scholarly journals Optimizing self-pollinated crop breeding employing genomic selection: from schemes to updating training sets

2021 ◽  
Author(s):  
Felipe Sabadin ◽  
Julio César DoVale ◽  
John Platten ◽  
Roberto Fritsche-Neto
Keyword(s):  
Genomic Selection ◽  
Phenotypic Selection ◽  
Training Data ◽  
Response To Selection ◽  
Crop Breeding ◽  
New Approach ◽  
Breeding Schemes ◽  
Selection Of ◽  
Better Than

Abstract Long-term breeding schemes employing genomic selection (GS) can boost the response to selection per year. Although several studies show that GS delivers a higher response to selection, only a few analyze the best strategy to employ it, specifically how often and in what manner the training set (TS) should be updated. Therefore, we used stochastic simulation to compare in a long-term breeding program of a hypothetical self-pollinated crop five different strategies to implement GS in the line fixation stage and four methods and sizes to update the TS. Moreover, among breeding schemes, we proposed a new approach for using GS to select the best individuals in each F2 progeny based on genomic estimated breeding and divergence and crossed them to generate a new recombination event. Finally, we compared these schemes to the traditional phenotypic selection and drift. Our results showed that using GS in F2 followed by the phenotypic selection of new parentals in F4 was the best scenario. Furthermore, adding a new set of training data every cycle (over 800) to update the TS maintains the accuracy at satisfactory levels for many more generations, showing that more data is better than optimizing the genetic relationship between TS and the targeted population in a closed system. Hence, we believe that these results may help breeders optimize GS in their programs and improve genetic gain in long-term schemes.

scholarly journals Selection of Satellite Image Series for the Determination of Forest Pathology Dynamics Taking Into Account Cloud Coverage and Image Distortions Based on the Data Obtained from the Key Point Detector

2019 ◽  
Author(s):  
Евгений Трубаков ◽  
Evgeniy Trubakov ◽  
Андрей Трубаков ◽  
Andrey Trubakov ◽  
Дмитрий Коростелёв ◽  
...  
Keyword(s):  
Satellite Image ◽  
Forest Pathology ◽  
Acceptable Result ◽  
New Approach ◽  
Cloud Coverage ◽  
Point Detector ◽  
Automated Methods ◽  
Selection Of

Remote sensing of the earth and monitoring of various phenomena have been and still remain an important task for solving various problems. One of them is the forest pathology dynamics determining. Assuming its dependence on various factors forest pathology can be either short-term or long-term. Sometimes it is necessary to analyze satellite images within a period of several years in order to determine the dynamics of forest pathology. So it is connected with some special aspects and makes such analysis in manual mode impossible. At the same time automated methods face the problem of identifying a series of suitable images even though they are not covered by clouds, shadows, turbulence and other distortions. Classical methods of nebulosity determination based either on neural network or decision functions do not always give an acceptable result, because the cloud coverage by itself can be either of cirrus intortus type or insignificant within the image, but in case of cloudiness it can be the reason for wrong analysis of the area under examination. The article proposes a new approach for the analysis and selection of images based on key point detectors connected neither with cloudiness determination nor distorted area identification, but with the extraction of suitable images eliminating those that by their characteristics are unfit for forest pathology determination. Experiments have shown that the accuracy of this approach is higher than of currently used method in GIS, which is based on cloud detector.

scholarly journals Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean

2021 ◽  
Vol 12 ◽  
Author(s):  
Vishnu Ramasubramanian ◽  
William D. Beavis
Keyword(s):  
Genomic Selection ◽  
Genetic Improvement ◽  
Genetic Variance ◽  
Phenotypic Selection ◽  
Breeding Strategies ◽  
Short Term ◽  
Mating Design ◽  
Breeding Populations ◽  
Hub Network

