Estimating genetic gains from alternative breeding strategies for clonal forestry

1992 ◽  
Vol 22 (1) ◽  
pp. 14-23 ◽  
Author(s):  
T.J. Mullin ◽  
Y.S. Park
Keyword(s):  
Linear Model ◽  
Genetic Parameters ◽  
Genetic Variance ◽  
Genetic Model ◽  
Large Fraction ◽  
Breeding Strategies ◽  
Clonal Forestry ◽  
Components Of Variance ◽  
Gene Effects ◽  
Dominance Variance

Concepts and procedures are presented for the analysis of progeny trials that incorporate clonal replication as a means to resolve variance arising from nonadditive gene effects. Components of variance from the linear model may be expressed in terms of expected covariances among relatives, and these, in turn, may be used to derive approximations of additive, dominance, and epistatic components of genetic variance. In addition to the usual assumptions applied to conventional progeny trials, the use of this expanded genetic model in the analysis of tests with clonal replicates assumes that the greatest portion of the total epistasis is due to interactions involving groups of more than two or three loci. If this assumption is not satisfied, estimates of additive and dominance variance, including those from trials without clonal replicates, will be contaminated by a large fraction of epistasis, and total epistasis will be underestimated by a corresponding amount. Heritability and gain formulae for alternative selection and deployment schemes are developed and illustrate the use of genetic parameters in the comparison of seedling and clonal reforestation strategies.

Modeling Epistasis of Quantitative Trait Loci Using Cockerham's Model

Genetics ◽  
2002 ◽  
Vol 160 (3) ◽  
pp. 1243-1261 ◽  
Author(s):  
Chen-Hung Kao ◽  
Zhao-Bang Zeng
Keyword(s):  
Quantitative Trait Loci ◽  
Qtl Mapping ◽  
Quantitative Trait ◽  
Genetic Variance ◽  
Genetic Model ◽  
Linkage Equilibrium ◽  
Gene Effects ◽  
Multiple Loci ◽  
Trait Loci ◽  
Orthogonal Property

AbstractWe use the orthogonal contrast scales proposed by Cockerham to construct a genetic model, called Cockerham's model, for studying epistasis between genes. The properties of Cockerham's model in modeling and mapping epistatic genes under linkage equilibrium and disequilibrium are investigated and discussed. Because of its orthogonal property, Cockerham's model has several advantages in partitioning genetic variance into components, interpreting and estimating gene effects, and application to quantitative trait loci (QTL) mapping when compared to other models, and thus it can facilitate the study of epistasis between genes and be readily used in QTL mapping. The issues of QTL mapping with epistasis are also addressed. Real and simulated examples are used to illustrate Cockerham's model, compare different models, and map for epistatic QTL. Finally, we extend Cockerham's model to multiple loci and discuss its applications to QTL mapping.

scholarly journals Heritability and correlation of litter traits in pigs determined by REML method

2012 ◽  
Vol 28 (4) ◽  
pp. 771-778
Author(s):  
D. Radojkovic ◽  
M. Petrovic ◽  
C. Radovic ◽  
N. Parunovic ◽  
M. Popovac ◽  
...  
Keyword(s):  
Variance Component ◽  
Genetic Parameters ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Weaned Piglets ◽  
Components Of Variance ◽  
Litter Traits ◽  
Pig Farms ◽  
Genetic Level ◽  
Selection Of

