Back to Search Start Over

Predicting yield of individual field-grown rapeseed plants from rosette-stage leaf gene expression.

Authors :
De Meyer S
Cruz DF
De Swaef T
Lootens P
De Block J
Bird K
Sprenger H
Van de Voorde M
Hawinkel S
Van Hautegem T
Inzé D
Nelissen H
Roldán-Ruiz I
Maere S
Source :
PLoS computational biology [PLoS Comput Biol] 2023 May 30; Vol. 19 (5), pp. e1011161. Date of Electronic Publication: 2023 May 30 (Print Publication: 2023).
Publication Year :
2023

Abstract

In the plant sciences, results of laboratory studies often do not translate well to the field. To help close this lab-field gap, we developed a strategy for studying the wiring of plant traits directly in the field, based on molecular profiling and phenotyping of individual plants. Here, we use this single-plant omics strategy on winter-type Brassica napus (rapeseed). We investigate to what extent early and late phenotypes of field-grown rapeseed plants can be predicted from their autumnal leaf gene expression, and find that autumnal leaf gene expression not only has substantial predictive power for autumnal leaf phenotypes but also for final yield phenotypes in spring. Many of the top predictor genes are linked to developmental processes known to occur in autumn in winter-type B. napus accessions, such as the juvenile-to-adult and vegetative-to-reproductive phase transitions, indicating that the yield potential of winter-type B. napus is influenced by autumnal development. Our results show that single-plant omics can be used to identify genes and processes influencing crop yield in the field.<br />Competing Interests: The authors have declared that no competing interests exist.<br /> (Copyright: © 2023 De Meyer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Details

Language :
English
ISSN :
1553-7358
Volume :
19
Issue :
5
Database :
MEDLINE
Journal :
PLoS computational biology
Publication Type :
Academic Journal
Accession number :
37253069
Full Text :
https://doi.org/10.1371/journal.pcbi.1011161