scholarly journals Mendelian sampling terms as a selective advantage in optimum breeding schemes with restrictions on the rate of inbreeding

2004 ◽  
Vol 83 (1) ◽  
pp. 55-64 ◽  
Author(s):  
S. AVENDAÑO ◽  
J. A. WOOLLIAMS ◽  
B. VILLANUEVA
Keyword(s):  
Genetic Variation ◽  
Genetic Gain ◽  
Selective Advantage ◽  
Breeding Values ◽  
Truncation Selection ◽  
Joint Management ◽  
Breeding Schemes ◽  
Estimated Breeding Values ◽  
Available Information ◽  
Mendelian Sampling

Quadratic indices are a general approach for the joint management of genetic gain and inbreeding in artificial selection programmes. They provide the optimal contributions that selection candidates should have to obtain the maximum gain when the rate of inbreeding is constrained to a predefined value. This study shows that, when using quadratic indices, the selective advantage is a function of the Mendelian sampling terms. That is, at all times, contributions of selected candidates are allocated according to the best available information about their Mendelian sampling terms (i.e. about their superiority over their parental average) and not on their breeding values. By contrast, under standard truncation selection, both estimated breeding values and Mendelian sampling terms play a major role in determining contributions. A measure of the effectiveness of using genetic variation to achieve genetic gain is presented and benchmark values of 0·92 for quadratic optimisation and 0·5 for truncation selection are found for a rate of inbreeding of 0·01 and a heritability of 0·25.

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.

scholarly journals AlphaPart—R implementation of the method for partitioning genetic trends

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Jana Obšteter ◽  
Justin Holl ◽  
John M. Hickey ◽  
Gregor Gorjanc
Keyword(s):  
Genetic Gain ◽  
R Package ◽  
Breeding Programme ◽  
Genetic Trend ◽  
Breeding Values ◽  
Poultry Breeding ◽  
Breeding Programmes ◽  
Estimated Breeding Values ◽  
The Relationship ◽  
Genetic Trends

Abstract Background In this paper, we present the AlphaPart R package, an open-source implementation of a method for partitioning breeding values and genetic trends to identify the contribution of selection pathways to genetic gain. Breeding programmes improve populations for a set of traits, which can be measured with a genetic trend calculated from estimated breeding values averaged by year of birth. While sources of the overall genetic gain are generally known, their realised contributions are hard to quantify in complex breeding programmes. The aim of this paper is to present the AlphaPart R package and demonstrate it with a simulated stylized multi-tier breeding programme mimicking a pig or poultry breeding programme. Results The package includes the main partitioning function AlphaPart, that partitions the breeding values and genetic trends by pre-defined selection paths, and a set of functions for handling data and results. The package is freely available from the CRAN repository at http://CRAN.R-project.org/package=AlphaPart. We demonstrate the use of the package by partitioning the nucleus and multiplier genetic gain of the stylized breeding programme by tier-gender paths. For traits measured and selected in the multiplier, the multiplier selection generated additional genetic gain. By using AlphaPart, we show that the additional genetic gain depends on accuracy and intensity of selection in the multiplier and the extent of gene flow from the nucleus. We have proven that AlphaPart is a valuable tool for understanding the sources of genetic gain in the nucleus and especially the multiplier, and the relationship between the sources and parameters that affect them. Conclusions AlphaPart implements the method for partitioning breeding values and genetic trends and provides a useful tool for quantifying the sources of genetic gain in breeding programmes. The use of AlphaPart will help breeders to improve genetic gain through a better understanding of the key selection points that are driving gains in each trait.

Effect of controlling future rate of inbreeding on expected genetic gain and genetic variability in small livestock populations

2020 ◽  
Vol 60 (14) ◽  
pp. 1681
Author(s):  
S. I. Mwangi ◽  
T. K. Muasya ◽  
E. D. Ilatsia ◽  
A. K. Kahi
Keyword(s):  
Genetic Variability ◽  
Milk Yield ◽  
Genetic Gain ◽  
Genetic Relationships ◽  
Breeding Population ◽  
Breeding Values ◽  
Sahiwal Cows ◽  
Improvement Programs ◽  
Small Livestock ◽  
Estimated Breeding Values

