F. Genotype by Environment Interaction

Genotype-by-Environment (G-by-E) Interaction is a topic that almost all scientists working with field crops know. Because of the varying environment conditions from year to year and location to location, we do not expect to observe same measurements for a wide range of agronomic traits. In fact, this almost determines that it is probably more challenging to work in agriculture than in other scientific disciplines.

Integrated analysis of genotype by environment can reveal the pattern and mechanistic interplay underlying the observed phenotype dynamics. A critical question needs to be answered to enhance our ability to conduct genomic and environmental analysis of varied phenotypic plasticity observed in natural field conditions: How to uncover patterns at different levels to facilitate complex trait dissection and performance prediction. Please check out our exciting findings from a 8-year study,

Genomic and environmental determinants and their interplay underlying phenotypic plasticity

"This is a small step in transforming a traditional approach, but a huge leap in understanding of plasticity and predicting the performance." - Jianming Yu, 2018

This is an outstanding review paper on G-by-E! Genotype-by-environment interaction and plasticity: Exploring genomic responses of plants to the abiotic environment.

Gene–environment-wide association studies: emerging approaches

Our recent research suggests that this G-by-E can be explained by a gene-by-gene interaction, which produces the genotypic values for different genotypes under a specific environment.

This is a new paper that works between Crop Model and Genomic Prediction: Integrating crop growth models with whole genome prediction through approximate Bayesian computation. Very interesting in general! Mark Cooper presented the research as the 2014 ASA meeting. Some comments: the ABC part may not be essential possibly due to my limited understanding. The integration of crop model and genomic prediction is interesting. The simulation part can be improved (one chromosome for all those 40 QTLs is too crowded?). Those nonlinear relationship between yield and other traits did not mean to allow GBLUP to be great in the first place. However, always easier to comment than doing something new. Great example showing the general direction!

Let's forget all statistics you know about. This is something many view as an ultimate synthesis platform that has the flexibility and capacity to deal with G x E, Crop Model, The Agricultural Production Systems sIMulator (APSIM) is internationally recognised as a highly advanced simulator of agricultural systems. Check out this review that combines crop models in physiology with genetic mapping in complex trait dissection, Adapting APSIM to model the physiology and genetics of complex adaptive traits in field crops.

Marcos Malosetti Jean-Marcel Ribaut and Fred A. van Eeuwijk published a comprehensive review and method demonstration paper at Frontiers in Physiology, The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis.

Scientists in Pioneer published this review and perspective paper, Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction.

Always interesting to see what human geneticists are doing with G x E (one contributor to the missing heritability?). Check out some of these presentation at G x E 2010.

Here is a simple diagram that we are trying to work on: Integrated Modeling Approach for Performance Prediction (IMAPP)