Abstract
Genomic prediction of complex traits, say yield, benefits from including information on correlated component traits. Statistical criteria to decide which yield components to consider in the prediction model include the heritability of the component traits and their genetic correlation with yield. Not all component traits are easy to measure. Therefore, it may be attractive to include proxies to yield components, where these proxies are measured in (high-throughput) phenotyping platforms during the growing season. Using the Agricultural Production Systems Simulator (APSIM)-wheat cropping systems model, we simulated phenotypes for a wheat diversity panel segregating for a set of physiological parameters regulating phenology, biomass partitioning, and the ability to capture environmental resources. The distribution of the additive quantitative trait locus effects regulating the APSIM physiological parameters approximated the same distribution of quantitative trait locus effects on real phenotypic data for yield and heading date. We use the crop growth model APSIM-wheat to simulate phenotypes in three Australian environments with contrasting water deficit patterns. The APSIM output contained the dynamics of biomass and canopy cover, plus yield at the end of the growing season. Each water deficit pattern triggered different adaptive mechanisms and the impact of component traits differed between drought scenarios. We evaluated multiple phenotyping schedules by adding plot and measurement error to the dynamics of biomass and canopy cover. We used these trait dynamics to fit parametric models and P-splines to extract parameters with a larger heritability than the phenotypes at individual time points. We used those parameters in multi-trait prediction models for final yield. The combined use of crop growth models and multi-trait genomic prediction models provides a procedure to assess the efficiency of phenotyping strategies and compare methods to model trait dynamics. It also allows us to quantify the impact of yield components on yield prediction accuracy even in different environment types. In scenarios with mild or no water stress, yield prediction accuracy benefitted from including biomass and green canopy cover parameters. The advantage of the multi-trait model was smaller for the early-drought scenario, due to the reduced correlation between the secondary and the target trait. Therefore, multi-trait genomic prediction models for yield require scenario-specific correlated traits.
Highlights
With the availability of low-cost genotyping, genomic prediction has become an attractive tool to increase the number of genotypes considered for selection (Poland et al, 2012; Crossa et al, 2013; Hickey et al, 2014) and to speed up the breeding cycle (Cooper et al, 2014; Haghighattalab et al, 2016; Araus et al, 2018)
Besides the simulated intermediate traits biomass and canopy cover, we evaluated the impact of using basic traits that are lower in the trait hierarchy, and that correspond to the physiological mechanisms of response to the environment (APSIM physiological parameters) on yield prediction accuracy
The temporal changes in trait correlations give insight about which traits are contributing to end-of-season yield outcomes at specific moments within the season. These dynamics influence the potential of secondary traits like biomass or canopy cover to improve prediction accuracy of the target trait when included simultaneously in a multi-trait model (Figure 8 in Bustos-Korts et al, 2019)
Summary
With the availability of low-cost genotyping, genomic prediction has become an attractive tool to increase the number of genotypes considered for selection (Poland et al, 2012; Crossa et al, 2013; Hickey et al, 2014) and to speed up the breeding cycle (Cooper et al, 2014; Haghighattalab et al, 2016; Araus et al, 2018). Additive and non-additive effects for the target trait (e.g. yield) are estimated in a training set of genotypes, which has genotypic and phenotypic observations. In some cases (in small plots, for example), breeders may wish to use the secondary traits directly for selection (e.g. for screening maturity or crop height) within season and discard unwanted genotypes prior to harvest for the generation of testing. In this case, the interest may be in correlating secondary traits in small plots with expected yield in larger plots in the season
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