0.16 for DBH, 0.14 for WV and close to zero for HT across all methods. For E. pellita, genomic predictive abilities were considerably higher, averaging 0.44 for DBH, 0.34 for HT and 0.42 for WV, suggesting the presence of a larger amount of additive genetic variation for these traits in this breeding population (Table 2). No difference was observed in the predictive abilities when using SNP sets including or not lower frequency SNPs. During cross-validation of genomic predictions a considerable variation was observed in the predictive abilities estimated across the different folds (Additional file 2: Table S3). This variation was larger for E. benthamii, where the predictive ability across folds ranged from a low −0.058 to 0.415 using BRR for DBH, with an average of 0.162 with a standard error (SE) of ±0.044. In E. pellita, the variation was smaller, with estimates ranging from 0.358 to 0.550 for DBH, with the ten-fold average equal to 0.441 ± 0.019 (Additional file 2: Table S3).
Impact of variable numbers of SNPs on genomic predictions
Genomic estimates of narrow-sense heritabilities (h 2 ) and predictive abilities (r gy ) for the E. benthamii and E. pellita breeding populations using different SNP sampling methods
SNP sampling method
Number of SNPs
Number of SNPs
h 2 (SE)
r gy (SE)
h 2 (SE)
r gy (SE)
h 2 (SE)
r gy (SE)
h 2 (SE)
r gy (SE)
h 2 (SE)
r gy (SE)
Evenly spaced 10 Kbp
Evenly spaced 50 Kbp
Evenly spaced 100 Kbp
Evenly spaced 250 Kbp
Evenly spaced 500 Kbp
Evenly spaced 1 Mbp
SNPs in LE (LD-pruning)
Impact of variable position-based SNP sampling methods
Overall, no difference was seen in the estimates of heritabilities and predictive abilities when different position-based SNP sampling schemes were used, as long as the total number of SNPs was close to 5000 (Table 3, Fig. 2). The predictive abilities estimated with a subset of evenly spaced SNPs every 1 Mbp windows (610 SNPs in E. benthamii and 609 SNPs in E. pellita), were slightly higher than those using 500 randomly sampled SNPs (Table 3). Although these results indicate that the number, and not the position of SNPs, determines the accuracy of predictions, they also suggest that even distribution might provide a small-added advantage when compared to random sampling. No significant differences in predictions were seen for any trait in both species when SNPs located in genic versus intergenic regions were used, and the predictions were equivalent to those obtained by random sampling of equivalent numbers of SNPs. The same result was observed with the LD-pruning approach, where estimates of predictive ability were similar either using LD-pruned SNPs in LE or all polymorphic SNPs (Table 3). There was no difference observed in the estimates of variance components when different sets of SNPs sampled based on position in the genome were used (Additional file 3).
Genomic estimates of narrow-sense heritabilities (h 2 ) and predictive abilities (r gy ) for the E. benthamii and E. pellita breeding populations using chromosome-specific SNP sets
Number of SNPs
Number of SNPs
h 2 (SE)
r gy (SE)
h 2 (SE)
r gy (SE)
h 2 (SE)
r gy (SE)
h 2 (SE)
r gy (SE)
h 2 (SE)
r gy (SE)
Impact of relatedness between training and validation sets
Association genetics models comparison
This study makes a further step towards the experimental assessment of whole-genomic prediction of complex traits in species of forest trees in general and of Eucalyptus in particular. Our results corroborate previous reports in forest trees showing encouraging perspectives of using genome-wide SNP data to capture large proportions of trait heritability and predict traits such as height and diameter growth with accuracies as good as or better than those attainable by conventional phenotypic selection.
Genomic heritabilities and predictions
Genomic heritabilities, irrespective of the method used, were generally lower than the pedigree-based estimates, with the exception of HT in E. benthamii (Table 2). Genomic heritability is considered to better reflect the true genetic relationships among individuals and as such, it corresponds to the proportion of phenotypic variance that can be explained by regression on molecular markers. The genomic heritability and trait heritability are expected to be equal only when all causal variants are typed. Additionally, when close relatives sharing long chromosome segments are analyzed, high prediction accuracy and very small bias in genomic heritability estimates are expected . Given the relatively long-range LD and relatedness in our populations, our estimates of genomic heritability should closely reflect the amount of additive genetic variance for the traits measured. Genomic heritabilities lower than the pedigree-based estimates were also reported in open-pollinated families of spruce [19, 21]. Pedigree-based heritability estimates from open-pollinated families could be inflated due to the presence of full-sibs or selfs and the inability of these estimates to disentangle the non-additive from the additive genetic components . For E. pellita, pedigree-based heritability could not be estimated. However, by using the SNP data, heritability estimates were obtained that breeders would not otherwise have had access to.
