The use of high-throughput phenotyping in genomic selection context

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. First, we provide a brief introduction showing the utility of genomic data in plant breeding applications. Then, 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.