Xinmin Hu ,Guihu Wng ,Xuemei Du ,Hongwei Zhng ,Zhenxing Xu ,Jie Wng ,Guo Chen ,Bo Wng ,Xuhui Li ,Xunji Chen ,Junjie Fu ,Jun Zheng ,Jinhu Wng ,*,Riling Gu ,*,Guoying Wng ,*

a Beijing Innovation Center for Crop Seed Technology and Beijing Key Laboratory of Crop Genetic Improvement,Ministry of Agriculture and Rural Affairs,Key Laboratory of Crop Heterosis Utilization,Ministry of Education,College of Agronomy and Biotechnology,China Agricultural University,Beijing 100193,China

b Institute of Crop Sciences,Chinese Academy of Agricultural Sciences,Beijing 100081,China

c Institute of Nuclear Technology and Biotechnology,Xinjiang Academy of Agricultural Sciences,Urumqi 830091,Xinjiang,China

ABSTRACT Drought is one of the most critical abiotic stresses influencing maize yield.Improving maize cultivars with drought tolerance using marker-assisted selection requires a better understanding of its genetic basis.In this study,a doubled haploid (DH) population consisting of 217 lines was created by crossing the inbred lines Han 21 (drought-tolerant) and Ye 478 (drought-sensitive).The population was genotyped with a 6 K SNP assay and 756 SNP (single nucleotide polymorphism) markers were used to construct a linkage map with a length of 1344 cM.Grain yield (GY),ear setting percentage (ESP),and anthesis–silking interval (ASI) were recorded in seven environments under well-watered (WW) and water-stressed (WS) regimes.High phenotypic variation was observed for all traits under both water regimes.Using the LSMEAN (least-squares mean) values from all environments for each trait,18 QTL were detected,with 9 associated with the WW and 9 with the WS regime.Four chromosome regions,Chr.3:219.8–223.7 Mb,Chr.5:191.5–194.7 Mb,Chr.7:132.2–135.6 Mb,and Chr.10:88.2–89.4 Mb,harbored at least 2 QTL in each region,and QTL co-located in a region inherited favorable alleles from the same parent.A set of 64 drought-tolerant BC3F6 lines showed preferential accumulation of the favorable alleles in these four regions,supporting an association between the four regions and maize drought tolerance.QTL-by-environment interaction analysis revealed 28 edQTL(environment-dependent QTL)associated with the WS regime and 22 associated with the WW regime for GY,ESP,and ASI.All WS QTL and 55.6%of WW QTL were located in the edQTL regions.The hotspot genomic regions identified in this work will support further fine mapping and marker-assisted breeding of drought-tolerant maize.

Keywords:Drought Yield Quantitative trait locus Introgression Maize

1.Introduction

Drought poses a grave threat to agricultural production worldwide [1].According to a simulation study [2],yield loss resulting from drought will increase under climate change,and the annual loss will reach 14.1%in 2030.Maize(Zea maysL.)is one of the most widely cultivated crops,providing food for humans,feed for animals,and materials for industry;however,maize is thought to be more susceptible to drought stress than other crops because of different drought responses of its physically separated male and female flowers.Female growth is strongly influenced by water stress,which results in delayed silk emergence,while the male flower is minimally influenced by drought,resulting in a long anthesis–silking interval (ASI) that reduces the ear setting percentage(ESP) and grain yield (GY) [3].Maize is extremely sensitive to drought in the period from 1 week before to 3 weeks after flowering,although drought affects the plant at almost all growth stages[4,5].

Using drought-tolerant cultivars is an effective way to address the drought threat,and genomics-based approaches such as marker-assisted selection can accelerate the breeding of tolerant maize [6].However,a lack of favorable alleles or genes has hindered breeding progress,given that drought tolerance is a complex trait controlled by several minor loci and strongly influenced by environmental factors[7,8].Quantitative trait locus(QTL)mapping is a powerful method to identify alleles or genes controlling complex traits.Over the past few decades,numerous maize QTL associated with drought-tolerance traits under both well-watered(WW) and water-stressed (WS) conditions have been reported in biparental populations such as F2:3[9–11],F3[12,13],F3:4[14],recombinant inbred line(RIL)[15],and doubled haploid(DH)populations [16].Although these studies made advances in dissecting the genetic mechanism of drought tolerance,there is still much to be learned about this trait in maize.In particular,it is still difficult to identify the most promising QTL for genetic cloning or markerassisted selection breeding.

