Identifying breeding goals to develop selection index for buffalo in Egypt using preference survey

The teacher is a major milestone in the world of education, the ability and achievement of students cannot be separated from the role of a teacher in teaching and guiding students. Based on the Law of the Republic of Indonesia No. 14 of 2005 concerning Teachers and Lecturers, in Article 1 explained that teachers are professional educators with the main task of educating, teaching, guiding, directing, training, evaluating, and evaluating students in early childhood education through formal education, basic education and education medium. Whereas in Article 4 of the Act, it is explained that the position of teachers as professionals serves to enhance the dignity and role of teachers as learning agents to function to improve the quality of national education.Decision making is an election process, among various alternatives that aim to meet one or several targets. The decision-making system has 4 phases, namely intelligence, design, choice and implementation. These phases are the basis for decision making, which ends with a recommendation.The Preferences Selection Index (PSI) method is a rarely used decision support system method. This method is a method developed by stevanie and Bhatt (2010) to solve the Multi Criteria Decision Making (MCDM). With the right consideration, this method can be one of the tools to determine policies in decision-making systems, especially the selection of outstanding teachers. Determination of policies taken as a basis for decision making, must use criteria that can be defined clearly and objectively.Keywords: Decision Support System, PSI, Selection of Achieving Teachers

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GENETIC IMPROVEMENT OF THE LOCAL SINAI STRAIN FOR EGG PRODUCTION BY USING GENERAL SELECTION INDEX

Menoufia Journal of Animal Poultry and Fish Production ◽

10.21608/mjapfp.2019.116387 ◽

2019 ◽

Vol 3 (1) ◽

pp. 43-44

Author(s):

M. E. Soltan ◽

A. A. Enab ◽

G. M. Gebril ◽

Kh. R. Mohamed

Keyword(s):

Genetic Improvement ◽

Egg Production ◽

Selection Index

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Further Notes Regarding Selection Index Numbers

The American Naturalist ◽

10.1086/279279 ◽

1912 ◽

Vol 46 (545) ◽

pp. 302-307

Author(s):

Raymond Pearl

Keyword(s):

Selection Index ◽

Index Numbers

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A Molecular Selection Index Method Based on Eigenanalysis

Genetics ◽

10.1534/genetics.108.087387 ◽

2008 ◽

Vol 180 (1) ◽

pp. 547-557 ◽

Cited By ~ 12

Author(s):

J. Jesús Cerón-Rojas ◽

Fernando Castillo-González ◽

Jaime Sahagún-Castellanos ◽

Amalio Santacruz-Varela ◽

Ignacio Benítez-Riquelme ◽

...

Keyword(s):

Selection Index ◽

Index Method ◽

Molecular Selection

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Breeding goals and selection criteria for intensive and semi-intensive dairy goat system in Brazil

Small Ruminant Research ◽

10.1016/j.smallrumres.2012.03.011 ◽

2012 ◽

Vol 106 (2-3) ◽

pp. 110-117 ◽

Cited By ~ 7

Author(s):

Fernando Brito Lopes ◽

Arcadio de los Reyes Borjas ◽

Marcelo Correia da Silva ◽

Olivardo Facó ◽

Raimundo Nonato Lôbo ◽

...

Keyword(s):

Selection Criteria ◽

Dairy Goat ◽

Breeding Goals

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Genetic Diversity of Landraces and Improved Varieties of Rice (Oryza sativa L.) in Taiwan

Rice ◽

10.1186/s12284-020-00445-w ◽

2020 ◽

Vol 13 (1) ◽

Author(s):

Ai-ling Hour ◽

Wei-hsun Hsieh ◽

Su-huang Chang ◽

Yong-pei Wu ◽

Han-shiuan Chin ◽

...

Keyword(s):

