A method for partitioning trends in genetic mean and variance to understand breeding practices

Thiago P. Oliveira; Jana Obšteter; Ivan Pocrnic; Nicolas Heslot; Gregor Gorjanc

doi:10.1186/s12711-023-00804-3

A method for partitioning trends in genetic mean and variance to understand breeding practices

Thiago P. Oliveira^*, Jana Obšteter, Ivan Pocrnic, Nicolas Heslot, Gregor Gorjanc

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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
Article number	36
Journal	Genetics Selection Evolution
Volume	55
Issue number	1
Early online date	2 Jun 2023
DOIs	https://doi.org/10.1186/s12711-023-00804-3
Publication status	Published - 2 Jun 2023

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10.1186/s12711-023-00804-3

s12711-023-00804-3Final published version, 3.77 MBLicence: Creative Commons: Attribution (CC-BY)

Cite this

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title = "A method for partitioning trends in genetic mean and variance to understand breeding practices",

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.",

author = "Oliveira, {Thiago P.} and Jana Ob{\v s}teter and Ivan Pocrnic and Nicolas Heslot and Gregor Gorjanc",

note = "Funding Information: The authors acknowledge support from the BBSRC Institute Strategic Programme Grant to The Roslin Institute (BBS/E/D/30002275). TPO acknowledges funding from Limagrain and the European Union{\textquoteright}s Horizon 2020 research and innovation programme under the Marie Sk{\l}odowska-Curie grant agreement No. 801215 and the University of Edinburgh Data-Driven Innovation programme part of the Edinburgh and South East Scotland City Region Deal. JO acknowledges funding from the Slovenian Research Agency (Grant P4-0133). For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) license to any Author Accepted Manuscript version arising from this submission. Publisher Copyright: {\textcopyright} 2023, The Author(s).",

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doi = "10.1186/s12711-023-00804-3",

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AU - Pocrnic, Ivan

AU - Heslot, Nicolas

AU - Gorjanc, Gregor

N1 - Funding Information: The authors acknowledge support from the BBSRC Institute Strategic Programme Grant to The Roslin Institute (BBS/E/D/30002275). TPO acknowledges funding from Limagrain and the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 801215 and the University of Edinburgh Data-Driven Innovation programme part of the Edinburgh and South East Scotland City Region Deal. JO acknowledges funding from the Slovenian Research Agency (Grant P4-0133). For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) license to any Author Accepted Manuscript version arising from this submission. Publisher Copyright: © 2023, The Author(s).

PY - 2023/6/2

Y1 - 2023/6/2

N2 - 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.

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A method for partitioning trends in genetic mean and variance to understand breeding practices

Abstract

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TRAIN@Ed: Transnational Research and Innovation Network at Edinburgh

Chancellor's Fellowship - Gregor Gorjanc

ISP 2017/22-Gregor Gorjanc

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A method for partitioning trends in genetic mean and variance to understand breeding practices

Abstract

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Projects

TRAIN@Ed: Transnational Research and Innovation Network at Edinburgh

Chancellor's Fellowship - Gregor Gorjanc

ISP 2017/22-Gregor Gorjanc

Cite this