Optimum breeding generation interval considering buildup of relatedness

2001 ◽  
Vol 31 (4) ◽  
pp. 722-729 ◽  
Author(s):  
R -P Wei ◽  
D Lindgren
Keyword(s):  
Population Size ◽  
Negative Impact ◽  
Large Population ◽  
Breeding Population ◽  
Forest Tree ◽  
Early Selection ◽  
Breeding Value ◽  
Generation Interval ◽  
Optimum Selection ◽  
Optimum Selection Age

Models taking genetic gain, relatedness, delay at generation turnover, and breeding population size into account have been developed to optimize selection age and generation interval. Relatedness (expressed as group coancestry) and average breeding value for the breeding population are merged into a joint index ("group merit"). The negative impact of group coancestry (like potential inbreeding depression) is expressed in a scale compatible with breeding value. Group merit measures the desirable characteristic of a breeding population. Annual increase of group merit is the criterion for comparing alternatives. Optimum generation interval is when annual group merit increase is highest. Generally the optimum selection age becomes higher when increase in relatedness is considered. We quantify the influence of relatedness penalty, early-mature genetic correlation, breeding population size, and delay at generation turnover on optimum selection age. A reasonable large population counteracts the increase of relatedness and, thus, favors early selection. Early selection can have a negative impact if a small early selection gain does not compensate for the buildup of relatedness at generation turnover. Conditions for this to occur are quantified. Early selection requires sufficient high juvenile-mature correlation to have a positive effect; this requirement can be reduced by using a large breeding population. The methods developed were applied to a number of situations relevant to forest tree improvement.

scholarly journals Comparison of Phenotype and Combined Index Selection at Optimal Breeding Population Size Considering Gain and Gene Diversity

2006 ◽  
Vol 55 (1-6) ◽  
pp. 13-19 ◽  
Author(s):  
H. Li ◽  
D. Lindgren
Keyword(s):  
Population Size ◽  
Gene Diversity ◽  
Phenotypic Selection ◽  
Breeding Population ◽  
Individual Performance ◽  
Breeding Value ◽  
Index Selection ◽  
Efficient Generation ◽  
The Difference ◽  
The Individual

Abstract A breeding program was simulated in this study. Two alternative ways of selecting the breeding population for the following generation was compared. Phenotypic selection, which means to select just on the individual performance, and combined index selection, which means selection on predicted breeding value for each individual obtained by weighting family average and individual phenotype, were compared. The plant number (testing resource) and gene diversity (status number, Ns) were kept constant, but the breeding population size was variable and chosen for maximizing gain for the particular breeding scenario. At low and medium heritability phenotypic selection was inferior to combined index selection. Only when heritability was high phenotypic selection was as efficient (generation 1) as or more efficient (generation 5) than combined index selection. This contrasts to earlier studies done under constant breeding population size, where selection methods appeared similar. The advantage in gain of combined index selection is usually at a larger breeding population size. At limited heritability and breeding population size the difference is considerable. When breeding population size was kept rather small (<100), and the heritability limited, combined index selection can result in slightly higher gain than phenotypic selection at the same gene diversity, but this was at the cost of a much larger breeding population. Phenotypic selection and combined index selection appears as rather similar for many cases in this simple model used in this study. Considering other advantages with phenotypic selection, it may often be regarded as a competitive alternative.

Update on the Boundaries of the Curly Birch Range

2020 ◽  
pp. 9-21
Author(s):  
L.V. Vetchinnikova ◽  
◽  
A.F. Titov ◽  
◽  
Keyword(s):  
Population Size ◽  
Natural Populations ◽  
Forest Tree ◽  
Published Data ◽  
Population Approach ◽  
Key Factors ◽  
History Of ◽  
Individual Trees ◽  
Karelian Birch

