Identification of Zinc Efficiency-Associated Loci (ZEALs) and Candidate Genes for Zn Deficiency Tolerance of Two Recombination Inbred Line Populations in Maize

Zinc (Zn) deficiency is one of the most common micronutrient disorders in cereal plants, greatly impairing crop productivity and nutritional quality. Identifying the genes associated with Zn deficiency tolerance is the basis for understanding the genetic mechanism conferring tolerance. In this study, the K22×BY815 and DAN340×K22 recombination inbred line (RIL) populations, which were derived from Zn-inefficient and Zn-efficient inbred lines, were utilized to detect the quantitative trait loci (QTLs) associated with Zn deficiency tolerance and to further identify candidate genes within these loci. The BLUP (Best Linear Unbiased Prediction) values under Zn-deficient condition (-Zn) and the ratios of the BLUP values under Zn deficient condition to the BLUP values under Zn-sufficient condition (-Zn/CK) were used to perform linkage mapping. In QTL analysis, 21 QTLs and 33 QTLs controlling the Zn score, plant height, shoot and root dry weight, and root-to-shoot ratio were detected in the K22×BY815 population and the DAN340×K22 population, explaining 5.5–16.6% and 4.2–23.3% of phenotypic variation, respectively. In addition, seventeen candidate genes associated with the mechanisms underlying Zn deficiency tolerance were identified in QTL colocalizations or the single loci, including the genes involved in the uptake, transport, and redistribution of Zn (ZmIRT1, ZmHMAs, ZmNRAMP6, ZmVIT, ZmNAS3, ZmDMAS1, ZmTOM3), and the genes participating in the auxin and ethylene signal pathways (ZmAFBs, ZmIAA17, ZmETR, ZmEIN2, ZmEIN3, ZmCTR3, ZmEBF1). Our findings will broaden the understanding of the genetic structure of the tolerance to Zn deficiency in maize.


Introduction
Zinc (Zn) deficiency is a common micronutrient disorder in cereal plants, reducing crop productivity and nutritional quality. Zn has been reported to be deficient in 30% of the agricultural soils worldwide [1], and 50% of cereal crops are cultivated on soils with low plant-available Zn [2]. Compared with the demand of plants, the concentration of free Zn ions in the rhizosphere is usually very small, and it is quickly depleted without the proliferation from other pools, leading to the expression of Zn deficiency in crop plants [3,4]. The low level of Zn in the soil restricts the growth of crops and impairs the quantity and quality of crops. Maize is grown on around 160 million hectares worldwide [5], which provides about 30% of the food calories to more than 4.5 billion people in 94 developing stunted growth, yellowish-white necrotic lesions on leaves, the necrosis of the leaf margins, and smaller leaves. By contrast, BY815 and DAN340 displayed no phenotypic differences between Zn-deficient and Zn-sufficient conditions. Regardless of treatments, all the traits associated with the tolerance to Zn deficiency showed significant differences between maternal and paternal parents (Tables 1 and 2). BY815 had high Zn efficiencies (ZEs) based on the shoot (116%) and root (111%) dry weights, which were 6.0-fold and 4.8-fold higher than those of K22, respectively. Under Zn-deficient condition, the shoot and root dry weights of BY815 were more than five times higher than K22 (Table 1). Zn efficiencies based on the shoot and root dry weights of DAN340 were 4.1-fold and 3.8-fold higher than those of K22 (Table 2), respectively. The root-to-shoot ratios (R/S) for the parents increased in response to Zn deficiency, especially for K22.

