Impact of mixing between parallel year groups on genomic prediction in Atlantic salmon breeding programmes under random selection

Panagiotis Kokkinias, Alastair Hamilton, Ross Houston, Chris S Haley, Ricardo Pong-Wong, Pau Navarro

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

A commercial breeding programme in Atlantic salmon utilises a four-year generation interval with four parallel breeding populations. In this study, we develop a computer simulation of a salmon breeding programme and explore the impact of gene flow between the parallel year groups on the accuracy of genomic prediction within and between breeding lines. We simulated four parallel lines for 10 discrete generations with random selection and different mixing rates between parallel year groups. The genetic distance between fish (as a measure of diversity) and the accuracy of estimated genomic breeding values were used as criteria of comparison. With no mixing the genetic distance increased between populations, the genetic variation within populations decreased and there was no increase in accuracy when combining data across populations. Even a low percentage of mixing decreased the genetic distance between populations and increased the genetic variation within populations. The higher the percentage of mixing the faster the lines became more similar. The accuracy of prediction climbed as the percentage of mixing increased. The increase in accuracy from the combined evaluation approach compared to the within evaluation approach was greater with an increased percentage of mixing. In conclusion, if there is no gene flow between populations the lines drift apart and there is no value in combining information across populations for genomic breeding value prediction. Only a low amount of mixing between lines brings the lines closer together and facilitates the use of information across lines to improve breeding value prediction. Optimising gene flow between lines should be an integral part of salmon breeding programme design.
Original languageEnglish
Article number739497
Pages (from-to)1-9
Number of pages9
Early online date21 Mar 2023
Publication statusPublished - 15 Jul 2023

Keywords / Materials (for Non-textual outputs)

  • salmon
  • genomic prediction
  • breeding programme
  • mixing
  • diversity
  • genetic distance
  • across-population prediction


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