Strategies to Exploit Genetic Variation While Maintaining Diversity

Kinghorn, Brian; Banks, Robert; Gondro, Cedric; Kremer, Valentin D.; Meszaros, S. A.; Newman, Scott; Shepherd, R. K.; Vagg, Rod D.; Werf, J.H.J. van der

doi:10.1007/978-1-4020-9005-9_13

Cited by 18 publications

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“…The optimal contribution selection assumes that contributions will be randomly paired, including selfing. An extension that delivers a practical crossing plan is to jointly optimise contributions and cross allocations (Kinghorn et al 2009; Kinghorn 2011). These methods are established in animal breeding (for a review, see Woolliams et al (2015)) and are increasingly common in plant breeding (Cowling et al 2016; Akdemir and Sánchez 2016; De Beukelaer et al 2017; Lin et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Optimal cross selection for long-term genetic gain in two-part programs with rapid recurrent genomic selection

2018

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Key message Optimal cross selection increases long-term genetic gain of two-part programs with rapid recurrent genomic selection. It achieves this by optimising efficiency of converting genetic diversity into genetic gain through reducing the loss of genetic diversity and reducing the drop of genomic prediction accuracy with rapid cycling. AbstractThis study evaluates optimal cross selection to balance selection and maintenance of genetic diversity in two-part plant breeding programs with rapid recurrent genomic selection. The two-part program reorganises a conventional breeding program into a population improvement component with recurrent genomic selection to increase the mean value of germplasm and a product development component with standard methods to develop new lines. Rapid recurrent genomic selection has a large potential, but is challenging due to genotyping costs or genetic drift. Here we simulate a wheat breeding program for 20 years and compare optimal cross selection against truncation selection in the population improvement component with one to six cycles per year. With truncation selection we crossed a small or a large number of parents. With optimal cross selection we jointly optimised selection, maintenance of genetic diversity, and cross allocation with AlphaMate program. The results show that the two-part program with optimal cross selection delivered the largest genetic gain that increased with the increasing number of cycles. With four cycles per year optimal cross selection had 78% (15%) higher long-term genetic gain than truncation selection with a small (large) number of parents. Higher genetic gain was achieved through higher efficiency of converting genetic diversity into genetic gain; optimal cross selection quadrupled (doubled) efficiency of truncation selection with a small (large) number of parents. Optimal cross selection also reduced the drop of genomic selection accuracy due to the drift between training and prediction populations. In conclusion optimal cross selection enables optimal management and exploitation of population improvement germplasm in two-part programs.Electronic supplementary materialThe online version of this article (10.1007/s00122-018-3125-3) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Optimal cross selection for long-term genetic gain in two-part programs with rapid recurrent genomic selection

2018

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“…A differential evolutionary 223 algorithm was implemented to find the set of crosses that is a Pareto-optimal solution of Eq. 10 224 (Storn and Price 1997;Kinghorn et al 2009;Kinghorn 2011). The direct consideration of ( ) in the 225 optimization allows to control the decrease in genetic diversity similarly to what was suggested for 226 controlling inbreeding rate in animal breeding (Woolliams et al 1998(Woolliams et al , 2015.…”

Section: Multi-objective Optimization Framework 216mentioning

confidence: 98%

“…Optimal contribution 62 selection aims at identifying the optimal contributions ( ) of candidate parents to the next generation 63 obtained by random mating, in order to maximize the expected genetic value in the progeny ( ) under 64 a certain constraint on inbreeding ( ) (Wray and Goddard 1994;Meuwissen 1997;Woolliams et al 65 2015). Optimal cross selection, further referred as OCS, is an extension of the optimal contribution 66 selection to deliver a crossing plan that maximizes under the constraint by considering additional 67 constraints on the allocation of mates in crosses (Kinghorn et al 2009;Kinghorn 2011;Akdemir and 68 Sánchez 2016;Akdemir et al 2018). In the era of genomic selection, the expected 69 genetic value in progeny ( ) to be maximized is defined as the mean of parental GEBV ( ) weighted 70 by parental contributions , i.e.…”

