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.

scholarly journals Genomics and breeding innovations for enhancing genetic gain for climate resilience and nutrition traits

2021 ◽  
Author(s):  
Pallavi Sinha ◽  
Vikas K. Singh ◽  
Abhishek Bohra ◽  
Arvind Kumar ◽  
Jochen C. Reif ◽  
...  
Keyword(s):  
Statistical Methods ◽  
Genetic Gain ◽  
Crop Improvement ◽  
Breeding Population ◽  
Breeding Cycle ◽  
Climate Resilience ◽  
Heritability Estimation ◽  
Improvement Programs ◽  
Statistical Designs ◽  
Novel Alleles

Abstract Key message Integrating genomics technologies and breeding methods to tweak core parameters of the breeder’s equation could accelerate delivery of climate-resilient and nutrient rich crops for future food security. Abstract Accelerating genetic gain in crop improvement programs with respect to climate resilience and nutrition traits, and the realization of the improved gain in farmers’ fields require integration of several approaches. This article focuses on innovative approaches to address core components of the breeder’s equation. A prerequisite to enhancing genetic variance (σ2g) is the identification or creation of favorable alleles/haplotypes and their deployment for improving key traits. Novel alleles for new and existing target traits need to be accessed and added to the breeding population while maintaining genetic diversity. Selection intensity (i) in the breeding program can be improved by testing a larger population size, enabled by the statistical designs with minimal replications and high-throughput phenotyping. Selection priorities and criteria to select appropriate portion of the population too assume an important role. The most important component of breeder′s equation is heritability (h2). Heritability estimates depend on several factors including the size and the type of population and the statistical methods. The present article starts with a brief discussion on the potential ways to enhance σ2g in the population. We highlight statistical methods and experimental designs that could improve trait heritability estimation. We also offer a perspective on reducing the breeding cycle time (t), which could be achieved through the selection of appropriate parents, optimizing the breeding scheme, rapid fixation of target alleles, and combining speed breeding with breeding programs to optimize trials for release. Finally, we summarize knowledge from multiple disciplines for enhancing genetic gains for climate resilience and nutritional traits.

scholarly journals Comparison of BLUP and Bayesian methods for different sizes of training population in genomic selection

2020 ◽  
Vol 44 (5) ◽  
pp. 994-1002
Author(s):  
Samet Hasan ABACI ◽  
Hasan ÖNDER
Keyword(s):  
Genomic Selection ◽  
Milk Yield ◽  
Predictive Ability ◽  
Breeding Value ◽  
Training Population ◽  
Breeding Values ◽  
Value Prediction ◽  
The Usa ◽  
Population Sizes ◽  
Estimated Breeding Values

This study aims to compare the accuracy of pedigree-based and genomic-based breeding value prediction for different training population sizes. In this study, Bayes (A, B, C, Cpi) and GBLUP methods for genomic selection and BLUP method for pedigree-based selection were used. Genomic and pedigree-based breeding values were estimated for partial milk yield (158 days) of Holstein cows (400 individuals) from a private enterprise in the USA. For this aim, populations were created for indirect breeding value estimates as training (322–360) and test (78–40) populations. In animals genotyped with a 54k SNP, the marker file was encoded as –10, 0, and 10 for AA, AB, and BB marker genotypes, respectively. Bayes and GBLUP methods were performed using GenSel 4.55 software. A total of 50,000 iterations were used, with the first 5000 excluded as the burn-in. Pedigree-based breeding values were estimated by REML using MTDFREML software employing an animal model. Correlations between partial milk yield and estimated breeding values were used to assess the predictive ability for methods. Bayes B method gave the highest accuracy for the indirect estimate of breeding value.

Stratified sublining: a new option for structuring breeding populations

2000 ◽  
Vol 30 (4) ◽  
pp. 596-604 ◽  
Author(s):  
Seppo Ruotsalainen ◽  
Dag Lindgren
Keyword(s):  
Genetic Gain ◽  
Breeding Population ◽  
Random Allocation ◽  
Genetic Quality ◽  
Seed Orchards ◽  
Breeding Values ◽  
Breeding Populations ◽  
Genetic Value ◽  
Interesting Alternative

When structuring a breeding population into sublines, the conventional approach is to assign parents to sublines randomly, so that each subline has approximately the same genetic value. By using deterministic infinitesimal model we study an alternative, stratified sublining system, where sublines are initially formed by positive assortative grouping of parents according to their breeding values. Stratified and random allocation to sublines are compared by evaluating the genetic quality of the seed orchards that each approach can provide. The seed orchards were established by selecting first the best individual from each subline and then a given best proportion from them. The greater among-subline variance in stratified sublining led to higher genetic gain in resulting seed orchards than did random sublining. For the case studied, stratified sublining gave considerably more genetic gain than random sublining, over 15% more, making it an interesting alternative that deserves further consideration and study.

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.

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.

