Genome-wide analysis of yield in Europe: allelic effects as functions of drought and heat scenarios

Abstract

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 3 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.With climate changes, crops will be subjected to more frequent episodes of drought and high temperature that may threaten food security (IPCC, 2014). Reducing the impacts of these effects is an urgent priority that (not exclusively) involves the genetic progress of plant performance under heat and drought stresses (Tester and Langridge, 2010;Lobell et al., 2011). Because hundreds of new genotypes of most cereals are commercialized every year, a generic approach is needed to avoid an endless series of experiments assessing the performances of the newly released genotypes. A systematic exploration of the natural genetic diversity used in breeding can provide information usable for large groups of genotypes. This entails the identification, among the thousands of accessions existing in gene banks, of allelic variants exhibiting specific adaptation traits by addressing three questions: (1) Is there a genetic variability for yield and related traits in dry and hot environments? (2) Can this genetic variability be dissected into the effect of genomic regions (quantitative trait loci, QTLs), and (3) have these genomic

Strategic crossing of biomass and harvest index—source and sink—achieves genetic gains in wheat

“…Nonetheless, since selecting among early generation progeny for expression of complex traits is not always feasible (due in part to very large numbers), it is expected that genomic selection-potentially in combination with high throughput phenotyping of few integrative traits like canopy temperature and vegetative indices (Rutkoski 2016)-will find valuable application in selection of early generations based on strategic crossing for complex physiological traits. Marker assisted selection (MAS) has not delivered as expected (Langridge and Reynolds 2015) but remains a possibility, especially if QTL 9 environment interactions can be more effectively modeled (Millet et al 2016). …”

Quantifying Wheat Sensitivities to Environmental Constraints to Dissect Genotype × Environment Interactions in the Field

“…Most physiological or genetic studies use conventional time for measuring periods of sensitivity (Ferris et al, 1998;Shah and Paulsen, 2003). Recently, Millet et al (2016) used thermal time centered on anthesis date for QTL detection in maize (Zea mays) for specific responses to four drought scenarios and three temperature scenarios (Chenu et al, 2013;Harrison et al, 2014). However, the environment was either still considered as a qualitative factor or analyzed in a quantitative way but only centered on anthesis.…”

“…Rather than using conventional time (Tashiro and Wardlaw, 1990;Ferris et al, 1998;Shah and Paulsen, 2003), we used percentage of flowering time expressed as thermal time to allow the periods to be linked to physiological plant stages, even for genotypes with contrasting phenology (Chenu et al, 2013;Harrison et al, 2014;Millet et al, 2016). By contrast with conventional indicators used in the field, such as thresholds of temperature or drought (Kuchel et al, 2007) or average yield at a site (Fleury et al, 2010), these periods of sensitivity together with the precise monitoring of environmental conditions allowed definition of the environmental conditions as quantitative variables.…”

“…Therefore, yield and its components are highly dependent on genotypeby-environment (G 3 E) interactions (Tardieu, 2012;Addison et al, 2016). The associated quantitative trait loci (QTLs) also are highly dependent on temperature and water scenarios (Kuchel et al, 2007;Pinto et al, 2010;Acuna-Galindo et al, 2015;Millet et al, 2016).…”

“…In the field, plants are grown under normal agricultural systems, but the environment is hard to define and quantify because almost all environmental variables fluctuate daily and along the crop cycle. As a result, the environment is often viewed as a qualitative factor rather than the sum of quantitative variables, with few exceptions (van Eeuwijk et al, 2010;Millet et al, 2016). Despite important progress in genetics, QTL-by-environment (QTL 3 E) interactions are most often analyzed in a qualitative way with significant or nonsignificant interactions across qualitative environments (El-Soda et al, 2015).…”