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Competitive gene flow does not necessarily maximize the genetic gain of genomic breeding programs in the presence of genotype-by-environment interaction

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  • L. Cao
  • ,
  • H. A. Mulder, Wageningen University & Research
  • ,
  • H. Liu
  • ,
  • H. M. Nielsen
  • A. C. S⊘rensen, SEGES Innovation P/S

National and international across-population selection is often recommended and fairly common in the current breeding practice of dairy cattle, with the primary aims to increase genetic gain and genetic variability. The aim of this study was to test the hypothesis that the strategy of truncation selection of sires across populations [i.e., competitive gene flow strategy (CGF)] may not necessarily maximize genetic gain in the long term in the presence of genotype-by-environment interaction (G×E). Two alternative strategies used to be compared with CGF were forced gene flow (FGF) strategies, with 10 or 50% of domestic dams forced to be mated with foreign sires (FGF10%, FGF50%). Two equal-size populations (Ndams = 1,000) that were selected for the same breeding goal trait (h2 = 0.3) under G×E correlation (rg) of either 0.9 or 0.8 were simulated to test these 3 different strategies. Each population first experienced either 5 or 20 differentiation generations (Gd), then 15 migration generations. Discrete generations were simulated for simplicity. Each population performed a within-population conventional breeding program during differentiation generations and the 3 across-population sire selection strategies based on joint genomic prediction during migration generations. The 4 Gd_rg combinations defined 4 different levels of differentiation degree between the 2 populations at the start of migration. The true rate of inbreeding over the last 10 migration generations in each scenario was constrained at 0.01 to provide a fair basis for comparison of genetic gain across scenarios. Results showed that CGF maximized the genetic gain after 15 migration generations in 5_0.9 combination only, the case of the lowest differentiation degree, with a superiority of 0.4% (0.04 genetic SD units) over the suboptimal strategy. While in 5_0.8, 20_0.9, and 20_0.8 combinations, 2 FGF strategies had a superiority in genetic gain of 2.3 to 12.5% (0.21–1.07 genetic SD units) over CGF after 15 migration generations, especially FGF50%. The superiority of FGF strategies over CGF was that they alleviated inbreeding, introduced new genetic variance in the early migration period, and improved accuracy in the entire migration period. Therefore, we concluded that CGF does not necessarily maximize the genetic gain of across-population genomic breeding programs given moderate G×E. The across-population selection strategy remains to be optimized to maximize genetic gain.

TidsskriftJournal of Dairy Science
Sider (fra-til)8122-8134
Antal sider13
StatusUdgivet - jul. 2021

Bibliografisk note

Funding Information:
We acknowledge GenSAP (Genomic Selection in Animals and Plants) project for providing academic financial support to this study, and CSC (China Scholarship Council) for covering the living stipend of the first author during this study. Mark Henryon (SEGES, Denmark) supported the programming of a key configuration of the used simulation software in this study (ADAM), without which the progress of this simulation study would have been slowed. We are also grateful to Jörn Bennewitz (Hohenheim University, Germany), Flavio Schramm Schenkel (Guelph University, Canada), and Just Jensen (Aarhus University, Denmark) for their constructive suggestions on how to improve the writing of this manuscript. At the same time, we appreciate Morten Kargo (Aarhus University, Denmark) for his efforts to facilitate the completion of this manuscript. The authors have not stated any conflicts of interest.

Publisher Copyright:
© 2021 American Dairy Science Association

Copyright 2021 Elsevier B.V., All rights reserved.

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