Abstract Early sowing is a key strategy to improve maize productivity and resilience under climate change, but it exposes plants to prolonged chilling stress that can severely compromise seedling establishment. While previous genetic studies have focused on germination or very early stages, tolerance to long-term chilling during the autotrophic transition remains poorly characterized. Here, we combined genome-wide association studies (GWAS) and transcriptome analysis on QTL near-isogenic lines (NILs) to dissect the genetic architecture of early vigor under chilling in maize. We identified a major genomic region on chromosome 4 (LD_COL4), harboring two QTLs within a 2.7 Mb interval, that were consistently associated with early vigor under long-term chilling conditions. Transcriptomic analysis of contrasted NILs revealed a cluster of differentially expressed genes co-localizing with LD_COL4, pointing to two strong candidate genes, Zm00001d048582 , an ortholog of the Arabidopsis OPS gene that regulates the brassinosteroid (BR) signaling pathway upstream of the key transcription factors BES1 and BZR1, and Zm00001d048612 , a brassinosteroid-signaling kinase (BSK). Multiple orthologs of BES1/BZR1 modulators were differentially expressed between genotypes under chilling, supporting the involvment of brassinosteroid signaling in this response. These findings highlight both genes as promising targets for marker-assisted breeding and gene editing to improve maize adaptation to early sowing.

ABSTRACT The evolution of maize yields under drought is of particular concern in the context of climate change and human population growth. To better understand the mechanisms associated with the genetic polymorphisms underlying the variations of traits related to drought tolerance, we used a systems genetics approach integrating high-throughput phenotypic, proteomics and genomics data acquired on 254 maize hybrids grown under two watering conditions. We show that water deficit, even mild, induced a strong proteome remodeling and a reprogramming of the genetic control of the abundance of many proteins. We identify close colocalizations between QTLs and pQTLs, thus highlighting environment-specific pleiotropic loci associated to the co-expression of drought-responsive proteins and to the variations of phenotypic traits. These findings bring several lines of evidence supporting candidate genes at many loci and provide novel insight into the molecular mechanisms of drought tolerance.

Table S6. Number of SNPs called, after QC filtering (MAF>1%) and useful for GWAS (MAF≥5%). Note that GBS1 have SNPs with 100% missing genotypes which were removed while GBS2 used external haplotype library which allow to impute loci with 100% missing data. It conducted to a smaller number of SNPs for GBS1 than GBS2. (XLS 33 kb)

Genotyping by sequencing is suitable for analysis of global diversity in maize. We showed the distinctiveness of flint maize inbred lines of interest to enrich the diversity of breeding programs.

Assessing the genetic variability of plant performance under heat and drought scenarios can contribute to reduce the negative effects of climate change. We propose here an approach that consisted of (1) clustering time courses of environmental variables simulated by a crop model in current (35 years × 55 sites) and future conditions into six scenarios of temperature and water deficit as experienced by maize (Zea mays L.) plants; (2) performing 29 field experiments in contrasting conditions across Europe with 244 maize hybrids; (3) assigning individual experiments to scenarios based on environmental conditions as measured in each field experiment; frequencies of temperature scenarios in our experiments corresponded to future heat scenarios (+5°C); (4) analyzing the genetic variation of plant performance for each environmental scenario. Forty-eight quantitative trait loci (QTLs) of yield were identified by association genetics using a multi-environment multi-locus model. Eight and twelve QTLs were associated to tolerances to heat and drought stresses because they were specific to hot and dry scenarios, respectively, with low or even negative allelic effects in favorable scenarios. Twenty-four QTLs improved yield in favorable conditions but showed nonsignificant effects under stress; they were therefore associated with higher sensitivity. Our approach showed a pattern of QTL effects expressed as functions of environmental variables and scenarios, allowing us to suggest hypotheses for mechanisms and candidate genes underlying each QTL. It can be used for assessing the performance of genotypes and the contribution of genomic regions under current and future stress situations and to accelerate breeding for drought-prone environments.

INRA, Laboratoire d’Ecophysiologie des Plantes sous Stress Environnementaux, 34060 Montpellier, France (E.J.M., C.W., A.C.-L., F.T.); Biometris — Applied Statistics, Department of Plant Science, Wageningen University, 6700AA Wageningen, Netherlands (W.K., F.V.E.); INRA, UMR 0320 / UMR 8120 Génétique Quantitative et Evolution, 91190 Gif-sur-Yvette, France (S.N, S.D.N., C.B., A.C.); INRA, SMH Maïs, Centre de recherche de Bordeaux Aquitaine, 40390 Saint-Martin-De-Hinx, France (J.L.); Centre de Recherche de Chappes, Biogemma, 63720 Chappes, France (S.P.); Syngenta France SAS, 12, Chemin de l’Hobit, BP 27, 31790, SaintSauveur, France (N.R.); KWS Saat SE, 37555 Einbeck, Germany (T.P.); Department of Agricultural Sciences, University of Bologna, 40127 Bologna, Italy (R.T.); MTA ATK/ AI CAR HAS, Martonvasar 2462, Hungary (Z.B.); UCL ELIA, 1348 Louvain-la-Neuve, Belgium (X.D.); and Institute for Botany and Molecular Genetics, BioSC, RWTH Aachen University, 52074 Aachen, Germany (B.U.)

Compilation of genetic maps combined to QTL meta-analysis has proven to be a powerful approach contributing to the identification of candidate genes underlying quantitative traits. One of the most interesting properties of meta-QTL (or consensus QTL) is its confidence interval (IC) often shorter than IC of corresponding QTLs, decreasing the number of candidate gene to consider. As map compilation and QTL meta-analysis do not rely on genotyping raw data or trait measure, they can be easily achieved even if user holds maps from the literature or genetic databases. BioMercator was the first software offering a complete set of algorithms and visualization tool covering all steps required to perform QTL meta-analysis. The fourth version of BioMercator propose additional methods and improve graphical representation of large datasets. In this version, user may import sequence and genome annotations datasets within the software in order to display and mine functional annotation related to QTL and meta-QTL. In order to improve candidates genes detection, we aim to inculde genetic association approach in the release of BioMercator. Association genetics allow to build a relationship between molecular polymorphism and phenotypic variation so, Genome-Wide Association Studies (GWAS) present a good potential for QTL's sharpening. We integrated GWAS results in Biomercator and provided new functionalities to display and exploit them.