Selection with control of inbreeding in populations with overlapping generations: a comparison of methods

2000 ◽  
Vol 70 (1) ◽  
pp. 1-8 ◽  
Author(s):  
A. K. Sonesson ◽  
B. Grundy ◽  
J. A. Woolliams ◽  
T. H. E. Meuwissen
Keyword(s):  
Computer Simulations ◽  
Optimization Algorithm ◽  
Genetic Gain ◽  
Overlapping Generations ◽  
Current Level ◽  
Comparison Of Methods ◽  
Genetic Response ◽  
Age Classes ◽  
Feasible Method ◽  
Breeding Schemes

AbstractMethods that maximize genetic response in populations with overlapping generations while controlling rate of inbreeding by constraining the average relationship among selection candidates were compared. Firstly, computer simulations of closed nucleus selection schemes showed that a two-stage optimization algorithm approach, where the distribution of parents within and thereafter over age classes was optimized resulted in different breeding schemes than an approach that performed an iteration on this distribution. It yielded significantly lower annual genetic gain (0·194 v. 0·223 σp units), fewer animals selected (21·9 v. 26·4) and longer generation intervals (2·38 v. 1·68 years) but maintained the rate of inbreeding closer to its constraint. In large schemes, iteration may be computationally the only feasible method for the optimization of parents across age classes. Secondly, the use of conventional relationships for constraining inbreeding was compared with that of augmented relationships, which do not depend on the level of inbreeding. Both relationships resulted in very similar breeding schemes, but the use of augmented relationships avoids correction of the current level of inbreeding. Thirdly, a constraint of the rate of inbreeding on a per year basis was compared with a constraint on a per generation basis. When optimizing per generation, the generation interval was shorter compared with a scheme where an analogous annual restriction was in place (2·01 v. 2·38 years) and the annual rate of genetic gain was higher (0·214 v. 0·194 σp units).

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 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.

scholarly journals Breeding schemes in reindeer husbandry

Rangifer ◽  
2003 ◽  
Vol 23 (2) ◽  
pp. 45 ◽  
Author(s):  
Lars Rönnegård ◽  
J. A. Woolliams ◽  
Öje Danell
Keyword(s):  
Genetic Gain ◽  
Mass Selection ◽  
Effective Population ◽  
Selection Programme ◽  
Breeding Schemes ◽  
The Difference ◽  
Genetic Contributions ◽  
Parameter Values ◽  
Open Nucleus

The objective of the paper was to investigate annual genetic gain from selection (G), and the influence of selection on the inbreeding effective population size (Ne), for different possible breeding schemes within a reindeer herding district. The breeding schemes were analysed for different proportions of the population within a herding district included in the selection programme. Two different breeding schemes were analysed: an open nucleus scheme where males mix and mate between owner flocks, and a closed nucleus scheme where the males in non-selected owner flocks are culled to maximise G in the whole population. The theory of expected long-term genetic contributions was used and maternal effects were included in the analyses. Realistic parameter values were used for the population, modelled with 5000 reindeer in the population and a sex ratio of 14 adult females per male. The standard deviation of calf weights was 4.1 kg. Four different situations were explored and the results showed: 1. When the population was randomly culled, Ne equalled 2400. 2. When the whole population was selected on calf weights, Ne equalled 1700 and the total annual genetic gain (direct + maternal) in calf weight was 0.42 kg. 3. For the open nucleus scheme, G increased monotonically from 0 to 0.42 kg as the proportion of the population included in the selection programme increased from 0 to 1.0, and Ne decreased correspondingly from 2400 to 1700. 4. In the closed nucleus scheme the lowest value of Ne was 1300. For a given proportion of the population included in the selection programme, the difference in G between a closed nucleus scheme and an open one was up to 0.13 kg. We conclude that for mass selection based on calf weights in herding districts with 2000 animals or more, there are no risks of inbreeding effects caused by selection.

Predicting genetic gain when rates of inbreeding are constrained to pre-defined values

2003 ◽  
Vol 2003 ◽  
pp. 46-46
Author(s):  
S. Avendaño ◽  
J.A. Woolliams ◽  
B. Villanueva
Keyword(s):  
Beef Cattle ◽  
Genetic Gain ◽  
Index Score ◽  
Optimal Selection ◽  
Dynamic Selection ◽  
Novel Approach ◽  
Breeding Schemes ◽  
The Uk ◽  
Selection Algorithms

Dynamic selection algorithms using quadratic indices to optimise the contributions of selection candidates for maximising rates of genetic gain (ΔG) while constraining the rate of inbreeding (ΔF) in the long-term to pre-defined values, are available (Grundy et al, 1998). Avendaño et al (2001 a,b) applied these optimal selection algorithms on the UK Meatlinc (sheep) and Aberdeen Angus (beef cattle) pedigree breeds and found substantial expected increases (of at least 17%) in the average index score at the observed ΔF. Although these algorithms constitute powerful operational tools for breeding schemes, the framework for deterministically predicting ΔG under optimal selection with restricted ΔF is not yet available. This study presents a novel approach to this problem.

