Factors Affecting Response to Recurrent Genomic Selection in Soybeans

Mapping Intimacies ◽

10.1101/2020.02.14.949008 ◽

2020 ◽

Author(s):

Vishnu Ramasubramanian ◽

William D Beavis

Keyword(s):

Genomic Selection ◽

Genetic Improvement ◽

Recurrent Selection ◽

Phenotypic Selection ◽

Support Vector ◽

Selection Methods ◽

Genetic Response ◽

The Impact ◽

Training Sets

AbstractHerein we report the impacts of applying five selection methods across 40 cycles of recurrent selection and identify interactions among factors that affect genetic responses in sets of simulated families of recombinant inbred lines derived from 21 homozygous soybean lines. Our use of recurrence equation to model response from recurrent selection allowed us to estimate the half-lives, asymptotic limits to recurrent selection for purposes of assessing the rates of response and future genetic potential of populations under selection. The simulated factors include selection methods, training sets, and selection intensity that are under the control of the plant breeder as well as genetic architecture and heritability. A factorial design to examine and analyze the main and interaction effects of these factors showed that both the rates of genetic improvement in the early cycles and limits to genetic improvement in the later cycles are significantly affected by interactions among all factors. Some consistent trends are that genomic selection methods provide greater initial rates of genetic improvement (per cycle) than phenotypic selection, but phenotypic selection provides the greatest long term responses in these closed genotypic systems. Model updating with training sets consisting of data from prior cycles of selection significantly improved prediction accuracy and genetic response with three parametric genomic prediction models. Ridge Regression, if updated with training sets consisting of data from prior cycles, achieved better rates of response than BayesB and Bayes LASSO models. A Support Vector Machine method, with a radial basis kernel, had the worst estimated prediction accuracies and the least long term genetic response. Application of genomic selection in a closed breeding population of a self-pollinated crop such as soybean will need to consider the impact of these factors on trade-offs between short term gains and conserving useful genetic diversity in the context of the goals for the breeding program.

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

Frontiers in Genetics ◽

10.3389/fgene.2021.675500 ◽

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.

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Strategies to assure optimal trade-offs among competing objectives for genetic improvement of soybean

10.1101/2021.02.19.431938 ◽

2021 ◽

Author(s):

Vishnu Ramasubramanian ◽

William Beavis

Keyword(s):

Genomic Selection ◽

Genetic Improvement ◽

Genetic Variance ◽

Breeding Strategies ◽

Short Term ◽

Genetic Gains ◽

Mating Design ◽

Genetic Potential ◽

Breeding Populations

AbstractPlant breeding is a decision making discipline based on understanding project objectives. Genetic improvement projects can have two competing objectives: maximize rate of genetic improvement and minimize 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 loss of useful genetic variance while realizing genetic improvements. Considerable research indicates that short term genetic gains from Genomic Selection (GS) are much greater than Phenotypic Selection (PS), while PS provides better long term genetic gains because PS 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 GS or PS? 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 ten cycles using GS, a hub network mating design in breeding populations organized as fully connected family islands and migration rules allowing 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 a genomic mating design, instead of a hub networked mating design, is used. This strategy also resulted in realizing the greatest proportion of genetic potential of the founder populations. Weighted genomic selection applied to both non-isolated and island populations also resulted in realization of the greatest proportion of genetic potential of the founders, but required more cycles than the best compromise strategy.

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Impacts of genotyping strategies on long-term genetic response in genomic selection

Animal Science Journal ◽

10.1111/asj.12184 ◽

2014 ◽

Vol 85 (5) ◽

pp. 511-516 ◽

Cited By ~ 3

Author(s):

Motohide Nishio ◽

Masahiro Satoh

Keyword(s):

Genomic Selection ◽

Genetic Response

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New cycle, same old mistakes? Overlapping vs. discrete generations in long-term recurrent selection

10.1101/2021.10.12.464059 ◽

2021 ◽

Author(s):

Marlee R. Labroo ◽

Jessica E. Rutkoski

Keyword(s):

