Millet Emilie J, Welcker Claude, Kruijer Willem, Negro Sandra, Coupel-Ledru Aude, Nicolas Stéphane D, Laborde Jacques, Bauland Cyril, Praud Sebastien, Ranc Nicolas, Presterl Thomas, Tuberosa Roberto, Bedo Zoltan, Draye Xavier, Usadel Björn, Charcosset Alain, Van Eeuwijk Fred, Tardieu François
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, Saint-Sauveur, 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.); andInstitute for Botany and Molecular Genetics, BioSC, RWTH Aachen University, 52074 Aachen, Germany (B.U.).
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, Saint-Sauveur, 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.); andInstitute for Botany and Molecular Genetics, BioSC, RWTH Aachen University, 52074 Aachen, Germany (B.U.)
Plant Physiol. 2016 Oct;172(2):749-764. doi: 10.1104/pp.16.00621. Epub 2016 Jul 19.
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.
评估植物在高温和干旱情况下表现的遗传变异性有助于减轻气候变化的负面影响。我们在此提出一种方法,该方法包括:(1)将作物模型在当前(35年×55个地点)和未来条件下模拟的环境变量时间进程聚类为玉米(Zea mays L.)植株所经历的六种温度和水分亏缺情景;(2)在欧洲不同条件下对244个玉米杂交种进行29次田间试验;(3)根据每个田间试验测量的环境条件将各个试验分配到情景中;我们试验中温度情景的频率与未来高温情景(+5°C)相对应;(4)分析每种环境情景下植物表现的遗传变异。使用多环境多基因座模型通过关联遗传学鉴定出48个产量数量性状位点(QTL)。其中8个和12个QTL分别与耐热性和耐旱性相关,因为它们分别特定于炎热和干旱情景,在有利情景下具有低甚至负的等位基因效应。24个QTL在有利条件下提高了产量,但在胁迫下表现出不显著的效应;因此它们与更高的敏感性相关。我们的方法显示了QTL效应作为环境变量和情景函数的模式,使我们能够提出每个QTL潜在机制和候选基因的假设。它可用于评估当前和未来胁迫情况下基因型的表现以及基因组区域的贡献,并加速针对易干旱环境的育种。