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Marcos Deon Vilela de Resende

Bio: Marcos Deon Vilela de Resende is an academic researcher from Universidade Federal de Viçosa. The author has contributed to research in topics: Population & Selection (genetic algorithm). The author has an hindex of 32, co-authored 335 publications receiving 5950 citations. Previous affiliations of Marcos Deon Vilela de Resende include Rothamsted Research & National Council for Scientific and Technological Development.


Papers
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Journal Article
TL;DR: In this article, the authors proposed a new approach for quality evaluation of variety trials for determination of cropping and use values (VCU), which considers three attributes simultaneously: magnitude of the residual variation, replication number, and genetic control of the trait under selection.
Abstract: This study had as objective to propose a new approach for quality evaluation of variety trials for determination of cropping and use values (VCU), which considers three attributes simultaneously: magnitude of the residual variation, replication number, and genetic control of the trait under selection. It was also emphasized the need for using shrinkage estimators/predictors of genotypic values instead of unshrunk phenotypic means of varieties, i.e., the procedures should consider the genetic coefficient of determination of the traits, as well as the eventual heterogeneity of residual variance within varieties. Targeting an accuracy of 90%, it was concluded that Snedecor F test values associated to treatment effects in the analysis of variance should be above 5.0. The magnitude of genotypic variability of the traits is also involved in the F statistics. This means that the approach of fixing minimum values for replication number and maximum values for residual variation coefficient (CVe) is not sufficient. For traits related to yield (with low genetic coefficient of determination) the normally used replication number, between two and four, does not permit to reach the targeted accuracy, even if residual variation coefficients below 10% are aimed, and the experimentation is conducted on several sites and years. For that target accuracy it is recommended the use of at least six replications. It was also shown that shrinkage estimators provide more precise and reliable inferences concerning genotypic means of the varieties, and their use is encouraged. KEY-WORDS: Accuracy; shrinkage estimator; variance heterogeneity; biased estimator; variation coefficent.

565 citations

Journal ArticleDOI
01 Apr 2012-Genetics
TL;DR: Four different original methods of genomic selection that differ with respect to assumptions regarding distribution of marker effects are presented, including ridge regression–best linear unbiased prediction (RR–BLUP), Bayes A, (iii) Bayes Cπ, and (iv) Bayesian LASSO, which suggest that alternative approaches to genomic selection prediction models may perform differently for traits with distinct genetic properties.
Abstract: Genomic selection can increase genetic gain per generation through early selection. Genomic selection is expected to be particularly valuable for traits that are costly to phenotype and expressed late in the life cycle of long-lived species. Alternative approaches to genomic selection prediction models may perform differently for traits with distinct genetic properties. Here the performance of four different original methods of genomic selection that differ with respect to assumptions regarding distribution of marker effects, including (i) ridge regression–best linear unbiased prediction (RR–BLUP), (ii) Bayes A, (iii) Bayes Cπ, and (iv) Bayesian LASSO are presented. In addition, a modified RR–BLUP (RR–BLUP B) that utilizes a selected subset of markers was evaluated. The accuracy of these methods was compared across 17 traits with distinct heritabilities and genetic architectures, including growth, development, and disease-resistance properties, measured in a Pinus taeda (loblolly pine) training population of 951 individuals genotyped with 4853 SNPs. The predictive ability of the methods was evaluated using a 10-fold, cross-validation approach, and differed only marginally for most method/trait combinations. Interestingly, for fusiform rust disease-resistance traits, Bayes Cπ, Bayes A, and RR–BLUB B had higher predictive ability than RR–BLUP and Bayesian LASSO. Fusiform rust is controlled by few genes of large effect. A limitation of RR–BLUP is the assumption of equal contribution of all markers to the observed variation. However, RR-BLUP B performed equally well as the Bayesian approaches.The genotypic and phenotypic data used in this study are publically available for comparative analysis of genomic selection prediction models.

362 citations

Journal ArticleDOI
TL;DR: The cautiously optimistic outlook is that GS has great potential to accelerate tree breeding, however, further simulation studies and proof-of-concept experiments of GS are needed before recommending it for operational implementation.
Abstract: Genomic selection (GS) involves selection decisions based on genomic breeding values estimated as the sum of the effects of genome-wide markers capturing most quantitative trait loci (QTL) for the target trait(s). GS is revolutionizing breeding practice in domestic animals. The same approach and concepts can be readily applied to forest tree breeding where long generation times and late expressing complex traits are also a challenge. GS in forest trees would have additional advantages: large training populations can be easily assembled and accurately phenotyped for several traits, and the extent of linkage disequilibrium (LD) can be high in elite populations with small effective population size (N e) frequently used in advanced forest tree breeding programs. Deterministic equations were used to assess the impact of LD (modeled by N e and intermarker distance), the size of the training set, trait heritability, and the number of QTL on the predicted accuracy of GS. Results indicate that GS has the potential to radically improve the efficiency of tree breeding. The benchmark accuracy of conventional BLUP selection is reached by GS even at a marker density ~2 markers/cM when N e ≤ 30, while up to 20 markers/cM are necessary for larger N e. Shortening the breeding cycle by 50% with GS provides an increase ≥100% in selection efficiency. With the rapid technological advances and declining costs of genotyping, our cautiously optimistic outlook is that GS has great potential to accelerate tree breeding. However, further simulation studies and proof-of-concept experiments of GS are needed before recommending it for operational implementation.