Phenotypic Variation in Zn Deficiency Tolerance
Under Zn-deficient condition, parents BY815 and DAN340 exhibited no visual Zndeficient symptoms (Figure 1). K22 showed severe symptoms of Zn deficiency, including stunted growth, yellowish-white necrotic lesions on leaves, the necrosis of the leaf margins, and smaller leaves. By contrast, BY815 and DAN340 displayed no phenotypic differences between Zn-deficient and Zn-sufficient conditions. Regardless of treatments, all the traits associated with the tolerance to Zn deficiency showed significant differences between maternal and paternal parents (Tables 1 and 2). BY815 had high Zn efficiencies (ZEs) based on the shoot (116%) and root (111%) dry weights, which were 6.0-fold and 4.8-fold higher than those of K22, respectively. Under Zn-deficient condition, the shoot and root dry weights of BY815 were more than five times higher than K22 (Table 1). Zn efficiencies based on the shoot and root dry weights of DAN340 were 4.1-fold and 3.8-fold higher than those of K22 (Table 2), respectively. The root-to-shoot ratios (R/S) for the parents increased in response to Zn deficiency, especially for K22.  a ZnSc, PH, SDW, RDW, and R/S represent Zn score, plant height, shoot dry weight, root dry weight, and R/S ratio, respectively. -Zn and -Zn/CK indicate the data under Zn-deficient condition and the ratio of the data under Zn-deficient condition to the data under Zn-sufficient condition. The same as below. b * and ** indicate significant differences between K22 and BY815 at p < 0.05 and p < 0.01, respectively. c NA indicates not available data. The same as below.   Since shoot Zn concentrations of K22, BY815, and DAN340 under Zn deficiency were no more than 20 µg g −1 , plants in the -Zn treatment were diagnosed to be Zn deficient. Deficiency in Zn substantially decreased Zn concentrations in the shoots and roots of BY815 and DAN340 by 60.8% and 54.7%, 45.5% and 64.4%, respectively (Figure 2a,b). However, Zn deficiency had no significant effects on the shoot Zn concentration of K22 ( Figure 2a). In addition, Zn concentration of the sensitive inbred line K22 was 2.2-fold and 1.4-fold higher than those of tolerant inbred lines BY815 and DAN340, respectively, indicating that Zn-efficient genotypes may not necessarily have higher shoot Zn concentrations when compared with Zn-inefficient genotypes. Furthermore, our previous results confirmed that Zn efficiencies based on shoot and root dry weights were not correlated with Zn concentrations in the shoot and root of twenty maize inbred lines which contained K22, BY815, and DAN340 [25]. Zn deficiency significantly enhanced shoot concentrations of iron (Fe) and manganese (Mn) for K22 by 96.2% and 34.9%, respectively, whereas it had no effects on Fe and Mn concentrations of BY815 and DAN340 (Figure 2c,d). By contrast, shoot copper (Cu) concentration and P/Zn ratio for each parent were markedly increased by Zn deficiency (Figure 2e,f).
Large variations of Zn-deficient symptoms were observed among RILs (Figure 3a). For each trait, the mean in each line was calculated by at least three replications from the uniform seedlings. The mean values for each trait of the K22×BY815 and DAN340×K22 RIL populations were between maternal and paternal parents (Tables 1 and 2; Figures S1 and S2). All traits associated with Zn deficiency tolerance exhibited abundant diversity among lines. The Coefficient of Variation (CV) for each trait among all RILs was calculated using the ratio of the mean to the standard error, ranging from 19.6% to 55.4%. All traits displayed normal distributions except for Zn score ( Figures S1 and S2). The broad-sense heritability for each trait varied from 64.7-86.8%, suggesting that a great proportion of phenotypic variation in each trait was genetically controlled (Tables 1 and 2).
