Factor-Analytic Variance-Covariance Structures for Prediction Into a Target Population of Environments.

Finlay-Wilkinson regression is a popular method for modeling genotype-environment interaction in plant breeding and crop variety testing. When environment is a random factor, this model may be cast as a factor-analytic variance-covariance structure, implying a regression on random latent environmental variables. This paper reviews such models with a focus on their use in the analysis of multi-environment trials for the purpose of making predictions in a target population of environments. We investigate the implication of random versus fixed effects assumptions, starting from basic analysis-of-variance models, then moving on to factor-analytic models and considering the transition to models involving observable environmental covariates, which promise to provide more accurate and targeted predictions than models with latent environmental variables.

Two-dimensional P-spline smoothing for spatial analysis of plant breeding trials.

Large agricultural field trials may display irregular spatial trends that cannot be fully captured by a purely randomization-based analysis. For this reason, paralleling the development of analysis-of-variance procedures for randomized field trials, there is a long history of spatial modeling for field trials, starting with the early work of Papadakis on nearest neighbor analysis, which can be cast in terms of first or second differences among neighboring plot values. This kind of spatial modeling is amenable to a natural extension using splines, as has been demonstrated in recent publications in the field. Here, we consider the P-spline framework, focusing on model options that are easy to implement in linear mixed model packages. Two examples serve to illustrate and evaluate the methods. A key conclusion is that first differences are rather competitive with second differences. A further key observation is that second differences require special attention regarding the representation of the null space of the smooth terms for spatial interaction, and that an unstructured variance-covariance structure is required to ensure invariance to translation and rotation of eigenvectors associated with that null space. We develop a strategy that permits fitting this model with ease, but the approach is more demanding than that needed for fitting models using first differences. Hence, even though in other areas, second differences are very commonly used in the application of P-splines, our conclusion is that with field trials, first differences have advantages for routine use.

Regression models for order-of-addition experiments.

The purpose of order-of-addition (OofA) experiments is to identify the best order in a sequence of m components in a system. Such experiments may be analyzed by various regression models, the most popular ones being based on pairwise ordering (PWO) factors or on component-position (CP) factors. This paper reviews these models and extensions and proposes a new class of models based on response surface (RS) regression using component position numbers as predictor variables. Using two published examples, it is shown that RS models can be quite competitive. In case of model uncertainty, we advocate the use of model averaging for analysis. The averaging idea leads naturally to a design approach based on a compound optimality criterion assigning weights to each candidate model.

Estimating the variance for heterogeneity in arm-based network meta-analysis.

Network meta-analysis can be implemented by using arm-based or contrast-based models. Here we focus on arm-based models and fit them using generalized linear mixed model procedures. Full maximum likelihood (ML) estimation leads to biased trial-by-treatment interaction variance estimates for heterogeneity. Thus, our objective is to investigate alternative approaches to variance estimation that reduce bias compared with full ML. Specifically, we use penalized quasi-likelihood/pseudo-likelihood and hierarchical (h) likelihood approaches. In addition, we consider a novel model modification that yields estimators akin to the residual maximum likelihood estimator for linear mixed models. The proposed methods are compared by simulation, and 2 real datasets are used for illustration. Simulations show that penalized quasi-likelihood/pseudo-likelihood and h-likelihood reduce bias and yield satisfactory coverage rates. Sum-to-zero restriction and baseline contrasts for random trial-by-treatment interaction effects, as well as a residual ML-like adjustment, also reduce bias compared with an unconstrained model when ML is used, but coverage rates are not quite as good. Penalized quasi-likelihood/pseudo-likelihood and h-likelihood are therefore recommended.

Neighbor balance and evenness of distribution of treatment replications in row-column designs.

Row-column designs allow error control in field experiments by blocking in two dimensions. While this strategy can capture spatial heterogeneity aligned with blocks and account for effects due to the farming operations along rows and columns, it suffers from the occasional clustered occurrence of several replications of the same treatment. This property of classical row-column designs has hampered their more widespread use in practice. A further issue of practical importance is the degree of neighbor balance of a design, that is, the frequency of adjacencies of pairs of treatments. This paper proposes two design strategies to simultaneously optimize the evenness of spatial distribution of treatment replication as well as neighbor balance. Three examples are given to illustrate the proposed methods and demonstrate that both approaches yield comparable and satisfactory results.