The article reports on the application of the best known principles for mapping natural populations of curly (Karelian) birch Betula pendula Roth var. carelica (Mercklin) Hämet-Ahti – one of the most appealing representatives of the forest tree flora. Relying on the synthesis and analysis of the published data amassed over nearly 100 years and the data from own full-scale studies done in the past few decades almost throughout the area where curly birch has grown naturally, it is concluded that its range outlined in the middle of the 20th century and since then hardly revised is outdated. The key factors and reasons necessitating its revision are specified. Herewith it is suggested that the range is delineated using the population approach, and the key element will be the critical population size below which the population is no longer viable in the long term. This approach implies that the boundaries of the taxon range depend on the boundaries of local populations (rather than the locations of individual trees or small clumps of trees), the size of which should not be lower than the critical value, which is supposed to be around 100–500 trees for curly birch. A schematic map of the curly birch range delineated using this approach is provided. We specially address the problem of determining the minimum population size to secure genetic diversity maintenance. The advantages of the population approach to delineating the distribution range of curly birch with regard to its biological features are highlighted. The authors argue that it enables a more accurate delineation of the range; shows the natural evolutionary history of the taxon (although it is not yet officially recognized as a species) and its range; can be relatively easily updated (e.g. depending on the scope of reintroduction); should be taken into account when working on the strategy of conservation and other actions designed to maintain and regenerate this unique representative of the forest tree flora.

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 Macroeconomic Impacts of Climate Change Driven by Changes in Crop Yields

2018 ◽  
Vol 10 (10) ◽  
pp. 3673 ◽  
Author(s):  
Shinichiro Fujimori ◽  
Toshichika Iizumi ◽  
Tomoko Hasegawa ◽  
Jun’ya Takakura ◽  
Kiyoshi Takahashi ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Population Size ◽  
Large Population ◽  
Food Prices ◽  
Production Volume ◽  
Total Uncertainty ◽  
Indicator Value ◽  
Macroeconomic Impacts ◽  
Macroeconomic Indicator

Changes in agricultural yields due to climate change will affect land use, agricultural production volume, and food prices as well as macroeconomic indicators, such as GDP, which is important as it enables one to compare climate change impacts across multiple sectors. This study considered five key uncertainty factors and estimated macroeconomic impacts due to crop yield changes using a novel integrated assessment framework. The five factors are (1) land-use change (or yield aggregation method based on spatially explicit information), (2) the amplitude of the CO2 fertilization effect, (3) the use of different climate models, (4) socioeconomic assumptions and (5) the level of mitigation stringency. We found that their global impacts on the macroeconomic indicator value were 0.02–0.06% of GDP in 2100. However, the impacts on the agricultural sector varied greatly by socioeconomic assumption. The relative contributions of these factors to the total uncertainty in the projected macroeconomic indicator value were greater in a pessimistic world scenario characterized by a large population size, low income, and low yield development than in an optimistic scenario characterized by a small population size, high income, and high yield development (0.00%).

scholarly journals Population structure of Czech cold-blooded breeds of horses

2011 ◽  
Vol 54 (1) ◽  
pp. 1-9
Author(s):  
L. Vostrý ◽  
Z. Čapková ◽  
J. Přibyl ◽  
B. Hofmanová ◽  
H. Vostrá Vydrová ◽  
...  
Keyword(s):  
Population Structure ◽  
Genetic Variability ◽  
Population Size ◽  
Effective Population Size ◽  
Inbreeding Coefficient ◽  
Effective Population ◽  
The Czech Republic ◽  
Generation Interval ◽  
Average Value ◽  
Inbreeding Coefficients

Abstract. In order to estimate effective population size, generation interval and the development of inbreeding coefficients (Fx) in three original breeds of cold-blooded horses kept in the Czech Republic: Silesian Noriker (SN), Noriker (N) and Czech-Moravian Belgian horse (CMB) all animals of the particular breeds born from 1990 to 2007 were analysed. The average values of generation interval between parents and their offspring were: 8.53 in SN, 8.88 in N and 8.56 in CMB. Average values of effective population size were estimated to be: 86.3 in SN, 162.3 in N and 104.4 in CMB. The average values of inbreeding coefficient were 3.13 % in SN stallions and 3.39 % in SN mares, in the N breed 1.76 % and 1.26 % and in the CMB breed 3.84 % and 3.26 % respectively. Overall averages of Fx were: 3.23 %, 1.51 % and 3.55 % for the breeds SN, N and CMB. The average value of inbreeding coefficient Fx increased by 1.22 % in SN, by 0.35 % in N and by 1.01 % in CMB, respectively. This may lead to a reduction in genetic variability. Reduction in genetic variability could be either controlled in cooperation with corresponding populations of cold-blooded breeds in other European countries or controlled by number of sires used in population

scholarly journals Estimating an Aggregate Economic Genotype to Facilitate Breeding and Selection of Strawberry Genotypes in Southeast Queensland

