Predicting yield traits of individual field-grownBrassica napusplants from rosette-stage leaf gene expression

Abstract

BackgroundIn the plant sciences, results of laboratory studies often do not translate well to the field because lab growth conditions are very different from field conditions. To help close this lab-field gap, we developed a new strategy for studying the wiring of plant traits directly in the field, based on molecular profiling and phenotyping of individual plants of the same genetic background grown in the same field. This single-plant omics strategy leverages uncontrolled micro-environmental variation across the field and stochastic variation among the individual plants as information sources, rather than controlled perturbations. Here, we use single-plant omics on winter-typeBrassica napus(rapeseed) plants to investigate to what extent rosette-stage gene expression profiles can be linked to the early and late phenotypes of individual field-grown plants.ResultsWe find that rosette leaf gene expression in autumn has substantial predictive power for both autumnal leaf phenotypes and final yield in spring. Many of the top predictor genes are linked to developmental processes known to occur in autumn in winter-typeB. napusaccessions, such as the juvenile-to-adult and vegetative-to-reproductive phase transitions, indicating that the yield potential of winter-typeB. napusis influenced by autumnal development.ConclusionsOur results show that profiling individual plants under uncontrolled field conditions is a valid strategy for identifying genes and processes influencing crop yield in the field.

The winner’s curse under dependence: repairing empirical Bayes using convoluted densities

“…For this simulation we use a dataset on 62 field-grown B. napus plants of the same genetic background for which the leaf transcriptome was measured in November 2016, and a number of phenotypes were measured at harvest in June 2017 [De Meyer et al, 2023]. Six phenotypes where chosen to be predicted from the transcriptome: leaf 8 width and length, leaf count, total seed count, plant height and total shoot dry weight.…”

Plant science in the age of simulation intelligence

“…On the other end of the spectrum, entirely data-driven models based on machine learning algorithms often lack interpretable parameters. The latter group of models is vital in breeding [e.g., genomic prediction (Korte and Farlow, 2013;Hickey et al, 2017;De Meyer et al, 2023)], but also in greenhouse climate control (Hemming et al, 2020). Consequently, models often only operate on a single point in the tempo-spatial domain, limiting their use beyond their initial conceptualization.…”