Plant breeding is a decision-making discipline based on understanding project objectives. Genetic improvement projects can have two competing objectives: maximize the rate of genetic improvement and minimize the loss of useful genetic variance. For commercial plant breeders, competition in the marketplace forces greater emphasis on maximizing immediate genetic improvements. In contrast, public plant breeders have an opportunity, perhaps an obligation, to place greater emphasis on minimizing the loss of useful genetic variance while realizing genetic improvements. Considerable research indicates that short-term genetic gains from genomic selection are much greater than phenotypic selection, while phenotypic selection provides better long-term genetic gains because it retains useful genetic diversity during the early cycles of selection. With limited resources, must a soybean breeder choose between the two extreme responses provided by genomic selection or phenotypic selection? Or is it possible to develop novel breeding strategies that will provide a desirable compromise between the competing objectives? To address these questions, we decomposed breeding strategies into decisions about selection methods, mating designs, and whether the breeding population should be organized as family islands. For breeding populations organized into islands, decisions about possible migration rules among family islands were included. From among 60 possible strategies, genetic improvement is maximized for the first five to 10 cycles using genomic selection and a hub network mating design, where the hub parents with the largest selection metric make large parental contributions. It also requires that the breeding populations be organized as fully connected family islands, where every island is connected to every other island, and migration rules allow the exchange of two lines among islands every other cycle of selection. If the objectives are to maximize both short-term and long-term gains, then the best compromise strategy is similar except that the mating design could be hub network, chain rule, or a multi-objective optimization method-based mating design. Weighted genomic selection applied to centralized populations also resulted in the realization of the greatest proportion of the genetic potential of the founders but required more cycles than the best compromise strategy.

Neighborhood Evaluation in Recommender Systems Using the Realization Based Entropy Approach

2014 ◽  
Vol 1 (4) ◽  
pp. 34-50
Author(s):  
Roee Anuar ◽  
Yossi Bukchin ◽  
Oded Maimon ◽  
Lior Rokach
Keyword(s):  
Recommender Systems ◽  
Entropy Measure ◽  
Continuous Data ◽  
System Evaluation ◽  
Rating Data ◽  
New Approach ◽  
Selection Of ◽  
Classical Entropy ◽  
Better Than

The task of a recommender system evaluation has often been addressed in the literature, however there exists no consensus regarding the best metrics to assess its performance. This research deals with collaborative filtering recommendation systems, and proposes a new approach for evaluating the quality of neighbor selection. It theorizes that good recommendations emerge from good selection of neighbors. Hence, measuring the quality of the neighborhood may be used to predict the recommendation success. Since user neighborhoods in recommender systems are often sparse and differ in their rating range, this paper designs a novel measure to asses a neighborhood quality. First it builds the realization based entropy (RBE), which presents the classical entropy measure from a different angle. Next it modifies the RBE and propose the realization based distance entropy (RBDE), which considers also continuous data. Using the RBDE, it finally develops the consent entropy, which takes into account the absence of rating data. The paper compares the proposed approach with common approaches from the literature, using several recommendation evaluation metrics. It presents offline experiments using the Netflix database. The experimental results confirm that consent entropy performs better than commonly used metrics, particularly with high sparsity neighborhoods. This research is supported by The Israel Science Foundation, Grant #1362/10. This research is supported by NHECD EC, Grant #218639.

scholarly journals Optimum selection intensities in artificial selection programmes: an experimental evaluation

1977 ◽  
Vol 30 (2) ◽  
pp. 115-119 ◽  
Author(s):  
R. Frankham
Keyword(s):  
Experimental Evaluation ◽  
Selection Intensity ◽  
Effective Population ◽  
Term Selection ◽  
Optimum Selection ◽  
Selection For ◽  
Selection Intensities ◽  
Selection Of ◽  
Better Than

SUMMARYAn experimental evaluation of Robertson's (1970) theory concerning optimum intensities of selection for selection of varying durations has been carried out using published results from a long term selection study in Drosophila. Agreement of predicted rankings of treatments with expectations was excellent for low values of t/T (generations/total number scored) but poor for larger values of t/T. This was due to the 20% selection intensity treatments responding worse than expected and the 40% treatments relatively better than expected. Several possible reasons for the discrepancies exist but the most likely explanation is considered to be the greater reduction in effective population size due to selection in treatments with more intense selection.

