Deterministic and stochastic modelling of impacts from genomic selection and phenomics on genetic gain for perennial ryegrass dry matter yield

M Z Z Jahufer,Mingzhu Sun,Wen-Hsi Yang,Alan V Stewart,Marty J Faville,Vivi Arief,Brent Barrett,Richard M George,Ian H Delacy,Kaye E Basford,Sai Krishna Arojju,Andrew G Griffiths,Dongwen Luo,Valerio Hoyos-Villegas,Colin Eady,Kioumars Ghamkhar,Will Clayton

doi:10.1038/s41598-021-92537-w

M Z Z Jahufer, Mingzhu Sun + Show 15 more

Open Access

https://doi.org/10.1038/s41598-021-92537-w

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Abstract

Increasing the efficiency of current forage breeding programs through adoption of new technologies, such as genomic selection (GS) and phenomics (Ph), is challenging without proof of concept demonstrating cost effective genetic gain (∆G). This paper uses decision support software DeltaGen (tactical tool) and QU-GENE (strategic tool), to model and assess relative efficiency of five breeding methods. The effect on ∆G and cost ($) of integrating GS and Ph into an among half-sib (HS) family phenotypic selection breeding strategy was investigated. Deterministic and stochastic modelling were conducted using mock data sets of 200 and 1000 perennial ryegrass HS families using year-by-season-by-location dry matter (DM) yield data and in silico generated data, respectively. Results demonstrated short (deterministic)- and long-term (stochastic) impacts of breeding strategy and integration of key technologies, GS and Ph, on ∆G. These technologies offer substantial improvements in the rate of ∆G, and in some cases improved cost-efficiency. Applying 1% within HS family GS, predicted a 6.35 and 8.10% ∆G per cycle for DM yield from the 200 HS and 1000 HS, respectively. The application of GS in both among and within HS selection provided a significant boost to total annual ∆G, even at low GS accuracy rA of 0.12. Despite some reduction in ∆G, using Ph to assess seasonal DM yield clearly demonstrated its impact by reducing cost per percentage ∆G relative to standard DM cuts. Open-source software tools, DeltaGen and QuLinePlus/QU-GENE, offer ways to model the impact of breeding methodology and technology integration under a range of breeding scenarios.

Highlights

Increasing the efficiency of current forage breeding programs through adoption of new technologies, such as genomic selection (GS) and phenomics (Ph), is challenging without proof of concept demonstrating cost effective genetic gain (∆G)
The precision of genetic gain estimated using breeding Eq (3) will depend on the accuracy of the phenomics method applied. (c) Standard phenotypic among HS family selection and within HS family genomic selection ( ApWgs): This breeding method consists of the steps: (i) select elite HS families, using a predetermined selection pressure, based on standard phenotypic measurements as per (a); (ii) equal numbers of remnant seed from each selected HS family are randomly sampled, seedlings established and individually genotyped, individual genomic-estimated breeding values (GEBV’s) determined using genomic prediction, and the best seedlings within each family based on their GEBV’s are selected on a predetermined selection pressure; (iii) the selected individuals become the parents of the generation
Applying the among HS family phenotypic selection method (Ap), at a selection pressure of 20%, to the seasonal dry matter (DM) yield data, predicted a 1.43% increase in DM yield above the population mean of 200 HS families (Table 2)