Inter-block information: to recover or not to recover it?

KEY MESSAGE: Comparing standard errors of treatment differences using fixed or random block effects with the approximation of Kackar and Harville helps in choosing the preferable assumption for blocks in the analysis of field experiments. Blocked designs are common in plant breeding field trials. Depending on the precision of variance estimates, recovery of inter-block information via random block effects may be worthwhile. A challenge in practice is to decide when recovery of information should be pursued. To investigate this question, a series of sugar beet trials laid out as α-designs were analysed assuming fixed or random block effects. Additionally, small trials laid out as α-designs or partially replicated designs were simulated and analysed assuming fixed or random block effects. Nine decision rules, including the Kackar-Harville adjustment, were used for choosing the better assumption regarding the block effects. In general, use of the Kackar-Harville adjustment works well and is recommended for partially replicated designs. For α-designs, using inter-block information is preferable for designs with four or more blocks.

Multiplicative interaction in network meta-analysis.

Meta-analysis of a set of clinical trials is usually conducted using a linear predictor with additive effects representing treatments and trials. Additivity is a strong assumption. In this paper, we consider models for two or more treatments that involve multiplicative terms for interaction between treatment and trial. Multiplicative models provide information on the sensitivity of each treatment effect relative to the trial effect. In developing these models, we make use of a two-way analysis-of-variance approach to meta-analysis and consider fixed or random trial effects. It is shown using two examples that models with multiplicative terms may fit better than purely additive models and provide insight into the nature of the trial effect. We also show how to model inconsistency using multiplicative terms.

Efficiency of augmented p-rep designs in multi-environmental trials.

KEY MESSAGE: The paper shows that unreplicated designs in multi-environmental trials are most efficient. If replication per environment is needed then augmented p-rep designs outperform augmented and replicated designs in triticale and maize. In plant breeding, augmented designs with unreplicated entries are frequently used for early generation testing. With limited amount of seed, this design allows to use a maximum number of environments in multi-environmental trials (METs). Check plots enable the estimation of block effects, error variances and a connection of otherwise unconnected trials in METs. Cullis et al. (J Agri Biol Environ Stat 11:381-393, 2006) propose to replace check plots from a grid-plot design by plots of replicated entries leading to partially replicated (p-rep) designs. Williams et al. (Biom J 53:19-27, 2011) apply this idea to augmented designs (augmented p-rep designs). While p-rep designs are increasingly used in METs, a comparison of the efficiency of augmented p-rep designs and augmented designs in the range between replicated and unreplicated designs in METs is lacking. We simulated genetic effects and allocated them according to these four designs to plot yields of a triticale and a maize uniformity trial. The designs varied in the number of environments, but have a fixed number of entries and total plots. The error model and the assumption of fixed or random entry effects were varied in simulations. We extended our simulation for the triticale data by including correlated entry effects which are common in genomic selection. Results show an advantage of unreplicated and augmented p-rep designs and a preference for using random entry effects, especially in case of correlated effects reflecting relationships among entries. Spatial error models had minor advantages compared to purely randomization-based models.

The use of fixed study main effects in arm-based network meta-analysis.

Methods of network meta-analysis (NMA) can be classified as arm-based and contrast-based approaches. There are several arm-based approaches, and some of these have been criticized because they recover inter-study information and hence do not obey the principle of concurrent control. Here, we point out that recovery of inter-study information in arm-based NMA can be prevented by fitting a fixed main effect for studies. Advantages of arm-based NMA are discussed.

Augmented p-rep designs.

Early generation variety trials are very important in plant and tree breeding programs. Typically many entries are tested, often with very little resources available. Unreplicated trials using control plots are popular and it is common to repeat the trials at a number of locations. An alternative is to use p-rep designs, where a proportion of the test entries are replicated at each location; this can obviate the need for control plots. α-Designs are commonly used for replicated variety trials and we show how these can be adapted to produce efficient p-rep designs.

Nearest neighbour adjustment and linear variance models in plant breeding trials.