Context In the present study we assessed the use of average relationship as a means to control future rates of inbreeding in small cattle closed nucleus and its effect on genetic gain for milk yield as a means of managing genetic variability in livestock improvement programs. Aim The aim was to strike an ideal balance between genetic gain and loss of genetic variability for Sahiwal population. Methods A total of 8452 milk yield records of Sahiwal cows from National Sahiwal Stud, Kenya, were used to estimate breeding values and 19315 records used to estimate average relatedness of all individuals. The estimated breeding values and genetic relationships were then used to optimise individual genetic contributions between the best two males and the top 210 females in 2000–2008-year group, as well as between the best four, six and eight males and top, 420, 630 and 840 females based on estimated breeding values for lactation milk yield. Weights on genetic merit and average relationship considered in this study were (1, 0), (1, −300), (1, −500), (1, −1000) and (0, −1). Key results When the best sires were selected and used for mating disregarding average relationship with their mates i.e. (0, –1), genetic gain of up to 213 kg was realised accompanied by a rate of inbreeding per generation of 4%. Restricting average relationship alone i.e. (0, –1), resulted in a future rate of inbreeding of 1.6% and average merit of 154 when top two sires were used for breeding. At the same restriction level but using eight top sires, the rate of inbreeding per generation was 0.9% accompanied by an average merit of 128.2 kg. Controlling average relationship between mates resulted in increased genetic variability i.e. lower rate of inbreeding though average merit declined. Conclusion A rate of inbreeding per generation of <1% is required for a population to maintain its long-term viability. For this level to be attained, the size of the breeding population should be increased from the current two sires vs 210 dams to eight sires vs 840 dams. Implications Practical implications for closed nucleus programs such as the Sahiwal program in Kenya should include expanding the nucleus to comprise other institutional and privately-owned herds.

Metabolic differences in Angus steers divergently selected for residual feed intake

2004 ◽  
Vol 44 (5) ◽  
pp. 441 ◽  
Author(s):  
E. C. Richardson ◽  
R. M. Herd ◽  
J. A. Archer ◽  
P. F. Arthur
Keyword(s):  
Genetic Variation ◽  
Feed Intake ◽  
Aspartate Aminotransferase ◽  
Dry Matter ◽  
Residual Feed Intake ◽  
Breeding Values ◽  
Dry Matter Digestibility ◽  
Total Plasma Protein ◽  
Estimated Breeding Values ◽  
Animal House

Residual feed intake measures variation in feed intake independent of liveweight and liveweight gain. First generation steer progeny (n = 33) of parents previously selected for low or high post-weaning residual feed intake were examined to determine metabolic processes contributing to variation in residual feed intake. Blood samples were taken from the steers from weaning through to slaughter. These samples were analysed for key metabolites and hormones. Total urine and total faecal collections were taken from the steers in an animal-house experiment to estimate dry matter digestibility, microbial protein production and protein turnover. At weaning, there were phenotypic correlations between concentrations in plasma of β-hydroxy butyrate (r = 0.55, P<0.001), aspartate aminotransferase (r = 0.34; P<0.001), urea (r = 0.26, P<0.1) and total plasma protein (r = 0.26, P<0.1), and subsequent residual feed intake over the whole experiment (feedlot plus animal-house phases), but no evidence of associations with genetic variation in residual feed intake. At the start of the feedlot residual feed intake test period plasma levels of glucose, creatinine and aspartate aminotransferase were correlated with residual feed intake over the experiment (r = 0.40, –0.45 and 0.43, respectively, P<0.05), providing evidence of phenotypic associations with residual feed intake, and concentrations of urea and triglycerides were correlated with sire estimated breeding values for residual feed intake (b = 1.20 and –0.08, respectively, P<0.05), providing evidence for genetic associations with residual feed intake. At the end of the experiment, concentrations of plasma insulin, cortisol and leptin were correlated with residual feed intake over the experiment (r = 0.43, –0.40 and 0.31, respectively, P<0.05). Plasma concentrations of urea, insulin and cortisol illustrated trends for an association with sire estimated breeding values for RFI (b = –0.35, 0.98 and 12.19, respectively, P<0.1). The ratio of allantoin : creatinine in urine, as a measure of rumen microbial production, tended to be correlated with residual feed intake in the animal house (r�=�0.32, P<0.1) but not with residual feed intake over the entire experiment (r = 0.10, P>0.05). Neither the ratio of 3-methyl histidine : creatinine in urine, as a measure of rate of muscle breakdown, nor the dry matter digestibility measured in the animal house were correlated with residual feed intake in the animal house (r = 0.04, P>0.05), or residual feed intake over the whole experiment (r = –0.22, P>0.05), and neither were associated with genetic variation in residual feed intake.It is hypothesised that high-RFI (low-efficiency) steers have higher tissue energy requirements, are more susceptible to stress and utilise different tissue substrates (partly as a consequence of differences in body composition) to generate energy required in response to exposure to a stressful stimulus.

