genotype by environment interactions (gxe) - van etten
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Presentation by Jacob van Etten. CCAFS workshop titled "Using Climate Scenarios and Analogues for Designing Adaptation Strategies in Agriculture," 19-23 September in Kathmandu, Nepal.TRANSCRIPT
Genotype by environment interactions (GxE) and climate change
Jacob van Etten
G x E
P = G + E + G*E
P = ProductionG = GenotypeE = Environment
In other words...
Suppose we have a continuous environmental variable two different genotypes i = {1,2}
Now the regression equation becomes:
P = (β0 +) β1Gi + β2E + β3GiE (+ e)
Purely genetic difference
P = β1Gi + β2E + β3GiE
β1 ≠ 0
β2 = 0
β3 = 0
Purely additive interaction
P = β1Gi + β2E + β3GiE
β1 ≠ 0
β2 ≠ 0
β3 = 0
Non-additive interaction
P = β1Gi + β2E + β3GiE
β1 ≠ 0
β2 ≠ 0
β3 ≠ 0
Cross-over: When does another variety take over?
climate change →
Lobell et al. (2011) study
Weather variables
YieldEcophysiological variables
Weather variables
YieldEcophysiological variables
+ 1 °C
Estimate statistical
modelCalculate
Recalculate
Predict using estimated
model
Lobell et al. (2011)
Climate change impact maps
GxE: OPV vs hybrid
GxE: duration
What breeders usually do: AMMI
Additive Main Effects and Multiplicative Interaction
Production = G + E + residuals
Then do a PCA on residuals to visualize the GxE interactions.
(An alternative is to only remove G – known as GGE.)
Example of AMMI Biplot
Stress
Normal
RDA: adding environmental variables
Variety adaptation zones
Final remarks
Statistical models complement mechanistic models Use real data for places where mechanistic models have not been calibrated Get an estimate of the error Link to daily plant breeding practice!
Package weatherData to get weather data for trial locations and derive ecophysiological variables