Maize green leaf area index dynamics: genetic basis of a new secondary trait for grain yield in optimal and drought conditions.
Journal
TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
ISSN: 1432-2242
Titre abrégé: Theor Appl Genet
Pays: Germany
ID NLM: 0145600
Informations de publication
Date de publication:
05 Mar 2024
05 Mar 2024
Historique:
received:
07
12
2022
accepted:
03
02
2024
medline:
5
3
2024
pubmed:
5
3
2024
entrez:
5
3
2024
Statut:
epublish
Résumé
Green Leaf Area Index dynamics is a promising secondary trait for grain yield and drought tolerance. Multivariate GWAS is particularly well suited to identify the genetic determinants of the green leaf area index dynamics. Improvement of maize grain yield is impeded by important genotype-environment interactions, especially under drought conditions. The use of secondary traits, that are correlated with yield, more heritable and less prone to genotype-environment interactions, can increase breeding efficiency. Here, we studied the genetic basis of a new secondary trait: the green leaf area index (GLAI) dynamics over the maize life cycle. For this, we used an unmanned aerial vehicle to characterize the GLAI dynamics of a diverse panel in well-watered and water-deficient trials in two years. From the dynamics, we derived 24 traits (slopes, durations, areas under the curve), and showed that six of them were heritable traits representative of the panel diversity. To identify the genetic determinants of GLAI, we compared two genome-wide association approaches: a univariate (single-trait) method and a multivariate (multi-trait) method combining GLAI traits, grain yield, and precocity. The explicit modeling of correlation structure between secondary traits and grain yield in the multivariate mixed model led to 2.5 times more associations detected. A total of 475 quantitative trait loci (QTLs) were detected. The genetic architecture of GLAI traits appears less complex than that of yield with stronger-effect QTLs that are more stable between environments. We also showed that a subset of GLAI QTLs explains nearly one fifth of yield variability across a larger environmental network of 11 water-deficient trials. GLAI dynamics is a promising grain yield secondary trait in optimal and drought conditions, and the detected QTLs could help to increase breeding efficiency through a marker-assisted approach.
Identifiants
pubmed: 38441678
doi: 10.1007/s00122-024-04572-6
pii: 10.1007/s00122-024-04572-6
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
68Subventions
Organisme : Association Nationale de la Recherche et de la technologie
ID : 2015/1190
Informations de copyright
© 2024. The Author(s).
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