GY is a key parameter for identifying drought tolerance in crops[17–19].However,GY is readily affected by environmental factors[20].ESP and ASI,secondary traits that are strongly correlated with GY,have been widely used [5,9,21–23] as supplementary criteria for evaluating maize drought tolerance.The objective of the present study was to evaluate drought-related traits,GY,ESP and ASI,in a DH population derived from two contrasting droughttolerant lines grown under two water regimes in seven environments and to map promising QTL for further drought-tolerant maize breeding and fine-mapping of the underlying genes.

2.Materials and methods

2.1.Plant materials

A DH population of 217 lines was derived from a cross of the inbred lines Han 21 and Ye 478.Han 21 is drought-tolerant and Ye 478 drought sensitive [24].A backcross (BC3F6) population was derived from the same parents with Han 21 as donor and Ye 478 as recurrent parent.

2.2.Field experiments

The DH population was planted in seven environments for two years at Langfang,Hebei (LF,3960′N,11661′E);Sanya,Hainan(SY,1840′N,10920′E);and Urumqi,Xinjiang (UM,4345′N,8736′E);and for one year at Changji,Xinjiang (CJ,4398′N,8751′E)(Table S1).The BC3F6population was planted in five environments for two years at LF and UM,and for one year at CJ.Trials were sown in winter of 2012 and 2013 in SY,spring of 2013 and 2014 in LF and UM,and spring of 2016 in CJ.

The experiments were laid out as randomized complete blocks with three replications;one-row plots were used in SY and LF,and two-row plots were used in UM and CJ for each replication.The row length was 3 m with 13 plants in SY and 5 m with 21 plants in LF,UM,and CJ.The space between rows was 0.6 m with a density of 66,700 plant ha-1.The precipitation in UM,CJ,and SY during the flowering season was normally less than 40 mm per month,too little for maize growth (Table S1).The soil type in the LF location was sandy,facilitating water drainage.Although the precipitation in LF was much higher (143–244 mm),it was also too little for maize growth.Accordingly,all the fields were sprinkler-irrigated with two water regimes:1) WW,with water application every two weeks;and 2) WS,with irrigation being suspended three weeks prior to anthesis until the end of flowering,when one additional irrigation was applied to ensure adequate development of the fertilized ovaries during the grain-filling period.The WS block was at least 15 m away from the WW block to ensure that there was no water contamination.Fertilizers,turf machinery,and weeding were applied according to local practices.

2.3.Trait evaluation

For each plot,anthesis date was recorded as the number of days from sowing until at least 50% of the plants had released pollen,and siking date was recorded as the number of days from sowing until silks had emerged on at least 50% of the plants.ASI was calculated as the difference between silking and anthesis dates.A plant was considered to have undergone anthesis or silking if at least one extruded anther or silk was visible.Some plots under severe drought stress failed to reach 50% silking even at 30 days after 50% anthesis,and these records were treated as missing values.Before harvest,the total standing plants and the total fertilized plants (with 5 or more kernels) were recorded in each plot,and ESP was calculated as the proportion (%) of fertilized in standing plants.All mature ears from each plot were harvested manually,bagged,air-dried,and shelled with an electric sheller.Total GY of each plot was determined on an electronic scale and adjusted to 14% moisture content.GY per plant was calculated as total GY divided by the number of fertilized plants.

2.4.Data analysis

The collected phenotypic data were analyzed with IBM SPSS 20.0 (IBM Corp.,Armonk,NY,USA) and the R statistical environment [25].Values for each trait under WS and WW conditions were separately calculated following the linear model

where observationYijris the plot-based phenotype as the sum of the mean (μ),the genetic effect (G) of theithline,the effect of thejthenvironment(E),their respective interactionsG×Eij,and the erroreijr.The PROC MIXED procedure in SAS 9.2 (SAS Institute Inc.,Cary,NC,USA)was used to estimate LSMEAN(least-squares mean)values for each trait with genotype as the fixed effect and environment and replication as random effects[26].These LSMEAN values were used for Pearson correlations and QTL mapping.