Predictive abilities of growth traits using GBLUP and different Bayesian methods reached similar results for all traits, in line with previous reports in forest trees [16, 20, 22]. These results provide further evidence that growth traits in Eucalyptus, and likely for all forest trees, are complex in architecture, controlled by a large number of small effect loci and fit adequately the infinitesimal model. The predictive ability estimates obtained for growth traits in E. pellita (0.34–0.44) using GBLUP were slightly lower than those reported for E. grandis x E. urophylla (0.46–0.55) . For E. benthamii, predictive abilities were lower (~0.16), possibly the result of (i) the larger effective population size; (ii) the relatively limited number of individuals used for model training (only ~500); and (iii) the limited genetic diversity available in this species and particularly so in this introduced population in Brazil, also indicated by the low heritability found in our study as well as in others with similar germplasm . From the applied breeding standpoint however, the genomic predictive abilities were as good as or better than the predictive abilities based on phenotypic data.
Prediction models using ~5000 SNPs provided predictive abilities almost equivalent to using all available SNPs for all traits and no difference was observed using different sets of SNPs. These results suggest that genomic prediction is largely driven by relatedness such that once a certain number of randomly sampled SNPs across the genome are used, suitable predictive ability is reached. This outcome indicates that low-density SNP chips could be contemplated as a way to reduce cost of GS in line to what has been the case for domestic animals [26, 49]. It is expected, however, that genomic predictions will decay over generations due to the combined effect of recombination and selection on the patterns of LD , unless continuous model retraining strategies are adopted . At this point, therefore, it is not clear whether the use of smaller SNP subsets is warranted for the long-term implementation of GS in Eucalyptus. A better assessment will be possible when predictions are carried out across breeding generations testing variable SNP densities.
We observed a major impact of relatedness on predictions, more so in E. benthamii than E. pellita (Fig. 3) consistent with theoretical expectations  and previous experimental results in forest trees [14, 18, 19]. The relative contributions of historical LD and relatedness are however difficult to disentangle. Predictive ability can be high even in the absence of LD when markers capture genetic relationships, but it will be even greater if markers are in LD with causal loci . The extent of LD detected in these populations reflected their differences in evolutionary and breeding history. A faster genome-wide LD decay was observed in E. benthamii (7.7 Kb, Fig. 1a) than in E. pellita (25.6 Kbp, Fig. 1c). While the E. benthamii population is derived from seeds collected in wild stands and its LD was similar to that found in natural populations of E. grandis (≈4–6 Kb) , the E. pellita population comes from a clonal seed orchard established with advanced selections such that a smaller effective population size and more extensive LD was expected.
The presence of some level of short-range historical LD could in part explain why predictions were still reasonable in E. pellita even after attempting to minimize relatedness between training and validation sets (Fig. 3b). However, another possibility is that our attempt to decrease relatedness was not completely efficient. To evaluate these alternative hypotheses we compared the predictive abilities obtained using the same number of markers concentrated on a single chromosome (capturing largely the effect of relatedness), versus distributed genome-wide (capturing relatedness and LD). Assuming an infinitesimal model in which growth traits are controlled by many QTLs with small effects distributed genome-wide, the difference between these two sets could be tentatively taken as the contribution of historical LD to predictions. An increase of 22 to 35% in predictive ability was seen (e.g. 0.306 versus 0.414 for DBH) when genome-wide SNPs were used, suggesting that some short-range historical LD between markers and causal loci could be accounted for in this population. Overall, our results corroborate previous reports on the major impact of relatedness on genomic prediction and further highlight the importance of properly planning the populations on which GS models will be trained and those where the models will be applied. If the training population is more or less related to the validation population than the future selection candidates, then the expected outcome of implementing genomic selection will be over- or underestimated, respectively.
