Optimising desired gain indices to maximise selection response

Reem Joukhadar; Yongjun Li; Rebecca Thistlethwaite; Kerrie L Forrest; Josquin F Tibbits; Richard Trethowan; Matthew J Hayden

doi:10.3389/fpls.2024.1337388

Optimising desired gain indices to maximise selection response

Front Plant Sci. 2024 Jun 24:15:1337388. doi: 10.3389/fpls.2024.1337388. eCollection 2024.

Authors

Reem Joukhadar¹, Yongjun Li¹, Rebecca Thistlethwaite², Kerrie L Forrest¹, Josquin F Tibbits¹, Richard Trethowan^{2

3}, Matthew J Hayden^{1

4}

Affiliations

¹ Agriculture Victoria, Centre for AgriBioscience, AgriBio, Bundoora, VIC, Australia.
² School of Life and Environmental Sciences, Plant Breeding Institute, Sydney Institute of Agriculture, The University of Sydney, Narrabri, NSW, Australia.
³ School of Life and Environmental Sciences, Plant Breeding Institute, Sydney Institute of Agriculture, The University of Sydney, Cobbitty, NSW, Australia.
⁴ School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia.

Abstract

Introduction: In plant breeding, we often aim to improve multiple traits at once. However, without knowing the economic value of each trait, it is hard to decide which traits to focus on. This is where "desired gain selection indices" come in handy, which can yield optimal gains in each trait based on the breeder's prioritisation of desired improvements when economic weights are not available. However, they lack the ability to maximise the selection response and determine the correlation between the index and net genetic merit.

Methods: Here, we report the development of an iterative desired gain selection index method that optimises the sampling of the desired gain values to achieve a targeted or a user-specified selection response for multiple traits. This targeted selection response can be constrained or unconstrained for either a subset or all the studied traits.

Results: We tested the method using genomic estimated breeding values (GEBVs) for seven traits in a bread wheat (Triticum aestivum) reference breeding population comprising 3,331 lines and achieved prediction accuracies ranging between 0.29 and 0.47 across the seven traits. The indices were validated using 3,005 double haploid lines that were derived from crosses between parents selected from the reference population. We tested three user-specified response scenarios: a constrained equal weight (INDEX1), a constrained yield dominant weight (INDEX2), and an unconstrained weight (INDEX3). Our method achieved an equivalent response to the user-specified selection response when constraining a set of traits, and this response was much better than the response of the traditional desired gain selection indices method without iteration. Interestingly, when using unconstrained weight, our iterative method maximised the selection response and shifted the average GEBVs of the selection candidates towards the desired direction.

Discussion: Our results show that the method is an optimal choice not only when economic weights are unavailable, but also when constraining the selection response is an unfavourable option.

Keywords: desired gain indices; genomic estimated breeding values; genomic prediction; genomic selection; selection indices.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The authors acknowledge the financial support of the University of Sydney, and Agriculture Victoria. The authors acknowledge the financial support of the Grains Research and Development Corporation. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.