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Novel methodology helps wheat breeders select for specific climate environments

In a recent study, researchers from Center for Quantitative Genetics and Genomics (QGG) proposed a novel methodology to account for the genotype-by-environment interactions in wheat. The methodology improved the currently available selection models and breeding methodologies.

Photo: Jette Odgaard Villemoes

Developing wheat varieties well adapted to local climate conditions around the world is a priority in wheat breeding programs (WBPs), and the use of efficient selection methods based on genomic selection (GS) is crucial for understanding and exploiting interactions between genotype and environment (G × E).

Bread wheat (Triticum aestivum L.) is the most widely cultivated cereal in the world and plays a critical role in food security, providing almost one-fifth of humans' dietary calories and proteins. Wheat production takes place under a wide range of climatic conditions and geographic regions worldwide. The production is annually affected by different biotic stressors like  insects or diseases and abiotic stressors like drought and water excess, extreme temperatures. The impact of these factors is further exacerbated by climate change, which causes volatility in production in many parts of the world, leading to yield reductions and crop losses. In addition, the differential response of genotypes to the environments in which they are grown (known as genotype-by-environment interactions, G × E) affects production.

Postdoc Miguel Raffo from QGG explains:

-‘In this study, we first incorporated high-dimensional environmental information obtained from soil maps and weather stations across Denmark (e.g. temperature, precipitation, solar radiation) to model interactions between molecular markers and environmental covariates, and then we extended the developed methodology for multi-trait (MT) evaluations which accounted for grain yield and protein content. The results of the study revealed that accounting for G × E via markers-by-environmental covariates interactions and the multi-trait modelling significantly enhanced the performance of genomic selection models, and benefits were boosted when both strategies were combined’.

The research results showed that the proposed methodology represents an efficient method to assist crop breeders in selecting breeding lines of superior adaptation to specific environments, and thus, it can help counteract the negative impact of climate change on production.


Read the full research article here: Integrating a growth degree-days based reaction norm methodology and multi-trait modeling for genomic prediction in wheat (Raffo et al., 2022) Frontiers in Plant Science.

Contact: Postdoc Miguel Raffo, mraffo@qgg.au.dk.