Expectation and variance of genetic gain in open and closed nucleus and progeny testing schemes

1991 ◽  
Vol 53 (2) ◽  
pp. 133-141 ◽  
Author(s):  
T. H. E. Meuwissen
Keyword(s):  
Genetic Gain ◽  
Variance Reduction ◽  
Deterministic Model ◽  
Finite Size ◽  
Response Rates ◽  
Progeny Test ◽  
Progeny Testing ◽  
Individual Record ◽  
Open Nucleus ◽  
Selection Of

AbstractOpen and closed nucleus and conventional and modern progeny testing schemes were compared for expectation and variance of genetic gain. Generation intervals were optimized, with minimum values of 2 and 6 years (progeny test results available) for males in nucleus and progeny testing schemes, respectively. Females had a minimum generation interval of 2 years, except in the conventional progeny testing schemes, which had a minimum of 4 years (one individual record available). Apart from the generation intervals and the progeny test, open nucleus and progeny testing schemes were identical, since ‘nucleus females’ are also born in progeny testing schemes, being full-sibs of the young bulls and dispersed over commercial herds. The number of nucleus sires (bull sires) selected was varied between four and 32. Selection was for milk production.A deterministic model was used, that accounted for variance reduction due to selection and the effects of finite size and family structure on the selection differentials. Prediction of the variance of the selection response accounted for selection of full- and paternal half-sibs.Closed nucleus schemes gave a factor 0·03, 0·13 and 0·19 larger response rates than open nucleus and modern and conventional progeny testing schemes, respectively. Reduction of genetic variance of open nucleus schemes was larger than that of closed nucleus schemes, which caused the slightly higher response rates of closed nucleus schemes. Standard deviations of selection responses of closed nucleus schemes were a factor 0·46, 0·79 and 0·86 larger, respectively.Preference for the schemes was assessed using a quadratic utility function expressing risk and inbreeding aversion. The increase in genetic gain due to shortening of generation intervals more than compensated for its increased variance. Whether the increased genetic gain due to closing the nucleus compensated for its increased variance depended on the amount of risk aversion. Selection of four sires and eight to 16 sires had the highest utility in progeny testing and nucleus schemes, respectively.

A deterministic model for the optimization of dairy cattle breeding based on BLUP breeding value estimates

1989 ◽  
Vol 49 (2) ◽  
pp. 193-202 ◽  
Author(s):  
T. H. E. Meuwissen
Keyword(s):  
Steady State ◽  
Dairy Cattle ◽  
Genetic Gain ◽  
Variance Reduction ◽  
Deterministic Model ◽  
Age Distribution ◽  
Breeding Value ◽  
Progeny Testing ◽  
Age Classes ◽  
Testing Scheme

ABSTRACTA deterministic model was developed to examine the optimization of open nucleus breeding schemes in order to maximize the rate of genetic response in dairy cattle. By changing the parameters, the model was able to simulate both a closed nucleus and a progeny testing scheme. The model implicitly optimized the generation interval and the selection across tiers by means of truncation across age classes and tiers respectively. The effects of size of the progeny test group and the nucleus size were assessed by comparing alternative plans. It is possible to optimize a breeding plan given the reproduction rates of the animals, the availability of different sources of information, the age distribution of the animals (survival rates) and the phenotypic and genetic parameters of the trait.The steady state selection response was assessed by calculating the genetic progress year after year until it stabilized. The genetic gain was corrected for the effects of reduced variances due to previous selections and increased variances due to genetic differences between parental age classes.In an example, the model was used to predict the improvement in milk yield in a closed artificial insemination breeding scheme. The genetic gain of a conventional progeny testing scheme was about one-third lower than the genetic gain of the optimized breeding plan. The variance reduction due to selection decreased the steady state genetic gain by a factor 0·3

A simulation study of open nucleus and closed nucleus breeding systems in a sheep population