Broad-sense heritabilities were separately calculated under WS and WW conditions using an ANOVA fitting the effects of genotype(G),environment (E),and G × E interactions,as

2.5.Genotyping and QTL identification

Fresh leaves were sampled from the parents and the DH and BC lines at the V5(five expanded leaves)stage.Leaf DNA was extracted using the CTAB method and genotyped on a 6K SNP(single nucleotide polymorphism)array(Illumina Inc.,San Diego,CA,USA).A linkage map was constructed using QTL IciMapping 4.0[28].SNP markers were rejected if they showed low(defined as<0.05)minor-allele frequency or contained<10%missing data.The accepted markers were binned based on their segregation patterns in the population using the BIN function,and one marker was chosen to represent each bin on the basis of the smallest amount of missing data or at random when two markers had the same amount of missing data.After binning,markers were grouped into linkage groups using logarithm of odds(LOD)threshold values≥3.Linkage groups were assigned based on the genomic position of SNP markers determined during SNP calling,and those from the same chromosome were merged into one group.Recombination frequencies between markers in a linkage group were converted into genetic distance(centiMorgans,cM)using the Kosambi mapping function.

Inclusive composite interval mapping of additive function(ICIM-ADD) in QTL IciMapping was used to detect additive QTL for the DH population [29].The mapping parameters step for ICIM-ADD was set to 1.0 cM,and a probability of 0.05 in stepwise regression was selected for each mapping method [29].The additive effect explained by each QTL was estimated from the values expressed by the QTL peaks obtained from ICIM-ADD,and the threshold LOD value was determined by 1000-permutation analysis using a type I error ofP<0.05.

QTL-by-environment interaction(QEI)mapping was performed to detect environment–dependent QTL (edQTL) using the Multi-Environment Trial (MET) functionality in QTL IciMapping [28,30–31].Additive × environment (AbyE) interactions were detected using 1.0-cM scanning steps and a probability of 0.001 for stepwise regression.As this MET functionality could simultaneously perform QEI mapping and additional QTL mapping across multiple environments,LOD for both additive QTL and QEI were calculated and referred to as LODAand LODAE,respectively.The threshold LOD value (sum of LODAand LODAE) was determined by 1000-permutation analysis using a type I error ofP<0.05 [32].

3.Results

3.1.GY,ASI,and ESP in the parental lines Han 21 and Ye 478 under contrasting water regimes

The GY,ESP,and ASI of the parental lines were recorded under contrasting water regimes (WW and WS) in six environments(Table 1).Han 21 showed a significantly higher GY than Ye 478 in five of the six WS environments (excluding only LF14);the parents showed similar GY performance in most WW environments(Table 1).The mean GY loss [(GY in WW–GY in WS)/GY in WW] for Han 21 was 43.6% across six environments,lower than the 63.6% loss for Ye 478.Han 21 showed a higher ESP (range 56.26% to 90.56%,mean 69.74%) and a lower ASI (range -0.36 to 3.78 days,mean 1.58 days) than Ye 478 (ESP range 40.82% to 69.77%,mean 58.86%;ASI range 0.33 to 5.33 days,mean 2.69 days)under the WS regime.Under the WW regime,the two lines showed similar performances for both traits (Table 1).The lower GY loss and ASI and higher ESP under the WS condition suggested that Han 21 is more tolerant to drought stress in the field than Ye 478,in agreement with their performances in the seedling stage[24].

3.2.Trait performance in the DH lines under contrasting water regimes

After drought treatment in all environments,population means for GY and ESP decreased significantly and those for ASI increased(Table 1).Across seven environments,GY decreased from a mean of 49.30 g plant-1in WW to 27.22 g plant-1in WS (44.8% reduction);the ESP mean decreased from 86.60%to 69.16%(20.1%reduction);and ASI mean increased from 1.17 to 2.25 days (92.3%increase).The coefficient of variations (CV) for GY and ESP increased from respectively 49.14% and 13.17% in WW to 68.86%and 30.71% in WS.

