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Call for a new paradigm for crop improvement

The use of mechanistic crop models can optimize the efficiency of breeding programs.

There is a need to accelerate breeding to meet growing food demand. Improved understanding of gene-to-phenotype (G2P) models of traits and effective crop modelling can play a large role in accelerating breeding, and thus help achieve this goal.

Breeding programs focus on multiple trait targets for genetic improvement. These traits can be anything from disease resistance, improved yield under drought, to grain quality. Breeding programs apply selection methods for multiple cycles to produce a continuous sequence of new cultivars with progressively improving trait performance.

A review by Professor Mark Cooper of The University of Queensland and colleagues proposes a framework linking mechanistic crop models (CGM) with statistical gene-to-phenotype models (CGM-G2P) to enhance the understanding of the genetic architecture of complex traits, such as grain yield of crops, and to improve prediction applications for breeding.

The breeder’s equation provides a modelling framework, grounded in quantitative genetics theory, for predicting response to selection for a breeding program. “We are considering developments of the breeder’s equation that incorporate a suitable CGM to capture our understanding of the genetic architecture of complex traits, such as grain yield of crops, and enhance prediction of response to selection. The CGM provides a robust mathematical framework, grounded by decades of ecophysiology research, to account for complex non-additive interactions due to trait-by-trait interactions and trait-by-environment interactions that are otherwise difficult to predict with our more classical G2P models of quantitative genetics,” says Cooper.

According to the authors, this work has important implications for plant improvement.  “Use of the CGM-G2P could significantly improve our ability to predict the links between genotypic information and phenotype expression in diverse environments, thus accelerating breeding, which is increasingly important as we attempt to meet yield improvement goals in the face climate change” says Cooper. “We have demonstrated proof-of-concept of the methodology to accelerate breeding of drought tolerant maize hybrids for the US corn-belt. We are now exploring a broader range of applications for additional crops and geographies.”

A note to educators: this article is an excellent educational resource. It provides a detailed description of the breeder’s equation together with the quantitative genetic theory behind the equation, a genetics primer, and definitions of common terms their uses for plant breeding and quantitative genetics.

Rachel Shekar

Rachel (she/her) is a Founding and Managing Editor of in silico Plants. She has a Master’s Degree in Plant Biology from the University of Illinois. She has over 15 years of academic journal editorial experience, including the founding of GCB Bioenergy and the management of Global Change Biology. Rachel has overseen the social media development that has been a major part of promotion of both journals.

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