GWAS versus genomic prediction in breeding populations
The objective of our GWAS was to assess the value of this approach in closed breeding populations under selection and compare it to whole-genome prediction from the standpoint of how much genetic variation could be captured for practical breeding. After duly controlling for population structure and experimental fixed effects, and applying experiment-wide corrections for multiple tests, we identified only one significant association for volume growth in E. pellita (Fig. 4c). Despite the relatively larger population size (n = 732) when compared to populations used in previous GWAS in forest trees (typically between ~300 and ~700 individuals), our results are consistent with the fact that very few associations were also found for growth in all those reported GWAS to date [52–59]. Population sizes used have been small, such that experiments have suffered from low power to detect the likely large number of small effect loci controlling growth. Integrating linkage mapping data from bi-parental pedigrees with association populations has been attempted but results have not improved and only a handful of associations have been found, again explaining very little of the genetic variation [56, 57, 59]. Our direct comparison between GS and GWAS is novel and more explicitly corroborates the fact that while genome-wide regression is able to account for large proportions of the pedigree-heritability (e.g. 73% for DBH in E. benthamii) and provide useful phenotype predictions, very little of the heritability is captured into significant associations using the GWAS approach. Reasons for this major discrepancy are not surprising and have been widely discussed in the plant, animal and human literature [60–62]. They derive essentially from the fact that GWAS by principle, relies on the application of stringent significance tests to declare an association. These very stringent tests typically result in only the largest effect QTLs being found, while the vast majority have too small an effect to be detectable in the limited power GWAS populations used. If no major effect exists, then no associations are found, which is most likely the case of the limited association results for growth targeted in our study.
A potential criticism to our GWAS is the fact that it was carried out in a breeding population with limited diversity and not in a canonical GWAS population sampled from the wild. GWAS studies for growth traits in forest trees have in fact targeted collections of trees derived from natural populations sampling large amounts of diversity. The goal of those studies has been to detect associations that would potentially allow gene discovery or even the identification of the elusive QTN (quantitative trait nucleotide) . However, notwithstanding the fact that very few associations were found for growth traits in those GWAS, explaining overall negligible fractions of trait heritability, it is not clear yet how marker-trait associations detected in undomesticated tree populations, genetically distant from improved germplasm, would be converted into useful information to breeding practice. This, in fact, has not been demonstrated yet in forest trees. Targeted alleles found by GWAS in natural populations might contribute relatively negligible effects, be already fixed or simply not be sampled in existing breeding populations . On the other hand, although genetic variation available in breeding populations is in principle more limited, associations detected in genetically improved material should be more relevant to breeding. A recent GWAS in a Eucalyptus breeding population reported promising results using a regional heritability mapping, an approach able to capture both common and rare allelic effects that individually contribute too little variance to be detected by conventional GWAS . The availability of GWAS data could be valuable to improve genomic predictions accuracies by assigning locus- or trait-specific priors to genomic prediction models , as recently shown in rice .
This study contributes further experimental data supporting the positive prospects of genomic selection to predict complex traits such as height and diameter growth in forest trees with accuracies equivalent or superior to those achievable by phenotypic selection. We show that genetic relatedness captured by the SNP data between training and validation populations and, by extension, to future selection candidates, is what will most likely determine the successful use of genomic selection in Eucalyptus breeding. We also conclude that more important to GS than the number and position of the SNPs fitted in the model, is the extensive LD created in closed breeding populations with small effective population sizes. Lower density SNP panels with ~5000 to 10,000 SNPs, distributed across the genome, should provide a good compromise between genotyping costs and predictive ability in such standard breeding populations advanced by open pollinated breeding. However, further experiments are necessary to evaluate the performance of such SNP densities across generations of breeding. Our results also illustrate the superiority of the whole-genome regression approach in accounting for large proportions of the heritability in contrast to the limited value of the local GWAS approach for breeding applications. To provide useful GWAS data toward breeding for growth traits in Eucalyptus and likely in all forest trees, it will be necessary first to massively increase the sample size, such that sufficient power is reached to detect at least part of the slightly larger effects segregating in the target breeding population. In the meantime, the encouraging results of genomic prediction that we, and others, have shown in this and other studies should probably receive greater attention if the objective is to impact breeding practice.
