PSXIV-27 Simulation controlling inbreeding for selection experiments on woody breast meat in broiler

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 258-258
Author(s):  
Hye Rin JEON ◽  
Seungjun SHIN ◽  
Sang-Hyon OH
Keyword(s):  
Average Rate ◽  
Additive Genetic Variance ◽  
Selection Response ◽  
Weighting Factor ◽  
Breast Meat ◽  
K Value ◽  
Breeding Values ◽  
Selection Experiments ◽  
Estimated Breeding Values ◽  
The Individual

Abstract The purpose of this study was to control inbreeding for selection experiments on woody breast (WB) meat in broiler. The simulation was designed to figure out which mating plan would show proper breeding values while optimizing inbreeding assuming that a selection study would be done on WB in broilers starting with 500 males and 500 females as a foundational population. The simulated selections were based on Optimum Genetic Contribution theory (OGC) under different conditions over 10 generations, which uses relationships among individuals as weighting factors. It is selecting individuals by weighting estimated breeding values with average relationships among individuals. From the 2nd generation, various selection plans were considered in each sex, which were top 10, 20, 50 and 100 males selected, and top 100 and 200 females selected every generation. Each female bird was assumed to have 10 eggs. The algorithm is as follows: 1) Identify the individual having the best EBV; 2) Calculate average relationships between selected and candidates; 3) Select the individual having the best EBV adjusted for average relationships using the weighting factor k; 4) Repeat process until the number of individuals selected equals number required. Three different weighting values (k=0, 1, 2) were used, which made a total 24 different conditions compared (4×2×3). Additive genetic variance of breast meat was 1.134. Mendelian sampling terms were also considered when the breeding values were generated. Results showed that higher k value (k=2) controlled effectively inbreeding and maintained consistent increases in selection response. Differences in breeding values among selection plans with OGC algorithm and by EBV only was 4% on average; however, average rate of inbreeding (0.1) was controlled by 27% after 10 generations. These results indicate that the OGC algorithm can be used effectively in a short-term selection program with the relatively smaller number of populations.

scholarly journals Combined Multistage Linear Genomic Selection Indices To Predict the Net Genetic Merit in Plant Breeding

2020 ◽  
Vol 10 (6) ◽  
pp. 2087-2101
Author(s):  
J. Jesus Cerón-Rojas ◽  
Jose Crossa
Keyword(s):  
Genomic Selection ◽  
Linear Combination ◽  
Selection Index ◽  
Selection Response ◽  
Single Stage ◽  
Multiple Traits ◽  
Breeding Values ◽  
Genetic Merit ◽  
Estimated Breeding Values ◽  
The Individual

A combined multistage linear genomic selection index (CMLGSI) is a linear combination of phenotypic and genomic estimated breeding values useful for predicting the individual net genetic merit, which in turn is a linear combination of the true unobservable breeding values of the traits weighted by their respective economic values. The CMLGSI is a cost-saving strategy for improving multiple traits because the breeder does not need to measure all traits at each stage. The optimum (OCMLGSI) and decorrelated (DCMLGSI) indices are the main CMLGSIs. Whereas the OCMLGSI takes into consideration the index correlation values among stages, the DCMLGSI imposes the restriction that the index correlation values among stages be zero. Using real and simulated datasets, we compared the efficiency of both indices in a two-stage context. The criteria we applied to compare the efficiency of both indices were that the total selection response of each index must be lower than or equal to the single-stage combined linear genomic selection index (CLGSI) response and that the correlation of each index with the net genetic merit should be maximum. Using four different total proportions for the real dataset, the estimated total OCMLGSI and DCMLGSI responses explained 97.5% and 90%, respectively, of the estimated single-stage CLGSI selection response. In addition, at stage two, the estimated correlations of the OCMLGSI and the DCMLGSI with the net genetic merit were 0.84 and 0.63, respectively. We found similar results for the simulated datasets. Thus, we recommend using the OCMLGSI when performing multistage selection.

scholarly journals A note on genetic parameters and accuracy of estimated breeding values in honey bees

2019 ◽  
Vol 51 (1) ◽  
Author(s):  
Evert W. Brascamp ◽  
Piter Bijma
Keyword(s):  
Honey Bees ◽  
Variance Components ◽  
Genetic Parameters ◽  
Additive Genetic Variance ◽  
Breeding Value ◽  
Phenotypic Variance ◽  
Breeding Values ◽  
Estimated Breeding Values ◽  
Additive Genetic Relationship ◽  
The Relationship

Abstract Background In honey bees, observations are usually made on colonies. The phenotype of a colony is affected by the average breeding value for the worker effect of the thousands of workers in the colony (the worker group) and by the breeding value for the queen effect of the queen of the colony. Because the worker group consists of multiple individuals, interpretation of the variance components and heritabilities of phenotypes observed on the colony and of the accuracy of selection is not straightforward. The additive genetic variance among worker groups depends on the additive genetic relationship between the drone-producing queens (DPQ) that produce the drones that mate with the queen. Results Here, we clarify how the relatedness between DPQ affects phenotypic variance, heritability and accuracy of the estimated breeding values of replacement queens. Second, we use simulation to investigate the effect of assumptions about the relatedness between DPQ in the base population on estimates of genetic parameters. Relatedness between DPQ in the base generation may differ considerably between populations because of their history. Conclusions Our results show that estimates of (co)variance components and derived genetic parameters were seriously biased (25% too high or too low) when assumptions on the relationship between DPQ in the statistical analysis did not agree with reality.

