Selection of alfalfa cultivars adapted for tropical environments with repeated measures using PROC MIXED of SAS (R) System

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Competition and fixation of cohorts of adaptive mutations under Fisher geometrical model

One of the simplest models of adaptation to a new environment is Fisher's Geometric Model (FGM), in which populations move on a multidimensional landscape defined by the traits under selection. The predictions of this model have been found to be consistent with current observations of patterns of fitness increase in experimentally evolved populations. Recent studies investigated the dynamics of allele frequency change along adaptation of microbes to simple laboratory conditions and unveiled a dramatic pattern of competition between cohorts of mutations, i.e., multiple mutations simultaneously segregating and ultimately reaching fixation. Here, using simulations, we study the dynamics of phenotypic and genetic change as asexual populations under clonal interference climb a Fisherian landscape, and ask about the conditions under which FGM can display the simultaneous increase and fixation of multiple mutations-mutation cohorts-along the adaptive walk. We find that FGM under clonal interference, and with varying levels of pleiotropy, can reproduce the experimentally observed competition between different cohorts of mutations, some of which have a high probability of fixation along the adaptive walk. Overall, our results show that the surprising dynamics of mutation cohorts recently observed during experimental adaptation of microbial populations can be expected under one of the oldest and simplest theoretical models of adaptation-FGM.

Forage peanut (Arachis spp.) genetic evaluation and selection.

Forage peanut improvement for use in grass?legume mixtures is expected to have a great impact on the sustainability of Brazilian livestock production. Eighteen cloned Arachis spp. ecotypes were evaluated under clipping in a Brazilian Cerrado region and results analysed using a mixed model methodology. The objective was to estimate genetic and phenotypic parameters and to select the best ecotypes based on selection index applied on their predicted genotypic value. The traits of total dry-matter (DM) and leaf DM yield presented moderate (0_30 < h2g < 0_50) to high (>0_50) broad-sense heritability, in contrast to the low genetic variability in nutritional quality-associated traits. Ecotypes of Arachis spp. contained average crude protein concentrations of 224 g kg _1 DM in leaves and 138 g kg _1 DM in stems, supporting the potential role of these species to overcome the low protein content in Cerrado pastures. The correlations between yield traits and traits associated with low nutritional value in leaves were consistently significant and positive. Genetic correlations among all the yield traits evaluated during the rainy or dry seasons were significant and positive. The ecotypes were ranked based on selection index. The next step is to validate long-term selection of grass?Arachis in combination with pastures under competition and adjusted grazing in the Cerrado region.

Long-term survivor mixture model with random effects: application to a multi-centre clinical trial of carcinoma

A mixture model incorporating long-term survivors has been adopted in the field of biostatistics where some individuals may never experience the failure event under study. The surviving fractions may be considered as cured. In most applications, the survival times are assumed to be independent. However, when the survival data are obtained from a multi-centre clinical trial, it is conceived that the environ mental conditions and facilities shared within clinic affects the proportion cured as well as the failure risk for the uncured individuals. It necessitates a long-term survivor mixture model with random effects. In this paper, the long-term survivor mixture model is extended for the analysis of multivariate failure time data using the generalized linear mixed model (GLMM) approach. The proposed model is applied to analyse a numerical data set from a multi-centre clinical trial of carcinoma as an illustration. Some simulation experiments are performed to assess the applicability of the model based on the average biases of the estimates formed. Copyright (C) 2001 John Wiley & Sons, Ltd.

Some vocabulary and grammar for the analysis of multi-environment trials, as applied to the analysis of FPB and PPB trials

