scholarly journals COMPARAÇÕES DE MÉTODOS DE SELEÇÃO EM POPULAÇÕES SIMULADAS DE FRANGOS CAIPIRAS

2017 ◽  
Vol 13 (Especial 2) ◽  
pp. 129-134
Author(s):  
Bruno Eduardo Damacena Silva ◽  
Jeferson Corrêa Ribeiro ◽  
Wallacy Barbacena Rosa dos Santos ◽  
Andreia Santos Cezário ◽  
Eliandra Maria Bianchini Oliveira ◽  
...  
Keyword(s):  
Genetic Gain ◽  
Overlapping Generations ◽  
Mating Systems ◽  
Random Mating ◽  
Selection Method ◽  
The Other ◽  
Individual Selection ◽  
Selection Methods ◽  
Slaughter Weight ◽  
Selection Systems

The purpose of this study was to compare the two types of selection methods known as BLUP and individual selection (IS) in populations of rustic chicken, evaluating the trait of slaughter weight and considering the averages of genetic gain and phenotypic value as response variables. A population of rustic chicken was simulated in the program Genesys, considering the basal population with 500 males and 500 females, from which was randomly chosen 120 males and 240 females. Then, the selected population was simulated considering the sexual ratios of 3, 4, 5 e 6 females per male in two selection types: Individual Selection (IS) and BLUP. In each selection method, the random mating system between breed chickens was used. Selection was performed during 15 consecutive and non-overlapping generations, with 10 simulation repetitions per generation, in order to decrease the genetic oscillation effects. The average genetic gain and the average phenotypic value in all methods and mating systems were evaluated. The results show that the ratio 6:1 was superior to the other sexual ratios in the two types of selection. When comparing the two selection systems, IS was superior to BLUP for the high phenotypic value of slaughter weight. Thus, the sexual ratio of six females for each male is recommended for individual 130 Colloquium Agrariae, vol. 13, n. Especial 2, Jan–Jun, 2017, p. 129-134. ISSN: 1809-8215. DOI: 10.5747/ca.2017.v13.nesp2.000218 selection, with which will allow high phenotypic values, with lower need of males

scholarly journals Genetic Gain and Diversity under Different Selection Methods in a Breeding Seed Orchard of Quercus serrata

2007 ◽  
Vol 56 (1-6) ◽  
pp. 277-281 ◽  
Author(s):  
K. S. Kang ◽  
B. H. Cheon ◽  
S. U. Han ◽  
C. S. Kim ◽  
W. Y. Choi
Keyword(s):  
Genetic Gain ◽  
Gene Diversity ◽  
Seed Orchard ◽  
Selection Method ◽  
Quercus Serrata ◽  
Selection Intensity ◽  
Individual Selection ◽  
Selection Methods ◽  
Family Selection ◽  
Status Number

Abstract Genetic gain and diversity were estimated in a 13- year old Quercus serrata breeding seed orchard under three selection (rouging) methods. The selections were based on individual selection, family selection, and family plus within family selection. Genetic gain was for stem volume and gene diversity was estimated by status number concept. Both estimated genetic gain and gene diversity were compared to those before selection and among selection scenarios. Estimated genetic gain for tree volume ranged from 4.0% to 9.1% for three selection methods under 50% selection intensity. Individual selection was better than family selection for retaining higher genetic gain and status number. Family plus within family selection was the best selection method, while individual selection was more efficient at the strong selection intensity. An optimal point, which maximized gain and diversity, was occurred at 50% selection intensity that would be applied for genetic thinning in the breeding seed orchard of Quercus serrata. The effect of genetic relatedness among families and possible pollen contamination on both genetic gain and gene diversity, although were not studied but their impact, are discussed. The selection method and intensity level applied should be chosen after careful consideration of the impacts on both genetic gain and diversity for seeds produced from the seed orchard.

scholarly journals Prospective genetic gain to improve salinity tolerance in a population of Panicum coloratum var. coloratum with two different selection methods

2021 ◽  
Vol 9 (2) ◽  
pp. 171-181
Author(s):  
Gabriela Pittaro ◽  
Mauro Lifschitz ◽  
Miguel Sánchez ◽  
Dolores Bustos ◽  
José Otondo ◽  
...  
Keyword(s):  
Genetic Control ◽  
Salinity Tolerance ◽  
Genetic Gain ◽  
Harsh Environment ◽  
Individual Selection ◽  
Selection Methods ◽  
Forage Production ◽  
Breeding Programs ◽  
Panicum Coloratum ◽  
Additive Genetic Effects

Panicum coloratum var. coloratum is a subtropical grass for potentially increasing forage production in lowly productive environments where cattle-raising activities have been relocated. Heritability was estimated for characters related to salinity tolerance under saline and non-saline conditions to explore the possibility of improving tolerance by selection. From a base germplasm collected in a very harsh environment, heritability and gain after selection were calculated using 2 recombination units: individual and phenotypic family mean (PFM). Heritability estimates were very low for all characters both in saline and non-saline conditions, suggesting a complex genetic control of salinity tolerance, with a high proportion of non-additive genetic effects. Estimates were higher using individual selection than with PFM and expected genetic gains were higher for individual selection. When compared in both saline and non-saline conditions, predicted means were greater than for plants of cv. Klein, the most common cultivar in use. It appears that the analyzed germplasm would be a valuable source of genes to be included in breeding programs to increase salinity tolerance in Panicum coloratum.