Table 1 Grain yield (GY),ear setting percentage (ESP),and anthesis–silking interval (ASI) in a doubled haploid (DH) population and its parents Han 21 and Ye 478 from well-watered(WW) and water-stressed (WS) regimes in seven environments.

GY and ASI showed continuous segregation and approximate fits to normal distributions according to histograms and density plots.This distribution suggested that the quantitative traits GYand ASI were controlled by several minor genes with small effects(Fig.S1).However,ESP showed a skewed distribution.

ANOVA showed that the effects of genotype,environment,and genotype × environment were significant for GY,ASI,and ESP in both WW and WS regimes (P=0.05) (Table 2).The broad-sense heritability (H2)was similar between WW and WS and slightly higher for GY (0.89 to 0.90) than for ESP (0.81 to 0.85) and ASI(0.81 to 0.82) (Table 2).

GY was correlated positively with ESP and negatively with ASI(Table S2).The highest correlations were found between GY and ESP (r=0.68 in WS and 0.69 in WW across environments),followed by those between GY and ASI (r=-0.48 in WS and -0.37 in WW),while those between ESP and ASI were lowest(r=-0.39 in WS and -0.29 in WW).

3.3.Identification of QTL for GY,ESP,and ASI under contrasting water regimes

In total,1691 SNP markers were polymorphic between the parents Han 21 and Ye 478,and were distributed across the whole genome with a sufficiently high density for QTL mapping.Genotyping revealed that all 217 DH lines were homozygous with average coverages of homozygous Han 21 and Y478 genotypes of 46.9%and 53.1%,respectively.Of the 1691 polymorphic markers,935 were eliminated because of low frequency (minor allele frequency<5%;425) in the population or because they were redundant (located in the same bin) in the linkage map (5 1 0).Finally,756 SNP markers were used to construct a linkage map with a total length of 1344 cM and a mean interval of 1.78 cM (Table S3).

QTL mapping was performed using the LSMEAN values separately calculated from the WS and WW regimes in seven environments.The threshold LOD values were 2.81 to 3.02 for different traits in different water regimes(Table S4).With these LOD thresholds,18 QTL were detected for GY,ESP,and ASI,with respectively 4,2,and 3 from the WS condition and 3,2,and 4 from the WW condition (Fig.1;Table S5).The phenotypic variance explained by each QTL was moderate for all traits,ranging from 4.11% to 12.26%.Among these QTL,10 (55.6%) inherited favorable alleles(trait-increasing alleles for GY and ESP and decreasing alleles for ASI) from the tolerant parent Han 21,while 8 (44.4%) inherited favorable alleles from the susceptible parent Ye 478 (Table S5).

After all QTL were mapped on the maize physical map (B73 RefGen_v2;www.maizegdb.org),four chromosome regions were found to harbor at least 2 QTL in each region.Region Chr.3:219.8–223.7 Mb harbored 4 QTL,with two from the WS (qWSGY3-1andqWS-ESP3-1) and two from the WW (qWW-GY3-1andqWW-ASI3-1) regimes.These 4 QTL inherited alleles that contributed to drought tolerance from the tolerant parent Han 21 and explained 4.11 to 8.94% of phenotypic variation.Region Chr.5:191.5–194.7 Mb contained two WS QTL (qWS-GY5-1,qWSESP5-1) and one WW QTL (qWW-GY5-1).These QTL inherited tolerance alleles from the susceptible parent Ye 478 and explained 7.24 to 12.26% of phenotypic variance.Chr.10:88.2–89.4 Mb harbored two ASI QTL that were detected in WS (qWS-ASI10-1) and WW (qWW-ASI10-1) regimes,and both inherited favorable alleles from Han 21.Chr.7:132.2–135.6 Mb harbored a GY QTL from WS (qWS-GY7-1) and an ESP QTL from WW (qWW-ESP7-1);both inherited favorable alleles from Ye 478.