For the improvement of genetic material suitable for on farm use under low-input conditions, participatory and formal plant breeding strategies are frequently presented as competing options. A common frame of reference to phrase mechanisms and purposes related to breeding strategies will facilitate clearer descriptions of similarities and differences between participatory plant breeding and formal plant breeding. In this paper an attempt is made to develop such a common framework by means of a statistically inspired language that acknowledges the importance of both on farm trials and research centre trials as sources of information for on farm genetic improvement. Key concepts are the genetic correlation between environments, and the heterogeneity of phenotypic and genetic variance over environments. Classic selection response theory is taken as the starting point for the comparison of selection trials (on farm and research centre) with respect to the expected genetic improvement in a target environment (low-input farms). The variance-covariance parameters that form the input for selection response comparisons traditionally come from a mixed model fit to multi-environment trial data. In this paper we propose a recently developed class of mixed models, namely multiplicative mixed models, also called factor-analytic models, for modelling genetic variances and covariances (correlations). Mixed multiplicative models allow genetic variances and covariances to be dependent on quantitative descriptors of the environment, and confer a high flexibility in the choice of variance-covariance structure, without requiring the estimation of a prohibitively high number of parameters. As a result detailed considerations regarding selection response comparisons are facilitated. ne statistical machinery involved is illustrated on an example data set consisting of barley trials from the International Center for Agricultural Research in the Dry Areas (ICARDA). Analysis of the example data showed that participatory plant breeding and formal plant breeding are better interpreted as providing complementary rather than competing information.

Application of spatial analysis techniques to adjust for fertility trends and identify interplot competition in early stage sugarcane selection trials

Most sugarcane breeding programs in Australia use large unreplicated trials to evaluate clones in the early stages of selection. Commercial varieties that are replicated provide a method of local control of soil fertility. Although such methods may be useful in detecting broad trends in the field, variation often occurs on a much smaller scale. Methods such as spatial analysis adjust a plot for variability by using information from immediate neighbours. These techniques are routinely used to analyse cereal data in Australia and have resulted in increased accuracy and precision in the estimates of variety effects. In this paper, spatial analyses in which the variability is decomposed into local, natural, and extraneous components are applied to early selection trials in sugarcane. Interplot competition in cane yield and trend in sugar content were substantial in many of the trials and there were often large differences in the selections between the spatial and current method used by the Bureau of Sugar Experiment Stations. A joint modelling approach for tonnes sugar per hectare in response to fertility trends and interplot competition is recommended.

Privatization and social welfare in an international mixed Stackelberg duopoly

Competition between public and private firms exists in a range of industries like telecommunications, electricity, natural gas, airlines industries, as weel as services including hospitals, banking and education. Some authors studied mixed oligopolies under Cournot competition (firms move simultaneously) and some others considered Stackelberg models (firms move sequentially). Tomaru [1] analyzed, in a Cournot model, how decision-making upon cost-reducing R&D investment by a domestic public firm is affected by privatization when competing in the domestic market with a foreign firm. He shows that privatization of the domestic public firm lowers productive efficiency and deteriorates domestic social welfare. In this paper, we examine the same question but in a Stackelberg formulation instead of Cournot. The model is a three-stage game. In the first stage, the domestic firm chooses the amount of cost-reducing R&D investment. Then, the firms compete à la Stackelberg. Two cases are considered: (i) The domestic firm is the leader; (ii) The foreign firm is the leader. We show that the results obtained in [1] for Cournot competition are robust in the sence that they are also true when firms move sequentially.

The evolution of XY recombination: sexually antagonistic selection versus deleterious mutation load.

Recombination arrest between X and Y chromosomes, driven by sexually antagonistic genes, is expected to induce their progressive differentiation. However, in contrast to birds and mammals (which display the predicted pattern), most cold-blooded vertebrates have homomorphic sex chromosomes. Two main hypotheses have been proposed to account for this, namely high turnover rates of sex-determining systems and occasional XY recombination. Using individual-based simulations, we formalize the evolution of XY recombination (here mediated by sex reversal; the "fountain-of-youth" model) under the contrasting forces of sexually antagonistic selection and deleterious mutations. The shift between the domains of elimination and accumulation occurs at much lower selection coefficients for the Y than for the X. In the absence of dosage compensation, mildly deleterious mutations accumulating on the Y depress male fitness, thereby providing incentives for XY recombination. Under our settings, this occurs via "demasculinization" of the Y, allowing recombination in XY (sex-reversed) females. As we also show, this generates a conflict with the X, which coevolves to oppose sex reversal. The resulting rare events of XY sex reversal are enough to purge the Y from its load of deleterious mutations. Our results support the "fountain of youth" as a plausible mechanism to account for the maintenance of sex-chromosome homomorphy.

