Daniel J. Tolhurst, R. Chris Gaynor, Brian Gardunia, John M. Hickey & Gregor Gorjanc

Theoretical and Applied Genetics Oct. 2022; vol. 135: 3393–3415

Key message

The integration of known and latent environmental covariates within a single-stage genomic selection approach provides breeders with an informative and practical framework to utilise genotype by environment interaction for prediction into current and future environments.

Abstract

This paper develops a single-stage genomic selection approach which integrates known and latent environmental covariates within a special factor analytic framework. The factor analytic linear mixed model of Smith et al. (2001) is an effective method for analysing multi-environment trial (MET) datasets, but has limited practicality since the underlying factors are latent so the modelled genotype by environment interaction (GEI) is observable, rather than predictable. The advantage of using random regressions on known environmental covariates, such as soil moisture and daily temperature, is that the modelled GEI becomes predictable. The integrated factor analytic linear mixed model (IFA-LMM) developed in this paper includes a model for predictable and observable GEI in terms of a joint set of known and latent environmental covariates. The IFA-LMM is demonstrated on a late-stage cotton breeding MET dataset from Bayer CropScience. The results show that the known covariates predominately capture crossover GEI and explain 34.4% of the overall genetic variance. The most notable covariates are maximum downward solar radiation (10.1%), average cloud cover (4.5%) and maximum temperature (4.0%). The latent covariates predominately capture non-crossover GEI and explain 40.5% of the overall genetic variance. The results also show that the average prediction accuracy of the IFA-LMM is 0.02−0.100.02−0.10 higher than conventional random regression models for current environments and 0.06−0.240.06−0.24 higher for future environments. The IFA-LMM is therefore an effective method for analysing MET datasets which also utilises crossover and non-crossover GEI for genomic prediction into current and future.

See https://link.springer.com/article/10.1007/s00122-022-04186-w

Figure 2: Regression plots for checks C1 and C2 in terms of the first two factors obtained from the a FAM4 and b/c FA4 models. Note: The simple main effects in a and the generalised main effects in b are denoted with closed circles and the growing regions are distinguished by shape. The percentage of additive genetic variance explained by each factor is labelled. The additive GE effects in c have been adjusted for those in b