The aim of this study was to determine the heritability coefficients and the correlation between the number of live born piglets (NBA), the number of stillborn piglets (NSB), the number of total born piglets (NTB) and the number of weaned piglets (NW) in the part of population in Swedish Landrace sows in R. Serbia. The results obtained should enable the selection of litter size traits that would be proposed to be included in the selection - breeding program for this breed. The analysis of parameters was carried out on the basis of data on fertility of 4.061 Swedish Landrace sows and their 15.209 litters realized on two pig farms in R. Serbia. There was a genetic relationship between animals among the farms. Components of variance and covariance of observed traits, the share of additive genetic variance component in the phenotypic and correlation of traits at phenotypic and genetic levels, were evaluated using the method of Restricted Maximum Likelihood (REML) using the Multitrait Model (MM). Heritability estimates for the NBA, NSB, NTB and NW amounted to 6.4, 1.6, 6.7 and 1.1%, respectively. Correlation between the NBA and NTB at the phenotypic and genetic level was complete (rP = 0986, rG = 0938). Correlation between the NBA and NW at the phenotypic level has not been established, while at the genetic level it was weak. We believe that this is the result of the procedure of equalizing of litters after farrowing. In order to obtain objective genetic parameters for NW this procedure should not be applied in pure breed sows.

scholarly journals ESTIMATION GENE EFFECTS BASED ON JOINT SCALING TEST AND SOME GENETIC PARAMETERS IN FOUR MAIZE CROSSES 1-YIELD COMPONENTS

2019 ◽  
Vol 49 (2) ◽  
Author(s):  
B. H. Hadi
Keyword(s):  
Genetic Parameters ◽  
Genetic Variance ◽  
Block Design ◽  
Dominance Model ◽  
Chi Square ◽  
Gene Effects ◽  
Randomized Complete Block Design ◽  
Maize Grain ◽  
Allelic Interaction

The objective of this study to estimate the components of genetic variation, phenotypic (PCV) ,genotypic(GCV)  coefficient of variation, genetic gain and its percentage. An experiment was conducted at the field of Field Crop Dept.Coll. Agric.-Univ. Baghdad, using four crosses (FI01301 Rustico), (AntignaoHi39× Nostred), (Lo1391× Rustico) and (Rusticocangini× Rustico) which developed  from crossing of genetically different of six inbred lines for maize (Zae mays L.), introduced from Italy. Genetic parameters were estimated according to the Joint scaling test using the randomized complete block design with four replications. The components  of genetic variance; Additive and dominance of the maize grain yield and some trait, were estimated. The results showed that the values of Chi square were significant  for all the studied some traits  of all crosses, thus the simple additive – dominance model in four crosses exhibited lack of good fit  for all traits, indicates the role of non-allelic interaction. Dominance gene action was higher than additive for most traits. Therefore the hybridization would be more effective than population selection to improve these traits for these crosses.

scholarly journals What can genetic variation tell us about the evolution of senescence?

2009 ◽  
Vol 276 (1665) ◽  
pp. 2271-2278 ◽  
Author(s):  
Jacob A. Moorad ◽  
Daniel E.L. Promislow
Keyword(s):  
Genetic Variation ◽  
Variance Components ◽  
Genetic Parameters ◽  
Genetic Variance ◽  
Mutation Accumulation ◽  
Antagonistic Pleiotropy ◽  
Age Classes ◽  
Dominance Variance ◽  
Quantitative Genetic ◽  
Genetic Techniques

Quantitative genetic approaches have been developed that allow researchers to determine which of two mechanisms, mutation accumulation (MA) or antagonistic pleiotropy (AP), best explain observed variation in patterns of senescence using classical quantitative genetic techniques. These include the creation of mutation accumulation lines, artificial selection experiments and the partitioning of genetic variances across age classes. This last strategy has received the lion's share of empirical attention. Models predict that inbreeding depression (ID), dominance variance and the variance among inbred line means will all increase with age under MA but not under those forms of AP that generate marginal overdominance. Here, we show that these measures are not, in fact, diagnostic of MA versus AP. In particular, the assumptions about the value of genetic parameters in existing AP models may be rather narrow, and often violated in reality. We argue that whenever ageing-related AP loci contribute to segregating genetic variation, polymorphism at these loci will be enhanced by genetic effects that will also cause ID and dominance variance to increase with age, effects also expected under the MA model of senescence. We suggest that the tests that seek to identify the relative contributions of AP and MA to the evolution of ageing by partitioning genetic variance components are likely to be too conservative to be of general value.