This work was supported by PRONEX-FAP-DF grant 2009/00106-8 ‘NEXTREE’, CNPq grant 400663/2012-0 and EMBRAPA grant 03.11.01.007.00.00 to DG. BSFM and JEAF had doctoral fellowships and DG a research fellowship from CNPq. We acknowledge the University of Brasília, the University of Florida and the Brazilian Agricultural Research Corporation (EMBRAPA) for their support during this project, as well as Ubirajara S. Oliveira from COMIGO and Luís Carlos Valtrin and Tayná J. Ben from GOLDEN TREE for providing logistic support in the field trials.
Availability of data and materials
The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.
BSFM performed the analysis of experiments and wrote the first version of the manuscript. PETS and EPF conducted the field experiments and collected the phenotype data. LGN and JEAF made substantial contributions to the bioinformatics, genomic prediction analysis and interpretation of data. MFRRJr, PRM and MK contributed to the interpretation of results. LGN was involved in manuscript editing. DG generated the SNP data, designed and coordinated the study and edited the final version of the manuscript. All authors have read and approved the manuscript.
The authors declare that they have no competing interests.
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- Myburg AA, Potts BM, Marques CM, Kirst M, Jm G, Grattapaglia D, Grima-Pettenati J. Eucalyptus. In: Kole C, editor. Genome mapping and molecular breeding in plants, vol. 7: Forest Trees. New York: Springer; 2007. p. 115–60.Google Scholar
- Harwood C. New introductions-doing it right. In: Proceedings of the conference “developing a eucalypt resource for New Zealand”: 2011; Blenheim, New Zealand; 2011. p. 10.Google Scholar
- Grattapaglia D, Kirst M. Eucalyptus applied genomics: from gene sequences to breeding tools. New Phytol. 2008;179(4):911–29.PubMedView ArticleGoogle Scholar
- Butcher PA, Skinner AK, Gardiner CA. Increased inbreeding and inter-species gene flow in remnant populations of the rare eucalyptus Benthamii. Conserv Genet. 2005;6(2):213–26.View ArticleGoogle Scholar
- Arnold R, Li B, Luo J, Bai F, Baker T: Selection of cold-tolerant Eucalyptus species and provenances for inland frost-susceptible, humid subtropical regions of southern China. Aust For 2015, 9158(August):1-14.Google Scholar
- RML DC, Estopa RA, Biernaski FA, Mori ES. Prediction of genetics gains in Eucalyptus Benthamii Maiden & Cambage Progenies by different selection methods. Sci For. 2016;44(109):105–13.Google Scholar
- Pirraglia A, Gonzalez R, Saloni D, Wright J, Denig J. Fuel properties and suitability of eucalyptus Benthamii and Eucalyptus Macarthurii for torrefied wood and pellets. Bioresources. 2012;7(1):217–35.Google Scholar
- Harwood CE, Alloysius D, Pomroy P, Robson KW, Haines MW. Early growth and survival of Eucalyptus Pellita provenances in a range of tropical environments, compared with E. Grandis, E. Urophylla and Acacia Mangium. New Forest. 1997;14(3):203–19.View ArticleGoogle Scholar
- Agustini L, Francis A, Glen M, Indrayadi H, Mohammed CL. Signs and identification of fungal root-rot pathogens in tropical Eucalyptus Pellita plantations. For Pathol. 2014;44(6):486–95.View ArticleGoogle Scholar
- Meuwissen TH, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001;157(4):1819–29.PubMedPubMed CentralGoogle Scholar
- Grattapaglia D, Resende MDV. Genomic selection in forest tree breeding. Tree Genet Genomes. 2011;7(2):241–55.View ArticleGoogle Scholar
- Iwata H, Hayashi T, Tsumura Y. Prospects for genomic selection in conifer breeding: a simulation study of Cryptomeria Japonica. Tree Genet Genomes. 2011;7(4):747. -758-758View ArticleGoogle Scholar
- Resende MFR, Munoz P, Acosta JJ, Peter GF, Davis JM, Grattapaglia D, Resende MDV, Kirst M. Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments. New Phytol. 2012;193(3):617–24.PubMedView ArticleGoogle Scholar
- Resende MDV, Resende MFR, Sansaloni CP, Petroli CD, Missiaggia AA, Aguiar AM, Abad JM, Takahashi EK, Rosado AM, Faria DA, et al. Genomic selection for growth and wood quality in eucalyptus: capturing the missing heritability and accelerating breeding for complex traits in forest trees. New Phytol. 2012;194(1):116–28.PubMedView ArticleGoogle Scholar