scholarly journals Cluster Partitioning Techniques for Data Path Synthesis

VLSI Design ◽  
1994 ◽  
Vol 1 (3) ◽  
pp. 181-192
Author(s):  
Chien-In Henry Chen ◽  
Gerald Sobelman
Keyword(s):  
Computation Time ◽  
Efficient Algorithms ◽  
Automated Synthesis ◽  
Data Path ◽  
New Approach ◽  
Path Design ◽  
Priority Ordering ◽  
Path Synthesis ◽  
Selection Of ◽  
Better Than

New, efficient algorithms for the automated synthesis of buses in data path design are presented. Modifications to the technique of Generalized Clique Partitioning (GCP) are discussed which lead to better designs and reduced computation time in large synthesis problems. The new approach, Weighted Cluster Partitioning (WCP), eliminates the need for backtracking. The algorithm guides the process of bus formation by assigning a higher weight to those interconnection units that should be combined first. The operation of the WCP algorithm is clearly demonstrated using a detailed example.A modified priority ordering in the selection of the candidate pair is also discussed which can improve the performance of GCP and WCP. We demonstrate that GCP II performs better than GCP, while WCP II consistently produces the best results of all these algorithms on a set of large synthesis examples.

scholarly journals MULTIPLICATIVE GENOTYPE-ENVIRONMENT INTERACTION AS A CAUSE OF REVERSED RESPONSE TO DIRECTIONAL SELECTION

Genetics ◽  
1986 ◽  
Vol 114 (1) ◽  
pp. 333-343
Author(s):  
A Gimelfarb
Keyword(s):  
Sudden Change ◽  
Phenotypic Selection ◽  
Directional Selection ◽  
Quantitative Character ◽  
Environment Interaction ◽  
Response To Selection ◽  
Random Drift ◽  
Genotype Environment Interaction ◽  
Possible Cause ◽  
Selection Of

ABSTRACT In experiments with directional selection on a quantitative character a "reversed response" to selection is occasionally observed, when selection of individuals for a higher (lower) value of the character results in a lower (higher) value of the character among their offspring. A sudden change in environments or random drift is often assumed to be responsible for this. It is demonstrated in this paper that these two causes cannot account for the reversed response at least in some of the experiments. Multiplicative genotype-environment interaction is discussed as a possible cause of a reversed response to directional selection. Such interaction entails either disruptive or stabilizing genotypic selection, even when the phenotypic selection is directional.

scholarly journals Undercatch Adjustments for Tipping-Bucket Gauge Measurements of Solid Precipitation

2020 ◽  
Vol 21 (6) ◽  
pp. 1193-1205 ◽  
Author(s):  
John Kochendorfer ◽  
Michael E. Earle ◽  
Daniel Hodyss ◽  
Audrey Reverdin ◽  
Yves-Alain Roulet ◽  
...  
Keyword(s):  
Transfer Functions ◽  
Test Results ◽  
Monitoring Networks ◽  
New Approach ◽  
Solid Precipitation ◽  
New Methods ◽  
Different Types ◽  
Precipitation Records ◽  
Better Than

AbstractHeated tipping-bucket (TB) gauges are used broadly in national weather monitoring networks, but their performance for the measurement of solid precipitation has not been well characterized. Manufacturer-provided TB gauges were evaluated at five test sites during the World Meteorological Organization Solid Precipitation Intercomparison Experiment (WMO-SPICE), with most gauge types tested at more than one site. The test results were used to develop and evaluate adjustments for the undercatch of solid precipitation by heated TB gauges. New methods were also developed to address challenges specific to measurements from heated TB gauges. Tipping-bucket transfer functions were created specifically to minimize the sum of errors over the course of the adjusted multiseasonal accumulation. This was based on the hypothesis that the best transfer function produces the most accurate long-term precipitation records, rather than accurate catch efficiency measurements or accurate daily or hourly precipitation measurements. Using this new approach, an adjustment function derived from multiple gauges was developed that performed better than traditional gauge-specific and multigauge catch efficiency derived adjustments. Because this new multigauge adjustment was developed using six different types of gauges tested at five different sites, it may be applicable to solid precipitation measurements from unshielded heated TB gauges that were not evaluated in WMO-SPICE. In addition, this new method of optimizing transfer functions may be useful for other types of precipitation gauges, as it has many practical advantages over the traditional catch efficiency methods used to derive undercatch adjustments.