2014 ◽  
Vol 139 (3) ◽  
pp. 253-260
Author(s):  
Mark E. Herrington ◽  
Craig Hardner ◽  
Malcolm Wegener ◽  
Louella Woolcock ◽  
Mark J. Dieters
Keyword(s):  
Rank Correlation ◽  
Breeding Population ◽  
Breeding Program ◽  
Production Costs ◽  
Breeding Value ◽  
Linear Quadratic ◽  
Gross Margin ◽  
Economic Weight ◽  
Non Linear ◽  
Gross Margins

The Queensland strawberry (Fragaria ×ananassa) breeding program in subtropical Australia aims to improve sustainable profitability for the producer. Selection must account for the relative economic importance of each trait and the genetic architecture underlying these traits in the breeding population. Our study used estimates of the influence of a trait on production costs and profitability to develop a profitability index (PI) and an economic weight (i.e., change in PI for a unit change in level of trait) for each trait. The economic weights were then combined with the breeding values for 12 plant and fruit traits on over 3000 genotypes that were represented in either the current breeding population or as progenitors in the pedigree of these individuals. The resulting linear combination (i.e., sum of economic weight × breeding value for all 12 traits) estimated the overall economic worth of each genotype as H, the aggregate economic genotype. H values were validated by comparisons among commercial cultivars and were also compared with the estimated gross margins. When the H value of ‘Festival’ was set as zero, the H values of genotypes in the pedigree ranged from –0.36 to +0.28. H was highly correlated (R2 = 0.77) with the year of selection (1945–98). The gross margins were highly linearly related (R2 > 0.98) to H values when the genotype was planted on less than 50% of available area, but the relationship was non-linear [quadratic with a maximum (R2 > 0.96)] when the planted area exceeded 50%. Additionally, with H values above zero, the variation in gross margin increased with increasing H values as the percentage of area planted to a genotype increased. High correlations among some traits allowed the omission of any one of three of the 12 traits with little or no effect on ranking (Spearman’s rank correlation 0.98 or greater). Thus, these traits may be dropped from the aggregate economic genotype, leading to either cost reductions in the breeding program or increased selection intensities for the same resources. H was efficient in identifying economically superior genotypes for breeding and deployment, but because of the non-linear relationship with gross margin, calculation of a gross margin for genotypes with high H is also necessary when cultivars are deployed across more than 50% of the available area.

POPULATION DYNAMICS OF SEX-DETERMINING ALLELES IN HONEY BEES AND SELF-INCOMPATIBILITY ALLELES IN PLANTS

Genetics ◽  
1979 ◽  
Vol 91 (3) ◽  
pp. 609-626 ◽  
Author(s):  
Shozo Yokoyama ◽  
Masatoshi Nei
Keyword(s):  
Population Dynamics ◽  
Population Size ◽  
Honey Bee ◽  
Honey Bees ◽  
Finite Population ◽  
Large Population ◽  
Allele Frequencies ◽  
Self Incompatibility ◽  
Overdominant Selection ◽  
Incompatibility Alleles

ABSTRACT Mathematical theories of the population dynamics of sex-determining alleles in honey bees are developed. It is shown that in an infinitely large population the equilibrium frequency of a sex allele is l/n, where n is the number of alleles in the population, and the asymptotic rate of approach to this equilibrium is 2/(3n) per generation. Formulae for the distribution of allele frequencies and the effective and actual numbers of alleles that can be maintained in a finite population are derived by taking into account the population size and mutation rate. It is shown that the allele frequencies in a finite population may deviate considerably from l/n. Using these results, available data on the number of sex alleles in honey bee populations are discussed. It is also shown that the number of self-incompatibility alleles in plants can be studied in a much simpler way by the method used in this paper. A brief discussion about general overdominant selection is presented.