Efficiency of nucleus breeding schemes in dual-purpose cattle of Tanzania

1990 ◽  
Vol 50 (2) ◽  
pp. 245-251 ◽  
Author(s):  
J. S. Kasonta ◽  
G. Nitter
Keyword(s):  
Artificial Insemination ◽  
Genetic Gain ◽  
Cattle Breed ◽  
Progeny Testing ◽  
Environment Interaction ◽  
Dual Purpose ◽  
Model Calculations ◽  
Dual Purpose Cattle ◽  
Total Profit ◽  
Breeding Schemes

ABSTRACTFor the Mpwapwa cattle breed in Tanzania, the efficiency of various breeding schemes including an open nucleus was investigated by model calculations. Artificial insemination and intensive recording of production are assumed to be applied in a nucleus which is the main breeding unit. As a pre-nucleus, associated herds with less intense data recording serve as the basis to screen superior cows for nucleus replacements, provide the capacity for progeny testing, and operate as bull multipliers for commercial herds.The criteria of efficiency were genetic gain and profit from selection for a dual purpose breeding objective (milk and beef) in a total population of 10 000 cows. Introducing a two breeding tier scheme through separating all recorded cows into a nucleus and pre-nucleus leads to an increase of the genetic gain rather than the profit. Further improvement is obtained by introduction of artificial insemination in pre-nucleus herds. The nucleus size should not exceed about 5% of the cow population and an optimum size of the pre-nucleus is about 15%. Opening the nucleus to replacement cows coming from the pre-nucleus affects the aggregate genetic gain very little although it can be recommended if milk yield is to be mainly improved or if the total profit is taken into account. Furthermore, the nucleus should be opened if there is little difference between the heritabilities in the nucleus and pre-nucleus and also in order to avoid detrimental effects of inbreeding and genotype × environment interaction.

Genetic response to within-family selection using molecular markers in some radiata pine breeding schemes

2001 ◽  
Vol 31 (5) ◽  
pp. 779-785 ◽  
Author(s):  
Satish Kumar ◽  
D J Garrick
Keyword(s):  
Genetic Gain ◽  
Radiata Pine ◽  
Forest Tree ◽  
Clonal Forestry ◽  
Genetic Response ◽  
Forest Tree Breeding ◽  
Forward Selection ◽  
Family Selection ◽  
Marker Haplotype ◽  
Selection For

Marker-assisted selection (MAS) provides an opportunity to increase the efficiency of within-family selection in forest tree breeding. Within-family MAS involves selection decisions first made on conventional breeding values and quantitative trait loci (QTL) information used for within-family selection. In this study genetic response obtained by using MAS was compared with conventional methods for three options: "full-sib family forestry," "clonal forestry," and "forward selection for deployment." This comparison was undertaken using stochastic simulation for a locus that explained 10 or 20% of the genetic variance. In the full-sib family forestry scenario, markers were used to select genotypes (among juvenile individuals in a family) for vegetative propagation. Markers were used to preselect genotypes for clonal testing in clonal forestry option. In case of forward selection for deployment option, offspring that have favourable marker haplotype and a superior phenotype were selected from each family. The comparison between the MAS and the conventional strategy was evaluated in genetic terms based on comparison of the average genetic merit of the genotypes used for deployment in production plantations. The relative genetic gain (%) using MAS were found to be 4–8% and 2–3% higher compared with conventional strategy for full-sib family forestry and clonal forestry options, respectively. In case of forward selection for deployment option, MAS was generally found to be providing higher genetic gain only when the heritability is low.

scholarly journals Designing dairy cattle breeding schemes under genomic selection: a review of international research

2012 ◽  
Vol 52 (3) ◽  
pp. 107 ◽  
Author(s):  
J. E. Pryce ◽  
H. D. Daetwyler
Keyword(s):  
Dairy Cattle ◽  
Genomic Selection ◽  
Genetic Gain ◽  
Reproductive Technologies ◽  
Progeny Testing ◽  
Genomic Breeding ◽  
Breeding Values ◽  
Scheme Design ◽  
Dairy Cattle Breeding ◽  
Breeding Schemes

High rates of genetic gain can be achieved through (1) accurate predictions of breeding values (2) high intensities of selection and (3) shorter generation intervals. Reliabilities of ~60% are currently achievable using genomic selection in dairy cattle. This breakthrough means that selection of animals can happen at a very early age (i.e. as soon as a DNA sample is available) and has opened opportunities to radically redesign breeding schemes. Most research over the past decade has focussed on the feasibility of genomic selection, especially how to increase the accuracy of genomic breeding values. More recently, how to apply genomic technology to breeding schemes has generated a lot of interest. Some of this research remains the intellectual property of breeding companies, but there are examples in the public domain. Here we review published research into breeding scheme design using genomic selection and evaluate which designs appear to be promising (in terms of rates of genetic gain) and those that may have unfavourable side-effects (i.e. increasing the rate of inbreeding). The schemes range from fairly conservative designs where bulls are screened genomically to reduce numbers entering progeny testing, to schemes where very large numbers of bull calves are screened and used as sires as soon as they reach sexual maturity. More radical schemes that incorporate the use of reproductive technologies (in juveniles) and genomic selection in nucleus herds are also described. The models used are either deterministic and more recently tend to be stochastic, simulating populations of cattle. A key driver of the rate of genetic gain is the generation interval, which could range from being similar to that in conventional testing (~5 years), down to as little as 1.5 years. Generally, the rate of genetic gain is between 12% and 100% more than in conventional progeny testing, while the rate of inbreeding tends to be lower per generation than in progeny testing because Mendelian sampling terms can be estimated more accurately. However, short generation intervals can lead to higher rates of inbreeding per year in genomic breeding programs.

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

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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