- Zapata-Valenzuela J, Whetten RW, Neale D, Mckeand S, Isik F. Genomic estimated breeding values using genomic relationship matrices in a cloned population of loblolly pine. G3-Genes Genom Genet. 2013;3(5):909–16.Google Scholar
- Resende MFR, Munoz P, Resende MDV, Garrick DJ, Fernando RL, Davis JM, Jokela EJ, Martin TA, Peter GF, Kirst M. Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus Taeda L.). Genetics. 2012;190(4):1503–10.PubMedPubMed CentralView ArticleGoogle Scholar
- De Almeida Filho JE, JFR G, Silva FF E, De Resende MDV, Muñoz P, Kirst M, Resende MFR. The contribution of dominance to phenotype prediction in a pine breeding and simulated population. Heredity. 2016;117(1):33–41.PubMedPubMed CentralView ArticleGoogle Scholar
- Beaulieu J, Doerksen TK, Mackay J, Rainville A, Bousquet J. Genomic selection accuracies within and between environments and small breeding groups in white spruce. BMC Genomics. 2014;15:1048.PubMedPubMed CentralView ArticleGoogle Scholar
- Beaulieu J, Doerksen T, Clement S, Mackay J, Bousquet J. Accuracy of genomic selection models in a large population of open-pollinated families in white spruce. Heredity. 2014;113:343–52.PubMedPubMed CentralView ArticleGoogle Scholar
- Ratcliffe B, El-Dien OG, Klapste J, Porth I, Chen C, Jaquish B, El-Kassaby YA. A comparison of genomic selection models across time in interior spruce (Picea Engelmannii X Glauca) using unordered Snp imputation methods. Heredity. 2015;115(6):547–55.PubMedPubMed CentralView ArticleGoogle Scholar
- El-Dien OG, Ratcliffe B, Klapste J, Chen C, Porth I, El-Kassaby YA. Prediction accuracies for growth and wood attributes of interior spruce in space using genotyping-by-sequencing. BMC Genomics. 2015;16:370.View ArticleGoogle Scholar
- Isik F, Bartholome J, Farjat A, Chancerel E, Raffin A, Sanchez L, Plomion C, Bouffier L. Genomic selection in maritime pine. Plant Sci. 2016;242:108–19.PubMedView ArticleGoogle Scholar
- Bartholomé J, Van Heerwaarden J, Isik F, Boury C, Vidal M, Plomion C, Bouffier L. Performance of genomic prediction within and across generations in maritime pine. BMC Genomics. 2016;17(1):604.PubMedPubMed CentralView ArticleGoogle Scholar
- Habier D, Fernando RL, Dekkers JCM. The impact of genetic relationship information on genome-assisted breeding values. Genetics. 2007;177(4):2389–97.PubMedPubMed CentralGoogle Scholar
- Heslot N, Jannink JL, Sorrells ME. Perspectives for genomic selection applications and research in plants. Crop Sci. 2015;55(1):1–12.View ArticleGoogle Scholar
- Van Eenennaam AL, Weigel KA, Young AE, Cleveland MA, Dekkers JCM. Applied animal genomics: results from the field. Annu Rev Anim Biosci. 2014;2:105–39.PubMedView ArticleGoogle Scholar
- Silva-Junior OB, Faria DA, Grattapaglia D. A flexible multi-species genome-wide 60k Snp chip developed from pooled resequencing 240 eucalyptus tree genomes across 12 species. New Phytol. 2015;206(4):1527–40.PubMedView ArticleGoogle Scholar
- Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, De Bakker PIW, Daly MJ, et al. Plink: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–75.PubMedPubMed CentralView ArticleGoogle Scholar
- Do C, Waples RS, Peel D, Macbeth GM, Tillett BJ, Ovenden JR. Neestimator V2: re-implementation of software for the estimation of contemporary effective population size (ne) from genetic data. Mol Ecol Resour. 2014;14(1):209–14.PubMedView ArticleGoogle Scholar
- Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155(2):945–59.PubMedPubMed CentralGoogle Scholar
- Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software structure: a simulation study. Mol Ecol. 2005;14(8):2611–20.PubMedView ArticleGoogle Scholar
- Earl DA, Vonholdt BM. Structure Harvester: a website and program for visualizing structure output and implementing the evanno method. Conserv Genet Resour. 2011;4(2):359–61.View ArticleGoogle Scholar
- Jakobsson M, Rosenberg NA. Clumpp: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics. 2007;23(14):1801–6.PubMedView ArticleGoogle Scholar
- Zheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS. A high-performance computing toolset for relatedness and principal component analysis of Snp data. Bioinformatics. 2012;28(24):3326–8.PubMedPubMed CentralView ArticleGoogle Scholar
- Mangin B, Siberchicot A, Nicolas S, Doligez A, This P, Cierco-Ayrolles C. Novel measures of linkage disequilibrium that correct the bias due to population structure and relatedness. Heredity. 2012;108(3):285–91.PubMedView ArticleGoogle Scholar
- Powell JE, Visscher PM, Goddard ME. Reconciling the analysis of Ibd and Ibs in complex trait studies. Nat Rev Genet. 2010;11(11):800–5.PubMedView ArticleGoogle Scholar
- Marroni F, Pinosio S, Zaina G, Fogolari F, Felice N, Cattonaro F, Morgante M. Nucleotide diversity and linkage disequilibrium in Populus Nigra cinnamyl alcohol Dehydrogenase (Cad4) gene. Tree Genet Genomes. 2011;7(5):1011–23.View ArticleGoogle Scholar
- Henderson CR. Best linear unbiased estimation and prediction under a selection model. Biometrics. 1975;31(2):423–47.PubMedView ArticleGoogle Scholar
- Vanraden PM. Efficient methods to compute genomic predictions. J Dairy Sci. 2008;91(11):4414–23.PubMedView ArticleGoogle Scholar
- Endelman JB. Ridge regression and other kernels for genomic selection with R package Rrblup. Plant Genome J. 2011;4(3):250–5.View ArticleGoogle Scholar
- Pérez P, De Los Campos G. Genome-wide regression and prediction with the Bglr statistical package. Genetics. 2014;198(2):483–95.PubMedPubMed CentralView ArticleGoogle Scholar
- Myburg AA, Grattapaglia D, Tuskan GA, Hellsten U, Hayes RD, Grimwood J, Jenkins J, Lindquist E, Tice H, Bauer D, et al. The genome of Eucalyptus Grandis. Nature. 2014;510(7505):356–62.PubMedGoogle Scholar
- Yang J, Lee SH, Goddard ME, Visscher PM. Gcta: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88(1):76–82.PubMedPubMed CentralView ArticleGoogle Scholar
- Yang J, Benyamin B, Mcevoy BP, Gordon S, Henders AK, Nyholt DR, Madden PA, Heath AC, Martin NG, Montgomery GW, et al. Common Snps explain a large proportion of the heritability for human height. Nat Genet. 2010;42(7):565–9.PubMedPubMed CentralView ArticleGoogle Scholar
- Benjamini Y, Hochberg Y. Controlling the false discovery rate - a practical and powerful approach to multiple testing. J Roy Stat Soc B Met. 1995;57(1):289–300.Google Scholar
- Turner SD. Qqman: an R Package For Visualizing Gwas Results Using Q-Q And Manhattan Plots. 2014; https://doi.org/10.1101/005165.
- De Los Campos G, Sorensen D, Gianola D. Genomic heritability: what is it? PLoS Genet. 2015;11(5):E1005048.PubMed CentralView ArticleGoogle Scholar
- Munoz PR, Resende MFR, Gezan SA, Resende MDV, De Los Campos G, Kirst M, Huber D, Peter GF. Unraveling additive from nonadditive effects using genomic relationship matrices. Genetics. 2014;198(4):1759.PubMedPubMed CentralView ArticleGoogle Scholar
- Habier D, Fernando RL, JCM D. Genomic selection using low-density marker panels. Genetics. 2009;182(1):343–53.PubMedPubMed CentralView ArticleGoogle Scholar
- Solberg TR, Sonesson AK, Woolliams JA, Meuwissen THE. Genomic selection using different marker types and densities. J Anim Sci. 2008;86(10):2447–54.PubMedView ArticleGoogle Scholar
- Silva-Junior OB, Grattapaglia D. Genome-wide patterns of recombination, linkage disequilibrium and nucleotide diversity from pooled resequencing and single nucleotide polymorphism genotyping unlock the evolutionary history of Eucalyptus Grandis. New Phytol. 2015;208(3):830–45.PubMedView ArticleGoogle Scholar
- Evans LM, Slavov GT, Rodgers-Melnick E, Martin J, Ranjan P, Muchero W, Brunner AM, Schackwitz W, Gunter L, Chen JG, et al. Population genomics of Populus Trichocarpa identifies signatures of selection and adaptive trait associations. Nat Genet. 2014;46(10):1089–96.PubMedView ArticleGoogle Scholar
- Fahrenkrog A, Neves L, Resende MFR, Vasquez A, De Los Campos G, Barbazuk WB, Kirst M. Genome-wide association study reveals putative regulators of bioenergy traits in Populus Deltoides. New Phytol. 2016; doi:https://doi.org/10.1111/Nph.14154.