scholarly journals On flexible finite polygenic models for multiple-trait evaluation

2002 ◽  
Vol 80 (3) ◽  
pp. 245-256 ◽  
Author(s):  
MARCO C. A. M. BINK
Keyword(s):  
Genetic Variance ◽  
Genetic Model ◽  
Random Variable ◽  
Random Selection ◽  
Full Potential ◽  
Multiple Trait ◽  
Gene Effects ◽  
Posterior Mean ◽  
Number Of Genes ◽  
Estimation Of Variance

Finite polygenic models (FPM) might be an alternative to the infinitesimal model (TIM) for the genetic evaluation of pedigreed multiple-generation populations for multiple quantitative traits. I present a general flexible Bayesian method that includes the number of genes in the FPM as an additional random variable. Markov-chain Monte Carlo techniques such as Gibbs sampling and the reversible jump sampler are used for implementation. Sampling of genotypes of all genes in the FPM is done via the use of segregation indicators. A broad range of FPM models, some combined with TIM, are empirically tested for the estimation of variance components and the number of genes in the FPM. Four simulation scenarios were studied, including genetic models with 5 or 50 additive independent diallelic genes affecting the traits, and random selection or selection on one of the traits was performed. The results in this study were based on ten replicates per simulation scenario. In the case of random selection, uniform priors on additive gene effects led to posterior mean estimates of genetic variance that were positively correlated with the number of genes fitted in the FPM. In the case of trait selection, assuming normal priors on gene effects also led to genetic variance estimates for the selected trait that were negatively correlated with the number of genes in the FPM. This negative correlation was not observed for the unselected trait. Treating the number of genes in the FPM as random revealed a positive correlation between prior and posterior mean estimates of this number, but the prior hardly affected the posterior estimates of genetic variance. Posterior inferences about the number of genes should be considered to be indicative where trait selection seems to improve the power of distinguishing between TIM and FPM. Based on the results of this study, I suggest not replacing TIM by the FPM, but combining TIM and FPM with the number of genes treated as random, to facilitate a highly flexible and thereby robust method for variance component estimation in pedigreed populations. Further study is required to explore the full potential of these models under different genetic model assumptions.

scholarly journals Sapling and coppice biomass heritabilities and potential gains from Eucalyptus polybractea progeny trials

2021 ◽  
Vol 17 (2) ◽  
Author(s):  
Beren Spencer ◽  
Richard Mazanec ◽  
Mark Gibberd ◽  
Ayalsew Zerihun
Keyword(s):  
Western Australia ◽  
Growth Rates ◽  
Genetic Parameters ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Short Rotation ◽  
Sapling Growth ◽  
Genetic And Phenotypic Correlations ◽  
Breeding Selections ◽  
Cross Site

AbstractEucalyptus polybractea has been planted as a short-rotation coppice crop for bioenergy in Western Australia. Historical breeding selections were based on sapling biomass and despite a long history as a coppice crop, the genetic parameters of coppicing are unknown. Here, we assessed sapling biomass at ages 3 and 6 from three progeny trials across southern Australia. After the second sapling assessment, all trees were harvested. Coppice biomass was assessed 3.5 years later. Mortality following harvest was between 1 and 2%. Additive genetic variance for the 6-sapling estimate at one site was not significant. Sapling heritabilities were between 0.06 and 0.36 at 3 years, and 0.18 and 0.20 at 6 years. The heritability for the coppice biomass was between 0.07 and 0.17. Within-site genetic and phenotypic correlations were strong between all biomass assessments. Cross-site correlations were not different from unity. Selections based on net breeding values revealed positive gains in sapling and coppice biomass. Lower or negative gains were estimated if 3-year sapling selections were applied to the coppice assessments (−7.1% to 3.4%) with useful families culled. Positive gains were obtained if 6-year sapling selections were applied to the coppice assessment (6.4% to 9.3%) but these were lower than those obtained by applying coppice selections to the coppice assessment (8.4% to 14.8%). Removal of poor performing families and families that displayed fast sapling growth rates but under-performed as coppice will benefit potential coppice production. These results indicate that selections should be made using coppice data.