scholarly journals Comparisons of Potentials for General Combining Ability Selection Methods Utilizing One or Two Random-Mating Populations

1963 ◽  
Vol 16 (4) ◽  
pp. 838 ◽  
Author(s):  
B Griffing
Keyword(s):  
Combining Ability ◽  
Arbitrary Number ◽  
Random Mating ◽  
Selection Method ◽  
General Combining Ability ◽  
Single Locus ◽  
Genetic Constitution ◽  
Selection Methods ◽  
Population Mean ◽  
Entire Class

This study is concerned with comparisons of potentials exhibited by the entire class of general combining ability methods which can be generated by one or two random-mating populations. By potential is meant the greatest value the population mean assumes with continued application of a given selection method initially applied to a population of specified genetic constitution. The argument is restricted to an arbitrary number of alleles at a single locus, and it is assumed that the populations are infinite in size.

COMPARISON OF FOUR METHODS OF SELECTION FOR RESISTANCE TO LEPTOSPHAERULINA BRIOSIANA IN ALFALFA

1979 ◽  
Vol 21 (2) ◽  
pp. 179-186 ◽  
Author(s):  
R. R. Hill Jr. ◽  
K. T. Leath
Keyword(s):  
Selection Method ◽  
The Other ◽  
Correlated Response ◽  
Response To Selection ◽  
Selection Methods ◽  
Family Selection ◽  
Colletotrichum Trifolii ◽  
Family Types ◽  
Medicago Sativa L ◽  
Selection For

Three cycles of selection for resistance to Leptosphaerulina briosiana (Poll.) Graham &Luttrell were conducted in two alfalfa (Medicago sativa, L.) germplasm pools, MSA and MSB. Each germplasm pool was used to compare four methods of selection: phenotypic recurrent, half-sib family, full-sib family, and alternating generations of selfed family and half-sib family. Response to selection for resistance to L. briosiana was greater in MSA than in MSB. Differences between selection methods were not significant. Selection for resistance to L. briosiana generally increased resistance to Stemphylium botryosum Wallr., but the magnitude of the correlated response varied with germplasm pool and selection method. The initial selfed families in both germplasm pools were significantly less resistant to Colletotrichum trifolii Bain than the other family types. Resistance to C. trifolii increased with selfed family selection for resistance to L. briosiana in MSA but not in MSB.

The Effectiveness of the Fused Weighted Filter Feature Selection Method to Improve Software Fault Prediction

2016 ◽  
Vol 8 ◽  
pp. 5 ◽  
Author(s):  
Fatemeh Alighardashi ◽  
Mohammad Ali Zare Chahooki
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Machine Learning Algorithms ◽  
Fault Prediction ◽  
Filter Method ◽  
Selection Methods ◽  
Software Projects ◽  
Software Fault Prediction ◽  
Software Fault

Improving the software product quality before releasing by periodic tests is one of the most expensive activities in software projects. Due to limited resources to modules test in software projects, it is important to identify fault-prone modules and use the test sources for fault prediction in these modules. Software fault predictors based on machine learning algorithms, are effective tools for identifying fault-prone modules. Extensive studies are being done in this field to find the connection between features of software modules, and their fault-prone. Some of features in predictive algorithms are ineffective and reduce the accuracy of prediction process. So, feature selection methods to increase performance of prediction models in fault-prone modules are widely used. In this study, we proposed a feature selection method for effective selection of features, by using combination of filter feature selection methods. In the proposed filter method, the combination of several filter feature selection methods presented as fused weighed filter method. Then, the proposed method caused convergence rate of feature selection as well as the accuracy improvement. The obtained results on NASA and PROMISE with ten datasets, indicates the effectiveness of proposed method in improvement of accuracy and convergence of software fault prediction.

A fuzzy gaussian rank aggregation ensemble feature selection method for microarray data

2021 ◽  
Vol 24 (4) ◽  
pp. 289-301
Author(s):  
B. Venkatesh ◽  
J. Anuradha
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Classification Accuracy ◽  
Performance Metrics ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Binary Particle Swarm Optimization ◽  
Selection Methods

In Microarray Data, it is complicated to achieve more classification accuracy due to the presence of high dimensions, irrelevant and noisy data. And also It had more gene expression data and fewer samples. To increase the classification accuracy and the processing speed of the model, an optimal number of features need to extract, this can be achieved by applying the feature selection method. In this paper, we propose a hybrid ensemble feature selection method. The proposed method has two phases, filter and wrapper phase in filter phase ensemble technique is used for aggregating the feature ranks of the Relief, minimum redundancy Maximum Relevance (mRMR), and Feature Correlation (FC) filter feature selection methods. This paper uses the Fuzzy Gaussian membership function ordering for aggregating the ranks. In wrapper phase, Improved Binary Particle Swarm Optimization (IBPSO) is used for selecting the optimal features, and the RBF Kernel-based Support Vector Machine (SVM) classifier is used as an evaluator. The performance of the proposed model are compared with state of art feature selection methods using five benchmark datasets. For evaluation various performance metrics such as Accuracy, Recall, Precision, and F1-Score are used. Furthermore, the experimental results show that the performance of the proposed method outperforms the other feature selection methods.

