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Integrated approach to maximize trait selection

There were many great articles concerning cross-disciplinary research at the interface between plant biology, mathematics and computer science before the launch in silico Plants (isP). We are excited for isP to be home to these types of articles in the future.

Breeding for desirable plant traits has always been difficult. Traits are moving targets controlled by a network of genes, which respond to environmental variation and management practices.

Chenu and coauthors propose an integrated approach to maximize trait selection in breeding. Using transpiration efficiency (TE) as an example of a complex trait of interest, the authors illustrate how an integrated approach can guide modelling, phenotyping, and selection in a breeding program. Their approach has three major components:

  1. Use predictive modeling to determine the value of traits, which can vary greatly under different environments and management practices. Modeling allows researchers to extensively characterize the current and projected target population of environments at large scales.
  2. Dissect complex traits (e.g. drought resistance) into underpinning component traits (e.g. TE) that are more environmentally stable and more closely linked to gene expression than the complex trait itself. Detailed experiments of component traits can be used to develop modules within a crop model in order to gain the biological functionality needed to predict crop performance. Insights gained both experimentally and from simulation studies with the improved model can guide the choice of relevant target traits for phenotyping purposes.
  3. Phenotype mapping populations using high-throughput platforms to link phenotype to genotype. Phenotyping of mapping populations allows high-throughput platforms to be the link between genotype and phenotype. A modelling platform can then be used to link crop modelling and whole-genome prediction models.

The authors believe that integrating insights from diverse disciplines can increase the resource use efficiency of breeding programs for improving yield gains in target populations of environments.

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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