EVALUATION OF DAIRY CATTLE FOR FINAL CLASS USING ANIMAL MODELS

1988 ◽  
Vol 68 (3) ◽  
pp. 639-645 ◽  
Author(s):  
J. JAMROZIK ◽  
L. R. SCHAEFFER
Keyword(s):  
Animal Models ◽  
Dairy Cattle ◽  
Fixed Effects ◽  
Genetic Relationships ◽  
Residual Effects ◽  
Breeding Values ◽  
Stage Of Lactation ◽  
Key Words Animal ◽  
Estimated Breeding Values ◽  
Genetic Evaluations

Estimated breeding values for final class of 364 868 Canadian Holstein Friesian cows and 10 186 bulls from three different animal models were compared. FIRST lactation, first classifications were described by a model with fixed effects of herd-round-classifier, linear and quadratic effects of age at calving and stage of lactation at classification, and random effects of additive genetic effects of cows, and residual effects. All additive genetic relationships among animals were included. A second model used the LATEST classification on each cow rather than the first and these observations were pre-adjusted for age and stage. The third model used ALL classifications on each cow, and these were also pre-adjusted for age and stage effects. Correlations among estimated breeding values between methods ranged from 0.92 to 0.95. Estimated breeding values from LATEST were most highly correlated to sire proofs from the currently official sire model which also used the latest classification of each cow. Correlations of estimated breeding values between sires and their sons showed that results from LATEST were more accurate than results from the other two models. A model similar to that for LATEST is proposed for official genetic evaluations for conformation in the Canadian Holstein population. Key words: Animal model, conformation, dairy cattle

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 Genetic and permanent environmental variations in daily milk yield and milk flow rates in Hungarian Holstein Friesian

2007 ◽  
Vol 50 (6) ◽  
pp. 535-548
Author(s):  
A. A. Amin
Keyword(s):  
Milk Yield ◽  
Genetic Correlations ◽  
Random Regression ◽  
Lactation Performance ◽  
Flow Rates ◽  
Breeding Values ◽  
Milk Flow ◽  
Holstein Friesian ◽  
Estimated Breeding Values ◽  
Daily Milk Yield

Abstract. Random regression animal model was applied for analyzing the relationships between test-day milk yields (DY), and milk flow rate (FR). The current study involved 169,491 sample test-day records of Hungarian Holstein- Friesian cows. A quadratic random regression was applied for declaring additive genetic variances in all studied traits during biweekly observations across the first three parities. Estimates of heritability for test-day milk yield and udder milk flow rates ranged from 0.09 to 0.58 and from 0.02 to 0.50, respectively through 42 milk-weeks (Wk). The highest heritability estimates occurred during the end of trajectory for both traits. In general DY tended to be more heritable than FR across lactation except during the first few weeks of lactation. Performance of DY was less affected by environmental variation than FR, while both values were moderate to high (0.63 to 0.75). Correlations among measurements showed that additive correlations (Ra) of 4WkFR with the reminder part of lactation were high during early and late lactation. Also 24WkFR was more genetically correlated with next measures and reached Ra = 0.94. Whereas 42WkFR was high additively correlated with other biweekly measurements and ranged from 0.53 to 0.99. Performance of early and late DY was negative additively correlated and ranged from −0.03 to −0.53. Heritability of DY within levels of FR ranged from 0.09 to 0.33 within very slow and medium milk flow, respectively. Correlations among both traits increased linearly toward lactation end. DY during 24Week and 42Week of lactation accounted the highest additive correlations with FR across lactation. Estimated breeding values for DY and FR increased in different rates with progressing lactation. These results may indicate that individual selection results would be favorably achieved during the late part of lactation. More details about estimates of breeding values, estimates of permanent environmental and additive genetic correlations for all traits were tabulated.

scholarly journals Implementation of an Economic Lifetime Net Merit for the Dual-Purpose German Black Pied Cattle Breed

Agriculture ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 41
Author(s):  
Saskia Meier ◽  
Danny Arends ◽  
Paula Korkuć ◽  
Sandra Kipp ◽  
Dierck Segelke ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Dairy Cattle ◽  
Milk Yield ◽  
Cattle Breed ◽  
Breeding Value ◽  
Dual Purpose ◽  
Breeding Values ◽  
Fitness Traits ◽  
Milk Performance ◽  
Estimated Breeding Values

Recently, a Total Merit Index (RZ€) has been developed for German Holstein dairy cattle on the basis of margin in Euro. Our aim was to adjust this lifetime net merit for the dual-purpose German Black Pied cattle breed (DSN) accounting for beef production in addition to milk performance and fitness traits. We used the estimated breeding values of DSN sires and developed a breeding value for carcass weight and quality. Furthermore, we adjusted the German Holstein marginal profits per standard deviation, which are used to calculate the estimated breeding values, to DSN-specific values. The DSN Net Merit is the sum of the three sub-indices DSN Net Milk, DSN Net Fitness, and DSN Net Beef, which contribute to the DSN Net Merit with 52.84%, 43.43%, and 3.73%, respectively. The DSN Net Merit that was calculated for 33 DSN sires ranged between EUR −1114 and +709. The DSN Net Merit strongly correlates with the Total Merit Index. The implementation of the DSN Net Merit is useful for selection and mating decisions. Especially, the sub-index DSN Net Beef, which does not correlate with existing breeding values, can be used to maintain the dual-purpose character of DSN while modestly improving milk yield. The approach can be easily adapted to other dual-purpose breeds.

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