Abstract
Abstract: One of the biggest challenges that breeders face is the development of improved cultivars in changing climate conditions posing extra challenges to their labor. On the other hand, the availability of data generated with automated systems offers an opportunity to characterize genetically and phenotypically genotypes with high detail. Modern sequencing technologies delivering hundreds of thousands of molecular makers, offered the opportunity of selecting genotypes without the need of observing these in fields and this methodology was coined as Genomic Selection (GS). More recently, sophisticated automated phenotyping platforms depending on sensors able to measure a large number of plant features were also developed and have shown potential in plant breeding applications. These modern phenotyping systems that attempt to efficiently deliver phenotypic information on secondary traits are also know as high-throughput phenotyping platforms (HTPPs). The integration of HTPP with GS models opened a new research front to improve the efficiency of the selection methods based on genomic data only, specially of those traits depending on a large number of genes with small effects (complex traits). However, there are still remaining some issues to solve for developing a robust methodology able to combine in an efficient and informed way these two high dimensional data types. In this document, we provide an overview of the statistical analysis of the data derived of the HTTPs for improving the predictive ability of conventional GS models. We provide a brief introduction showing the utility of genomic data in plant breeding applications. After, we provide an overview of the field-based HTPPs considering the light detection and ranging and the unmanned aerial vehicles and how the image data derived from these platforms can be used to accelerate genetic gains. After that, we discuss about the extension of the conventional GS models to allow the incorporation of data derived of the HTPPs as main effects and also in interaction with environmental factors. The availability of several sources of information have opened a venue to investigate besides the univariate or single trait model, models based on multiple traits and also models that consider multiple time measures allowing longitudinal GS studies. Finally, we provide some conclusions as well as we mention some the current issues that do not allow to fully exploit the potential of HTTPs in plant breeding applications.
Highlights
The world faces an increased demand in the production of food for human consumption for a growing population, there is increased demand for land and water resources to establish plant cultivars (Fróna et al 2019)
The indoors tools are designed for small experiments in a greenhouse where the environmental conditions can be controlled and manipulated up to some extent
High-throughput phenotyping and phenomics are essential tools that provide different ways to record plant development, to learn about the genetic basis of quantitative traits, and to leverage the interaction between genotypes and environment in prediction models. The use of this methodology (HTP) has the potential to change the way of how breeders select their material for advancement in breeding programs
Summary
The world faces an increased demand in the production of food for human consumption for a growing population, there is increased demand for land and water resources to establish plant cultivars (Fróna et al 2019). Technological advancements based on remote sensing have been used to develop High Throughput Phenotyping (HTP) platforms with applications in plant breeding programs These platforms have revolutionized the way for collecting phenotypic information in a faster and efficient way helping breeders to reduce. HTP platforms can examine, quantify, and precisely assess phenotypes in agricultural experiments by using sensors and data collection systems employing low or no physical human intervention known as labor non-destructive (Li et al 2021) This methodology is able to produce useful data for selecting new lines and for developing prediction models to predict new traits. The generated data may be used as the primary trait response or as a covariate associated with the principal trait response, usually grain yield
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