359 citations

Journal ArticleDOI
TL;DR: Genomic selection brings a new perspective to the understanding of quantitative trait variation in forest trees and provides a revolutionary tool for applied tree improvement, although population-specific predictive models will likely drive the initial applications of GS in forest tree breeding.
Abstract: Summary •Genomic selection (GS) is expected to cause a paradigm shift in tree breeding by improving its speed and efficiency By fitting all the genome-wide markers concurrently, GS can capture most of the ‘missing heritability’ of complex traits that quantitative trait locus (QTL) and association mapping classically fail to explain Experimental support of GS is now required •The effectiveness of GS was assessed in two unrelated Eucalyptus breeding populations with contrasting effective population sizes (Ne = 11 and 51) genotyped with > 3000 DArT markers Prediction models were developed for tree circumference and height growth, wood specific gravity and pulp yield using random regression best linear unbiased predictor (BLUP) •Accuracies of GS varied between 055 and 088, matching the accuracies achieved by conventional phenotypic selection Substantial proportions (74–97%) of trait heritability were captured by fitting all genome-wide markers simultaneously Genomic regions explaining trait variation largely coincided between populations, although GS models predicted poorly across populations, likely as a result of variable patterns of linkage disequilibrium, inconsistent allelic effects and genotype × environment interaction •GS brings a new perspective to the understanding of quantitative trait variation in forest trees and provides a revolutionary tool for applied tree improvement Nevertheless population-specific predictive models will likely drive the initial applications of GS in forest tree breeding

299 citations

Journal ArticleDOI
TL;DR: The software Selegen-REML/BLUP uses mixed models, and was developed to optimize the routine of plant breeding programs, and is friendly, easy to use and interpret, and allows dealing efficiently with most of the situations in plant breeding.
Abstract: The software Selegen-REML/BLUP uses mixed models, and was developed to optimize the routine of plant breeding programs. It addresses the following plants categories: allogamous, automagous, of mixed mating system, and of clonal propagation. It considers several experimental designs, mating designs, genotype x environment interaction, experiments repeated over sites, repeated measures, progenies belonging to several populations, among other factors. The software adjusts effects, estimates variance components, genetic additive, dominance and genotypic values of individuals, genetic gain with selection, effective population size, and other parameters of interest to plant breeding. It allows testing the significance of the effects by means of likelihood ratio test (LRT) and analysis of deviance. It addresses continuous variables (linear models) and categorical variables (generalized linear models). Selegen-REML/ BLUP is friendly, easy to use and interpret, and allows dealing efficiently with most of the situations in plant breeding. It is free and available at http://www. det.ufv.br/ppestbio/corpo_docente.php under the author?s name.

258 citations


Cited by
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Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal Article
TL;DR: For the next few weeks the course is going to be exploring a field that’s actually older than classical population genetics, although the approach it’ll be taking to it involves the use of population genetic machinery.
Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

9,847 citations

Journal Article
TL;DR: In this article, the authors present a document, redatto, voted and pubblicato by the Ipcc -Comitato intergovernativo sui cambiamenti climatici - illustra la sintesi delle ricerche svolte su questo tema rilevante.
Abstract: Cause, conseguenze e strategie di mitigazione Proponiamo il primo di una serie di articoli in cui affronteremo l’attuale problema dei mutamenti climatici. Presentiamo il documento redatto, votato e pubblicato dall’Ipcc - Comitato intergovernativo sui cambiamenti climatici - che illustra la sintesi delle ricerche svolte su questo tema rilevante.

4,187 citations

01 Jan 2001
TL;DR: Genetical genomics as discussed by the authors combines the power of genomics and genetics in a way that is likely to become instrumental in the further unravelling of metabolic, regulatory and developmental pathways.
Abstract: The recent successes of genome-wide expression profiling in biology tend to overlook the power of genetics. We here propose a merger of genomics and genetics into ‘genetical genomics’. This involves expression profiling and marker-based fingerprinting of each individual of a segregating population, and exploits all the statistical tools used in the analysis of quantitative trait loci. Genetical genomics will combine the power of two different worlds in a way that is likely to become instrumental in the further unravelling of metabolic, regulatory and developmental pathways.

952 citations