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A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data
- Source :
- Scientific Reports 12 (2022) 1, Scientific Reports, 12 (1), Scientific Reports, Scientific Reports, 2022, 12 (1), pp.3177. ⟨10.1038/s41598-022-06935-9⟩, Scientific Reports, 12(1)
- Publication Year :
- 2022
-
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.<br />Scientific Reports, 12 (1)<br />ISSN:2045-2322
- Subjects :
- Agricultural genetics
[SDV.GEN]Life Sciences [q-bio]/Genetics
Multidisciplinary
Statistics
PE&RC
[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
Wiskundige en Statistische Methoden - Biometris
Plant breeding
2417.14 Genética Vegetal
Biometris
FOS: Mathematics
[SDV.BV]Life Sciences [q-bio]/Vegetal Biology
Life Science
1209.03 Análisis de Datos
P-splines, mixed models, spatio-temporal modelling, hierarquical modelling
Mathematical and Statistical Methods - Biometris
Subjects
Details
- Language :
- English
- ISSN :
- 20452322
- Database :
- OpenAIRE
- Journal :
- Scientific Reports 12 (2022) 1, Scientific Reports, 12 (1), Scientific Reports, Scientific Reports, 2022, 12 (1), pp.3177. ⟨10.1038/s41598-022-06935-9⟩, Scientific Reports, 12(1)
- Accession number :
- edsair.doi.dedup.....c593fc0fdf980b928f19ae350aec039e