scholarly journals Predicting Rates of Inbreeding in Populations Undergoing Selection

Genetics ◽  
2000 ◽  
Vol 154 (4) ◽  
pp. 1851-1864 ◽  
Author(s):  
John A Woolliams ◽  
Piter Bijma
Keyword(s):  
Overlapping Generations ◽  
Linear Models ◽  
Selection Process ◽  
Correction Term ◽  
Random Mating ◽  
Nonrandom Mating ◽  
Selection Processes ◽  
Genetic Contributions ◽  
The Relationship

AbstractTractable forms of predicting rates of inbreeding (ΔF) in selected populations with general indices, nonrandom mating, and overlapping generations were developed, with the principal results assuming a period of equilibrium in the selection process. An existing theorem concerning the relationship between squared long-term genetic contributions and rates of inbreeding was extended to nonrandom mating and to overlapping generations. ΔF was shown to be ~¼(1 − ω) times the expected sum of squared lifetime contributions, where ω is the deviation from Hardy-Weinberg proportions. This relationship cannot be used for prediction since it is based upon observed quantities. Therefore, the relationship was further developed to express ΔF in terms of expected long-term contributions that are conditional on a set of selective advantages that relate the selection processes in two consecutive generations and are predictable quantities. With random mating, if selected family sizes are assumed to be independent Poisson variables then the expected long-term contribution could be substituted for the observed, providing ¼ (since ω = 0) was increased to ½. Established theory was used to provide a correction term to account for deviations from the Poisson assumptions. The equations were successfully applied, using simple linear models, to the problem of predicting ΔF with sib indices in discrete generations since previously published solutions had proved complex.

scholarly journals Expected Genetic Contributions and Their Impact on Gene Flow and Genetic Gain

Genetics ◽  
1999 ◽  
Vol 153 (2) ◽  
pp. 1009-1020 ◽  
Author(s):  
J A Woolliams ◽  
P Bijma ◽  
B Villanueva
Keyword(s):  
Gene Flow ◽  
Genetic Gain ◽  
Overlapping Generations ◽  
Predictive Accuracy ◽  
Index Theory ◽  
Previous Theory ◽  
Selection Indices ◽  
Breeding Groups ◽  
Genetic Contributions

Abstract Long-term genetic contributions (ri) measure lasting gene flow from an individual i. By accounting for linkage disequilibrium generated by selection both within and between breeding groups (categories), assuming the infinitesimal model, a general formula was derived for the expected contribution of ancestor i in category q (μi(q)), given its selective advantages (si(q)). Results were applied to overlapping generations and to a variety of modes of inheritance and selection indices. Genetic gain was related to the covariance between ri and the Mendelian sampling deviation (ai), thereby linking gain to pedigree development. When si(q) includes ai, gain was related to E[μi(q)ai], decomposing it into components attributable to within and between families, within each category, for each element of si(q). The formula for μi(q) was consistent with previous index theory for predicting gain in discrete generations. For overlapping generations, accurate predictions of gene flow were obtained among and within categories in contrast to previous theory that gave qualitative errors among categories and no predictions within. The generation interval was defined as the period for which μi(q), summed over all ancestors born in that period, equaled 1. Predictive accuracy was supported by simulation results for gain and contributions with sib-indices, BLUP selection, and selection with imprinted variation.

scholarly journals The Application of Computer Image Analysis Based on Textural Features for the Identification of Barley Kernels Infected with Fungi of the Genus Fusarium

2018 ◽  
Vol 22 (3) ◽  
pp. 49-56 ◽  
Author(s):  
Ewa Ropelewska
Keyword(s):  
Image Analysis ◽  
Selection Method ◽  
Color Model ◽  
Textural Features ◽  
Discriminative Power ◽  
Selection Methods ◽  
Computer Image Analysis ◽  
Genus Fusarium ◽  
Color Models ◽  
Lab Color Model

AbstractThe aim of this study was to develop discrimination models based on textural features for the identification of barley kernels infected with fungi of the genus Fusarium and healthy kernels. Infected barley kernels with altered shape and discoloration and healthy barley kernels were scanned. Textures were computed using MaZda software. The kernels were classified as infected and healthy with the use of the WEKA application. In the case of RGB, Lab and XYZ color models, the classification accuracies based on 10 selected textures with the highest discriminative power ranged from 95 to 100%. The lowest result (95%) was noted in XYZ color model and Multi Class Classifier for the textures selected using the Ranker method and the OneR attribute evaluator. Selected classifiers were characterized by 100% accuracy in the case of all color models and selection methods. The highest number of 100% results was obtained for the Lab color model with Naive Bayes, LDA, IBk, Multi Class Classifier and J48 classifiers in the Best First selection method with the CFS subset evaluator.

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