scholarly journals AlphaPart - R implementation of the method for partitioning genetic trends

2020 ◽  
Author(s):  
Jana Obšteter ◽  
Justin Holl ◽  
John M. Hickey ◽  
Gregor Gorjanc
Keyword(s):  
Gene Flow ◽  
Open Source ◽  
Open Source Software ◽  
Genetic Gain ◽  
R Package ◽  
Genetic Trend ◽  
Breeding Values ◽  
Breeding Programmes ◽  
Estimated Breeding Values ◽  
Genetic Trends

AbstractBackgroundIn this paper we present the AlphaPart R package, an open-source software that implements a method for partitioning breeding values and genetic trends to identify sources of genetic gain. Breeding programmes improve populations for a set of traits, which can be measured with a genetic trend calculated from averaged year of birth estimated breeding values of selection candidates. While sources of the overall genetic gain are generally known, their realised contributions are hard to quantify in complex breeding programmes. The aim of this paper is to present the AlphaPart R package and demonstrate it with a simulated pig breeding example.ResultsThe package includes the main partitioning function AlphaPart, that partitions the breeding values and genetic trends by analyst defined paths, and a set of functions for handling data and results. The package is freely available from CRAN repository at http://CRAN.R-project.org/package=AlphaPart. We demonstrate the use of the package by examining the genetic gain in a pig breeding example, in which the multiplier achieved higher breeding values than the nucleus for traits measured and selected in the multiplier. The partitioning analysis revealed that these higher values depended on the accuracy and intensity of selection in the multiplier and the extent of gene flow from the nucleus. For traits measured only in the nucleus, the multiplier achieved comparable or smaller genetic gain than the nucleus depending on the amount of gene flow.ConclusionsAlphaPart implements a method for partitioning breeding values and genetic trends and provides a useful tool for quantifying the sources of genetic gain in breeding programmes. The use of AlphaPart will help breeders to better understand or improve their breeding programmes.

scholarly journals Identification of superior parental lines for biparental crossing via genomic prediction

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243159
Author(s):  
Ping-Yuan Chung ◽  
Chen-Tuo Liao
Keyword(s):  
Genetic Gain ◽  
Genomic Prediction ◽  
Genomic Diversity ◽  
Breeding Value ◽  
Selection Strategy ◽  
Parental Selection ◽  
Breeding Values ◽  
Truncation Selection ◽  
Selection Approach ◽  
Parental Lines

A parental selection approach based on genomic prediction has been developed to help plant breeders identify a set of superior parental lines from a candidate population before conducting field trials. A classical parental selection approach based on genomic prediction usually involves truncation selection, i.e., selecting the top fraction of accessions on the basis of their genomic estimated breeding values (GEBVs). However, truncation selection inevitably results in the loss of genomic diversity during the breeding process. To preserve genomic diversity, the selection of closely related accessions should be avoided during parental selection. We thus propose a new index to quantify the genomic diversity for a set of candidate accessions, and analyze two real rice (Oryza sativa L.) genome datasets to compare several selection strategies. Our results showed that the pure truncation selection strategy produced the best starting breeding value but the least genomic diversity in the base population, leading to less genetic gain. On the other hand, strategies that considered only genomic diversity resulted in greater genomic diversity but less favorable starting breeding values, leading to more genetic gain but unsatisfactorily performing recombination inbred lines (RILs) in progeny populations. Among all strategies investigated in this study, compromised strategies, which considered both GEBVs and genomic diversity, produced the best or second-best performing RILs mainly because these strategies balance the starting breeding value with the maintenance of genomic diversity.

scholarly journals Genetic gain of pure line selection and combined crossbred purebred selection with constrained inbreeding