scholarly journals Effects of Different Strategies for Exploiting Genomic Selection in Perennial Ryegrass Breeding Programs

2020 ◽  
Vol 10 (10) ◽  
pp. 3783-3795
Author(s):  
Hadi Esfandyari ◽  
Dario Fè ◽  
Biructawit Bekele Tessema ◽  
Lucas L. Janss ◽  
Just Jensen
Keyword(s):  
Genomic Selection ◽  
Perennial Ryegrass ◽  
Genetic Gain ◽  
Additive Genetic Variance ◽  
Phenotypic Selection ◽  
Breeding Program ◽  
Breeding Programs ◽  
Traditional Program ◽  
Breeding Schemes ◽  
Estimated Breeding Values

Genomic selection (GS) is a potential pathway to accelerate genetic gain for perennial ryegrass (Lolium perenne L.). The main objectives of the present study were to investigate the level of genetic gain and accuracy by applying GS in commercial perennial ryegrass breeding programs. Different scenarios were compared to a conventional breeding program. Simulated scenarios differed in the method of selection and structure of the breeding program. Two scenarios (Phen-Y12 and Phen) for phenotypic selection and three scenarios (GS-Y12, GS and GS-SP) were considered for genomic breeding schemes. All breeding schemes were simulated for 25 cycles. The amount of genetic gain achieved was different across scenarios. Compared to phenotypic scenarios, GS scenarios resulted in substantially larger genetic gain for the simulated traits. This was mainly due to more efficient selection of plots and single plants based on genomic estimated breeding values. Also, GS allows for reduction in waiting time for the availability of the superior genetic materials from previous cycles, which led to at least a doubling or a trebling of genetic gain compared to the traditional program. Reduction in additive genetic variance levels were higher with GS scenarios than with phenotypic selection. The results demonstrated that implementation of GS in ryegrass breeding is possible and presents an opportunity to make very significant improvements in genetic gains.

scholarly journals A Two-Part Strategy using Genomic Selection in Hybrid Crop Breeding Programs

2020 ◽  
Author(s):  
Owen Powell ◽  
R. Chris Gaynor ◽  
Gregor Gorjanc ◽  
Christian R. Werner ◽  
John M. Hickey
Keyword(s):  
Genomic Selection ◽  
Genetic Gain ◽  
Genetic Variance ◽  
New Technologies ◽  
Stochastic Simulations ◽  
Practical Implementation ◽  
Crop Breeding ◽  
Breeding Programs ◽  
Multiple Generations

AbstractHybrid crop breeding programs using a two-part strategy produced the most genetic gain, but a maximum avoidance of inbreeding crossing scheme was required to increase long-term genetic gain. The two-part strategy uses outbred parents to complete multiple generations per year to reduce the generation interval of hybrid crop breeding programs. The maximum avoidance of inbreeding crossing scheme manages genetic variance by maintaining uniform contributions and inbreeding coefficients across all crosses. This study performed stochastic simulations to quantify the potential of a two-part strategy in combination with two crossing schemes to increase the rate of genetic gain in hybrid crop breeding programs. The two crossing schemes were: (i) a circular crossing scheme, and (ii) a maximum avoidance of inbreeding crossing scheme. The results from this study show that the implementation of genomic selection increased the rate of genetic gain, and that the two-part hybrid crop breeding program generated the highest genetic gain. This study also shows that the maximum avoidance of inbreeding crossing scheme increased long-term genetic gain in two-part hybrid crop breeding programs completing multiple selection cycles per year, as a result of maintaining higher levels of genetic variance over time. The flexibility of the two-part strategy offers further opportunities to integrate new technologies to further increase genetic gain in hybrid crop breeding programs, such as the use of outbred training populations. However, the practical implementation of the two-part strategy will require the development of bespoke transition strategies to fundamentally change the data, logistics, and infrastructure that underpin hybrid crop breeding programs.Key messageHybrid crop breeding programs using a two-part strategy produced the most genetic gain by using outbred parents to complete multiple generations per year. However, a maximum avoidance of inbreeding crossing scheme was required to manage genetic variance and increase long-term genetic gain.

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