scholarly journals Optimal contribution selection in highly fecund species with overlapping generations

2020 ◽  
Author(s):  
Matthew G Hamilton
Keyword(s):  
Overlapping Generations ◽  
Family Relationship ◽  
Age Groups ◽  
Breeding Strategies ◽  
Breeding Programs ◽  
Genetic Gains ◽  
Breeding Populations ◽  
Outcrossing Species ◽  
Number Of Individuals

Abstract Optimal contributions approaches to parental selection in closed breeding populations aim to maximise genetic gains, while restraining long-term inbreeding. The adoption of optimal contribution selection (OCS) in highly-fecund outcrossing species presents a number of challenges not applicable to species of low fecundity (e.g. livestock) for which they were developed. This is particularly true if overlapping-generations or rolling-front breeding strategies are applied, in which case the number of individuals per family in juvenile (i.e. sexually immature) age groups is not necessarily known but is likely to be large. In these circumstances, conventional OCS procedures must be modified or a large number of dummy individuals defined, making computations onerous. Here, an approach to OCS is presented that involves the use of ‘between-family relationship matrices’ instead of ‘between-individual relationship matrices’. The method is applicable to breeding programs involving highly fecund outcrossing species with overlapping generations, including circumstances where the number of juveniles per family is unknown but large.

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

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.

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.

scholarly journals Impact of Genomic Prediction Model, Selection Intensity, and Breeding Strategy on the Long-Term Genetic Gain and Genetic Erosion in Soybean Breeding

2021 ◽  
Vol 12 ◽  
Author(s):  
Éder David Borges da Silva ◽  
Alencar Xavier ◽  
Marcos Ventura Faria
Keyword(s):  
Random Forest ◽  
Bayesian Methods ◽  
Genomic Prediction ◽  
Genetic Variance ◽  
Breeding Strategies ◽  
Selection Intensity ◽  
Soybean Breeding ◽  
Genetic Gains ◽  
Selection Strategies

Genomic-assisted breeding has become an important tool in soybean breeding. However, the impact of different genomic selection (GS) approaches on short- and long-term gains is not well understood. Such gains are conditional on the breeding design and may vary with a combination of the prediction model, family size, selection strategies, and selection intensity. To address these open questions, we evaluated various scenarios through a simulated closed soybean breeding program over 200 breeding cycles. Genomic prediction was performed using genomic best linear unbiased prediction (GBLUP), Bayesian methods, and random forest, benchmarked against selection on phenotypic values, true breeding values (TBV), and random selection. Breeding strategies included selections within family (WF), across family (AF), and within pre-selected families (WPSF), with selection intensities of 2.5, 5.0, 7.5, and 10.0%. Selections were performed at the F4 generation, where individuals were phenotyped and genotyped with a 6K single nucleotide polymorphism (SNP) array. Initial genetic parameters for the simulation were estimated from the SoyNAM population. WF selections provided the most significant long-term genetic gains. GBLUP and Bayesian methods outperformed random forest and provided most of the genetic gains within the first 100 generations, being outperformed by phenotypic selection after generation 100. All methods provided similar performances under WPSF selections. A faster decay in genetic variance was observed when individuals were selected AF and WPSF, as 80% of the genetic variance was depleted within 28–58 cycles, whereas WF selections preserved the variance up to cycle 184. Surprisingly, the selection intensity had less impact on long-term gains than did the breeding strategies. The study supports that genetic gains can be optimized in the long term with specific combinations of prediction models, family size, selection strategies, and selection intensity. A combination of strategies may be necessary for balancing the short-, medium-, and long-term genetic gains in breeding programs while preserving the genetic variance.

scholarly journals ДИНАМІКА ВІКОВОЇ СТРУКТУРИ ПОПУЛЯЦІЇ CROCUS HEUFFELIANUS HERB. У ПРИРОДНОМУ ЗАПОВІДНИКУ "МЕДОБОРИ" ЗА ОСТАННЄ ДЕСЯТИРІЧЧЯ

2019 ◽  
Vol 75 (1) ◽  
pp. 8-12
Author(s):  
L. Ia. Kozyra ◽  
N. J. Semenovych
Keyword(s):  
Plant Density ◽  
Age Groups ◽  
Absolute Number ◽  
Entire Area ◽  
Left Handed ◽  
Beginning Of Flowering ◽  
Average Plant ◽  
Number Of Individuals

Based on the long-term observations of Crocus heuffelianus Herb., the Red Book species of "Medobory" Nature Reserve, growing on the boundary of the area, the dynamics of the population age structure has been analysed. The study of the population and phenology has been conducted since 1995 at the Botanical Experimental Area (BP-2), which is located in Viknianske forestry (square 32, board 7) and covers an area of 0.1925 hectares.The number and density of the cenopopulation have been studied. The ratio of the number of individuals to the area of the population is taken for the density, and the number of individuals in its entire area – for the absolute number. The number of species ranged from 347 to 753 individuals. The average plant density is 0.27 individuals per square meter. The highest index was in the years 2016 and 2018 – 0.39 and 0.38 individuals per square meter, and the lowest was in 2005 – 0.09 individuals per square meter.During the investigating over the past decade, surveys of plants of different ages (juvenile, virgin and generative), as well as phenological observations were conducted. Allocation of age groups was carried out in accordance with the classification of Melnyk V.I.The population of Crocus heuffelianus Herb in the Reserve is established as a full-fledged and left-handed. Only in 2011 it was intermediate.According to the results of phenological observations, the average long-term date of the vegetation onset is the 15th of March, the beginning of flowering – the 24th of March, the mass flowering – the 1st of April and the end - the 12th of April. The entire flowering cycle is 21 days on average.An important factor that has a significant impact on the state of the population of the species in the Reserve is the spring sowing of bulbs and tufts of plants by sultry European. It is pointed out almost every year, and evidenced by the presence of numerous fresh lanterns and ditches.

