Combining crop growth modelling and statistical genetic modelling to evaluate phenotyping strategies

Transcription

1 Combining crop growth modelling and statistical genetic modelling to evaluate phenotyping strategies Daniela Bustos-Korts, Marcos Malosetti, Martin Boer, Scott Chapman, Karine Chenu, Fred van Eeuwijk

2 Background Yield is a complex trait, strong context-dependencies (GxE, epistasis). GxE is the result of the interplay between multiple traits and the environment during the growing season. High throughput phenotyping (HTP) allows to improve GxE characterization through and increased phenotyping intensity The value of additional phenotyping should be assessed by its potential to improve yield predictions. 2

3 Modelling steps for target trait prediction 3

4 Removing extraneous variation (spatial adjustment) Modelling phenotypes over time 3D-approaches also possible / desirable (rows, columns, time) 4

5 Yield as a function of genotype, environment and underlying phenotypes 5

6 Statistical G2P models to incorporate additional phenotypic information ) ρρ 2,(ss,ff h 2,ss > h 2,ff Correlated response to selection Select genotypes in the platform Advantageous IF correlated selection response is larger than when selecting directly on the field itself Platform Field 2,(ss,ff ) ρρ = pppppppppppppppp ffffffffff gggggggggggggg cccccccccccccccccccccc h 2,ss = heeeeeeeeeeeeeeeeeeeeee aaaaaaaaaaaaaaaaaaaa ppppppppppppppppppppp h 2,ss = heeeeeeeeeeeeeeeeeeeeee oooo ttttt tttttttttttt tttttttttt yy ss ii = μμ ss ii + GG ss ss ii + εε ii yy ff ii = μμ ff ii + GG ff ff ii + εε ii Multi-trait prediction yy kk iiii = μμ kk iiii + GG kk kk iiii + εε iiii ΣΣ: {GG iijj kk }~MMMMMM(00, ΣΣ) μμ kk iijj fixed intercept for trait k (field and platform) GG kk iijj the random trait-specific genetic effect Factorial regression yy iiii ff = μμ jj ff + gg ii ff + gggg iiii ff + εε iiii ff gggg iiii ff = cc CC,dd DD θθ cccc yy(xx ii ) ss cc zz ee ff jjjj + δδ iiii yy(xx ii ) ss cc = gggggggggggggggggg cccccccccccccccccccccc (pppppppppppppppp) zz jjjj ee = eeeeeeeeeeeeeeeeeeeeeeeeee ccccccccccccccccccccccccccccccc Structural equations and networks Conditional (in)dependencies between variables (see presentations by Willem Kruijer and Pariya Berhouzi) δδ iiii ff = llllllll oooo ffffff 6

7 When is this condition fulfilled in our phenotyping schedule? ) ρρ 2,(ss,ff h 2,ss > h 2,ff ) Trait correlations (ρρ 2,(ss,ff ) change across environments and over time Heritability in the platform/phenotyping device (h 2,ss ) is technology and time-dependent Difficult to evaluate phenotyping scenarios with real data Statistical genetic + crop growth models: Provide a characterization of the genotypic response over time (population), over multiple environments and traits This simulated data is useful to compare models and assess phenotyping schedules 7

8 Using G2P models to generate phenotypic data Add experimental and measurement error Sensitivities to the environment in a hypothetical population Fully deterministic data for multiple genotypes, environments and over time Genotypic data to get estimates for ranges and genotypic covariances Environmental covariables (climate, soil, management) 8

9 Application 1: Long-term characterization of adaptation patterns Which locations are more likely to represent the relevant ) environment types? ρρ 2,(ss,ff is reduced by GxE Training environments for genomic prediction How many trials per environment type? GxE clearly driven by water deficit patterns C= no drought A= drought Example: when environment types are homogeneous, replication can be reduced. Environment type 4 trials per environment type 2 trials per environment type B= late drought Accuracy avsed Accuracy avsed A (drought) B (late drought) C (no drought)

10 Application 2: Using APSIM output to identify traits that are relevant for adaptation Networks help visualizing trait relationships (here; biomass plays a central role for yield) Adaptive mechanisms differ between environments (dry=ll, lower limit for water uptake) Traits can work in modules (clusters of highly correlated traits, which one to phenotype?) Drought No drought Networks estimated with pcgen algorithm: see presentations Willem Kruijer 10

11 Application 3: Assessing the potential of traits measured during the growing season to improve yield predictions ) ρρ 2,(ss,ff h 2,ss > h 2,ff 2,(ss,ff ) ρρ = tttttttttt gggggggggggggg cccccccccccccccccccccc h 2,ss = heeeeeeeeeeeeeeeeeeeeee tttttttttt mmmmmmmmmmmmmmmm wwwwwww HHHHHH h 2,ss = heeeeeeeeeeeeeeeeeeeeee iiii ttttt ffffffffff Trait correlations over time are a natural output from crop growth models h 2,ss can be improved by modelling time points simultaneously 22,(ss,ff) ρρ A= drought B= late drought C= no drought Biomass Asymptote Maximum rate Inflection point Days after sowing 11

12 Phenotyping and genomic prediction: How precise and how often do we need to measure additional traits to improve yield prediction? Heritability increases when using mixed model p-splines to summarize biomass over time (compared to single time points) Martin Boer h 2,ss curve parameters Phenotyping frequency 25 days 15 days Phenotyping frequency Single-time point h 2,ss 5 days

13 Phenotyping and genomic prediction Yield prediction accuracy benefits from including spline predictions in a multi-trait mixed model. Larger improvement in those environments with a large trait correlation A= drought B= late drought C= no drought Single trait Measurement + - error

14 Summary Incorporating additional phenotypic information can increase prediction accuracy for the target trait. Prediction accuracy will be improved if traits are genetically correlated and if the heritability of the secondary phenotype is larger than the heritability of yield A combination of crop growth models and statistical genetic models can help designing a phenotyping strategy by generating multiple traits across environments and over time Questions that can be addressed with the crop growth model output concern: How many trials per environment type to use (related to GxE structure) Overview of trait correlations (networks can give insight) Evaluate effects of phenotyping frequency and heritability on prediction accuracy. 14

15 Combining crop growth modelling and statistical genetic modelling to evaluate phenotyping strategies Daniela Bustos-Korts, Marcos Malosetti, Martin Boer, Scott Chapman, Karine Chenu, Fred van Eeuwijk Thanks for your attention!!!

16 Characterizing the genetic basis for GxE dynamics QTL effects change over time Relate QTL effects to the timing and magnitude of environmental stress A= drought B= late drought C= no drought 16