1995 ◽  
Vol 60 (1) ◽  
pp. 117-124 ◽  
Author(s):  
J. A. Roden
Keyword(s):  
Genetic Gain ◽  
Live Weight ◽  
Breeding Systems ◽  
Breeding Value ◽  
Linear Unbiased Prediction ◽  
Sheep Population ◽  
Best Linear Unbiased ◽  
Nucleus Breeding ◽  
Open Nucleus ◽  
Selection Of

AbstractStochastic simulation was used to compare the results of alternative breeding systems in a sheep population divided into 10 flocks of 120 ewes. The breeding systems compared were selection within closed flocks (CF), a closed nucleus system (CNS), an open nucleus system (ONS) and open nucleus systems with the selection of nucleus replacements being restricted to either nucleus born males (ONSRm) or nucleus born females (ONSRf). Selection was for a best linear unbiased prediction of breeding value for lamb live weight which had a heritability of 0·17. The open nucleus breeding systems (ONS, ONSRm, ONSRf) resulted in higher rates of genetic gain, more predictable selection responses and lower rates of inbreeding than either the closed nucleus system (CNS) or selection within closed flocks (CF). Initial genetic differences between flocks resulted in higher rates of genetic gain in the nucleus breeding systems due to the use of between flock genetic variance. In the ONS system up to 25% of nucleus sires and approximately 50% of nucleus dams were born in base flocks. Nevertheless if selection of either nucleus sires or dams was restricted to nucleus born animals there was very little change in genetic gain or rate of inbreeding.

Responses of multi-trait selection in open nucleus schemes for dairy cattle breeding

1993 ◽  
Vol 56 (3) ◽  
pp. 293-299 ◽  
Author(s):  
T. H. E. Meuwissen ◽  
J. A. Woolliams
Keyword(s):  
Food Intake ◽  
Genetic Correlation ◽  
Milk Production ◽  
Deterministic Model ◽  
Economic Value ◽  
Estimation Method ◽  
Breeding Value ◽  
Progeny Testing ◽  
Secondary Traits ◽  
Open Nucleus

AbstractResponses of selection for milk production and secondary traits were predicted in open nucleus schemes using a deterministic model. Secondary traits considered were: traits recorded during lactation (e.g. mastitis resistance; calving ease); traits recorded in the nucleus only (e.g. food intake); traits recorded early in life (e.g. growth rate); and traits recorded late in life (e.g. longevity). Also, genotype × environment interactions between nucleus and commercial herds and predictors of merit in juveniles were considered.Extension of the breeding goal to include an uncorrelated secondary trait, which was recorded at each lactation, had the same heritability as milk production (assumed throughout to be 0·25) and half its economic value, increased total economic gain by a factor of 0·12. This increase was only 0·04, if the heritability of the secondary trait was 0·1. The situation for traits of low heritability was not improved by progeny testing of young bulls due to the short optimized generation intervals. Gain increased only by a factor of 0·04, if the economic value was 0·25.Including a secondary trait of heritability 0·25 and a genetic correlation with yield of 0·5 in the index, only increased economic response rates by a factor of 0·04. However, when the genetic correlation was –0·5 the benefits were greater with increases of 0·09, 0·10 and 0·22 for heritabilities of 0·05, 0·10 and 0·25, respectively. Hence, including traits with low heritability but with strong negative correlations with yield, which might apply to fertility and disease resistance, increased rates of gain moderately.If an uncorrelated secondary trait was recorded in the nucleus only, e.g. food intake, and had half the economic value of milk production, total gains increased by a factor of 0·10. Hence, recording of secondary traits can be restricted to the nucleus with only minor loss of gain. The extra economic benefit was greatest from secondary traits measured early in life compared with late in life, e.g. longevity, with benefits increased by factors of 0·24 and 0·06, respectively.Open nucleus schemes are robust in the presence of genotype × environment interactions between nucleus and commercial herds, if the breeding value estimation method accounts for these interactions, juvenile indicator traits of milk production may increase rates of gain by a factor of 0·11, if the heritability of the indicator trait is 0·25 and the correlation with milk production is 0·5.