Section: Introductionmentioning

confidence: 99%

“…To obtain optimal solutions for the vector of contributions and the 72 crossing plan, i.e. pairing of candidates, differential evolutionary algorithms have been suggested (Storn 73 and Price 1997;Kinghorn et al 2009;Kinghorn 2011). Using the concept of optimal contribution 74 selection for mating decisions is common in animal breeding (Woolliams et al 2015) and is increasingly 75 adopted in plant breeding (Akdemir and Sánchez 2016;De Beukelaer et al 2017;Lin et al 2017;76 Gorjanc et al 2018;Akdemir et al 2018).…”

Section: Introductionmentioning

confidence: 99%

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Improving short and long term genetic gain by accounting for within family variance in optimal cross selection

Allier

Lehermeier²,

Charcosset

et al. 2019

Preprint

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The implementation of genomic selection in recurrent breeding programs raised several concerns, especially that a higher inbreeding rate could compromise the long term genetic gain. An optimized mating strategy that maximizes the performance in progeny and maintains diversity for long term genetic gain on current and yet unknown future targets is essential. The optimal cross selection approach aims at identifying the optimal set of crosses maximizing the expected genetic value in the progeny under a constraint on diversity in the progeny. Usually, optimal cross selection does not account for within family selection, i.e. the fact that only a selected fraction of each family serves as candidate parents of the next generation. In this study, we consider within family variance accounting for linkage disequilibrium between quantitative trait loci to predict the expected mean performance and the expected genetic diversity in the selected progeny of a set of crosses. These predictions rely on the method called usefulness criterion parental contribution (UCPC). We compared UCPC based optimal cross selection and optimal cross selection in a long term simulated recurrent genomic selection breeding program considering overlapping generations. UCPC based optimal cross selection proved to be more efficient to convert the genetic diversity into short and long term genetic gains than optimal cross selection. We also showed that using the UCPC based optimal cross selection, the long term genetic gain can be increased with only limited reduction of the short term commercial genetic gain.

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“…The optimal contribution selection assumes that contributions will 89 be randomly paired, including selfing. An extension that delivers a practical crossing 90 plan is to jointly optimise contributions and cross allocations (Kinghorn et al 2009;91 Kinghorn 2011). These methods are established in animal breeding (for a review see 92 Woolliams et al (2015)) and are increasingly common in plant breeding (Cowling et The aim of this study was to evaluate the potential of optimal cross selection to 95 balance selection and maintenance of genetic diversity in a two-part program with 96 rapid recurrent genomic selection.…”

mentioning

confidence: 99%

Optimal cross selection for long-term genetic gain in two-part programs with rapid recurrent genomic selection

Gaynor

Hickey

2017

Preprint

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Key message 8Optimal cross selection increases long-term genetic gain of two-part programs with 9 rapid recurrent genomic selection. It achieves this by optimising efficiency of 10 converting genetic diversity into genetic gain through reducing the loss of genetic 11 diversity and reducing the drop of genomic prediction accuracy with rapid cycling. 12 13. CC-BY-NC-ND 4.0 International license not peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/227215 doi: bioRxiv preprint first posted online with optimal cross selection delivered the largest genetic gain that increased with the 27 increasing number of cycles. With four cycles per year optimal cross selection had 28 78% (15%) higher long-term genetic gain than truncation selection with a small 29 (large) number of parents. Higher genetic gain was achieved through higher 30 efficiency of converting genetic diversity into genetic gain; optimal cross selection 31 quadrupled (doubled) efficiency of truncation selection with a small (large) number of 32 parents. Optimal cross selection also reduced the drop of genomic selection accuracy 33 due to the drift between training and prediction populations. In conclusion, optimal 34 cross-selection enables optimal management and exploitation of population 35 improvement germplasm in two-part programs.

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Strategies to Exploit Genetic Variation While Maintaining Diversity

Cited by 18 publications

References 18 publications

Optimal cross selection for long-term genetic gain in two-part programs with rapid recurrent genomic selection

Optimal cross selection for long-term genetic gain in two-part programs with rapid recurrent genomic selection

Improving short and long term genetic gain by accounting for within family variance in optimal cross selection

Optimal cross selection for long-term genetic gain in two-part programs with rapid recurrent genomic selection

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