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Showing papers on "Selection (genetic algorithm) published in 2014"


Journal ArticleDOI
TL;DR: This article uses causal graphs (direct acyclic graphs, or DAGs) to highlight that endogenous selection bias stems from conditioning on a so-called collider variable, i.e., a variable that is itself caused by two other variables, one that is (or is associated with) the treatment and another that is not associated with the outcome.
Abstract: Endogenous selection bias is a central problem for causal inference. Recognizing the problem, however, can be difficult in practice. This article introduces a purely graphical way of characterizing endogenous selection bias and of understanding its consequences (Hernan et al. 2004). We use causal graphs (direct acyclic graphs, or DAGs) to highlight that endogenous selection bias stems from conditioning (e.g., controlling, stratifying, or selecting) on a so-called collider variable, i.e., a variable that is itself caused by two other variables, one that is (or is associated with) the treatment and another that is (or is associated with) the outcome. Endogenous selection bias can result from direct conditioning on the outcome variable, a post-outcome variable, a post-treatment variable, and even a pre-treatment variable. We highlight the difference between endogenous selection bias, common-cause confounding, and overcontrol bias and discuss numerous examples from social stratification, cultural sociology, s...

668 citations


Journal ArticleDOI
TL;DR: The comparative analysis has shown that the Fuzzy TOPSIS method is better suited to the problem of supplier selection in regard to changes of alternatives and criteria, agility and number of criteria and alternative suppliers.

641 citations


Journal ArticleDOI
TL;DR: Of the 51 solvents considered, an acceptable alignment of the classifications could be met, permitting a ranking into four categories: recommended, problematic, hazardous and highly hazardous.

636 citations


Journal ArticleDOI
TL;DR: Hamburg Registration and Organization Online Tool (Hroot) as discussed by the authors is a web-based software designed for managing participants of economic experiments, which provides important features to assure a randomized invitation process based on a filtered, pre-specified subject pool.

544 citations


Journal ArticleDOI
TL;DR: Several genomic selection (GS) models are reviewed with respect to both the prediction accuracy and genetic gain from selection.

479 citations


Journal ArticleDOI
01 May 2014
TL;DR: Experiments on twenty benchmark datasets show that PSO with the new initialisation strategies and/or the new updating mechanisms can automatically evolve a feature subset with a smaller number of features and higher classification performance than using all features.
Abstract: In classification, feature selection is an important data pre-processing technique, but it is a difficult problem due mainly to the large search space. Particle swarm optimisation (PSO) is an efficient evolutionary computation technique. However, the traditional personal best and global best updating mechanism in PSO limits its performance for feature selection and the potential of PSO for feature selection has not been fully investigated. This paper proposes three new initialisation strategies and three new personal best and global best updating mechanisms in PSO to develop novel feature selection approaches with the goals of maximising the classification performance, minimising the number of features and reducing the computational time. The proposed initialisation strategies and updating mechanisms are compared with the traditional initialisation and the traditional updating mechanism. Meanwhile, the most promising initialisation strategy and updating mechanism are combined to form a new approach (PSO(4-2)) to address feature selection problems and it is compared with two traditional feature selection methods and two PSO based methods. Experiments on twenty benchmark datasets show that PSO with the new initialisation strategies and/or the new updating mechanisms can automatically evolve a feature subset with a smaller number of features and higher classification performance than using all features. PSO(4-2) outperforms the two traditional methods and two PSO based algorithm in terms of the computational time, the number of features and the classification performance. The superior performance of this algorithm is due mainly to both the proposed initialisation strategy, which aims to take the advantages of both the forward selection and backward selection to decrease the number of features and the computational time, and the new updating mechanism, which can overcome the limitations of traditional updating mechanisms by taking the number of features into account, which reduces the number of features and the computational time.