Selection on purebred and crossbred Performance for litter size in pigs*

2000 ◽  
Vol 43 (3) ◽  
pp. 249-262 ◽  
Author(s):  
M. Bösch ◽  
R. Röhe ◽  
H. Looft ◽  
E. Kalm
Keyword(s):  
Genetic Correlation ◽  
Litter Size ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Genetic Parameter ◽  
Selection Method ◽  
Data Sets ◽  
Phenotypic Variance ◽  
Breeding Values ◽  
Estimated Breeding Values

Abstract. The present study deals with estimation of genetic parameter for purebred and crossbred Performance of live born piglets, in order to choose the optimal selection method. Data sets of two pure breeds, line L03 and L04, with 5,422 sows, a two line crossbred, L303, with 3,553 sows and a three line crossbred, L350, with 3,609 sows of a North-German breeding Company were recorded. Estimated genetic correlation between purebred and crossbred Performance were rg = 0.59 and 0.40 for reciprocal crosses L03xL04 and L04xL03, respectively. Further investigations showed that the genetic correlation is influenced by genotype-environment interactions between a nucleus farm and a farm on production level. Full-sib effects showed a proportion of FS = 0.06 on the phenotypic variance of litter size. They were confounded with additive genetic variance and permanent environment variance, when full-sib effects were neglected. The percentage of equal selected purebred sires of line L03 were 80% when 30% of the sires selected on purebred or crossbred breeding values. Accuracy of estimated breeding values of purebred sires increased when crossbred Information were considered additionally from 0.32 to 0.38 for line L03 and 0.46 to 0.47 for line L04. Genetic correlation between purebred and crossbred Performance, the genetic connectedness between nucleus and production and the presence of genotype-environment interactions were analysed to have high influence on the value of additionally considered crossbred Performance.

Joint estimation of (co) variance components and breeding values for mean and dispersion of days from calving to first service in Holstein cow

2017 ◽  
Vol 57 (4) ◽  
pp. 760 ◽  
Author(s):  
Heydar Ghiasi ◽  
Majbritt Felleki
Keyword(s):  
Standard Deviation ◽  
Genetic Correlation ◽  
Additive Genetic Variance ◽  
Joint Estimation ◽  
Breeding Value ◽  
Genetic Trend ◽  
Breeding Values ◽  
The Mean ◽  
Estimated Breeding Values ◽  
Genetic Standard Deviation

The present study explored the possibility of selection for uniformity of days from calving to first service (DFS) in dairy cattle. A double hierarchical generalised linear model with an iterative reweighted least-squares algorithm was used to estimate covariance components for the mean and dispersion of DFS. Data included the records of 27 113 Iranian Holstein cows (parity, 1–6) in 15 herds from 1981 to 2007. The estimated additive genetic variance for the mean and dispersion were 32.25 and 0.0139; both of these values had low standard errors. The genetic standard deviation for dispersion of DFS was 0.117, indicating that decreasing the estimated breeding value of dispersion by one genetic standard deviation can increase the uniformity by 12%. A strong positive genetic correlation (0.689) was obtained between the mean and dispersion of DFS. This genetic correlation is favourable since one of the aims of breeding is to simultaneously decrease the mean and increase the uniformity of DFS. The Spearman rank correlations between estimated breeding values in the mean and dispersion for sires with a different number of daughter observations were 0.907. In the studied population, the genetic trend in the mean of DFS was significant and favourable (–0.063 days/year), but the genetic trend in the dispersion of DFS was not significantly different from zero. The results obtained in the present study indicated that the mean and uniformity of DFS can simultaneously be improved in dairy cows.