This paper reviews methods for nearest neighbour analysis that adjust for local trend in one dimension. Such methods are commonly used in plant breeding and variety testing. The focus is on simple differencing methods, including first differences and the Papadakis method. We discuss mixed model representations of these methods on the scale of the observed data. Modelling observed data has a number of practical advantages compared to differencing, for example the facility to conveniently compute adjusted cultivar means. Most models considered involve a linear variance-covariance structure and can be represented as state-space models. The reviewed methods and models are exemplified using three datasets.

A microsatellite-based consensus linkage map for species of Eucalyptus and a novel set of 230 microsatellite markers for the genus.

BACKGROUND: Eucalypts are the most widely planted hardwood trees in the world occupying globally more than 18 million hectares as an important source of carbon neutral renewable energy and raw material for pulp, paper and solid wood. Quantitative Trait Loci (QTLs) in Eucalyptus have been localized on pedigree-specific RAPD or AFLP maps seriously limiting the value of such QTL mapping efforts for molecular breeding. The availability of a genus-wide genetic map with transferable microsatellite markers has become a must for the effective advancement of genomic undertakings. This report describes the development of a novel set of 230 EMBRA microsatellites, the construction of the first comprehensive microsatellite-based consensus linkage map for Eucalyptus and the consolidation of existing linkage information for other microsatellites and candidate genes mapped in other species of the genus. RESULTS: The consensus map covers approximately 90% of the recombining genome of Eucalyptus, involves 234 mapped EMBRA loci on 11 linkage groups, an observed length of 1,568 cM and a mean distance between markers of 8.4 cM. A compilation of all microsatellite linkage information published in Eucalyptus allowed us to establish the homology among linkage groups between this consensus map and other maps published for E. globulus. Comparative mapping analyses also resulted in the linkage group assignment of other 41 microsatellites derived from other Eucalyptus species as well as candidate genes and QTLs for wood and flowering traits published in the literature. This report significantly increases the availability of microsatellite markers and mapping information for species of Eucalyptus and corroborates the high conservation of microsatellite flanking sequences and locus ordering between species of the genus. CONCLUSION: This work represents an important step forward for Eucalyptus comparative genomics, opening stimulating perspectives for evolutionary studies and molecular breeding applications. The generalized use of an increasingly larger set of interspecific transferable markers and consensus mapping information, will allow faster and more detailed investigations of QTL synteny among species, validation of expression-QTL across variable genetic backgrounds and positioning of a growing number of candidate genes co-localized with QTLs, to be tested in association mapping experiments.

Postprandial adiponectin and gelatinase response to a high-fat versus an isoenergetic low-fat meal in lean, healthy men.

OBJECTIVE: Evidence suggests that an acute systemic inflammatory response is invoked after consumption of a high-energy meal. Postprandial regulation of adiponectin, an adipose tissue-derived, anti-inflammatory hormone, and the gelatinases, matrix metalloproteinase (MMP)-2 and MMP-9, endopeptidases implicated in a diverse range of inflammatory processes, remain inconclusive. The aim of this study was to assess the postprandial effect of a high-energy (1212 kcal) meal on plasma adiponectin, MMP-2 and MMP-9 activity, glucose, insulin, triacylglycerols, total cholesterol, high-density lipoprotein cholesterol, and the differential effects on these parameters depending on whether the test meal was high fat (HF; 46 g fat, 1210 kcal) or isoenergetic and low fat (LF; 15 g fat, 1214 kcal energy). METHODS: Test meals were consumed by 17 lean, healthy men on two separate occasions with blood samples collected by venipuncture at baseline (0 h) and 1 and 3 h after consumption of each test meal. RESULTS: At baseline, no significant difference was seen in the parameters between the two groups, except for MMP-2, MMP-9, and total cholesterol. Over the 3-h postprandial period, no significant differential effect of the HF versus the LF test meal was observed on adiponectin, MMP-2, MMP-9, or on metabolic markers other than triacylglycerol, which increased significantly in response to the HF test meal (time × treatment, P = 0.002). When analyzed independent of time, MMP-2 (treatment, P = 0.006), MMP-9 (treatment, P = 0.022), and glucose (treatment, P = 0.026) were lower after consumption of the HF meal compared with the LF test meal. When analyzed independent of treatment, adiponectin increased over the 3-h postprandial period (time, P = 0.031), but there was no change in MMP-2 or MMP-9 (time, P = 0.503 and P = 0.525, respectively). Over the 3-h postprandial period, insulin (time, P < 0.001) and total cholesterol (time, P = 0.002) increased, whereas glucose (time, P < 0.001) and high-density lipoprotein cholesterol (time, P < 0.001) decreased. CONCLUSION: No differential effects of a HF versus a LF isoenergetic meal were seen on postprandial adiponectin or the gelatinases. Adiponectin increased in response to a high-energy meal independent of treatment, and the gelatinases were lower in response to the HF versus the LF isoenergetic meal, independent of time point. Given the considerable amount of time that humans spend in the postprandial state, additional research is necessary to further understand inflammatory changes in this state.