Relative efficiency of individual selection and reference sire progeny test schemes for beef production

1980 ◽  
Vol 31 (3) ◽  
pp. 601 ◽  
Author(s):  
CA Morris ◽  
LP Jones ◽  
IR Hopkins
Keyword(s):  
Genetic Gain ◽  
Progeny Test ◽  
Progeny Testing ◽  
Selection Intensity ◽  
Individual Selection ◽  
Test Results ◽  
Genetic Progress ◽  
Test Selection ◽  
Artificial Breeding ◽  
Natural Service

Individual selection on the basis of adjusted yearling weight records (policy 1) was compared with selection of proven sires based on progeny test results ('progeny test selection'). The major assumptions in the comparisons were that herd sizes were 100 recorded cows, and that each herd used four joining groups. It was assumed that 25 herds cooperated in using two reference sires in artificial breeding to link progeny test data from young bulls in natural service, thereby increasing selection intensity without the loss in accuracy normally suffered in a single multi-sired herd. In the progeny test comparisons, preselection of young bulls for progeny testing (policy 2) was also compared with random selection among young bulls for progeny testing (policy 3). This paper contains a limited number of comparisons only, in order to indicate the possible extent of selection pressure with different policies. Comparisons in terms of annual genetic progress ranked the policies in the order 2 (greatest), 1, 3, with policy 2 being better than 3 by 90-110%. The advantage of policy 2 over policy 1 was 26-38%. In all cases, using bulls first as yearlings was preferable to 2 1/4 years in terms of annual genetic gain. With individual selection, keeping bulls for 1 year compared with 2 or 3 years had little effect on annual gain, as the rise in selection intensity balanced the rise in generation interval. Inbreeding change per year was more affected, lower rates resulting from bulls being used for 1 year only. Inbreeding rates were small with progeny test selection as described here, as long as proven sons came from young bulls as well as proven sires. The effect of selection intensity under progeny test selection with preselection becomes diluted to 25% in its contribution to annual genetic change. Thus some degree of assortative mating may be useful, or wider use of proven sires relative to young sires. With preselection the break-even number of cooperating progeny test herds was low (three herds), compared with equal rates of genetic gain from individual selection.

The use of increased female reproductive rates in dairy cattle breeding schemes

1991 ◽  
Vol 52 (1) ◽  
pp. 21-31 ◽  
Author(s):  
T. H. E. Meuwissen
Keyword(s):  
Order Statistics ◽  
Genetic Gain ◽  
Progeny Testing ◽  
Base Population ◽  
Female Reproduction ◽  
Reproductive Rates ◽  
Efficiency Of Selection ◽  
Efficient Selection ◽  
Open Nucleus ◽  
Selection Differentials

ABSTRACTThe effect of increased female reproductive rates on selection response, on efficiency of progeny testing and on the openness of the nucleus was investigated in open nucleus breeding plans. Conventional progeny testing plans and closed nucleus plans are special classes of open nucleus plans. In the open nucleus plans, generation intervals and selection across tiers were optimized. The number of offspring per elite dam was varied from 1 to 41, progeny testing of young bulls in the female base population was varied from 0 to 100 test records and the size of the nucleus was varied from 250 to 2000 young bulls born per year. Also efficiency of selection was varied: efficient selection in T(heoretical)-schemes and less efficient selection in P(ractical)-schemes. Especially, selection of base parents was less efficient i n P-schemes.The deterministic prediction model took account of variance reduction due to selection and reduction of selection differentials due to correlations between estimated breeding values of relatives (order statistics). For closed nucleus plans, the results of the model were verified with Monte Carlo simulation results.By increasing female reproductive rates, genetic gain increased by a factor 0·08 and 0·16 for the T- and P-schemes respectively. The nuclei in P-schemes were less open, due to the less efficient selection in the female base population. Schemes that were less open benefited more from increased female reproductive rates because selection differentials in small nuclei increased more than those in large base populations. The optimal open nucleus plan became less open with increasing female reproduction. Generally, progeny testing of bulls reduced genetic gain (by up to a factor 0·1) but it also reduced inbreeding rates. Progeny testing was more efficient in schemes that were less open: in P-schemes with 41 offspring per dam, progeny testing increased genetic gain. With many offspring per dam there were fewer full-sib families, causing lower selection differentials due to order statistics effects. This effect could be prevented by increasing the size of the nucleus.

