Abstract
High throughput phenotyping (HTP) platforms and devices are increasingly used for the characterization of growth and developmental processes for large sets of plant genotypes. Such HTP data require challenging statistical analyses in which longitudinal genetic signals need to be estimated against a background of spatio-temporal noise processes. We propose a two-stage approach for the analysis of such longitudinal HTP data. In a first stage, we correct for design features and spatial trends per time point. In a second stage, we focus on the longitudinal modelling of the spatially corrected data, thereby taking advantage of shared longitudinal features between genotypes and plants within genotypes. We propose a flexible hierarchical three-level P-spline growth curve model, with plants/plots nested in genotypes, and genotypes nested in populations. For selection of genotypes in a plant breeding context, we show how to extract new phenotypes, like growth rates, from the estimated genotypic growth curves and their first-order derivatives. We illustrate our approach on HTP data from the PhenoArch greenhouse platform at INRAE Montpellier and the outdoor Field Phenotyping platform at ETH Zürich.
Highlights
High throughput phenotyping (HTP) platforms and devices are increasingly used for the characterization of growth and developmental processes for large sets of plant genotypes
References for stage-wise analysis of HTP data include the work by van Eeuwijk et al.[2], where the authors propose to first estimate time-series of spatially adjusted genotypic means from low-level phenotypic traits, that are subject to temporal dynamic modelling
In agricultural and breeding research, non-destructive data acquisition of phenotypic traits by HTP platforms has emerged in recent years as a rich source of new information on plant growth and development as well as on genotypic performance
Summary
High throughput phenotyping (HTP) platforms and devices are increasingly used for the characterization of growth and developmental processes for large sets of plant genotypes. References for stage-wise analysis of HTP data include the work by van Eeuwijk et al.[2], where the authors propose to first estimate time-series of spatially adjusted genotypic means from low-level phenotypic traits, that are subject to temporal dynamic modelling. The purpose of this stage and subsequent correction is two-fold: (1) to remove nuisance spatial variation from the phenotypic data; and (2) to keep the data resolution for the second stage at the level of the experimental unit (through the incorporation in the correction of the residual component) This is one of the main differences to the proposals described in van Eeuwijk et al.[2] and Kar et al.[12], and it is routinely applied for data derived from the field phenotyping platform of ETH Zürich[15,16,17]. We choose SpATS for this first stage
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