457 citations


Journal ArticleDOI
TL;DR: This paper proposes a bandit-based AOS method, fitness-rate-rank-based multiarmed bandit (FRRMAB), which uses a sliding window to record the recent fitness improvement rates achieved by the operators, while employing a decaying mechanism to increase the selection probability of the best operator.
Abstract: Adaptive operator selection (AOS) is used to determine the application rates of different operators in an online manner based on their recent performances within an optimization process. This paper proposes a bandit-based AOS method, fitness-rate-rank-based multiarmed bandit (FRRMAB). In order to track the dynamics of the search process, it uses a sliding window to record the recent fitness improvement rates achieved by the operators, while employing a decaying mechanism to increase the selection probability of the best operator. Not much work has been done on AOS in multiobjective evolutionary computation since it is very difficult to measure the fitness improvements quantitatively in most Pareto-dominance-based multiobjective evolutionary algorithms. Multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem into a number of scalar optimization subproblems and optimizes them simultaneously. Thus, it is natural and feasible to use AOS in MOEA/D. We investigate several important issues in using FRRMAB in MOEA/D. Our experimental results demonstrate that FRRMAB is robust and its operator selection is reasonable. Comparison experiments also indicate that FRRMAB can significantly improve the performance of MOEA/D.

343 citations


Journal ArticleDOI
TL;DR: This work considers bootstrap methods for computing standard errors and confidence intervals that take model selection into account, also known as bootstrap smoothing, to tame the erratic discontinuities of selection-based estimators.
Abstract: Classical statistical theory ignores model selection in assessing estimation accuracy. Here we consider bootstrap methods for computing standard errors and confidence intervals that take model selection into account. The methodology involves bagging, also known as bootstrap smoothing, to tame the erratic discontinuities of selection-based estimators. A useful new formula for the accuracy of bagging then provides standard errors for the smoothed estimators. Two examples, nonparametric and parametric, are carried through in detail: a regression model where the choice of degree (linear, quadratic, cubic, …) is determined by the Cp criterion and a Lasso-based estimation problem.

329 citations


Journal ArticleDOI
TL;DR: It is found that it is possible to greatly reduce error rates by considering the results of all three methods when identifying outlier loci, and the relative ranking between the methods is impacted by the consideration of polygenic selection.
Abstract: The recent availability of next-generation sequencing (NGS) has made possible the use of dense genetic markers to identify regions of the genome that may be under the influence of selection. Several statistical methods have been developed recently for this purpose. Here, we present the results of an individual-based simulation study investigating the power and error rate of popular or recent genome scan methods: linear regression, Bayescan, BayEnv and LFMM. Contrary to previous studies, we focus on complex, hierarchical population structure and on polygenic selection. Additionally, we use a false discovery rate (FDR)-based framework, which provides an unified testing framework across frequentist and Bayesian methods. Finally, we investigate the influence of population allele frequencies versus individual genotype data specification for LFMM and the linear regression. The relative ranking between the methods is impacted by the consideration of polygenic selection, compared to a monogenic scenario. For strongly hierarchical scenarios with confounding effects between demography and environmental variables, the power of the methods can be very low. Except for one scenario, Bayescan exhibited moderate power and error rate. BayEnv performance was good under nonhierarchical scenarios, while LFMM provided the best compromise between power and error rate across scenarios. We found that it is possible to greatly reduce error rates by considering the results of all three methods when identifying outlier loci.

288 citations


Journal ArticleDOI
Aicha Aguezzoul1
TL;DR: It is revealed that 3PL selection is empirical in nature and is related to a region/country, industrial sector, and logistics activities outsourced, as well as 11 key criteria, which are identified.
Abstract: This paper presents a literature review on Third-Party Logistics (3PL) selection decision in terms of criteria and methods. Based on the analysis of 67 articles published within 1994–2013 period, this review reveals that 3PL selection is empirical in nature and is related to a region/country, industrial sector, and logistics activities outsourced. In terms of 3PL selection criteria, 11 key criteria are identified; each one is defined by a set of attributes. Cost is the most widely adopted criterion, followed by relationship, services, and quality. In terms of methods for 3PL evaluation, they can be categorized in 5 groups, namely: MCDM techniques, statistical approaches, artificial intelligence, mathematical programming, and hybrid methods.