Of the four regions of interest,three harbored different QTL for the same trait,but expressed under different water regimes.Chr.3:219.8–223.7 Mb and Chr.5:191.5–194.7 Mb each harbored two GY QTL,with one from WW and the other from WS.Chr.10:88.2–89.4 Mb contained two ASI QTL (qWS-ASI10-1andqWW-ASI10-1),with one each from WS and WW conditions.Colocalization of GY and ASI QTL from different water regimes suggested that these QTL might function under both WS and WW conditions.

3.4.Identification of QEI for GY,ESP,and ASI under contrasting water regimes

The QEI method revealed 28 edQTL from the WS condition and 22 from the WW condition for GY,ESP,and ASI(Table S6).On average,edQTL of the WS regime explained 2.08%and 2.84%of the phenotypic variance from additive effect (A) and additive-byenvironmental effect (AbyE),respectively.Similarly,edQTL of the WW regime explained on average respectively 2.60% and 2.34%of the phenotypic variance from A and AbyE effects.Comparing the chromosome locations of QTL and edQTL revealed that all WS QTL and 56%(5 of 9)WW QTL were located within edQTL regions,a finding consistent with a previous hypothesis that most QTL identified by joint analysis of several environments can also be detected by QEI mapping [33].

Table 2 Analysis of variance (ANOVA) and heritability (H2) for grain yield (GY),ear setting percentage (ESP),and anthesis–silking interval (ASI) in a doubled haploid (DH) population grown under well-watered (WW) and water-stressed (WS) regimes in seven environments.

3.5.Verification of chromosome regions associated with drought tolerance in a backcrossed population

The BC3F1lines(more than 500 plants)were screened under the WS condition for three generations (from BC3F1to BC3F3).In eachgeneration,lines above the 50th percentile for GY were selected.Finally,64 BC3F3lines were selected for generating the BC3F6population.These selected BC3F6lines were grown under five WS conditions in LF13,LF14,UM13,UM14 and UM16.They showed mean GY,ESP,and ASI values of respectively 41.94 g plant-1,81.83%,and 1.66 days,values 35.95% higher,12.29% higher,and 28.45% lower than those of the DH lines grown under the same conditions.Thus,the selected BC3F6lines were more tolerant to drought stress than the DH lines (Fig.2).

Fig.1.QTL for grain yield(GY),ear setting percentage(ESP),and anthesis–silking interval(ASI)in a doubled haploid population grown under well-watered(WW)and waterstressed(WS)regimes.Triangles,circles,and squares represent the GY,ESP,and ASI traits,respectively.Red and blue indicate QTL detected from the WS and WW conditions,respectively.

Fig.2.Drought-related field performance and genotype components of the BC3F6 population.(a–c) Comparison of grain yield (GY;A),ear setting percentage (ESP;B),and anthesis–silking interval(ASI;C)in the BC3F6 and DH population evaluated under five drought environments:LF13,LF14,UM13,UM14,and CJ16.Environments LF,UM,and CJ denote Langfang,Hebei;Urumqi,Xinjiang;and Changji,Xinjiang,respectively.Years 13,14,and 16 denote 2013,2014 and 2016,respectively.*,** and **** represent significance at P<0.05,P<0.01,and P<0.001,respectively.ns denotes no significance.(d)Introgression ratios of the Han 21 genotype on chromosome 3,5,7,and 10 in the BC3F6 population.Red and blue dashed lines represent respectively the observed mean introgression ratio of all markers and the expected ratio in the BC3F6 population.

These BC3F6lines were genotyped with the same SNP array that was used in the DH lines.The average coverage of heterozygous SNPs was only 0.50%,suggesting an extremely low heterosis effect in these BC3F6lines.The average coverage by homozygous Han 21 genotypes in these lines was 10.62%,higher than the expected coverage of 6.25% in the BC3population.As phenotypic selection was conducted in early generations (from BC3F1to BC3F3),it was reasonable that lines with more Han 21 segments introgressed into the Ye 478 background were preferentially selected,because more introgressions produced more heterogeneous loci that impart heterosis effects.

