Justin Blancon, Clément Buet, Pierre Dubreuil, Marie‑Hélène Tixier, Frédéric Baret, Sébastien Praud.

Theoretical and Applied Genetics; March 2024; vol.137; article 68

Abstract

Key message 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. Abstract 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 genotypeenvironment interactions, can increase breeding efciency. 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-defcient 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-efect QTLs that are more stable between environments. We also showed that a subset of GLAI QTLs explains nearly one ffth of yield variability across a larger environmental network of 11 water-defcient trials. GLAI dynamics is a promising grain yield secondary trait in optimal and drought conditions, and the detected QTLs could help to increase breeding efciency through a marker-assisted approach.

See https://link.springer.com/article/10.1007/s00122-024-04572-6

Figure 1: Estimation of 24 parameters derived from maize GLAI dynamics. A Two bent-cable regressions were fitted, one during the vegetative phase and the other during the senescence phase, to estimate four slopes (S_EV, S_LV, S_SS, S_RS) and to define five distinct phases during the development cycle. B The phases previously defined were used to derive eight areas under the curve (AUC_EV, AUC_LV, AUC_V, AUC_F, AUC_SS, AUC_RS, AUC_S, AUC_C) and the eight corresponding durations (D_EV, D_LV, D_V, D_F, D_SS, D_RS, D_S, D_C). Finally, the maximum GLAI reached during the cycle (GLAI_M) was extracted from the model and then used to derive three additional durations (C: D₇₅, D₅₀, D₂₅) (color figure online)