Micronutrient malnutrition is a global health problem. An improved understanding of the genetic variation of important micronutrient traits within a potato breeding population will help devise breeding strategies for the biofortification of this important food staple. The dataset consisted of 556 individuals from 17 full‐sib diploid families grown in 2006 in Huanuco, Peru, and 1329 individuals from 32 full‐sib families grown in 2009 in Ayacucho, Peru. Genetic parameters were estimated using univariate and multivariate individual Bayesian models for micronutrient tuber content including Fe and Zn. Genetic variance was additive and heritability estimates were moderate (0.36 to 0.57) and inflated if the common environment of full‐sibs was not taken into account. Posterior modes of genetic correlation estimates between minerals, when analyzed on a dry‐weight basis, were all positive (0.04 to 0.72) and between minerals and tuber dry matter were negative (−0.14 to −0.38). On a fresh‐weight basis, genetic correlations between minerals and tuber dry matter were small but positive (0.05 to 0.18). The implications and challenges for selective breeding to enhance micronutrient content in potato tubers are discussed.

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Genetic evaluation aims to identify genotypes with high empirical breeding values (EBVs) for selection as parents. In this study, 2157 potato genotypes were evaluated for tuber yield using 8 years of early‐stage trial data collected from a potato breeding programme. Using linear mixed models, spatial parameters to target greater control of localised spatial heterogeneity within trials were estimated and variance models to account for across‐trial genetic heterogeneity were tested. When spatial components improved model fit, correlations of errors were mostly small and negative for marketable tuber yield (MTY) and total tuber yield (TTY), suggesting the presence of interplot competition in some years. For the analysis of multi‐environment trials, a variance model with a simple correlation structure (with heterogeneous variances) was the most favourable variance structure fitted for TTY and PTY (per cent marketable yield). There was very little difference in model fit when comparing a factor analytic structure of order 2 (FA2) with either FA1 or simple correlation structures for MTY, indicating that simple variance models may be preferable for early‐stage genetic evaluation of potato yield.

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Traditional tree improvement is cumbersome and costly. Our main objective was to assess the extent to which genomic data can currently accelerate and improve decision making in this field. We used diameter at breast height (DBH) and wood density (WD) data for 4430 tree genotypes and single-nucleotide polymorphism (SNP) data for 2446 tree genotypes. Pedigree reconstruction was performed using a combination of maximum likelihood parentage assignment and matching based on identity-by-state (IBS) similarity. In addition, we used best linear unbiased prediction (BLUP) methods to predict phenotypes using SNP markers (GBLUP), recorded pedigree information (ABLUP), and single-step “blended” BLUP (HBLUP) combining SNP and pedigree information. We substantially improved the accuracy of pedigree records, resolving the inconsistent parental information of 506 tree genotypes. This led to substantially increased predictive ability (i.e., by up to 87%) in HBLUP analyses compared to a baseline from ABLUP. Genomic prediction was possible across populations and within previously untested families with moderately large training populations (N = 800–1200 tree genotypes) and using as few as 2000–5000 SNP markers. HBLUP was generally more effective than traditional ABLUP approaches, particularly after dealing appropriately with pedigree uncertainties. Our study provides evidence that genome-wide marker data can significantly enhance tree improvement. The operational implementation of genomic selection has started in radiata pine breeding in New Zealand, but further reductions in DNA extraction and genotyping costs may be required to realise the full potential of this approach.

1957ReseaRch W hen testing selection candidates over multiple environments, uncertainty in the estimates of genotype values increases with the magnitude of G E. This increases the difficulty of identifying superior genotypes and compromises genetic progress from selection (e.g., Annicchiarico, 2002;Bos and Caligari, 2008; DeLacy et al., 1996a,b). A better understanding of GE effects within a MET testing regime allows a reevaluation of resource allocation and selection strategy in a breeding program. The type and extent of G  E is of particular interest to plant breeders as the characterization of environments will help, in part, to define selection strategies. For example, measures of quantitative G E (heterogeneity of variance or the scale ABSTRACT Differences in trait responses of genotypes across environments, or genotype  environment interactions (G E), hinder the progress of genetic improvement. Characterization of these effects helps to determine breeding strategies and improve resource allocation in a cultivar development program. This study used historical multienvironment trial (MET) data (34 trials in five locations) for the analysis of marketable yield of advanced selections in a New Zealand potato (Solanum tuberosum L.) breeding program. A factor analytic (FA) model was used for the analysis of these MET data. Contrasts based on the environmental loadings were observed between the program's main trial locations in the North Island (pukekohe) and the South Island (Lincoln), indicating that these locations optimized differentiation between genotypes in terms of G E effects. Genetic correlation estimates between trial environments were mostly moderately high (>0.5) to high (>0.8) and ranged from zero to positive with a maximum coefficient of 0.97, suggesting that quantitative (rescaling) rather than qualitative (crossover) G E effects were of greater importance. A number of newly developed varieties were shown to have higher genetic yield potential than older and established commercial cultivars but did not necessarily show better yield stability over the locations tested.

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Genomic selection (GS) is currently being used in the New Zealand radiata pine (Pinus radiata D. Don) breeding program to accelerate genetic gain. GS also has the potential to accelerate the deployment of genetic gain to the production forest through early selection. The increased rate of genetic gain in the breeding cycle will need to be transferred more quickly to realise that gain in the deployment population. GS selections will have lower accuracies than selections based on phenotypic data as currently practised; however, it is unknown how this will affect the genetic gain from GS-based deployment. Moreover, census size and turnover rate need to be optimised to cope with the influx of new marker-based selected material into a commercial orchard. We utilised a stochastic simulation approach to investigate these concepts, comparing three deployment scenarios: half-sib open-pollinated orchards (OP), full-sib control-pollinated orchards (CP) and clonal deployment through somatic embryogenesis. When accounting for time, genomic selection in OP, CP and clonal deployment pathways increased genetic gain by 9.5%, 15.9% and 44.6% respectively compared to phenotypic selection. The optimal orchard scenario would be genomic-selected control-pollination with a low census size (n = 40, males and females combined), low female turnover (5%) and a high male turnover (15–25%). This scheme balances high genetic gain with high seed yield while moderating the rate of inbreeding.