As regions Chr.10:88.2–89.4 Mb and Chr.3:219.8–223.7 Mb harbored several drought-related QTL with favorable alleles inherited from Han 21,it was interesting that the Han 21 genotypes in both regions preferentially accumulated in these BC3F6lines,with average proportions of 26.63%for the Chr.10:88.2–89.4 Mb region and 15.63% for the Chr.3:219.8–223.7 Mb region.These proportions were much higher than the observed mean proportions of 10.62% for all chromosome regions (Fig.2).Similarly,Chr.5:191.5–194.7 Mb and Chr.7:132.2–135.6 Mb contained QTL with Ye 478-derived alleles contributing to traits.The Y478 genotypes were also enriched in this BC population.The proportions sharply increased to 98.44% in the Chr.7:132.2–135.6 Mb region and slightly increased to 92.19% in the Chr.5:191.5–194.7 Mb region from the mean Ye 478 proportion of 89.38%.The enrichment of positive contributing alleles in these selected BC3F6lines indicated a strong correlation between the pyramiding of the four chromosome regions and maize drought tolerance.

4.Discussion

In this study,phenotypic analysis showed that GY of maize grown under the WS condition was highly correlated with ESP and ASI (Table S2);genetic analysis found that 2 of the 4 GY QTL co-localized with ESP QTL under the WS condition (Fig.1).Increased introgression of the ASI QTL in the Chr.10:88.2–89.4 Mb region in the BC3F6population improved GY under WS conditions(Fig.2).These results support the hypothesis that selection on ASI or ESP is effective for breeding higher GY maize under drought conditions [34].

In the past decades,many drought-related QTL have been determined using different populations [13–18,35–39].Meta-QTL analysis have also revealed consistent QTL for this trait [39,40].While dozens of QTL have been identified,the most promising QTL for gene cloning or marker–assisted selection are still difficult to determine.In this study,four chromosome regions were detected after QTL identification in a DH population under seven environments and were further verified by an introgression effect test in BC3F6lines under five drought environments (Figs.1 and 2;Table S5).

The region Chr.10:88.2–89.4 Mb harbored two ASI QTL from the WW and WS regimes,with favorable alleles inherited from the tolerant parent Han 21(Table S5).The presence of drought–related QTL in this region was also observed in previous studies.Almeida et al.[9] found a GY QTL in Chr.10:86.3–89.4 Mb from an RIL population under the WS condition and an ASI QTL from a combination of WW and WS conditions.By meta-analysis of data from the RIL population and two F2:3populations,the authors proposed a meta-QTL located at Chr.10:86.3–109.6 Mb for the GY and ASI traits under the WS condition.In another study,Hao et al.[34]identified a significant genomic region around Chr.10:88.2–89.4 Mb for GY under the WS conditions from a meta-QTL analysis of 12 mapping experiments.Li et al.[41] also conducted a meta-QTL analysis from seven populations and detected a meta-QTL in this region for GY under drought condition.Ribaut et al.[13]identified several GY QTL under both intermediate and severe drought stresses;that study also revealed a QTL associated with marker umc64 located at a position (85.8 Mb) adjacent to the region Chr.10:88.2–89.4 Mb.Together with the finding that introgression of Chr.10:88.2–89.4 Mb in the BC3F6lines significantly improved GY under drought conditions (Fig.2),this result suggests that the genomic region Chr.10:88.2–89.4 Mb plays important roles in conferring ASI-mediated GY advantages under drought stress across different populations and in different environments.

Chr.3:219.8–223.7 Mb harbored four QTL for GY,ESP,and ASI traits,with favorable alleles inherited from the tolerant parent Han 21(Table S5).Increasing the accumulation of the 4 QTL in the BC3F6population improved GY performance under drought conditions,suggesting an important role for this region in maize drought tolerance (Fig.2).Consistent with this finding,Almeida et al.[9]detected an ASI QTL from an RIL population grown under drought conditions;and then Almeida et al.[10] proposed that there is a hotspot in a nearby position within approximately 180 Mb of Chr.3,based on several QTL associated with staygreen,leaf senescence,and chlorophyll content identified under normal growth or drought-stressed conditions[22,42–45].In this nearby region,two ASI QTL(qWS-ASI3-1andqWW-ASI3-1) were also identified in this study,but they inherited favorable alleles from the susceptible parent Ye 478(Table S5).Consistently,Ye 478 genotype accumulation in this region improved the BC3F6lines,suggesting that the Ye 478 alleles contribute to drought tolerance (Fig.2;Table S5).Taken together,these results suggest that Chr.3:219.8–223.7 Mb and its extended region harbor several QTL responsible for the effects of maize drought tolerance identified in the present and previous studies.

