Temporal and genomic analysis of additive genetic variance in breeding programmes

Lara, Letícia Aparecida de Castro; Pocrnić, Ivan; Oliveira, Thiago de Paula; Gaynor, R. Chris

doi:10.1038/s41437-021-00485-y

Cited by 21 publications

(36 citation statements)

References 68 publications

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“…Hence, they should not be analysed in isolation from each other. Future work will apply the method to estimate trends in the genetic mean and variance following the work of Sorensen et al (2001) and Lara et al (2021).…”

Section: Discussionmentioning

confidence: 99%

“…This partitioning is for a priori or true breeding values. To estimate partitions of genetic variance from data collected in a breeding programme (posteriori) we would have to use methods from Sorensen et al (2001) and Lara et al (2021). While this is straightforward, we left this to future work.…”

Section: Methodsmentioning

confidence: 99%

“…To gain insight into time-trends or other data partitions, they summarised group-specific contributions by a variable such as year, gender, population, etc. While García-Cortés et al (2008) addressed the contribution of different groups to changes in genetic mean, breeding programmes should also consider the contribution of different groups to changes in genetic variance to fully understand drivers of genetic change in a population (Sorensen et al, 2001;Lara et al, 2021). Here we extend the method of García-Cortés et al (2008) to analyse changes in genetic variance.…”

Section: Introductionmentioning

confidence: 99%

“…We assume throughout we know the true breeding value. Future work will extend the method to estimated breeding values by applying the method on posterior samples of breeding values following the work of Sorensen et al (1994;2001) and Lara et al (2021).…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the contribution of paths to changes in genetic mean, breeding programmes should also consider analysing changes in genetic variance to fully understand the drivers of genetic change in a population [6, 7]. Managing the change in genetic mean and variance in breeding programmes is essential to ensure a long-term genetic gain [8, 9].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Oliveira

Obšteter

Pocrnić

2022

Preprint

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Quantifying the sources of genetic change is essential for optimising breeding programmes. However, breeding programmes are often complex because many breeding groups are subject to different breeding actions. Understanding the contribution of these groups to changes in genetic mean and variance is essential to understanding genetic change in breeding programmes. Here we extend the previously developed method for analysing the contribution of groups to changes in genetic mean to analysing changes in genetic variance. We, expectedly, show that the contribution of females and males to change in genetic variance can differ and are not independent, indicating we should not look at the contributions in isolation.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Oliveira

Obšteter

Pocrnić

2022

Preprint

Self Cite

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Simulation‐based decision‐making and implementation of tools in hybrid crop breeding pipelines

Peixoto,

Coelho,

Leach

et al. 2023

Crop Science

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New technologies have been developed over the last few years aiming to support breeding pipeline optimization for long‐term genetic gains. However, the implementation of these new tools and their impact on any breeding program's budget are not well studied. Here, we compare multiple breeding pipeline strategies accounting for genomic selection and high‐throughput phenotyping by means of hybrid gain and cost effectiveness. We simulated a hybrid crop breeding program through coalescent theory. We compared two strategies for parental updates and four breeding pipelines: conventional breeding pipeline; conventional breeding pipeline with high‐throughput phenotyping; conventional breeding pipeline with genomic selection; conventional breeding pipeline with genomic selection and high‐throughput phenotyping. All analyses were implemented under three different levels of genotype‐by‐environment interaction (G×E) and two traits heritabilities (0.3 and 0.7). Overall, the results show that scenarios with early parental selection perform better than the others. In addition, the implementation of high‐throughput phenotyping (HTP) delivered the highest hybrid gain in the long‐term, whereas the implementation of genomic selection seems to be more cost‐effective. We suggest, considering breeding programs with complex trait inheritance and accounting for higher levels of G×E, to invest in breeding pipelines accounting for genomic selection as a strategy to create and maintain long‐term hybrid gain. Moreover, considering an unconstrained budget, the investment in both, genomic selection and HTP, represents the best strategy. Hence, these results provide strategies that may aid breeders in optimizing self‐pollination breeding programs.This article is protected by copyright. All rights reserved

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Influence of the mating design on the additive genetic variance in plant breeding populations

Lanzl,

Melchinger,

Schön

2023

Theor Appl Genet

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Key message Mating designs determine the realized additive genetic variance in a population sample. Deflated or inflated variances can lead to reduced or overly optimistic assessment of future selection gains. Abstract The additive genetic variance $${V}_{A}$$ V A inherent to a breeding population is a major determinant of short- and long-term genetic gain. When estimated from experimental data, it is not only the additive variances at individual loci (QTL) but also covariances between QTL pairs that contribute to estimates of $${V}_{A}$$ V A . Thus, estimates of $${V}_{A}$$ V A depend on the genetic structure of the data source and vary between population samples. Here, we provide a theoretical framework for calculating the expectation and variance of $${V}_{A}$$ V A from genotypic data of a given population sample. In addition, we simulated breeding populations derived from different numbers of parents (P = 2, 4, 8, 16) and crossed according to three different mating designs (disjoint, factorial and half-diallel crosses). We calculated the variance of $${V}_{A}$$ V A and of the parameter b reflecting the covariance component in $${V}_{A},$$ V A , standardized by the genic variance. Our results show that mating designs resulting in large biparental families derived from few disjoint crosses carry a high risk of generating progenies exhibiting strong covariances between QTL pairs on different chromosomes. We discuss the consequences of the resulting deflated or inflated $${V}_{A}$$ V A estimates for phenotypic and genome-based selection as well as for applying the usefulness criterion in selection. We show that already one round of recombination can effectively break negative and positive covariances between QTL pairs induced by the mating design. We suggest to obtain reliable estimates of $${V}_{A}$$ V A and its components in a population sample by applying statistical methods differing in their treatment of QTL covariances.

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Temporal and genomic analysis of additive genetic variance in breeding programmes

Cited by 21 publications

References 68 publications

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

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

Simulation‐based decision‐making and implementation of tools in hybrid crop breeding pipelines

Influence of the mating design on the additive genetic variance in plant breeding populations

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