2001 ◽  
Vol 72 (2) ◽  
pp. 225-232 ◽  
Author(s):  
P. Bijma ◽  
J.A. Woolliams ◽  
J.A.M. van Arendonk
Keyword(s):  
Genetic Gain ◽  
Selection Index ◽  
Computing Time ◽  
Genetic Correlations ◽  
Pure Line ◽  
Selection Strategies ◽  
Line Selection ◽  
Breeding Schemes ◽  
Estimated Breeding Values ◽  
Genetic Contributions

AbstractUsing deterministic methods, rates of genetic gain (Δ G) and inbreeding (Δ F) were compared between pure line selection (PLS) and combined crossbred purebred selection (CCPS), for the sire line of a three-way crossbreeding scheme. Purebred performance and crossbred performance were treated as genetically correlated traits assuming the infinitesimal model. Breeding schemes were compared at a fixed total number of purebred selection candidates, i.e. including crossbred information did not affect the size of the purebred nucleus. Selection was by truncation on estimated breeding values for crossbred performance. Rates of genetic gain were predicted using a pseudo-BLUP selection index. Rates of inbreeding were predicted using recently developed methods based on long-term genetic contributions. Results showed that changing from PLS to CCPS may increase ΔF by a factor of 2·14. In particular with high heritabilities and low purebred-crossbred genetic correlations, CCPS requires a larger number of parents than PLS, to avoid excessive ΔF. The superiority of CCPS over PLS was judged by comparing ΔG from both selection strategies at the same ΔF. At the same ΔF, CCPS was superior to PLS and the superiority of CCPS was only moderately reduced compared with the situation without a restriction on ΔF. This paper shows that the longterm genetic contribution theory can be used to balance ΔF and ΔG in animal breeding schemes within very limited computing time.

scholarly journals Pre-selection against a lethal recessive allele in breeding schemes with optimum-contribution selection or truncation selection

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Line Hjortø ◽  
Mark Henryon ◽  
Huiming Liu ◽  
Peer Berg ◽  
Jørn Rind Thomasen ◽  
...  
Keyword(s):  
Genetic Gain ◽  
Recessive Allele ◽  
Selection Strategy ◽  
Carrier Status ◽  
Truncation Selection ◽  
Selection Strategies ◽  
Significant Difference ◽  
Selection Step ◽  
Breeding Schemes ◽  
Male Carriers

Abstract Background We tested the hypothesis that breeding schemes with a pre-selection step, in which carriers of a lethal recessive allele (LRA) were culled, and with optimum-contribution selection (OCS) reduce the frequency of a LRA, control rate of inbreeding, and realise as much genetic gain as breeding schemes without a pre-selection step. Methods We used stochastic simulation to estimate true genetic gain realised at a 0.01 rate of true inbreeding (ΔFtrue) by breeding schemes that combined one of four pre-selection strategies with one of three selection strategies. The four pre-selection strategies were: (1) no carriers culled, (2) male carriers culled, (3) female carriers culled, and (4) all carriers culled. Carrier-status was known prior to selection. The three selection strategies were: (1) OCS in which $$\Delta {\text{F}}_{{{\text{true}}}}$$ Δ F true was predicted and controlled using pedigree relationships (POCS), (2) OCS in which $$\Delta {\text{F}}_{{{\text{true}}}}$$ Δ F true was predicted and controlled using genomic relationships (GOCS), and (3) truncation selection of parents. All combinations of pre-selection strategies and selection strategies were tested for three starting frequencies of the LRA (0.05, 0.10, and 0.15) and two linkage statuses with the locus that has the LRA being on a chromosome with or without loci affecting the breeding goal trait. The breeding schemes were simulated for 10 discrete generations (t = 1, …, 10). In all breeding schemes, ΔFtrue was calibrated to be 0.01 per generation in generations t = 4, …, 10. Each breeding scheme was replicated 100 times. Results We found no significant difference in true genetic gain from generations t = 4, …, 10 between breeding schemes with or without pre-selection within selection strategy. POCS and GOCS schemes realised similar true genetic gains from generations t = 4, …, 10. POCS and GOCS schemes realised 12% more true genetic gain from generations t = 4, …, 10 than truncation selection schemes. Conclusions We advocate for OCS schemes with pre-selection against the LRA that cause animal suffering and high costs. At LRA frequencies of 0.10 or lower, OCS schemes in which male carriers are culled reduce the frequency of LRA, control rate of inbreeding, and realise no significant reduction in true genetic gain compared to OCS schemes without pre-selection against LRA.

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