284 citations


Journal ArticleDOI
TL;DR: A combination of whole-genome selection scans and association analyses of Populus trichocarpa trees are used to reveal genomic bases of adaptive variation across a wide latitudinal range, providing unexpected insights into the evolutionary dynamics of duplicated genes and their roles in adaptive trait variation.
Abstract: Forest trees are dominant components of terrestrial ecosystems that have global ecological and economic importance. Despite distributions that span wide environmental gradients, many tree populations are locally adapted, and mechanisms underlying this adaptation are poorly understood. Here we use a combination of whole-genome selection scans and association analyses of 544 Populus trichocarpa trees to reveal genomic bases of adaptive variation across a wide latitudinal range. Three hundred ninety-seven genomic regions showed evidence of recent positive and/or divergent selection and enrichment for associations with adaptive traits that also displayed patterns consistent with natural selection. These regions also provide unexpected insights into the evolutionary dynamics of duplicated genes and their roles in adaptive trait variation.


Journal ArticleDOI
TL;DR: Four state-of-the-art Random Forest-based feature selection methods were compared and the post-selection classifier error rate was found to be a potentially deceptive measure of gene selection quality.
Abstract: Gene selection is an important part of microarray data analysis because it provides information that can lead to a better mechanistic understanding of an investigated phenomenon. At the same time, gene selection is very difficult because of the noisy nature of microarray data. As a consequence, gene selection is often performed with machine learning methods. The Random Forest method is particularly well suited for this purpose. In this work, four state-of-the-art Random Forest-based feature selection methods were compared in a gene selection context. The analysis focused on the stability of selection because, although it is necessary for determining the significance of results, it is often ignored in similar studies. The comparison of post-selection accuracy of a validation of Random Forest classifiers revealed that all investigated methods were equivalent in this context. However, the methods substantially differed with respect to the number of selected genes and the stability of selection. Of the analysed methods, the Boruta algorithm predicted the most genes as potentially important. The post-selection classifier error rate, which is a frequently used measure, was found to be a potentially deceptive measure of gene selection quality. When the number of consistently selected genes was considered, the Boruta algorithm was clearly the best. Although it was also the most computationally intensive method, the Boruta algorithm’s computational demands could be reduced to levels comparable to those of other algorithms by replacing the Random Forest importance with a comparable measure from Random Ferns (a similar but simplified classifier). Despite their design assumptions, the minimal optimal selection methods, were found to select a high fraction of false positives.

Journal ArticleDOI
TL;DR: This study illustrates the potential of population genetic approaches for identifying genomic regions affecting domestication-related phenotypes and further helps to identify specific regions targeted by selection during speciation, domestication and breed formation of cattle.
Abstract: Human driven selection during domestication and subsequent breed formation has likely left detectable signatures within the genome of modern cattle. The elucidation of these signatures of selection is of interest from the perspective of evolutionary biology, and for identifying domestication-related genes that ultimately may help to further genetically improve this economically important animal. To this end, we employed a panel of more than 15 million autosomal SNPs identified from re-sequencing of 43 Fleckvieh animals. We mainly applied two somewhat complementary statistics, the integrated Haplotype Homozygosity Score (iHS) reflecting primarily ongoing selection, and the Composite of Likelihood Ratio (CLR) having the most power to detect completed selection after fixation of the advantageous allele. We find 106 candidate selection regions, many of which are harboring genes related to phenotypes relevant in domestication, such as coat coloring pattern, neurobehavioral functioning and sensory perception including KIT, MITF, MC1R, NRG4, Erbb4, TMEM132D and TAS2R16, among others. To further investigate the relationship between genes with signatures of selection and genes identified in QTL mapping studies, we use a sample of 3062 animals to perform four genome-wide association analyses using appearance traits, body size and somatic cell count. We show that regions associated with coat coloring significantly (P<0.0001) overlap with the candidate selection regions, suggesting that the selection signals we identify are associated with traits known to be affected by selection during domestication. Results also provide further evidence regarding the complexity of the genetics underlying coat coloring in cattle. This study illustrates the potential of population genetic approaches for identifying genomic regions affecting domestication-related phenotypes and further helps to identify specific regions targeted by selection during speciation, domestication and breed formation of cattle. We also show that Linkage Disequilibrium (LD) decays in cattle at a much faster rate than previously thought.