Risk factors for acute respiratory infection in the Australian community.

OBJECTIVES: The objective of this study was to identify the risk factors for ARI in the Australian community. METHODS: We used a national survey of 7578 randomly selected respondents in 2008-2009 to identify the risk factors of ARI. A case was defined as a person experiencing cold or flu with one or more symptoms of: fever, chills, sore throat, runny nose, or cough in the previous four weeks. RESULTS: There were 19.8% (1505/7578) of respondents who reported ARI in the four weeks prior to the survey. Age was an independent risk factor for ARI, with the risk of acquiring ARI decreasing as age increased. Respondents reporting asthma (OR 1.4, 95%CI: 1.2-1.7) or having someone in their house attending childcare (OR 1.6, 95%CI: 1.2-2.1) were more likely to report ARI. CONCLUSIONS: It is important to identify ways of interrupting transmission of ARI amongst children. Improving identification of risk factors will enable targeted interventions for this exceedingly common syndrome.

Soil warming increases plant species richness but decreases germination from the alpine soil seed bank.

Global warming is occurring more rapidly above the treeline than at lower elevations and alpine areas are predicted to experience above average warming in the future. Temperature is a primary factor in stimulating seed germination and regulating changes in seed dormancy status. Thus, plant regeneration from seed will be crucial to the persistence, migration and post disturbance recruitment of alpine plants in future climates. Here, we present the first assessment of the impact of soil warming on germination from the persistent alpine soil seed bank. Contrary to expectations, soil warming lead to reduced overall germination from the soil seed bank. However, germination response to soil temperature was species specific such that total species richness actually increased by nine with soil warming. We further explored the system by assessing the prevalence of seed dormancy and germination response to soil disturbance, the frequency of which is predicted to increase under climate change. Seeds of a significant proportion of species demonstrated physiological dormancy mechanisms and germination of several species appeared to be intrinsically linked to soil disturbance. In addition, we found no evidence of subalpine species and little evidence of exotic weed species in the soil, suggesting that the soil seed bank will not facilitate their invasion of the alpine zone. In conclusion, changes in recruitment via the alpine soil seed bank can be expected under climate change, as a result of altered dormancy alleviation and germination cues. Furthermore, the alpine soil seed bank, and the species richness therein, has the potential to help maintain local species diversity, support species range shift and moderate species dominance. Implications for alpine management and areas for further study are also discussed.

Associations between the sensory attributes and volatile composition of Cabernet Sauvignon wines and the volatile composition of the grapes used for their production.

The sensory properties of wine are influenced by the chemical composition of the grapes used to produce them. Identification of grape and wine chemical markers associated with the attributes perceived by the consumer of the wine will enable better prediction of the potential of a parcel of grapes to produce wine of a certain flavor. This study explores the relationships between Cabernet Sauvignon grape volatile composition and wine volatile profiles with the sensory properties of wines. Twenty grape samples were obtained from nine vineyard sites across three vintages and wines vinified from these parcels using controlled winemaking methods. The volatile composition of the grapes were analyzed by SBSE-GCMS, the wines were analyzed by SPME-GCMS, and these data sets were compared to that obtained from the sensory analysis of the wines. Statistical treatment of the data to account for vintage and region effects allowed underlying relationships to be seen between wine sensory attributes and wine or grape volatile components. The observed associations between grape or wine volatile compounds and wine sensory attributes has revealed target compounds and pathways whose levels may reflect the biochemical effects on grape composition by differing growth conditions during berry development and ripening. The compounds identified in this study may be useful grape or wine markers for potential wine sensory characteristics.