Responses to selection for growth and backfat in closed nucleus herds of Hampshire and Duroc pigs

2001 ◽  
Vol 81 (1) ◽  
pp. 17-23 ◽  
Author(s):  
J. P. Gibson ◽  
V. M. Quinton ◽  
P. Simedrea
Keyword(s):  
Selection Index ◽  
High Standard ◽  
Selection Response ◽  
Genetic Trend ◽  
Genetic Progress ◽  
Breeding Values ◽  
Frozen Semen ◽  
Key Words Pig ◽  
Breeding Selection ◽  
Estimated Breeding Values

A herd of purebred Hampshire and a herd of purebred Duroc pigs were created between 1987 and 1989 and subsequently selected on an index of growth rate and backfat from 1989 to 1995. Strict rules were put in place to promote rapid turnover of generations and allow only minimal culling for structural soundness. In 1990, 1991, 1993 and 1994, a number of sows coming from the Hampshire and Duroc selection lines were bred using frozen semen collected from a sample of control boars born in 1988 and 1989. Estimates of genetic trend based on single trait animal model analyses of age at 100 kg and backfat estimated rates of response to selection of approximately –1.4 and –0.4 days and –0.5 and –0.3 mm backfat per annum in Durocs and Hampshires, respectively. These were close to the original predictions based on the index employed for Durocs, but somewhat less than expected for Hampshires. Comparison of progeny of selected versus control boars was consistent with significant selection responses having been achieved, though estimates of the magnitude of the response had high standard errors. During the period of the trial, both the Hampshire and Duroc herds went from slightly below average on the Canadian National Genetic Evaluation Program to become the number one herds within their breed for the sire line index based on backfat and growth. The trial demonstrated that selection indexes based on estimated breeding values can be used effectively to achieve genetic progress. Key words: Pig breeding, selection index, estimated breeding values, selection response

scholarly journals Validation of single-step genomic predictions using the linear regression method for milk yield and heat tolerance in a Thai-Holstein population

2021 ◽  
pp. 3119-3125
Author(s):  
Piriyaporn Sungkhapreecha ◽  
Ignacy Misztal ◽  
Jorge Hidalgo ◽  
Daniela Lourenco ◽  
Sayan Buaban ◽  
...  
Keyword(s):  
Linear Regression ◽  
Milk Yield ◽  
Heat Tolerance ◽  
Selection Response ◽  
Single Step ◽  
Linear Regression Method ◽  
Genomic Information ◽  
Phenotypic Data ◽  
Breeding Values ◽  
Estimated Breeding Values

Background and Aim: Genomic selection improves accuracy and decreases the generation interval, increasing the selection response. This study was conducted to assess the benefits of using single-step genomic best linear unbiased prediction (ssGBLUP) for genomic evaluations of milk yield and heat tolerance in Thai-Holstein cows and to test the value of old phenotypic data to maintain the accuracy of predictions. Materials and Methods: The dataset included 104,150 milk yield records collected from 1999 to 2018 from 15,380 cows. The pedigree contained 33,799 animals born between 1944 and 2016, of which 882 were genotyped. Analyses were performed with and without genomic information using ssGBLUP and BLUP, respectively. Statistics for bias, dispersion, the ratio of accuracies, and the accuracy of estimated breeding values were calculated using the linear regression (LR) method. A partial dataset excluded the phenotypes of the last generation, and 66 bulls were identified as validation individuals. Results: Bias was considerable for BLUP (0.44) but negligible (–0.04) for ssGBLUP; dispersion was similar for both techniques (0.84 vs. 1.06 for BLUP and ssGBLUP, respectively). The ratio of accuracies was 0.33 for BLUP and 0.97 for ssGBLUP, indicating more stable predictions for ssGBLUP. The accuracy of predictions was 0.18 for BLUP and 0.36 for ssGBLUP. Excluding the first 10 years of phenotypic data (i.e., 1999-2008) decreased the accuracy to 0.09 for BLUP and 0.32 for ssGBLUP. Genomic information doubled the accuracy and increased the persistence of genomic estimated breeding values when old phenotypes were removed. Conclusion: The LR method is useful for estimating accuracies and bias in complex models. When the population size is small, old data are useful, and even a small amount of genomic information can substantially improve the accuracy. The effect of heat stress on first parity milk yield is small.

scholarly journals A SIMULATION STUDY OF TRUNCATION SELECTION FOR A QUANTITATIVE TRAIT OPPOSED BY NATURAL SELECTION

Genetics ◽  
1980 ◽  
Vol 94 (4) ◽  
pp. 989-1000
Author(s):  
Francis Minvielle
Keyword(s):  
Natural Selection ◽  
Artificial Selection ◽  
Random Mating ◽  
Selection Response ◽  
Quantitative Character ◽  
Mass Selection ◽  
Frequency Selection ◽  
Selection Experiments ◽  
Selection For ◽  
Selective Process

ABSTRACT A quantitative character controlled at one locus with two alleles was submitted to artificial (mass) selection and to three modes of opposing natural selection (directional selection, overdominance and underdominance) in a large random-mating population. The selection response and the limits of the selective process were studied by deterministic simulation. The lifetime of the process was generally between 20 and 100 generations and did not appear to depend on the mode of natural selection. However, depending on the values of the parameters (initial gene frequency, selection intensity, ratio of the effect of the gene to the environmental standard deviation, fitness values) the following outcomes of selection were observed: fixation of the allele favored by artificial selection, stable nontrivial equilibrium, unstable equilibrium and loss of the allele favored by artificial selection. Finally, the results of the simulation were compared to the results of selection experiments.

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