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Genomic selection methods are particularly useful for traits that are difficult or expensive to measure. We investigated the impact of using predictor growth traits and/or genomic information to increase the breeding value (BV) predictive accuracies for target scarcely recorded wood quality traits in an open-pollinated Eucalyptus grandis population. The performance of single- and multiple-trait single-step genomic best linear unbiased prediction and conventional pedigree-based models were compared in terms of the predictive accuracies (PA) of estimated BV for the target traits. We also derived the contributions of the BV for candidate trees to better understand our results. The inclusion of predictor traits in both, the training and the validation sets, together with genomic information, improved the PA (up to 17.7%) for pulp yield and cellulose. However, significant improvements in PA were not observed when predictor traits were recorded only in the training set or when the impact of genomic information alone was assessed. Changes in the PA were explained by the variations in the maternal contributions, contribution/s from all the predictor/s trait/s, and from genotyped trees. We conclude that there is not a "universal" rule regarding the use of genomic information and records on predictor traits. However, assessing the contributions to the BV of validation trees may help to better design how to benefit from predictor traits in forest tree breeding. [ABSTRACT FROM AUTHOR]

Perennial shrub willow are increasingly being promoted in short-rotation coppice systems as biomass feedstocks, for phytoremediation applications, and for the diverse ecosystem services that can accrue. This renewed interest has led to widespread willow cultivation, particularly of non-native varieties. However, Canadian willow species have not been widely adopted and their inherent diversity has not yet been thoroughly investigated. In this study, 324 genotypes of Salix famelica and Salix eriocephala collected from 33 sites of origin were analyzed using 26,016 single nucleotide polymorphisms to reveal patterns of population structure and genetic diversity. Analyses by Bayesian methods and principal component analysis detected five main clusters that appeared to be largely shaped by geoclimatic variables including mean annual precipitation and the number of frost-free days. The overall observed (HO) and expected (HE) heterozygosity were 0.126 and 0.179, respectively. An analysis of molecular variance revealed that the highest genetic variation occurred within genotypes (69%), while 8% of the variation existed among clusters and 23% between genotypes within clusters. These findings provide new insights into the extent of genetic variation that exists within native shrub willow species which could be leveraged in pan-Canadian willow breeding programs. [ABSTRACT FROM AUTHOR]

Non-spatial and spatial analyses were carried out to estimate genetic parameters for growth and stem straightness traits between ages 4 and 7 in two series of eight progeny trials of Pinus elliottii Engelm. var. elliottii (PEE) established in 2004 and 2008, comprising 429 open-pollinated families from Instituto Nacional de Tecnología Agropecuaria (INTA) tree breeding population in Argentina. A first-order autoregressive spatial mixed model was found to significantly improve the fit of the model, compared with the standard non-spatial mixed model approach, due to substantial spatial heterogeneity within sites. The average individual-tree narrow-sense heritability estimates based on this spatial analysis were 0.49 for diameter at breast height, 0.36 for total height, and 0.48 for volume, with stem straightness being low to moderate (average = 0.11). The additive genetic correlation estimates between growth traits were positive (0.51 ≤ r ^ a ≤ 0.99) and statistically significant from zero. In contrast, the genetic correlations between growth traits and stem straightness although in general also positive were not significantly different from zero (− 0.22 ≤ r ^ a ≤ 0.59). Age-to-age genetic correlations were consistently higher for growth traits ( r ^ a ≥ 0.81) than for stem straightness ( r ^ a ≥ 0.33). The average additive genetic correlation estimated between sites within test series was high for all traits evaluated (average > 0.72). In contrast, average additive genetic correlation estimated between sites across series was slightly lower (average < 0.63). Implications of all these parameter estimates for genetic improvement of PEE in Argentina are discussed. [ABSTRACT FROM AUTHOR]

Simple sequence repeats (SSR) are the most widely used molecular markers for relatedness inference due to their multi-allelic nature and high informativeness. However, there is a growing trend toward using high-throughput and inter-specific transferable single-nucleotide polymorphisms (SNP) and Diversity Arrays Technology (DArT) in forest genetics owing to their wide genome coverage. We compared the efficiency of 15 SSRs, 181 SNPs and 2816 DArTs to estimate the relatedness coefficients, and their effects on genetic parameters’ precision, in a relatively small data set of an open-pollinated progeny trial of Eucalyptus grandis (Hill ex Maiden) with limited relationship from the pedigree. Both simulations and real data of Eucalyptus grandis were used to study the statistical performance of three relatedness estimators based on co-dominant markers. Relatedness estimates in pairs of individuals belonging to the same family (related) were higher for DArTs than for SNPs and SSRs. DArTs performed better compared to SSRs and SNPs in estimated relatedness coefficients in pairs of individuals belonging to different families (unrelated) and showed higher ability to discriminate unrelated from related individuals. The likelihood-based estimator exhibited the lowest root mean squared error (RMSE); however, the differences in RMSE among the three estimators studied were small. For the growth traits, heritability estimates based on SNPs yielded, on average, smaller standard errors compared to those based on SSRs and DArTs. Estimated relatedness in the realized relationship matrix and heritabilities can be accurately inferred from co-dominant or sufficiently dense dominant markers in a relatively small E. grandis data set with shallow pedigree. [ABSTRACT FROM AUTHOR]

Many traits of biological interest in tree breeding are assessed using more than two ordered discrete categories. These scores have a more or less arbitrary and subjective assignment by the assessors, which could lead to a strong departure from the Gaussian distribution. Different assessors may also use different regions of the available scale. This study describes the use of the multi-threshold mixed model proposed by Varona et al. (J Anim Sci 87:1210–1217, 2009 ), which allows different thresholds for different assessors on an underlying Gaussian distribution. This method was applied to a six-point score for stem quality in an open-pollinated progeny trial of Prosopis alba Griseb. Four mixed models were used: (1) a linear mixed model with observed score (LMM); (2) a linear mixed model with transformed “normal scores” (LMM_NS); (3) a threshold mixed model (TMM); and (4) an assessor-specific multi-threshold mixed model (MTMM). Dispersion parameters were estimated using Bayesian techniques via the Gibbs sampling with a data augmentation step. The proposed MTMM produced higher posterior mean heritabilities (0.096) than the commonly used LMM (0.077). Posterior mean heritabilities from LMM_NS (0.094) and TMM (0.097) were comparable to those obtained using MTMM; however, MTMM yielded slightly more precise estimates than TMM. Although correlations of the estimated breeding values were high between different models (from 0.88 to 0.99), the heterogeneity in the estimated posterior means of the thresholds between the three assessors caused notable changes in the top 10 families between TMM and MTMM. The proposed model is helpful in fitting subjective ordered categorical traits assessed by different assessors in tree breeding. [ABSTRACT FROM AUTHOR]