Genome-Wide Analysis of Yield in Europe: Allelic Effects Vary with 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 × 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.

Figure 1.

Time courses of soil water potential and temperature in each environmental scenario. A and B, Each line represents a time course corresponding to one experiment of the field network in Europe (A )or Chile (B). C to G, time courses were centered (time 0) on the day of anthesis of the reference hybrid (vertical dashed line). Dark lines represent smoothed mean values. Thermal time is in equivalent days at 20°C. C, experiments with cool temperatures during both day and night (Cool); D, experiments with hot temperatures during the day but cool temperatures during the night (Hot(day)); E, experiments with hot temperatures (Hot, mean maximum temperatures > 33°C and mean night temperatures > 20°C). C to E, The upper series of lines represents maximum temperature and the lower series represents mean night temperature. F, Well-watered experiments (WW) with experiments in which mean soil water potential was higher than −0.10 MPa (WW cycle, blue lines) and experiments with well-watered conditions during flowering time and water deficit during grain filling (late Term, red); G, Water deficit experiments (WD) with early deficit followed by recovery at flowering time (Rec, yellow lines) and experiments with water deficit from vegetative stage to maturity (early Term, dark red lines).

Figure 2.

Relationship between grain yield and grain number (A) and between grain size and grain number (B) for the reference hybrid. Each symbol corresponds to one experiment. Blue and green symbols, Well-watered experiments (WW cycle); brown, experiments with early deficit followed by recovery (Rec) at flowering time; red, experiments with water deficit from vegetative stage to maturity (early Term); orange, experiments with water deficit during grain filling (late Term). Symbol shape indicates temperature at flowering time. Circle, Cool temperatures; triangles, hot temperatures; squares, temperatures cool during the night but hot during the day. Blue shapes in A and B indicate the region of the panel with maximum density of well-watered experiments during grain filling (WW cycle and Rec). Salmon shape indicates experiments with water deficit during grain filling (Term).

Figure 3.

Genetic variability of grain yield in three typical experiments. A and B, Well-watered soil and cool air temperature at flowering time (Gai12W); C and D, well-watered soil and hot air temperature at flowering time (Cam13W); E and F, soil water deficit plus hot air temperature (Bol12R). Histograms are based on the BLUEs values of grain yield (A, C, and E) estimated with a mixed model (Supplemental Methods S1). Manhattan plots show results of single-environment GWAS (B, D, and F), with the red line indicating the -log₁₀(P value) threshold of 5.

Figure 4.

Genetic variability of grain yield in the six studied environmental scenarios. Variability of grain yield for six hybrids in the environmental scenarios identified in Fig. 1 (WW cycle and lateTerm are grouped as WW, Rec, and early Term are grouped as WD, see text). A to C, Hybrids with high performance in WW-Cool (first quartile of yield), D to F, hybrids with lower performance in WW-Cool (third quartile of yield). Three hybrids are shown in each category, classified by yield values in the scenario WW-HotDN, ranked as first, median, and last for yield in each quartile. Accessions of genotypes are as follows: A, PHG47_usda; B, B84_inra; C, Lo1087_bergamo; D, Pa36_inra; E, F7058_inra; F, Mo15W_inra.

Figure 5.

Final set of significant QTLs for grain yield in the 29 experiments. Circle diameters are proportional to the absolute value of allelic effect. Colors indicate the direction of effect: green when the reference hybrid allele increases grain yield, and blue when the other allele increases grain yield. Physical positions of the markers are based on the B73 reference genome RefGen_v2. Each horizontal line contains QTLs of one experiment, organized by scenarios of water status and temperature. Vertical white lines indicate bin position (bins are subdivisions of chromosomes in maize).

Figure 6.

Heat map of the allelic effects of 12 QTLs of grain yield in the six environmental scenarios. Green, The allele increases grain yield; orange, the allele decreases grain yield; yellow, the effect is null. By convention, the plus allele is the one that is favorable for mean performance. Allelic effects were estimated per experiment and then averaged across experiments per environmental scenario. A to D, QTLs conferring tolerance to heat with high effect in HotN and null effect in Cool: A, bin 6.01 (18.9 Mb); B, bin 6.01 (25.6 Mb); C, bin 10.06 (141.6 Mb); D, bin 5.01 (5.8 Mb). E to H, QTLs conferring tolerance to drought with effects in WD and lesser or reversed effect in WW: E, bin 3.05 (150.4 Mb); E, bin 1.03 (50.4 Mb); G, bin 6.01 (84.4 Mb); H, bin 2.04 (44.5 Mb). I to L, QTLs of plant performance under favorable conditions with lesser effect in dry and hot conditions, including two genes affecting flowering time (I,K): I, bin 3.05 (159.0 Mb); J, bin 8.06 (159.5 Mb); K, bin 5.01 (5.4 Mb); L, bin 1.01 (2.4 Mb).

Figure 7.

Allelic effects of QTLs on grain yield in relation to environmental variables. A, Allelic effect of QTL on chromosome 6.01 (19.0 Mb) as a function of VPD_max; B, allelic effect of QTL on chromosome 3.05 (150.4 Mb) as a function of soil water potential; C, allelic effect of QTL on chromosome 3.05 (159.0 Mb) as a function of soil water potential; D, allelic effect of QTL on chromosome 1.01 (2.4 Mb) as function of T_night. Colors and shapes of symbols as in Fig. 2. Allelic effects of grain yield (t ha⁻¹) were estimated using model M3. Environmental variables were averaged within a period of ± 10 d_20°C around the flowering time of the reference hybrid. A to D, coefficients of Pearson’s correlation were 0.32, 0.63, -0.64, and -0.42, respectively.