One-sided tolerance interval in a two-way balanced nested model with mixed effects

In many research areas (such as public health, environmental contamination, and others) one deals with the necessity of using data to infer whether some proportion (%) of a population of interest is (or one wants it to be) below and/or over some threshold, through the computation of tolerance interval. The idea is, once a threshold is given, one computes the tolerance interval or limit (which might be one or two - sided bounded) and then to check if it satisfies the given threshold. Since in this work we deal with the computation of one - sided tolerance interval, for the two-sided case we recomend, for instance, Krishnamoorthy and Mathew [5]. Krishnamoorthy and Mathew [4] performed the computation of upper tolerance limit in balanced and unbalanced one-way random effects models, whereas Fonseca et al [3] performed it based in a similar ideas but in a tow-way nested mixed or random effects model. In case of random effects model, Fonseca et al [3] performed the computation of such interval only for the balanced data, whereas in the mixed effects case they dit it only for the unbalanced data. For the computation of twosided tolerance interval in models with mixed and/or random effects we recomend, for instance, Sharma and Mathew [7]. The purpose of this paper is the computation of upper and lower tolerance interval in a two-way nested mixed effects models in balanced data. For the case of unbalanced data, as mentioned above, Fonseca et al [3] have already computed upper tolerance interval. Hence, using the notions persented in Fonseca et al [3] and Krishnamoorthy and Mathew [4], we present some results on the construction of one-sided tolerance interval for the balanced case. Thus, in order to do so at first instance we perform the construction for the upper case, and then the construction for the lower case.

Spatial statistical analysis and selection of genotypes in plant breeding

The objective of this study was to evaluate the efficiency of spatial statistical analysis in the selection of genotypes in a plant breeding program and, particularly, to demonstrate the benefits of the approach when experimental observations are not spatially independent. The basic material of this study was a yield trial of soybean lines, with five check varieties (of fixed effect) and 110 test lines (of random effects), in an augmented block design. The spatial analysis used a random field linear model (RFML), with a covariance function estimated from the residuals of the analysis considering independent errors. Results showed a residual autocorrelation of significant magnitude and extension (range), which allowed a better discrimination among genotypes (increase of the power of statistical tests, reduction in the standard errors of estimates and predictors, and a greater amplitude of predictor values) when the spatial analysis was applied. Furthermore, the spatial analysis led to a different ranking of the genetic materials, in comparison with the non-spatial analysis, and a selection less influenced by local variation effects was obtained.

Designs and model effects definitions in the initial stage of a plant breeding program

The objective of this work was to compare the relative efficiency of initial selection and genetic parameter estimation, using augmented blocks design (ABD), augmented blocks twice replicated design (DABD) and group of randomised block design experiments with common treatments (ERBCT), by simulations, considering fixed effect model and mixed model with regular treatment effects as random. For the simulations, eight different conditions (scenarios) were considered. From the 600 simulations in each scenario, the mean percentage selection coincidence, the Pearsons´s correlation estimates between adjusted means for the fixed effects model, and the heritability estimates for the mixed model were evaluated. DABD and ERBCT were very similar in their comparisons and slightly superior to ABD. Considering the initial stages of selection in a plant breeding program, ABD is a good alternative for selecting superior genotypes, although none of the designs had been effective to estimate heritability in all the different scenarios evaluated.

Genetic parameters and simultaneous selection for root yield, adaptability and stability of cassava genotypes

The objective of this work was to estimate genetic parameters and to evaluate simultaneous selection for root yield and for adaptability and stability of cassava genotypes. The effects of genotypes were assumed as fixed and random, and the mixed model methodology (REML/Blup) was used to estimate genetic parameters and the harmonic mean of the relative performance of genotypic values (HMRPGV), for simultaneous selection purposes. Ten genotypes were analyzed in a complete randomized block design, with four replicates. The experiment was carried out in the municipalities of Altamira, Santarém, and Santa Luzia do Pará in the state of Pará, Brazil, in the growing seasons of 2009/2010, 2010/2011, and 2011/2012. Roots were harvested 12 months after planting, in all tested locations. Root yield had low coefficients of genotypic variation (4.25%) and broad-sense heritability of individual plots (0.0424), which resulted in low genetic gain. Due to the low genotypic correlation (0.15), genotype classification as to root yield varied according to the environment. Genotypes CPATU 060, CPATU 229, and CPATU 404 stood out as to their yield, adaptability, and stability.