scholarly journals INBREEDING IN POPULATIONS WITH OVERLAPPING GENERATIONS

Genetics ◽  
1977 ◽  
Vol 87 (3) ◽  
pp. 581-591 ◽  
Author(s):  
D L Johnson
Keyword(s):  
Overlapping Generations ◽  
Matrix Theory ◽  
Age Groups ◽  
Diagonal Matrix ◽  
Age Group ◽  
Effective Number ◽  
Short Term ◽  
Uniform Rate ◽  
Inbreeding Coefficients ◽  
Number Of Individuals

ABSTRACT An inbreeding matrix is defined for populations with overlapping generations. In the short term it can be expressed in terms of a matrix specifying the passage of genes between the different age groups (and sexes) and a diagonal matrix whose elements depend on the number of individuals in each age group. Formulae for the inbreeding effective number are derived using matrix theory. A comparison is made between the inbreeding coefficients predicted by this theory and those obtained by assuming a uniform rate of inbreeding from the outset, and these in turn are compared with the exact inbreeding coefficients.

COVID - 19 AND THE PROBLEM OF PERSONALITY

2020 ◽  
pp. 31-35
Author(s):  
Mazaeva N.A. ◽  
Golovina A.G.
Keyword(s):  
General Population ◽  
Age Groups ◽  
Personal Transformation ◽  
Personality Changes ◽  
Age Dependent

In order to determine possible trends in the dynamics and characterological structure of personality in the General population caused by the COVID-19 pandemic, which is a long-term strong stressful effect and clinically and psychopathologically comparable to chronic personality changes after experiencing a disaster, the conditions predisposing to personal transformation, including clinical and prognostic patterns, are analyzed. The age-dependent nature of these changes is shown, and a number of features identified for different age groups are discussed.

scholarly journals Prediction of Genetic Contributions and Generation Intervals in Populations With Overlapping Generations Under Selection

Genetics ◽  
1999 ◽  
Vol 151 (3) ◽  
pp. 1197-1210 ◽  
Author(s):  
Piter Bijma ◽  
John A Woolliams
Keyword(s):  
Gene Flow ◽  
Overlapping Generations ◽  
Selective Advantage ◽  
Turnover Time ◽  
Breeding Value ◽  
Age Classes ◽  
Generation Interval ◽  
Young Parents ◽  
Genetic Contributions

Abstract A method to predict long-term genetic contributions of ancestors to future generations is studied in detail for a population with overlapping generations under mass or sib index selection. An existing method provides insight into the mechanisms determining the flow of genes through selected populations, and takes account of selection by modeling the long-term genetic contribution as a linear regression on breeding value. Total genetic contributions of age classes are modeled using a modified gene flow approach and long-term predictions are obtained assuming equilibrium genetic parameters. Generation interval was defined as the time in which genetic contributions sum to unity, which is equal to the turnover time of genes. Accurate predictions of long-term genetic contributions of individual animals, as well as total contributions of age classes were obtained. Due to selection, offspring of young parents had an above-average breeding value. Long-term genetic contributions of youngest age classes were therefore higher than expected from the age class distribution of parents, and generation interval was shorter than the average age of parents at birth of their offspring. Due to an increased selective advantage of offspring of young parents, generation interval decreased with increasing heritability and selection intensity. The method was compared to conventional gene flow and showed more accurate predictions of long-term genetic contributions.

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 The Quality of Life and Satisfaction with Continuous Glucose Monitoring Therapy in Children under 7 Years of Age with T1D Using the rtCGM System Integrated with Insulin Pump—A Caregivers Point of View

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3683
Author(s):  
Ewa Rusak ◽  
Natalia Ogarek ◽  
Karolina Wolicka ◽  
Anna Mrówka ◽  
Sebastian Seget ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Insulin Pump ◽  
Age Groups ◽  
Glucose Monitoring ◽  
Point Of View ◽  
Therapy Assessment ◽  
Monitoring Therapy ◽  
Original Survey

Quality of life (QoL) is an important parameter that affects the choice of therapy. Assessment of QoL and satisfaction with therapy using the rtCGM in children with T1D aged < 7 years was conducted. The study group consisted of 38 children with T1D aged < 7 years (34% aged 2–4, 66% aged 5–7 years), HbA1c: 6.53 ± 0.63%, duration of diabetes: 2.6 ± 1.6 years, treated with an rtCGM-augmented insulin pump for 1.92 ± 1.15 years. Two anonymous surveys were conducted: a. PedsQL3.0 diabetes standardized questionnaire—QoL assessment among age groups: 2–4/5–7 years. b. An original survey assessing the CGM use satisfaction. The mean scores in PedsQL3.0: communication 75%, worries 30%, treatment 70%, and problems associated with diabetes 65%. The QoL scale is: 0–19% very low, 20–39% low, 40–59% moderate, 60–79% high, 80–100% very high. The most frequently reported concerns were long-term diabetes complications and prick pain. Satisfaction with CGM use was high (68% in group aged 5–7 and 92% 2–4 years). Twenty-seven (71%) caregivers confirmed the positive effect of CGM on sleep. During the use of rtCGM a high quality of life was reported, and the quality of sleep in their caregivers was increased.

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.

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