Genetic Gain ◽

Overlapping Generations ◽

Recurrent Selection ◽

Phenotypic Selection ◽

Breeding Value ◽

Breeding Values ◽

Truncation Selection ◽

Breeding Cycles ◽

Error Bias

Background: Recurrent selection is a foundational breeding method for quantitative trait improvement. It typically features rapid breeding cycles that can lead to high rates of genetic gain. In recurrent phenotypic selection, generations do not overlap, which means that breeding candidates are evaluated and considered for selection for only one cycle. With recurrent genomic selection, candidates can be evaluated based on genomic estimated breeding values indefinitely, therefore facilitating overlapping generations. Candidates with true high breeding values that were discarded in one cycle due to underestimation of breeding value could be identified and selected in subsequent cycles. The consequences of allowing generations to overlap in recurrent selection are unknown. We assessed whether maintaining overlapping and discrete generations led to differences in genetic gain for phenotypic, genomic truncation, and genomic optimum contribution recurrent selection by simulation of traits with various heritabilities and genetic architectures across fifty breeding cycles. We also assessed differences of overlapping and discrete generations in a conventional breeding scheme with multiple stages and cohorts. Results: With phenotypic selection, overlapping generations led to decreased genetic gain compared to discrete generations due to increased selection error bias. Selected individuals, which were in the upper tail of the distribution of phenotypic values, tended to also have high absolute error relative to their true breeding value compared to the overall population. Without repeated phenotyping, these erroneously outlying individuals were repeatedly selected across cycles, leading to decreased genetic gain. With genomic truncation selection, overlapping and discrete generations performed similarly as updating breeding values precluded repeatedly selecting individuals with inaccurately high estimates of breeding values in subsequent cycles. Overlapping generations did not outperform discrete generations in the presence of a positive genetic trend with genomic truncation selection, as past generations had lower mean genetic values than the current generation of selection candidates. With genomic optimum contribution selection, overlapping and discrete generations performed similarly, but overlapping generations slightly outperformed discrete generations in the long term if the targeted inbreeding rate was extremely low. Conclusions: Maintaining discrete generations in recurrent phenotypic mass selection leads to increased genetic gain, especially at low heritabilities, by preventing selection error bias. With genomic truncation selection and genomic optimum contribution selection, genetic gain does not differ between discrete and overlapping generations assuming non-genetic effects are not present. Overlapping generations may increase genetic gain in the long term with very low targeted rates of inbreeding in genomic optimum contribution selection.

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THE IMPACT OF MOLECULAR GENETICS IN PLANT BREEDING: REALITIES AND PERSPECTIVES

Journal of Basic and Applied Genetics ◽

10.35407/bag.2019.xxx.01.02 ◽

2019 ◽

Vol 30 (1) ◽

pp. 11-15

Author(s):

A. Zambelli

Keyword(s):

Plant Breeding ◽

Molecular Genetics ◽

Molecular Breeding ◽

Recurrent Selection ◽

Phenotypic Selection ◽

Selection Marker ◽

Breeding Strategies ◽

Practical Applications ◽

Dna Technology ◽

The Impact

Even when conventional breeding was effective in achieving a continuous improvement in yield, Molecular Genetics tools applied in plant breeding contributed to maximize genetic gain. Thus, the use of DNA technology applied in agronomic improvement gave rise to Molecular Breeding, discipline which groups the different breeding strategies where genotypic selection, based on DNA markers, are used in combination with or in replacement of phenotypic selection. These strategies can be listed as: marker-assisted selection; marker-assisted backcrossing; marker assisted recurrent selection; and genomic selection. Strong arguments have been made about the potential advantages that Molecular Breeding brings, although little has been devoted to discussing its feasibility in practical applications. The consequence of the lack of a deep analysis when implementing a strategy of Molecular Breeding is its failure, leading to many undesirable outcomes and discouraging breeders from using the technology. The aim of this work is to trigger a debate about the convenience of the use of Molecular Breeding strategies in a breeding program considering the DNA technology of choice, the complexity of the trait of agronomic interest to be improved, the expected accuracy in the selection, and the demanded resources. Key words: DNA marker, selection, plant improvement.

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The Impact of Feature Selection Methods for Classifying Arabic Textual Data

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d7163.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 1333-1338

Keyword(s):

Feature Selection ◽

Text Classification ◽

Information Gain ◽

Feature Space ◽

Support Vector ◽

Selection Methods ◽

K Nearest Neighbors ◽

Chi Square ◽

Selection Algorithms ◽

The Impact

Text classification is a vital process due to the large volume of electronic articles. One of the drawbacks of text classification is the high dimensionality of feature space. Scholars developed several algorithms to choose relevant features from article text such as Chi-square (x2 ), Information Gain (IG), and Correlation (CFS). These algorithms have been investigated widely for English text, while studies for Arabic text are still limited. In this paper, we investigated four well-known algorithms: Support Vector Machines (SVMs), Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Decision Tree against benchmark Arabic textual datasets, called Saudi Press Agency (SPA) to evaluate the impact of feature selection methods. Using the WEKA tool, we have experimented the application of the four mentioned classification algorithms with and without feature selection algorithms. The results provided clear evidence that the three feature selection methods often improves classification accuracy by eliminating irrelevant features.

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Deterministic and stochastic modelling of impacts from genomic selection and phenomics on genetic gain for perennial ryegrass dry matter yield

Scientific Reports ◽

10.1038/s41598-021-92537-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

M. Z. Z. Jahufer ◽

Sai Krishna Arojju ◽

Marty J. Faville ◽

Kioumars Ghamkhar ◽

Dongwen Luo ◽

...