Book ChapterDOI
TL;DR: A method to predict the genetic value of selection candidates based on the genomic estimated breeding value (GEBV) predicted from high-density markers positioned throughout the genome may capture more of the genetic variation for the particular trait under selection.
Abstract: Genomic selection (GS) is a method to predict the genetic value of selection candidates based on the genomic estimated breeding value (GEBV) predicted from high-density markers positioned throughout the genome. Unlike marker-assisted selection, the GEBV is based on all markers including both minor and major marker effects. Thus, the GEBV may capture more of the genetic variation for the particular trait under selection.

Journal ArticleDOI
TL;DR: This paper proposes a global and local structure preservation framework for feature selection (GLSPFS) which integrates both global pairwise sample similarity and local geometric data structure to conduct feature selection and shows that the best feature selection performance is always obtained when the two factors are appropriately integrated.
Abstract: The recent literature indicates that preserving global pairwise sample similarity is of great importance for feature selection and that many existing selection criteria essentially work in this way. In this paper, we argue that besides global pairwise sample similarity, the local geometric structure of data is also critical and that these two factors play different roles in different learning scenarios. In order to show this, we propose a global and local structure preservation framework for feature selection (GLSPFS) which integrates both global pairwise sample similarity and local geometric data structure to conduct feature selection. To demonstrate the generality of our framework, we employ methods that are well known in the literature to model the local geometric data structure and develop three specific GLSPFS-based feature selection algorithms. Also, we develop an efficient optimization algorithm with proven global convergence to solve the resulting feature selection problem. A comprehensive experimental study is then conducted in order to compare our feature selection algorithms with many state-of-the-art ones in supervised, unsupervised, and semisupervised learning scenarios. The result indicates that: 1) our framework consistently achieves statistically significant improvement in selection performance when compared with the currently used algorithms; 2) in supervised and semisupervised learning scenarios, preserving global pairwise similarity is more important than preserving local geometric data structure; 3) in the unsupervised scenario, preserving local geometric data structure becomes clearly more important; and 4) the best feature selection performance is always obtained when the two factors are appropriately integrated. In summary, this paper not only validates the advantages of the proposed GLSPFS framework but also gains more insight into the information to be preserved in different feature selection tasks.

Journal ArticleDOI
TL;DR: The literature on insect genital evolution is synthesised, and this synthesis is used to address the debate over the mechanisms of selection most likely to explain observed patterns of macroevolutionary divergence in genital morphology.
Abstract: Male genitalia show patterns of divergent evolution, and sexual selection is recognised as being responsible for this taxonomically widespread phenomenon. Much of the empirical support for the sexual selection hypothesis comes from studies of insects. Here, I synthesise the literature on insect genital evolution, and use this synthesis to address the debate over the mechanisms of selection most likely to explain observed patterns of macroevolutionary divergence in genital morphology. Studies of seven insect orders provide evidence that non-intromittent genitalia are subject to sexual selection through their effects on mating success, while intromittent genitalia are subject to selection through their effects on fertilisation success. However, studies that use quantitative methods to analyse the form of selection are necessary to identify the mechanisms of sexual selection involved. Phylogenetic analyses from diverse taxonomic groups confirm that divergence in male genital morphology can be predicted from variation in the opportunity for sexual selection. Much debate revolves around the importance of female choice and sexual conflict in the evolution of male genitalia, the resolution of which lies in economic studies of mating interactions and in recognising sexual selection as a continuum between male competition, sexual conflict and female choice. The species isolating lock-and-key hypothesis is frequently dismissed as unimportant in genital evolution because in part of a perceived lack of variation in female genitalia across species. Increasingly, however, studies report species-specific variation in female genital morphology and its coevolutionary divergence with male genital morphology. Contemporary views recognise a continuum between female choice that enforces species isolation and female choice that targets variation in male quality within populations, placing lock-and-key processes into the realm of sexual selection. Distinguishing between species-isolating and directional forms of female choice will require studies that examine both the tempo and mode of divergence, both within and among species.

