Appropriate selection indices for functional traits in dairy cattle breeding schemes

2018 ◽  
Vol 86 (1) ◽  
pp. 13-18 ◽  
Author(s):  
Arash Chegini ◽  
Navid Ghavi Hossein-Zadeh ◽  
Seyed Hossein Hosseini Moghaddam ◽  
Abdol Ahad Shadparvar
Keyword(s):  
Genetic Gain ◽  
Deterministic Model ◽  
Selection Index ◽  
Index Theory ◽  
Multiple Trait ◽  
Cattle Breeding ◽  
Udder Health ◽  
Selection Indices ◽  
Dairy Cattle Breeding ◽  
Breeding Schemes

AbstractThe objective of this study was to establish different single or multiple trait selection indices to calculate genetic and economic gains by combining some production, reproduction and udder health traits in a population similar to the overall practical situation in Iran, with and without imposing restrictions on genetic change for some traits. The SelAction software was used to perform the analyses based on selection index theory through a deterministic model. Results indicated that among established indices, the index that showed the highest genetic gain for milk yield did not maximize the total genetic and economic gains. Rather, the index that included all production, reproduction and udder health traits yielded the highest genetic and economic gains. When we placed restriction on the selection indices, the economic gain decreased and the amount of reduction depended on the heritability and the correlation of restricted trait(s) with other traits. Generally, regarding the economic genetic gain per generation, the indices based on records of 200 offspring were 4.819% more efficient than those that used information of 100 offspring.

scholarly journals Expected Genetic Contributions and Their Impact on Gene Flow and Genetic Gain

Genetics ◽  
1999 ◽  
Vol 153 (2) ◽  
pp. 1009-1020 ◽  
Author(s):  
J A Woolliams ◽  
P Bijma ◽  
B Villanueva
Keyword(s):  
Gene Flow ◽  
Genetic Gain ◽  
Overlapping Generations ◽  
Predictive Accuracy ◽  
Index Theory ◽  
Previous Theory ◽  
Selection Indices ◽  
Breeding Groups ◽  
Genetic Contributions

Abstract Long-term genetic contributions (ri) measure lasting gene flow from an individual i. By accounting for linkage disequilibrium generated by selection both within and between breeding groups (categories), assuming the infinitesimal model, a general formula was derived for the expected contribution of ancestor i in category q (μi(q)), given its selective advantages (si(q)). Results were applied to overlapping generations and to a variety of modes of inheritance and selection indices. Genetic gain was related to the covariance between ri and the Mendelian sampling deviation (ai), thereby linking gain to pedigree development. When si(q) includes ai, gain was related to E[μi(q)ai], decomposing it into components attributable to within and between families, within each category, for each element of si(q). The formula for μi(q) was consistent with previous index theory for predicting gain in discrete generations. For overlapping generations, accurate predictions of gene flow were obtained among and within categories in contrast to previous theory that gave qualitative errors among categories and no predictions within. The generation interval was defined as the period for which μi(q), summed over all ancestors born in that period, equaled 1. Predictive accuracy was supported by simulation results for gain and contributions with sib-indices, BLUP selection, and selection with imprinted variation.

scholarly journals Genetic Progress in Multistage Dairy Cattle Breeding Schemes Using Genetic Markers

2005 ◽  
Vol 88 (4) ◽  
pp. 1569-1581 ◽  
Author(s):  
C. Schrooten ◽  
H. Bovenhuis ◽  
J.A.M. van Arendonk ◽  
P. Bijma
Keyword(s):  
Dairy Cattle ◽  
Genetic Markers ◽  
Genetic Progress ◽  
Cattle Breeding ◽  
Dairy Cattle Breeding ◽  
Breeding Schemes

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 Across-Family Marker-Assisted Selection Using Selective Genotyping Strategies in Dairy Cattle Breeding Schemes

2008 ◽  
Vol 91 (4) ◽  
pp. 1628-1639 ◽  
Author(s):  
S. Ansari-Mahyari ◽  
A.C. Sørensen ◽  
M.S. Lund ◽  
H. Thomsen ◽  
P. Berg
Keyword(s):  
Dairy Cattle ◽  
Marker Assisted Selection ◽  
Selective Genotyping ◽  
Cattle Breeding ◽  
Dairy Cattle Breeding ◽  
Breeding Schemes

Calculation of response and variance reduction due to multi-stage and multiple trait selection

1994 ◽  
Vol 58 (1) ◽  
pp. 1-9 ◽  
Author(s):  
S. Andersen
Keyword(s):  
Variance Reduction ◽  
Selection Index ◽  
Breeding Value ◽  
Multiple Trait ◽  
Multi Stage ◽  
Breeding Schemes ◽  
Unselected Population ◽  
Best Linear Unbiased ◽  
Selection Intensities ◽  
Set Up

AbstractIn deterministic comparisons of breeding schemes it is necessary to take account of variance reduction due to selection. This can take place as multi-stage selection within generations and it takes place across generations when offspring of selected parents are selected. A standard way to deal with this is to set up selection index equations where the parameters are altered as a consequence of selection. It is shown that if the breeding schemes use a univariate or multivariate best linear unbiased prediction (BLUP) animal model for prediction of breeding values this procedure can be simplified. This is done by modelling the distribution of estimated breeding value (EBV) utilizing that changes in EBV of an individual are independent of selection. In the univariate case the variance reduction and the resulting genetic gain can be calculated from the selection intensities and the accuracies in the unselected population. An expression is given for the response in each generation when selection is started in a base population with complete pedigree. This shows that a limiting value is obtained within three to four generations. The asymptotic response for several traits is described in the case where selection is on multitrait BLUP.

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