Coincidence of QTL detected in this study with those of previous studies also occurred for Chr.5:191.1–194.7 Mb and Chr.7:132.2–135.6 Mb.Chr.5:191.1–194.7 Mb contained 3 QTL for GY and ESP,and the favorable alleles were contributed by the susceptible line Ye 478 (Table S5).In this region,Almeida et al.[9] identified a mQTL controlling the GY trait under drought conditions in the Chr.5:171.7–199.7 Mb interval,and Semagn et al.[39] identified a mQTL from 18 populations for drought-stressed GY and ASI in the Chr.5:199.9–203.7 Mb interval.Chr.7:132.2–135.6 Mb contained 2 QTL for GY and ESP,with the favorable alleles inherited from Ye 478(Table S5).Almeida et al.[10]proposed seven drought-related QTL clusters,with one located in this region.Li et al.[37]reported 79 mQTL for drought-related traits,with one falling in this region.Increasing the accumulation of the favorable allele from Ye 478 in both regions improved GY performance in the BC3F6population(Fig.2).These results suggest that both regions influenced GY performance under water-limited conditions.

As a benefit of the high marker density used in this work,the potential QTL interval in region Chr.10:88.2–89.4 Mb was narrowed to 1.2 Mb.This interval contained 31 annotated genes,with 24 encoding proteins larger than 100 amino acids (Table S7).Two of these genes had previously been linked to ASI-mediated promotion of GY under drought conditions in maize or other species.Zm00001d024813encodes a maize embryonic flower 1-like protein(EMF1) that was transcriptionally induced by drought stress [46].TheEMF1ortholog inArabidopsiscontrols flowering time and the transition of vegetative to reproductive growth [47,48].TheEMF1ortholog inDimocarpus longanLour.is expressed specifically in seeds in a drought-responsive manner [49].The other gene isZm00001d024803,which encodes an S-adenosyl-L-methioninedependent methyltransferase protein.Ectopic expression of itsArabidopsishomolog in tomato increased drought tolerance by suppressing gibberellin activity via methyl transferase function[50].Thus,both genes are promising candidates in the Chr.10:88.2–89.4 Mb interval for promoting GY under drought conditions.However,candidate genes in other regions were difficult to identify,owing to the large confidence intervals.

In conclusion,GY-related traits undergo a complex genetic mechanism and strong genotype–environment interactions in maize.Multienvironment experiments identified 18 GY-related QTL from WW and WS regimes and distributed over 10 chromosome regions.Each of four chromosome regions,Chr.3:219.8–223.7 Mb,Chr.5:191.5–194.7 Mb,Chr.7:132.2–135.6 Mb,and Chr.10:88.2–89.4 Mb,harbored at least two QTL.Accumulation in these four regions of alleles contributing to traits increased GY in the BC3F6population under drought conditions.Thus,these four genomic regions are candidates for further fine mapping and marker-assisted breeding in maize

CRediT authorship contribution statement

Jun Zheng,Jianhua Wang,Riliang Gu,and Guoying Wangdesigned the study.Xinmin Hu,Hongwei Zhang,Zhenxiang Xu,Jie Wang,Guo Chen,Bo Wang,Xuhui Li,and Xunji Chenconducted experiments.Xinmin Hu,Guihua Wang,Hongwei Zhang,and Xuemei Duanalyzed the data.Xinmin Hu and Riliang Guwrote the manuscript.Xuemei Du and Riliang Gurevised the manuscript.All authors read and approved the manuscript.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

We thank the anonymous reviewers for improving this manuscript.This work was supported by the National Key Research and Development Program of China (2016YFD0101803),the Key Transgenic Breeding Program of the Ministry of Agriculture of China (2016ZX08003-002),and the China Agriculture Research System (CARS-02-10).

Appendix A.Supplementary data

Supplementary data for this article can be found online at https://doi.org/10.1016/j.cj.2020.10.004.