Literature DB >> 10511574

Expected genetic contributions and their impact on gene flow and genetic gain.

J A Woolliams1, P Bijma, B Villanueva.   

Abstract

Long-term genetic contributions (r(i)) 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 (mu(i)(q)), given its selective advantages (s(i)(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 r(i) and the Mendelian sampling deviation (a(i)), thereby linking gain to pedigree development. When s(i)(q) includes a(i), gain was related to E[mu(i)(q))a(i)], decomposing it into components attributable to within and between families, within each category, for each element of s(i)(q). The formula for mu(i)(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 mu(i)(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.

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Mesh:

Year:  1999        PMID: 10511574      PMCID: PMC1460791     

Source DB:  PubMed          Journal:  Genetics        ISSN: 0016-6731            Impact factor:   4.562


  2 in total

1.  Prediction of genetic contributions and generation intervals in populations with overlapping generations under selection.

Authors:  P Bijma; J A Woolliams
Journal:  Genetics       Date:  1999-03       Impact factor: 4.562

2.  Maximizing the response of selection with a predefined rate of inbreeding.

Authors:  T H Meuwissen
Journal:  J Anim Sci       Date:  1997-04       Impact factor: 3.159

  2 in total
  21 in total

Review 1.  Genetic hitchhiking.

Authors:  N H Barton
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2000-11-29       Impact factor: 6.237

2.  Predicting rates of inbreeding in populations undergoing selection.

Authors:  J A Woolliams; P Bijma
Journal:  Genetics       Date:  2000-04       Impact factor: 4.562

3.  A general procedure for predicting rates of inbreeding in populations undergoing mass selection.

Authors:  P Bijma; J A Van Arendonk; J A Woolliams
Journal:  Genetics       Date:  2000-04       Impact factor: 4.562

4.  Prediction of rates of inbreeding in populations selected on best linear unbiased prediction of breeding value

Authors: 
Journal:  Genetics       Date:  2000-09       Impact factor: 4.562

5.  Prediction of rates of inbreeding in populations selected on best linear unbiased prediction of breeding value.

Authors:  P Bijma; J A Woolliams
Journal:  Genetics       Date:  2000-09       Impact factor: 4.562

6.  Impact of nonrandom mating on genetic variance and gene flow in populations with mass selection.

Authors:  Leopoldo Sánchez; John A Woolliams
Journal:  Genetics       Date:  2004-01       Impact factor: 4.562

7.  The impact of male-killing bacteria on host evolutionary processes.

Authors:  Jan Engelstädter; Gregory D D Hurst
Journal:  Genetics       Date:  2006-12-06       Impact factor: 4.562

Review 8.  Genomic prediction in animals and plants: simulation of data, validation, reporting, and benchmarking.

Authors:  Hans D Daetwyler; Mario P L Calus; Ricardo Pong-Wong; Gustavo de Los Campos; John M Hickey
Journal:  Genetics       Date:  2012-12-05       Impact factor: 4.562

9.  Breeding Top Genotypes and Accelerating Response to Recurrent Selection by Selecting Parents with Greater Gametic Variance.

Authors:  Piter Bijma; Yvonne C J Wientjes; Mario P L Calus
Journal:  Genetics       Date:  2019-11-26       Impact factor: 4.562

10.  Optimization of selection contribution and mate allocations in monoecious tree breeding populations.

Authors:  Jon Hallander; Patrik Waldmann
Journal:  BMC Genet       Date:  2009-11-06       Impact factor: 2.797

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