Keyword(s):

Genomic Selection ◽

Perennial Ryegrass ◽

Genetic Gain ◽

Dry Matter ◽

New Technologies ◽

Stochastic Modelling ◽

Phenotypic Selection ◽

Breeding Strategy ◽

Yield Data ◽

The Impact

AbstractIncreasing the efficiency of current forage breeding programs through adoption of new technologies, such as genomic selection (GS) and phenomics (Ph), is challenging without proof of concept demonstrating cost effective genetic gain (∆G). This paper uses decision support software DeltaGen (tactical tool) and QU-GENE (strategic tool), to model and assess relative efficiency of five breeding methods. The effect on ∆G and cost ($) of integrating GS and Ph into an among half-sib (HS) family phenotypic selection breeding strategy was investigated. Deterministic and stochastic modelling were conducted using mock data sets of 200 and 1000 perennial ryegrass HS families using year-by-season-by-location dry matter (DM) yield data and in silico generated data, respectively. Results demonstrated short (deterministic)- and long-term (stochastic) impacts of breeding strategy and integration of key technologies, GS and Ph, on ∆G. These technologies offer substantial improvements in the rate of ∆G, and in some cases improved cost-efficiency. Applying 1% within HS family GS, predicted a 6.35 and 8.10% ∆G per cycle for DM yield from the 200 HS and 1000 HS, respectively. The application of GS in both among and within HS selection provided a significant boost to total annual ∆G, even at low GS accuracy rA of 0.12. Despite some reduction in ∆G, using Ph to assess seasonal DM yield clearly demonstrated its impact by reducing cost per percentage ∆G relative to standard DM cuts. Open-source software tools, DeltaGen and QuLinePlus/QU-GENE, offer ways to model the impact of breeding methodology and technology integration under a range of breeding scenarios.

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Using Machine Learning and Feature Selection for Alfalfa Yield Prediction

AI ◽

10.3390/ai2010006 ◽

2021 ◽

Vol 2 (1) ◽

pp. 71-88

Author(s):

Christopher D. Whitmire ◽

Jonathan M. Vance ◽

Hend K. Rasheed ◽

Ali Missaoui ◽

Khaled M. Rasheed ◽

...

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Crop Yields ◽

Bayesian Regression ◽

Weather Data ◽

Support Vector ◽

Yield Prediction ◽

Selection Methods ◽

Yield Data ◽

The Impact

Predicting alfalfa biomass and crop yield for livestock feed is important to the daily lives of virtually everyone, and many features of data from this domain combined with corresponding weather data can be used to train machine learning models for yield prediction. In this work, we used yield data of different alfalfa varieties from multiple years in Kentucky and Georgia, and we compared the impact of different feature selection methods on machine learning (ML) models trained to predict alfalfa yield. Linear regression, regression trees, support vector machines, neural networks, Bayesian regression, and nearest neighbors were all developed with cross validation. The features used included weather data, historical yield data, and the sown date. The feature selection methods that were compared included a correlation-based method, the ReliefF method, and a wrapper method. We found that the best method was the correlation-based method, and the feature set it found consisted of the Julian day of the harvest, the number of days between the sown and harvest dates, cumulative solar radiation since the previous harvest, and cumulative rainfall since the previous harvest. Using these features, the k-nearest neighbor and random forest methods achieved an average R value over 0.95, and average mean absolute error less than 200 lbs./acre. Our top R2 of 0.90 beats a previous work’s best R2 of 0.87. Our primary contribution is the demonstration that ML, with feature selection, shows promise in predicting crop yields even on simple datasets with a handful of features, and that reporting accuracies in R and R2 offers an intuitive way to compare results among various crops.

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Reduction of genotyping marker density for genomic selection is not an affordable approach to long-term breeding in cross-pollinated crops

10.1101/2021.03.05.434084 ◽

2021 ◽

Author(s):

Júlio César DoVale ◽

Humberto Fanelli Carvalho ◽

Felipe Sabadin ◽

Roberto Fritsche-Neto

Keyword(s):

Genomic Selection ◽

Recurrent Selection ◽

Predictive Ability ◽

Computational Time ◽

Marker Density ◽

Additive Variance ◽

Selection Scheme ◽

Performance Accuracy ◽

Breeding Cycles

ABSTRACTThe selection of informative markers has been studied massively as an alternative to reduce genotyping costs for the genomic selection (GS) application. Low-density marker panels are attractive for GS because they decrease computational time-consuming and multicollinearity beyond more individuals can be genotyped with the same cost. Nevertheless, these inferences are usually made empirically using “static” training sets and populations, which are adequate only to predict a breeding program’s initial cycles but might not for long-term cycles. Moreover, to the best of our knowledge, none of these inferences considered the inclusion of dominance into the GS models, which is particularly important to predict cross-pollinated crops. Therefore, that reveals an important and unexplored topic for allogamous long-term breeding. To achieve this goal, we employed two approaches: the former used empirical maize datasets, and the latter simulations of long-term breeding cycles of phenotypic and genomic recurrent selection (intrapopulation and reciprocal). Then, we observed the reducing marker density effect on populations (mean, the best genotypes performance, accuracy, additive variance) over cycles and models (additive, additive-dominance, specific combining ability (SCA)). Our results indicate that the markers reduction based on different linkage disequili brium (LD) levels is viable only within a cycle and brings a significant decrease in predictive ability over generations. Furthermore, in the long-term, regardless of the selection scheme adopted, the more makers, the better because they buffer LD losses caused by recombination over breeding cycles. Finally, regarding the accuracy, the additive-dominant models tend to outperform the additive ones and perform similar to the SCA.

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