Journal ArticleDOI
TL;DR: It is demonstrated that optimal neighborhoods for individual 3D points significantly improve the results of scene interpretation and that the selection of adequate feature subsets may even further increase the quality of the derived results.
Abstract: D scene analysis by automatically assigning 3D points a semantic label has become an issue of major interest in recent years. Whereas the tasks of feature extraction and classification have been in the focus of research, the idea of using only relevant and more distinctive features extracted from optimal 3D neighborhoods has only rarely been addressed in 3D lidar data processing. In this paper, we focus on the interleaved issue of extracting relevant, but not redundant features and increasing their distinctiveness by considering the respective optimal 3D neighborhood of each individual 3D point. We present a new, fully automatic and versatile framework consisting of four successive steps: (i) optimal neighborhood size selection, (ii) feature extraction, (iii) feature selection, and (iv) classification. In a detailed evaluation which involves 5 different neighborhood definitions, 21 features, 6 approaches for feature subset selection and 2 different classifiers, we demonstrate that optimal neighborhoods for individual 3D points significantly improve the results of scene interpretation and that the selection of adequate feature subsets may even further increase the quality of the derived results.

Journal ArticleDOI
TL;DR: It is suggested that deeper ecological perspectives on sexual selection may alter some of the fundamental assumptions of sexual selection theory and rapidly lead to new discoveries.
Abstract: Sexual selection has resulted in some of the most captivating features of insects, including flashy colors, bizarre structures, and complex pheromones. These features evolve in dynamic environments, where conditions can change rapidly over space and time. However, only recently has ecological complexity been embraced by theory and practice in sexual selection. We review replicated selection studies as well as studies on variation in the agents of selection to delineate gaps in current knowledge and clarify exciting new directions for research. Existing work suggests that fluctuations in sexual selection may be extremely common, though work on the ecological factors influencing these fluctuations is scarce. We suggest that deeper ecological perspectives on sexual selection may alter some of the fundamental assumptions of sexual selection theory and rapidly lead to new discoveries.


Journal ArticleDOI
TL;DR: An advanced method to predict hybrid performance, in which a subset of all potential hybrids is used as a training sample to predict trait values of all possible hybrids, is proposed and applied, called genomic best linear unbiased prediction.
Abstract: Genomic selection is an upgrading form of marker-assisted selection for quantitative traits, and it differs from the traditional marker-assisted selection in that markers in the entire genome are used to predict genetic values and the QTL detection step is skipped Genomic selection holds the promise to be more efficient than the traditional marker-assisted selection for traits controlled by polygenes Genomic selection for pure breed improvement is based on marker information and thus leads to cost-saving due to early selection before phenotypes are measured When applied to hybrid breeding, genomic selection is anticipated to be even more efficient because genotypes of hybrids are predetermined by their inbred parents Hybrid breeding has been an important tool to increase crop productivity Here we proposed and applied an advanced method to predict hybrid performance, in which a subset of all potential hybrids is used as a training sample to predict trait values of all potential hybrids The method is called genomic best linear unbiased prediction The technology applied to hybrids is called genomic hybrid breeding We used 278 randomly selected hybrids derived from 210 recombinant inbred lines of rice as a training sample and predicted all 21,945 potential hybrids The average yield of top 100 selection shows a 16% increase compared with the average yield of all potential hybrids The new strategy of marker-guided prediction of hybrid yields serves as a proof of concept for a new technology that may potentially revolutionize hybrid breeding

Journal ArticleDOI
TL;DR: In this article, a Bayesian approach to variable selection in the presence of high dimensional covariates based on a hierarchical model that places prior distributions on the regression coefficients as well as on the model space is proposed.
Abstract: We consider a Bayesian approach to variable selection in the presence of high dimensional covariates based on a hierarchical model that places prior distributions on the regression coefficients as well as on the model space. We adopt the well-known spike and slab Gaussian priors with a distinct feature, that is, the prior variances depend on the sample size through which appropriate shrinkage can be achieved. We show the strong selection consistency of the proposed method in the sense that the posterior probability of the true model converges to one even when the number of covariates grows nearly exponentially with the sample size. This is arguably the strongest selection consistency result that has been available in the Bayesian variable selection literature; yet the proposed method can be carried out through posterior sampling with a simple Gibbs sampler. Furthermore, we argue that the proposed method is asymptotically similar to model selection with the $L_0$ penalty. We also demonstrate through empirical work the fine performance of the proposed approach relative to some state of the art alternatives.

