Effect of manipulating recombination rates on response to selection in livestock breeding programs

There is an increasing concern about losing genetic diversity in farm animals, and poultry genetic resources are considered to be one of the most endangered (Crawford, 1990). A large number of local dual-purpose breeds used at the beginning of the last century have been replaced with highly specialised lines. Market orientated intensive livestock breeding programs tend to concentrate on just a limited number of breeds, and the proportion of low-input, low-output breeds used in agricultural production in developed countries has been decreased almost to zero. Decreasing numbers of breeds results in reduced genetic variability, and limits the flexibility of future breeding programs. On the other hand, an increase in income in these countries leads to a rise in demands for specialised food, diversification in the product supply, and changes in preferences of production conditions.

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A Strategy To Exploit Surrogate Sire Technology in Livestock Breeding Programs

G3 Genes|Genome|Genetics ◽

10.1534/g3.118.200890 ◽

2018 ◽

Vol 9 (1) ◽

pp. 203-215 ◽

Cited By ~ 8

Author(s):

Paolo Gottardo ◽

Gregor Gorjanc ◽

Mara Battagin ◽

R. Chris Gaynor ◽

Janez Jenko ◽

...

Keyword(s):

Breeding Programs ◽

Livestock Breeding

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Potential of gene drives with genome editing to increase genetic gain in livestock breeding programs

Genetics Selection Evolution ◽

10.1186/s12711-016-0280-3 ◽

2017 ◽

Vol 49 (1) ◽

Cited By ~ 17

Author(s):

Serap Gonen ◽

Janez Jenko ◽

Gregor Gorjanc ◽

Alan J. Mileham ◽

C. Bruce A. Whitelaw ◽

...

Keyword(s):

Genome Editing ◽

Genetic Gain ◽

Breeding Programs ◽

Livestock Breeding ◽

Gene Drives

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Potential of promotion of alleles by genome editing to improve quantitative traits in livestock breeding programs

Genetics Selection Evolution ◽

10.1186/s12711-015-0135-3 ◽

2015 ◽

Vol 47 (1) ◽

Cited By ~ 66

Author(s):

Janez Jenko ◽

Gregor Gorjanc ◽

Matthew A Cleveland ◽

Rajeev K Varshney ◽

C. Bruce A Whitelaw ◽

...

Keyword(s):

Genome Editing ◽

Quantitative Traits ◽

Breeding Programs ◽

Livestock Breeding

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Statistical model and testing designs to increase response to selection with constrained inbreeding in genomic breeding programs for pigs affected by social genetic effects

Genetics Selection Evolution ◽

10.1186/s12711-020-00598-8 ◽

2021 ◽

Vol 53 (1) ◽

Author(s):

Thinh Tuan Chu ◽

Mark Henryon ◽

Just Jensen ◽

Birgitte Ask ◽

Ole Fredslund Christensen

Keyword(s):

Statistical Model ◽

Statistical Models ◽

Genetic Effects ◽

Response To Selection ◽

Heritable Variation ◽

Fixed Rate ◽

Random Group ◽

Breeding Programs ◽

The Difference ◽

Family Groups

Abstract Background Social genetic effects (SGE) are the effects of the genotype of one animal on the phenotypes of other animals within a social group. Because SGE contribute to variation in economically important traits for pigs, the inclusion of SGE in statistical models could increase responses to selection (RS) in breeding programs. In such models, increasing the relatedness of members within groups further increases RS when using pedigree-based relationships; however, this has not been demonstrated with genomic-based relationships or with a constraint on inbreeding. In this study, we compared the use of statistical models with and without SGE and compared groups composed at random versus groups composed of families in genomic selection breeding programs with a constraint on the rate of inbreeding. Results When SGE were of a moderate magnitude, inclusion of SGE in the statistical model substantially increased RS when SGE were considered for selection. However, when SGE were included in the model but not considered for selection, the increase in RS and in accuracy of predicted direct genetic effects (DGE) depended on the correlation between SGE and DGE. When SGE were of a low magnitude, inclusion of SGE in the model did not increase RS, probably because of the poor separation of effects and convergence issues of the algorithms. Compared to a random group composition design, groups composed of families led to higher RS. The difference in RS between the two group compositions was slightly reduced when using genomic-based compared to pedigree-based relationships. Conclusions The use of a statistical model that includes SGE can substantially improve response to selection at a fixed rate of inbreeding, because it allows the heritable variation from SGE to be accounted for and capitalized on. Compared to having random groups, family groups result in greater response to selection in the presence of SGE but the advantage of using family groups decreases when genomic-based relationships are used.

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Community based livestock breeding programs in Bangladesh: Present status and challenges

Journal of Animal Breeding and Genomics ◽

10.12972/jabng.20170009 ◽

2017 ◽

Vol 1 (2) ◽

Cited By ~ 2

Keyword(s):

Present Status ◽

Community Based ◽

Breeding Programs ◽

Livestock Breeding

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The Equation for Response to Selection and Its Use for Prediction

Evolutionary Computation ◽

10.1162/evco.1997.5.3.303 ◽

1997 ◽

Vol 5 (3) ◽

pp. 303-346 ◽

Cited By ~ 295

Author(s):

Heinz Mühlenbein

Keyword(s):

Genetic Algorithms ◽

Genetic Variance ◽

Additive Genetic Variance ◽

Linkage Equilibrium ◽

Realized Heritability ◽

Response To Selection ◽

Infinite Population ◽

Livestock Breeding ◽

Genotype Frequencies ◽

Tournament Selection

The Breeder Genetic Algorithm (BGA) was designed according to the theories and methods used in the science of livestock breeding. The prediction of a breeding experiment is based on the response to selection (RS) equation. This equation relates the change in a population's fitness to the standard deviation of its fitness, as well as to the parameters selection intensity and realized heritability. In this paper the exact RS equation is derived for proportionate selection given an infinite population in linkage equilibrium. In linkage equilibrium the genotype frequencies are the product of the univariate marginal frequencies. The equation contains Fisher's fundamental theorem of natural selection as an approximation. The theorem shows that the response is approximately equal to the quotient of a quantity called additive genetic variance, VA, and the average fitness. We compare Mendelian two-parent recombination with gene-pool recombination, which belongs to a special class of genetic algorithms that we call univariate marginal distribution (UMD) algorithms. UMD algorithms keep the genotypes in linkage equilibrium. For UMD algorithms, an exact RS equation is proven that can be used for long-term prediction. Empirical and theoretical evidence is provided that indicates that Mendelian two-parent recombination is also mainly exploiting the additive genetic variance. We compute an exact RS equation for binary tournament selection. It shows that the two classical methods for estimating realized heritability—the regression heritability and the heritability in the narrow sense—may give poor estimates. Furthermore, realized heritability for binary tournament selection can be very different from that of proportionate selection. The paper ends with a short survey about methods that extend standard genetic algorithms and UMD algorithms by detecting interacting variables in nonlinear fitness functions and using this information to sample new points.

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