Evaluation of several initialization methods on arithmetic optimization algorithm performance

2021 ◽  
Vol 31 (1) ◽  
pp. 70-94
Author(s):  
Jeffrey O. Agushaka ◽  
Absalom E. Ezugwu
Keyword(s):  
Population Size ◽  
Optimization Algorithm ◽  
Beta Distribution ◽  
Random Number ◽  
Large Population ◽  
Latin Hypercube Sampling ◽  
Rayleigh Distribution ◽  
Population Based ◽  
Algorithm Performance ◽  
Number Of Iterations

Abstract Arithmetic optimization algorithm (AOA) is one of the recently proposed population-based metaheuristic algorithms. The algorithmic design concept of the AOA is based on the distributive behavior of arithmetic operators, namely, multiplication (M), division (D), subtraction (S), and addition (A). Being a new metaheuristic algorithm, the need for a performance evaluation of AOA is significant to the global optimization research community and specifically to nature-inspired metaheuristic enthusiasts. This article aims to evaluate the influence of the algorithm control parameters, namely, population size and the number of iterations, on the performance of the newly proposed AOA. In addition, we also investigated and validated the influence of different initialization schemes available in the literature on the performance of the AOA. Experiments were conducted using different initialization scenarios and the first is where the population size is large and the number of iterations is low. The second scenario is when the number of iterations is high, and the population size is small. Finally, when the population size and the number of iterations are similar. The numerical results from the conducted experiments showed that AOA is sensitive to the population size and requires a large population size for optimal performance. Afterward, we initialized AOA with six initialization schemes, and their performances were tested on the classical functions and the functions defined in the CEC 2020 suite. The results were presented, and their implications were discussed. Our results showed that the performance of AOA could be influenced when the solution is initialized with schemes other than default random numbers. The Beta distribution outperformed the random number distribution in all cases for both the classical and CEC 2020 functions. The performance of uniform distribution, Rayleigh distribution, Latin hypercube sampling, and Sobol low discrepancy sequence are relatively competitive with the Random number. On the basis of our experiments’ results, we recommend that a solution size of 6,000, the number of iterations of 100, and initializing the solutions with Beta distribution will lead to AOA performing optimally for scenarios considered in our experiments.

Anadromous Sturgeons: Habitats, Threats, and Management

2007 ◽  
Keyword(s):  
Growth Rate ◽  
Population Size ◽  
Yellow River ◽  
Large Population ◽  
Leading Edge ◽  
The Yellow River ◽  
Gulf Sturgeon ◽  
Successful Recruitment ◽  
Viable Population ◽  
Size Growth

<em>Abstract.</em>—The Gulf sturgeon <em>Acipenser oxyrinchus desotoi</em> is an anadromous species listed as threatened under the Endangered Species Act in 1991. We conducted a 3year tagging study to estimate population size, growth, mortality, and age composition for sturgeon in the Yellow River. Capture probabilities and population size were estimated using Program MARK and a Cormack-Jolly–Seber model. Total mortality of Gulf sturgeon was estimated using a Beverton–Holt mortality equation. Growth rate was determined from annuli on the leading edge of pectoral fin-ray. A total of 522 Gulf sturgeon captures were made, and 399 individual fish were tagged. The population estimates for the Gulf sturgeon over 3 years ranged from 500 to 911 fish. The age structure of the population suggests successful recruitment and a viable population. The total annual mortality estimate for Yellow River Gulf sturgeon was 11.9%. Growth rate for the Yellow River population was comparable to other populations of Gulf sturgeon. The Yellow River Gulf sturgeon population is a dynamic population based upon consistent age-classes as an indicator of successful recruitment, a large population size relative to most rivers where Gulf sturgeon are found, and estimates of mortality below the reported range for the species.

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