Literature DB >> 24169831

Balancing selection response and rate of inbreeding by including genetic relationships in selection decisions.

J R Brisbane1, J P Gibson.   

Abstract

An iterative selection strategy, based on estimated breeding values (EBV) and average relationship among selected individuals, is proposed to optimise the balance between genetic response and inbreeding. Stochastic simulation was used to compare rates of inbreeding and genetic gain with those of other strategies. For a range of heritabilities, population sizes and mating ratios, the iterative strategy, denoted ADJEBV, outperforms other strategies, giving the greatest genetic gain at a given rate of inbreeding and the least breeding at a given genetic gain. Where selection is currently by truncation on the EBV, with a restriction on the number of full-sibs selected, it should be possible to maintain similar levels of genetic gain and inbreeding with a reduction in population size of 10-30%, by changing to the iterative strategy. If performance is measured by the reduction in cumulative inbreeding without losing more than a given amount of genetic gain relative to results obtained under truncation selection on the EBV, then with the EBV based on a family index, the performance of ADJEBV is greater at low heritability, and is generally greater than where EBV are based on individual records. When comparisons of genetic response and inbreeding are made for alternative breeding scheme designs, schemes which give higher genetic gain within acceptable inbreeding levels would usually be favoured. If comparisons are made on this basis, then the selection method used should be ADJEBV, which maximises the genetic gain for a given level of inbreeding. The results indicated that all selection strategies used to reduce inbreeding had very small effects on the variance of gain, and so differences in this respect are unlikely to affect choices among selection strategies. Selection criteria are recommended based on maximising a selection objective which specifies the desired balance between genetic gain and inbreeding.

Year:  1995        PMID: 24169831     DOI: 10.1007/BF00222969

Source DB:  PubMed          Journal:  Theor Appl Genet        ISSN: 0040-5752            Impact factor:   5.699


  9 in total

1.  Comparison of selection methods at the same level of inbreeding.

Authors:  M Quinton; C Smith; M E Goddard
Journal:  J Anim Sci       Date:  1992-04       Impact factor: 3.159

2.  The Genetic Basis for Constructing Selection Indexes.

Authors:  L N Hazel
Journal:  Genetics       Date:  1943-11       Impact factor: 4.562

3.  The Theoretical Variance within and among Subdivisions of a Population That Is in a Steady State.

Authors:  S Wright
Journal:  Genetics       Date:  1952-05       Impact factor: 4.562

4.  Including genetic relationships in selection decisions: alternative methodologies.

Authors:  J R Brisbane; J P Gibson
Journal:  Theor Appl Genet       Date:  1995-10       Impact factor: 5.699

5.  Long-term effects of selection based on the animal model BLUP in a finite population.

Authors:  E Verrier; J J Colleau; J L Foulley
Journal:  Theor Appl Genet       Date:  1993-12       Impact factor: 5.699

6.  Analysis of levels of inbreeding and inbreeding depression in Jersey cattle.

Authors:  F Miglior; B Szkotnicki; E B Burnside
Journal:  J Dairy Sci       Date:  1992-04       Impact factor: 4.034

7.  Prediction of rates of inbreeding in selected populations.

Authors:  N R Wray; R Thompson
Journal:  Genet Res       Date:  1990-02       Impact factor: 1.588

8.  A note on increasing the limit of selection through selection within families.

Authors:  L Dempfle
Journal:  Genet Res       Date:  1974-10       Impact factor: 1.588

9.  Effects of mild inbreeding on productive and reproductive performance of Guernsey cattle.

Authors:  S A Hermas; C W Young; J W Rust
Journal:  J Dairy Sci       Date:  1987-03       Impact factor: 4.034

  9 in total
  6 in total

1.  Including genetic relationships in selection decisions: alternative methodologies.

Authors:  J R Brisbane; J P Gibson
Journal:  Theor Appl Genet       Date:  1995-10       Impact factor: 5.699

2.  Assessment of breeding programs sustainability: application of phenotypic and genomic indicators to a North European grain maize program.

Authors:  Antoine Allier; Simon Teyssèdre; Christina Lehermeier; Bruno Claustres; Stéphane Maltese; Stéphane Melkior; Laurence Moreau; Alain Charcosset
Journal:  Theor Appl Genet       Date:  2019-01-21       Impact factor: 5.699

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Authors:  M Penasa; A Cecchinato; M Battagin; M De Marchi; D Pretto; M Cassandro
Journal:  J Appl Genet       Date:  2010       Impact factor: 2.653

4.  A Bayesian Decision Theory Approach for Genomic Selection.

Authors:  Bartolode Jesús Villar-Hernández; Sergio Pérez-Elizalde; José Crossa; Paulino Pérez-Rodríguez; Fernando H Toledo; Juan Burgueño
Journal:  G3 (Bethesda)       Date:  2018-08-30       Impact factor: 3.154

5.  Challenges and opportunities in genetic improvement of local livestock breeds.

Authors:  Filippo Biscarini; Ezequiel L Nicolazzi; Alessandra Stella; Paul J Boettcher; Gustavo Gandini
Journal:  Front Genet       Date:  2015-02-25       Impact factor: 4.599

6.  Efficient Breeding by Genomic Mating.

Authors:  Deniz Akdemir; Julio I Sánchez
Journal:  Front Genet       Date:  2016-11-29       Impact factor: 4.599

  6 in total

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