Projects per year
Abstract
Background: In breeding programmes, the observed genetic change is a sum of the contributions of different selection paths represented by groups of individuals. Quantifying these sources of genetic change is essential for identifying the key breeding actions and optimizing breeding programmes. However, it is difficult to disentangle the contribution of individual paths due to the inherent complexity of breeding programmes. Here we extend the previously developed method for partitioning genetic mean by paths of selection to work both with the mean and variance of breeding values. Methods: First, we extended the partitioning method to quantify the contribution of different paths to genetic variance assuming that the breeding values are known. Second, we combined the partitioning method with the Markov Chain Monte Carlo approach to draw samples from the posterior distribution of breeding values and use these samples for computing the point and interval estimates of partitions for the genetic mean and variance. We implemented the method in the R package AlphaPart. We demonstrated the method with a simulated cattle breeding programme. Results: We show how to quantify the contribution of different groups of individuals to genetic mean and variance and that the contributions of different selection paths to genetic variance are not necessarily independent. Finally, we observed that the partitioning method under the pedigree-based model has some limitations, which suggests the need for a genomic extension. Conclusions: We presented a partitioning method to quantify sources of change in genetic mean and variance in breeding programmes. The method can help breeders and researchers understand the dynamics in genetic mean and variance in a breeding programme. The developed method for partitioning genetic mean and variance is a powerful method for understanding how different selection paths interact within a breeding programme and how they can be optimised.
Original language | English |
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Article number | 36 |
Journal | Genetics Selection Evolution |
Volume | 55 |
Issue number | 1 |
Early online date | 2 Jun 2023 |
DOIs | |
Publication status | Published - 2 Jun 2023 |
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Dive into the research topics of 'A method for partitioning trends in genetic mean and variance to understand breeding practices'. Together they form a unique fingerprint.Projects
- 3 Finished
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TRAIN@Ed: Transnational Research and Innovation Network at Edinburgh
Gorjanc, G. (Principal Investigator), Bell, C. (Co-investigator), Duncan, A. (Co-investigator), Farrington, S. (Co-investigator), Florian, L. (Co-investigator), Forde, M. (Co-investigator), Hickey, J. (Co-investigator), Lacka, E. (Co-investigator), Ma, T. (Co-investigator), Mcneill, G. (Co-investigator), Medina-Lopez, E. (Co-investigator), Rosser, S. (Co-investigator), Rossi, R. (Co-investigator), Sabanis, S. (Co-investigator), Szpruch, L. (Co-investigator), Tenesa, A. (Co-investigator), Wake, D. (Co-investigator), Williamson, B. (Co-investigator) & Yang, Y. (Co-investigator)
EU government bodies, Non-EU industry, commerce and public corporations
1/11/19 → 19/04/23
Project: Research
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Chancellor's Fellowship - Gregor Gorjanc
Gorjanc, G. (Principal Investigator)
1/01/19 → 31/12/23
Project: Research
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