scholarly journals Estimation of environmental effects and genetic parameters of carcass traits on Chikso (Korean brindle cattle)

2020 ◽  
Vol 33 (4) ◽  
pp. 525-530 ◽  
Author(s):  
Byoungho Park ◽  
Tae Jeong Choi ◽  
Mi Na Park ◽  
Sang-Hyon Oh
Keyword(s):  
Linear Model ◽  
Environmental Effects ◽  
Genetic Parameters ◽  
Coat Color ◽  
Carcass Traits ◽  
National Level ◽  
Pedigree Information ◽  
Small Scale ◽  
Muscle Area ◽  
Eye Muscle

Objective: The purpose of this study was i) to identify the characteristics of carcass traits in Chikso by gender, region, age at slaughter, and coat color using the carcass data collected from the nationwide pedigree information and coat color investigation, and ii) to estimate genetic parameters for breed improvement.Methods: A linear model was used to analyze the environmental effects on the carcass traits and to estimate genetic parameters. Analysis of variance was performed using TYPE III sum of squares for the unbalanced data provided by the general linear model procedure. Variance components for genetic parameters was estimated using REMLF90 of the BLUPF90 family programs.Results: Phenotypic performance of carcass weight (CW), eye muscle area (EMA), and backfat thickness (BF) in Chikso were lower than those of Hanwoo. This is a natural outcome because Hanwoo have undergone significant efforts for improvement at the national level, a phenomenon not observed in Chikso. Another factor influencing the above outcome was the smaller population size of Chikso compared to that of Hanwoo’s. The heritabilities of CW, EMA, BF, and marbling score in Chikso were estimated as 0.50, 0.37, 0.35, and 0.53, respectively, which were was higher than those of Hanwoo.Conclusion: Based on the genetic parameters that were estimated in this study, it is expected that the carcass traits will improve when the livestock research institutes at each province conduct small-scale performance tests and the semen is provided to farmers after selecting proven bulls using the state-of-art selection technique such as genomic selection.

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 Some observations on asymmetrical correlated responses to selection

1966 ◽  
Vol 7 (1) ◽  
pp. 44-57 ◽  
Author(s):  
B. B. Bohren ◽  
W. G. Hill ◽  
A. Robertson
Keyword(s):  
Model Selection ◽  
Gene Frequency ◽  
Genetic Parameters ◽  
Correlated Response ◽  
Correlated Responses ◽  
Gene Frequencies ◽  
Genetic Covariance ◽  
Gene Effects ◽  
Selection Experiments ◽  
Relative Change

The pattern of changes of the genetic covariance between two characters on selection was examined in an effort to explain the asymmetry of correlated responses in two traits, or of the same trait in two environments, frequently observed in experimental results.The algebraic conclusions were further examined by model selection experiments using a computer. The computer was programmed to calculate the change in gene frequency from generation to generation and to calculate from it the expected changes in genetic variances and covariance as selection proceeded. This procedure was carried out with several models of gene effects and gene frequencies.Asymmetry of the genetic covariance, and consequently of the correlated responses, resulted when the relative change in gene frequency at the loci contributing positively and negatively to the covariance depended on the trait selected. The conditions necessary for the development of asymmetry were examined and the results suggest that any symmetry found in an experiment is perhaps more surprising than asymmetry. Probably the most frequent contribution to asymmetry in practice will be from loci contributing negatively to the covariance and having frequencies other than 0·5.Accurate prediction of correlated response over many generations is therefore not possible without prior knowledge of the composition of the genetic covariance, as well as its magnitude. The validity of existing theory for the prediction of correlated responses is likely to be much poorer than for the prediction of direct responses. Predictions would then have to be based on the genetic parameters estimated in each generation.

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