scholarly journals Genomic Analysis, Progress and Future Perspectives in Dairy Cattle Selection: A Review

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 599
Author(s):  
Miguel A. Gutierrez-Reinoso ◽  
Pedro M. Aponte ◽  
Manuel Garcia-Herreros
Keyword(s):  
Dairy Cattle ◽  
Production Efficiency ◽  
Association Studies ◽  
Phenotypic Traits ◽  
Progeny Testing ◽  
Genome Wide Association Studies ◽  
Genetic Progress ◽  
Breeding Programs ◽  
Increased Risk ◽  
Selection Of

Genomics comprises a set of current and valuable technologies implemented as selection tools in dairy cattle commercial breeding programs. The intensive progeny testing for production and reproductive traits based on genomic breeding values (GEBVs) has been crucial to increasing dairy cattle productivity. The knowledge of key genes and haplotypes, including their regulation mechanisms, as markers for productivity traits, may improve the strategies on the present and future for dairy cattle selection. Genome-wide association studies (GWAS) such as quantitative trait loci (QTL), single nucleotide polymorphisms (SNPs), or single-step genomic best linear unbiased prediction (ssGBLUP) methods have already been included in global dairy programs for the estimation of marker-assisted selection-derived effects. The increase in genetic progress based on genomic predicting accuracy has also contributed to the understanding of genetic effects in dairy cattle offspring. However, the crossing within inbred-lines critically increased homozygosis with accumulated negative effects of inbreeding like a decline in reproductive performance. Thus, inaccurate-biased estimations based on empirical-conventional models of dairy production systems face an increased risk of providing suboptimal results derived from errors in the selection of candidates of high genetic merit-based just on low-heritability phenotypic traits. This extends the generation intervals and increases costs due to the significant reduction of genetic gains. The remarkable progress of genomic prediction increases the accurate selection of superior candidates. The scope of the present review is to summarize and discuss the advances and challenges of genomic tools for dairy cattle selection for optimizing breeding programs and controlling negative inbreeding depression effects on productivity and consequently, achieving economic-effective advances in food production efficiency. Particular attention is given to the potential genomic selection-derived results to facilitate precision management on modern dairy farms, including an overview of novel genome editing methodologies as perspectives toward the future.

Catalog Design: Design Using Available Assets

1992 ◽  
Author(s):  
S. Vadde ◽  
J. K. Allen ◽  
F. Mistree
Keyword(s):  
Deterministic Model ◽  
Limited Resources ◽  
Complex Task ◽  
Cooling Fluid ◽  
Selection Problems ◽  
Catalog Design ◽  
Standard Framework ◽  
Selection Of ◽  
Do So

Abstract Catalog design is a procedure in which a system is assembled by selecting standard components from catalogs of available components. Selection in design involves making a choice among a number of alternatives taking into account several attributes. The information available to a designer to do so during the early stages of project initiation may be uncertain. The uncertainty in information may be imprecise or stochastic. Under these circumstances, a designer has to balance limited resources against the quality of solution obtained or decisions made by accounting for uncertainty in information available. This complex task becomes formidable when dealing with coupled selection problems, that is problems that should be solved simultaneously. Coupled selection problems share a number of coupling attributes among them. In an earlier paper we have shown how selection problems, both coupled and uncoupled can be reformulated as a single compromise Decision Support Problem (DSP) using a deterministic model. In this paper, we show how the traditional compromise DSP can be extended to represent a nondeterministic case. We use fuzzy set theory to model imprecision and Bayesian statistics to model stochastic information. Formulations that can be solved with the same solution scheme are presented to handle both fuzzy and stochastic information in the standard framework of a compromise DSP. The approaches are illustrated by an example involving the coupled selection of a heat exchanger concept and a cooling fluid for a specific application. The emphasis in this paper is placed on explaining the methods.