Genetic Diversity ◽

Genetic Variation ◽

Indigenous Peoples ◽

Oryza Sativa L ◽

Crop Improvement ◽

Genetic Erosion ◽

Germplasm Conservation ◽

Gene Pools ◽

Improved Varieties ◽

Breeding Goals

Abstract Background Rice, the most important crop in Asia, has been cultivated in Taiwan for more than 5000 years. The landraces preserved by indigenous peoples and brought by immigrants from China hundreds of years ago exhibit large variation in morphology, implying that they comprise rich genetic resources. Breeding goals according to the preferences of farmers, consumers and government policies also alter gene pools and genetic diversity of improved varieties. To unveil how genetic diversity is affected by natural, farmers’, and breeders’ selections is crucial for germplasm conservation and crop improvement. Results A diversity panel of 148 rice accessions, including 47 cultivars and 59 landraces from Taiwan and 42 accessions from other countries, were genotyped by using 75 molecular markers that revealed an average of 12.7 alleles per locus with mean polymorphism information content of 0.72. These accessions could be grouped into five subpopulations corresponding to wild rice, japonica landraces, indica landraces, indica cultivars, and japonica cultivars. The genetic diversity within subpopulations was: wild rices > landraces > cultivars; and indica rice > japonica rice. Despite having less variation among cultivars, japonica landraces had greater genetic variation than indica landraces because the majority of Taiwanese japonica landraces preserved by indigenous peoples were classified as tropical japonica. Two major clusters of indica landraces were formed by phylogenetic analysis, in accordance with immigration from two origins. Genetic erosion had occurred in later japonica varieties due to a narrow selection of germplasm being incorporated into breeding programs for premium grain quality. Genetic differentiation between early and late cultivars was significant in japonica (FST = 0.3751) but not in indica (FST = 0.0045), indicating effects of different breeding goals on modern germplasm. Indigenous landraces with unique intermediate and admixed genetic backgrounds were untapped, representing valuable resources for rice breeding. Conclusions The genetic diversity of improved rice varieties has been substantially shaped by breeding goals, leading to differentiation between indica and japonica cultivars. Taiwanese landraces with different origins possess various and unique genetic backgrounds. Taiwanese rice germplasm provides diverse genetic variation for association mapping to unveil useful genes and is a precious genetic reservoir for rice improvement.

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Estimation of Genetic Parameters and Wood Yield Selection Index in a Clonal Trial of Korean Pine (Pinus koraiensis) in Northeastern China

Sustainability ◽

10.3390/su13084167 ◽

2021 ◽

Vol 13 (8) ◽

pp. 4167

Author(s):

David Kombi Kaviriri ◽

Huan-Zhen Liu ◽

Xi-Yang Zhao

Keyword(s):

Selection Index ◽

Tree Height ◽

Stem Volume ◽

Korean Pine ◽

Branch Number ◽

Basal Diameter ◽

Diameter At Breast Height ◽

Breast Height ◽

Stem Straightness ◽

Clonal Trial

In order to determine suitable traits for selecting high-wood-yield Korean pine materials, eleven morphological characteristics (tree height, basal diameter, diameter at breast height, diameter at 3 meter height, stem straightness degree, crown breadth, crown height, branch angle, branch number per node, bark thickness, and stem volume) were investigated in a 38-year-old Korean pine clonal trial at Naozhi orchard. A statistical approach combining variance and regression analysis was used to extract appropriate traits for selecting elite clones. Results of variance analysis showed significant difference in variance sources in most of the traits, except for the stem straightness degree, which had a p-value of 0.94. Moderate to high coefficients of variation and clonal repeatability ranged from 10.73% to 35.45% and from 0.06% to 0.78%, respectively. Strong significant correlations on the phenotypic and genotypic levels were observed between the straightness traits and tree volume, but crown breadth was weakly correlated to the volume. Four principal components retaining up to 80% of the total variation were extracted, and stem volume, basal diameter, diameter at breast height, diameter at 3 meter height, tree height, and crown height displayed high correlation to these components (r ranged from 0.76 to 0.98). Based on the Type III sum of squares, tree height, diameter at breast height, and branch number showed significant information to explain the clonal variability based on stem volume. Using the extracted characteristics as the selection index, six clones (PK105, PK59, PK104, PK36, PK28, and K101) displayed the highest Qi values, with a selection rate of 5% corresponding to the genetic gain of 42.96% in stem volume. This study provides beneficial information for the selection of multiple traits for genetically improved genotypes of Korean pine.

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Optimal breeding-value prediction using a Sparse Selection Index

Genetics ◽

10.1093/genetics/iyab030 ◽

2021 ◽

Author(s):

Marco Lopez-Cruz ◽

Gustavo de los Campos

Keyword(s):

Sample Size ◽

Dna Sequences ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Regularization Parameter ◽

Selection Index ◽

Prediction Method ◽

Training Data ◽

Breeding Value ◽

Data Set

Abstract Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and in linkage disequilibrium patterns can lead to heterogeneity in SNP effects. In this context, calibrating genomic predictions using a large, potentially heterogeneous, training data set may not lead to optimal prediction accuracy. Some studies tried to address this sample size/homogeneity trade-off using training set optimization algorithms; however, this approach assumes that a single training data set is optimum for all individuals in the prediction set. Here, we propose an approach that identifies, for each individual in the prediction set, a subset from the training data (i.e., a set of support points) from which predictions are derived. The methodology that we propose is a Sparse Selection Index (SSI) that integrates Selection Index methodology with sparsity-inducing techniques commonly used for high-dimensional regression. The sparsity of the resulting index is controlled by a regularization parameter (λ); the G-BLUP (the prediction method most commonly used in plant and animal breeding) appears as a special case which happens when λ = 0. In this study, we present the methodology and demonstrate (using two wheat data sets with phenotypes collected in ten different environments) that the SSI can achieve significant (anywhere between 5-10%) gains in prediction accuracy relative to the G-BLUP.

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