Journal ArticleDOI
TL;DR: Key aspects related to disease resistance in salmonid species are reviewed, from both a genetic and genomic perspective, with emphasis in the applicability of disease resistance traits into breeding programs in salmonids.
Abstract: Infectious and parasitic diseases generate large economic losses in salmon farming. A feasible and sustainable alternative to prevent disease outbreaks may be represented by genetic improvement for disease resistance. To include disease resistance into the breeding goal, prior knowledge of the levels of genetic variation for these traits is required. Furthermore, the information from the genetic architecture and molecular factors involved in resistance against diseases may be used to accelerate the genetic progress for these traits. In this regard, marker assisted selection and genomic selection are approaches which incorporate molecular information to increase the accuracy when predicting the genetic merit of selection candidates. In this article we review and discuss key aspects related to disease resistance in salmonid species, from both a genetic and genomic perspective, with emphasis in the applicability of disease resistance traits into breeding programs in salmonids.

Journal ArticleDOI
TL;DR: The proposed approach to DEA cross-efficiency evaluation in portfolio selection can be a promising tool for stock portfolio selection by showing that the selected portfolio yields higher risk-adjusted returns than other benchmark portfolios for a 9-year sample period from 2002 to 2011.

Journal ArticleDOI
TL;DR: The authors found that migrants outperform domestic scientists when instrumenting migration for reasons of work or study with migration in childhood to minimize the effect of selection, consistent with theories of knowledge recombination and specialty matching.

Journal ArticleDOI
TL;DR: This protocol describes the steps that must be carried out to generate a large-scale mutagenesis data set consisting of functional scores for up to hundreds of thousands of variants of a protein of interest.
Abstract: Deep mutational scanning marries selection for protein function to high-throughput DNA sequencing in order to quantify the activity of variants of a protein on a massive scale. First, an appropriate selection system for the protein function of interest is identified and validated. Second, a library of variants is created, introduced into the selection system and subjected to selection. Third, library DNA is recovered throughout the selection and deep-sequenced. Finally, a functional score for each variant is calculated on the basis of the change in the frequency of the variant during the selection. This protocol describes the steps that must be carried out to generate a large-scale mutagenesis data set consisting of functional scores for up to hundreds of thousands of variants of a protein of interest. Establishing an assay, generating a library of variants and carrying out a selection and its accompanying sequencing takes on the order of 4-6 weeks; the initial data analysis can be completed in 1 week.

Journal ArticleDOI
TL;DR: Computer simulations suggest that, with an adequate experimental design, E&R studies are a powerful tool to identify adaptive mutations from standing genetic variation and thereby provide an excellent means to analyze the trajectories of selected alleles in evolving populations.
Abstract: Standing genetic variation provides a rich reservoir of potentially useful mutations facilitating the adaptation to novel environments. Experimental evolution studies have demonstrated that rapid and strong phenotypic responses to selection can also be obtained in the laboratory. When combined with the next-generation sequencing technology, these experiments promise to identify the individual loci contributing to adaption. Nevertheless, until now, very little is known about the design of such evolve & resequencing (E&R) studies. Here, we use forward simulations of entire genomes to evaluate different experimental designs that aim to maximize the power to detect selected variants. We show that low linkage disequilibrium in the starting population, population size, duration of the experiment, and the number of replicates are the key factors in determining the power and accuracy of E&R studies. Furthermore, replication of E&R is more important for detecting the targets of selection than increasing the population size. Using an optimized design, beneficial loci with a selective advantage as low as s = 0.005 can be identified at the nucleotide level. Even when a large number of loci are selected simultaneously, up to 56% can be reliably detected without incurring large numbers of false positives. Our computer simulations suggest that, with an adequate experimental design, E&R studies are a powerful tool to identify adaptive mutations from standing genetic variation and thereby provide an excellent means to analyze the trajectories of selected alleles in evolving populations.

