Growth dynamics and heritability for plant high-throughput phenotyping studies using hierarchical functional data analysis

Mapping QTLs for spring green-up, plant vigor, and plant biomass in two lowland switchgrass populations

Switchgrass (Panicum virgatum L.) is an important perennial C4 species due to its large potential for cellulosic bioenergy feedstock production. Identification of quantitative trait loci (QTL) controlling important developmental traits is valuable to understanding the genetic basis and using marker-assisted selection (MAS) in switchgrass breeding. One F1 hybrid population derived from NL94 (♀) × SL93 (♂) and one S1 (first-generation selfed) population from NL94 were used in this study. Both the populations showed significant variations for genotype and genotype by environment interactions for three traits studied: plant vigor, spring green-up, and plant biomass. Plant vigor had strong and positive correlations with plant biomass in both populations. Broad-sense heritability estimates for plant vigor ranged from 0.46 to 0.74 and 0.45 to 0.74 in the hybrid and selfed population, respectively. Spring green-up had similar heritability estimates, 0.42-0.78 in the hybrid population, and 0.47-0.82 in the selfed population. Heritability of plant biomass was 0.54-0.64 in the hybrid population and 0.64-0.74 in the selfed population. Fifteen QTLs for spring green-up, 6 QTLs for plant vigor, and 3 QTLs for biomass yield were detected in the hybrid population, whereas 4 QTLs for spring green-up, 4 QTLs for plant vigor, and 1 QTL for biomass yield were detected in the selfed population. Markers associated with these QTLs can be used in MAS to accelerate switchgrass breeding program. This study provided new information in understanding the genetic control of biomass components and demonstrated substantial heterotic vigor that could be explored for breeding hybrid cultivars in switchgrass.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01296-7.

From multivariate to functional data analysis: fundamentals, recent developments, and emerging areas

Functional data analysis (FDA), which is a branch of statistics on modeling infinite dimensional random vectors resided in functional spaces, has become a major research area for Journal of Multivariate Analysis. We review some fundamental concepts of FDA, their origins and connections from multivariate analysis, and some of its recent developments, including multi-level functional data analysis, high-dimensional functional regression, and dependent functional data analysis. We also discuss the impact of these new methodology developments on genetics, plant science, wearable device data analysis, image data analysis, and business analytics. Two real data examples are provided to motivate our discussions.

A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data

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.