Selection response of growth rate in rabbits for meat production

Genetic and environmental trends in 2 lines of rabbit (B and R) selected on individual weight gain (WG) from weaning (4 wk) to slaughter (11 wk) were estimated using mixed model methodology. Line B was derived from the California breed and line R was a synthetic of stock of different origin. The data were collected from a single herd and comprised 7 718 individuals in line B and 9 391 in line R, the lines having 12 and 9 generations of selection respectively. Realized responses in the 2 lines were 2.7% and 2.2% of the initial mean per year respectively and showed that selection on WG was effective but was less than expected. Selection on slaughter weight (SW) and effects of selection on other economic traits are discussed. It is concluded that selection on either WG or SW is a simple method for improving growth rate in rabbit sire line stocks.

REPEATABILITY OF FRUITS AND SEEDS PRODUCTION AND SELECTION OF BRAZIL NUT GENOTYPES IN NATIVE POPULATIONS IN RORAIMA1

ABSTRACT This study estimates the repeatability coefficients of two production traits in two native populations of Brazil nut trees. It determines the number of years of suitable evaluations for an efficient selection process, determines the permanent phenotypic correlation between production traits and also the selection of promising trees in these populations. Populations, located in the Itã region (ITA) and in the in the Cujubim region (CUJ), are both belonging to the municipality of Caracaraí, state of Roraima - Brazil, and consist of 85 and 51 adult trees, respectively. Each tree was evaluated regarding the number of fruits per plant (NFP) and fresh seed weight per plant (SWP), for eight (ITA) and five consecutive years (CUJ). Statistical analyses were performed according to the mixed model methodology, using Software Selegen-REML/BLUP (RESENDE, 2007). The repeatability coefficients were low for NFP (0.3145 and 0.3269 for ITA and CUJ, respectively) and also for SWP (0.2957 and 0.3436 for ITA and CUJ, respectively). It on average takes nine evaluation years to reach coefficients of determination higher than 80%. Permanent phenotypic correlation values higher than 0.95 were obtained for NFP and SWP in both populations. Although trees with a high number of fruits and seed weight were identified, more evaluation years are needed to perform the selection process more efficiently.

Attentional Selection in Object Recognition

A key problem in object recognition is selection, namely, the problem of identifying regions in an image within which to start the recognition process, ideally by isolating regions that are likely to come from a single object. Such a selection mechanism has been found to be crucial in reducing the combinatorial search involved in the matching stage of object recognition. Even though selection is of help in recognition, it has largely remained unsolved because of the difficulty in isolating regions belonging to objects under complex imaging conditions involving occlusions, changing illumination, and object appearances. This thesis presents a novel approach to the selection problem by proposing a computational model of visual attentional selection as a paradigm for selection in recognition. In particular, it proposes two modes of attentional selection, namely, attracted and pay attention modes as being appropriate for data and model-driven selection in recognition. An implementation of this model has led to new ways of extracting color, texture and line group information in images, and their subsequent use in isolating areas of the scene likely to contain the model object. Among the specific results in this thesis are: a method of specifying color by perceptual color categories for fast color region segmentation and color-based localization of objects, and a result showing that the recognition of texture patterns on model objects is possible under changes in orientation and occlusions without detailed segmentation. The thesis also presents an evaluation of the proposed model by integrating with a 3D from 2D object recognition system and recording the improvement in performance. These results indicate that attentional selection can significantly overcome the computational bottleneck in object recognition, both due to a reduction in the number of features, and due to a reduction in the number of matches during recognition using the information derived during selection. Finally, these studies have revealed a surprising use of selection, namely, in the partial solution of the pose of a 3D object.