Journal ArticleDOI
TL;DR: The effects of selection primarily for productivity traits on temperament and welfare are discussed and future opportunities include automated data collection methods and the wider use of genomic information in selection.
Abstract: Animal temperament can be defined as a response to environmental or social stimuli. There are a number of temperament traits in cattle that contribute to their welfare, including their response to handling or milking, response to challenge such as human approach or intervention at calving, and response to conspecifics. In a number of these areas, the genetic basis of the trait has been studied. Heritabilities have been estimated and in some cases quantitative trait loci (QTL) have been identified. The variation is sometimes considerable and moderate heritabilities have been found for the major handling temperament traits, making them amenable to selection. Studies have also investigated the correlations between temperament and other traits, such as productivity and meat quality. Despite this, there are relatively few examples of temperament traits being used in selection programmes. Most often, animals are screened for aggression or excessive fear during handling or milking, with extreme animals being culled, or EBVs for temperament are estimated, but these traits are not commonly included routinely in selection indices, despite there being economic, welfare and human safety drivers for their. There may be a number of constraints and barriers. For some traits and breeds, there may be difficulties in collecting behavioral data on sufficiently large populations of animals to estimate genetic parameters. Most selection indices require estimates of economic values, and it is often difficult to assign an economic value to a temperament trait. The effects of selection primarily for productivity traits on temperament and welfare are discussed. Future opportunities include automated data collection methods and the wider use of genomic information in selection.

Journal ArticleDOI
TL;DR: It is concluded that while discrete-morph variation provides the most unambiguous cases of protected polymorphism, they represent only a fraction of the balancing selection at work in plants.
Abstract: Balancing selection refers to a variety of selective regimes that maintain advantageous genetic diversity within populations. We review the history of the ideas regarding the types of selection that maintain such polymorphism in flowering plants, notably heterozygote advantage, negative frequency-dependent selection, and spatial heterogeneity. One shared feature of these mechanisms is that whether an allele is beneficial or detrimental is conditional on its frequency in the population. We highlight examples of balancing selection on a variety of discrete traits. These include the well-referenced case of self-incompatibility and recent evidence from species with nuclear-cytoplasmic gynodioecy, both of which exhibit trans-specific polymorphism, a hallmark of balancing selection. We also discuss and give examples of how spatial heterogeneity in particular, which is often thought unlikely to allow protected polymorphism, can maintain genetic variation in plants (which are rooted in place) as a result of microhabitat selection. Lastly, we discuss limitations of the protected polymorphism concept for quantitative traits, where selection can inflate the genetic variance without maintaining specific alleles indefinitely. We conclude that while discrete-morph variation provides the most unambiguous cases of protected polymorphism, they represent only a fraction of the balancing selection at work in plants.

Journal ArticleDOI
TL;DR: A significant correlation between biases induced by VDJ recombination and inferred selection factors together with a reduction of diversity during selection suggest that natural selection acting on the recombination process has anticipated the selection pressures experienced during somatic evolution.
Abstract: The efficient recognition of pathogens by the adaptive immune system relies on the diversity of receptors displayed at the surface of immune cells. T-cell receptor diversity results from an initial random DNA editing process, called VDJ recombination, followed by functional selection of cells according to the interaction of their surface receptors with self and foreign antigenic peptides. Using high-throughput sequence data from the β-chain of human T-cell receptors, we infer factors that quantify the overall effect of selection on the elements of receptor sequence composition: the V and J gene choice and the length and amino acid composition of the variable region. We find a significant correlation between biases induced by VDJ recombination and our inferred selection factors together with a reduction of diversity during selection. Both effects suggest that natural selection acting on the recombination process has anticipated the selection pressures experienced during somatic evolution. The inferred selection factors differ little between donors or between naive and memory repertoires. The number of sequences shared between donors is well-predicted by our model, indicating a stochastic origin of such public sequences. Our approach is based on a probabilistic maximum likelihood method, which is necessary to disentangle the effects of selection from biases inherent in the recombination process.