scholarly journals Genetic parameters and estimated genetic gains in young rubber tree progenies

2013 ◽  
Vol 48 (4) ◽  
pp. 411-416 ◽  
Author(s):  
Cecília Khusala Verardi ◽  
Erivaldo José Scaloppi Junior ◽  
Guilherme Augusto Peres Silva ◽  
Lígia Regina Lima Gouvêa ◽  
Paulo de Souza Gonçalves
Keyword(s):  
Genetic Gain ◽  
Genetic Parameters ◽  
Block Design ◽  
Rubber Tree ◽  
First Year ◽  
Genetic Gains ◽  
Selection Gain ◽  
Randomized Block Design ◽  
Using Data ◽  
Selection Of

The objective of this work was to assess the genetic parameters and to estimate genetic gains in young rubber tree progenies. The experiments were carried out during three years, in a randomized block design, with six replicates and ten plants per plot, in three representative Hevea crop regions of the state of São Paulo, Brazil. Twenty-two progenies were evaluated, from three to five years old, for rubber yield and annual girth growth. Genetic gain was estimated with the multi-effect index (MEI). Selection by progenies means provided greater estimated genetic gain than selection based on individuals, since heritability values of progeny means were greater than the ones of individual heritability, for both evaluated variables, in all the assessment years. The selection of the three best progenies for rubber yield provided a selection gain of 1.28 g per plant. The genetic gains estimated with MEI using data from early assessments (from 3 to 5-year-old) were generally high for annual girth growth and rubber yield. The high genetic gains for annual girth growth in the first year of assessment indicate that progenies can be selected at the beginning of the breeding program. Population effective size was consistent with the three progenies selected, showing that they were not related and that the population genetic variability is ensured. Early selection with the genetic gains estimated by MEI can be made on rubber tree progenies.

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.

scholarly journals Genomic Selection for Any Dairy Breeding Program via Optimized Investment in Phenotyping and Genotyping

2021 ◽  
Vol 12 ◽  
Author(s):  
Jana Obšteter ◽  
Janez Jenko ◽  
Gregor Gorjanc
Keyword(s):  
Genomic Selection ◽  
Genetic Gain ◽  
Breeding Program ◽  
Progeny Testing ◽  
Initial Training ◽  
Training Population ◽  
Breeding Programs ◽  
Use Of Resources ◽  
Dairy Cattle Breeding ◽  
Close Relatives

This paper evaluates the potential of maximizing genetic gain in dairy cattle breeding by optimizing investment into phenotyping and genotyping. Conventional breeding focuses on phenotyping selection candidates or their close relatives to maximize selection accuracy for breeders and quality assurance for producers. Genomic selection decoupled phenotyping and selection and through this increased genetic gain per year compared to the conventional selection. Although genomic selection is established in well-resourced breeding programs, small populations and developing countries still struggle with the implementation. The main issues include the lack of training animals and lack of financial resources. To address this, we simulated a case-study of a small dairy population with a number of scenarios with equal available resources yet varied use of resources for phenotyping and genotyping. The conventional progeny testing scenario collected 11 phenotypic records per lactation. In genomic selection scenarios, we reduced phenotyping to between 10 and 1 phenotypic records per lactation and invested the saved resources into genotyping. We tested these scenarios at different relative prices of phenotyping to genotyping and with or without an initial training population for genomic selection. Reallocating a part of phenotyping resources for repeated milk records to genotyping increased genetic gain compared to the conventional selection scenario regardless of the amount and relative cost of phenotyping, and the availability of an initial training population. Genetic gain increased by increasing genotyping, despite reduced phenotyping. High-genotyping scenarios even saved resources. Genomic selection scenarios expectedly increased accuracy for young non-phenotyped candidate males and females, but also proven females. This study shows that breeding programs should optimize investment into phenotyping and genotyping to maximize return on investment. Our results suggest that any dairy breeding program using conventional progeny testing with repeated milk records can implement genomic selection without increasing the level of investment.

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