- Mckown AD, Klapste J, Guy RD, Geraldes A, Porth I, Hannemann J, Friedmann M, Muchero W, Tuskan GA, Ehlting J, et al. Genome-wide association implicates numerous genes underlying ecological trait variation in natural populations of Populus Trichocarpa. New Phytol. 2014;203(2):535–53.PubMedView ArticleGoogle Scholar
- Cappa EP, El-Kassaby YA, Garcia MN, Acuna C, Borralho NMG, Grattapaglia D, Poltri SNM. Impacts of population structure and analytical models in genome-wide association studies of complex traits in Forest trees: a case study in Eucalyptus Globulus. PLoS One. 2013;8(11):E81267.PubMedPubMed CentralView ArticleGoogle Scholar
- Du Q, Gong C, Wang Q, Zhou D, Yang H, Pan W, Li B, Zhang D. Genetic architecture of growth traits in populus revealed by integrated quantitative trait locus (Qtl) analysis and association studies. New Phytol. 2016;209(3):1067–82.PubMedView ArticleGoogle Scholar
- Bartholome J, Bink M, Van Heerwaarden J, Chancerel E, Boury C, Lesur I, Isik F, Bouffier L, Plomion C. Linkage and association mapping for two major traits used in the maritime pine breeding program: height growth and stem straightness. PLoS One. 2016;11(11):e0165323.PubMedPubMed CentralView ArticleGoogle Scholar
- Resende RT, MDV R, Silva FF, Azevedo CF, Takahashi EK, Silva-Junior OB, Grattapaglia D. Regional heritability mapping and genome-wide association identify loci for complex growth, wood and disease resistance traits in eucalyptus. New Phytol. 2017;213(3):1287–300.PubMedView ArticleGoogle Scholar
- Allwright MR, Payne A, Emiliani G, Milner S, Viger M, Rouse F, Keurentjes JJB, Bérard A, Wildhagen H, Faivre-Rampant P, et al. Biomass traits and candidate genes for bioenergy revealed through association genetics in coppiced European Populus Nigra (L.). Biotechnol Biofuels. 2016;9(1):1–22.View ArticleGoogle Scholar
- Lorenz AJ, Chao SM, Asoro FG, Heffner EL, Hayashi T, Iwata H, Smith KP, Sorrells ME, Jannink JL. Genomic selection in plant breeding: knowledge and prospects. Adv Agron. 2011;110:77–123.View ArticleGoogle Scholar
- Meuwissen TH, Hayes B, Goddard ME. Genomic selection: a paradigm shift in animal breeding. Anim Front. 2016;6:6–14.View ArticleGoogle Scholar
- Robinson MR, Wray NR, Visscher PM. Explaining additional genetic variation in complex traits. Trends Genet. 2014;30(4):124–32.PubMedPubMed CentralView ArticleGoogle Scholar
- Rockman MV. The Qtn program and the alleles that matter for evolution: all that’s gold does not glitter. Evolution. 2012;66(1):1–17.PubMedView ArticleGoogle Scholar
- Hamblin MT, Buckler ES, Jannink JL. Population genetics of genomics-based crop improvement methods. Trends Genet. 2011;27(3):98–106.PubMedView ArticleGoogle Scholar
- Daetwyler HD, Pong-Wong R, Villanueva B, Woolliams JA. The impact of genetic architecture on genome-wide evaluation methods. Genetics. 2010;185(3):1021–31.PubMedPubMed CentralView ArticleGoogle Scholar
- Spindel JE, Begum H, Akdemir D, Collard B, Redona E, Jannink JL, Mccouch SR. Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement. Heredity. 2016;11:395–408.View ArticleGoogle Scholar