Zn score, which was to assess the ability to tolerate Zn deficiency and was ranked from 0 to 5 (Figure 2b-g, Figures 4a and 5a), had the highest broad-sense heritability (Tables 1 and 2). As shown in Figures 4 and 5, the 75th percentiles of shoot and root dry weights in the -Zn/CK treatment in these two RIL populations were no more than 1, indicating that shoot and root biomass accumulation in most of the RILs were reduced in response to Zn deficiency. However, the 90th percentiles of R/S ratio in the -Zn/CK treatment were higher than 1 (Figure 4e,h and Figure 5e,h), suggesting that the root-to-shoot ratios of the RILs responded positively to low Zn stress. In these two RIL populations, the Zn score was significantly correlated with plant height, shoot and root dry weights, and R/S ratio using Spearman correlation analysis (p < 0.01). Furthermore, significant correlation was found among any other two traits using Pearson correlation analysis (p < 0.01) ( Figure 6).  conditions for 21 days after transplanting. Different uppercase (lowercase) letters indicate significant differences among K22, BY815, and DAN340 in the -Zn (CK) treatments at p < 0.05. ** indicates significant differences between the -Zn and CK treatments at p < 0.01.

QTL Detection
In the K22×BY815 RIL population, based on the empirical threshold of the LOD score for each trait, a total of 21 QTLs were identified in the -Zn and -Zn/CK treatments ( Figure 7, Table S1). Five QTLs (qKB-ZnSc1-1, qKB-ZnSc2-1, qKB-ZnSc6-1, qKB-ZnSc9-1, and qKB-ZnSc9-2) controlling Zn score were identified on chromosome 1, 2, 6, and 9, together explained 39.5% of phenotypic variation. Alleles from BY815 at four QTLs except for qKB-ZnSc1-1 had an additive effect of 0.31-0.42 for increased Zn score. Five QTLs for plant height were mapped on chromosome 1, 2, 6, and 9, explaining 5.5-12.0% of phenotypic variation. The allele associated with increased plant height at the four QTLs, except for qKB-PH1-1 on chromosome 1, came from BY815. The second largest-effect QTL, qKB-PH2-2, that accounted for 12.0% of phenotypic variation, was located between PZE-102017472 and SYN18069. Four loci (qKB-SDW2-1, qKB-SDW2-2, qKB-SDW9-1, and qKB-SDW3-1) controlling shoot dry weight were identified on chromosome 2, 3, and 9. At the three QTLs (qKB-SDW2-1, qKB-SDW2-2, and qKB-SDW9-1) on chromosome 2 and 9, the allele from BY815 increased the shoot dry weight by 0.09-0.13 g. Four QTLs associated with the root dry weight were detected on chromosome 1 and 9, explaining 5.9-11.2% of phenotypic variation. The allele from K22 at two loci (qKB-RDW1-1, qKB-RDW1-2) on chromosome 1 had additive effects for the increased root dry weight. In addition, the allele that increased the ZE based on root dry weight at two loci (qKB-RDW9-1, qKB-RDW9-2) (a) In the -Zn treatment, two plants for each parent and 53 RILs were grown in hydroponics for 21 days after transplanting in each container. Large variations of Zn-deficient symptoms were observed among RILs. Zn score for each plant has been visually recorded for three times since 15th day after transplanting. (b) All leaves show wrinkled leaf margins and small malformed leaves, and more than 50% of areas on middle and young leaves show chlorosis and turn pale yellow. The whole leaf with obvious Zn-deficient symptoms tends to be dead with declines in physiological functions. (c) Clustering and malformed leaves are prominent symptoms, and 30-50% of areas on middle and young leaves show Zn deficient chlorosis with necrotic patches distributed on margins and tips of leaves. (d) All leaves are clustering and small leaves, and 20-30% of areas on middle and young appear brown patches. Moreover, Zn-deficient chlorosis has been improved compared with plants in score-0 and 1. (e) The stretch for all leaves of score-3 plant is strongly inhibited and necrotic spots are shown on 10-20% of areas in middle and young leaves. (f) There are no obvious differences of score-4 plant between the -Zn and CK treatments. However, banded chlorosis is still shown on leaf margins of middle and young leaves. (g) Score-5 plants are green and healthy without any Zn-deficient symptoms.
Zn score, which was to assess the ability to tolerate Zn deficiency and was ranked from 0 to 5 (Figures 2b-g, 4a and 5a), had the highest broad-sense heritability (Tables 1  and 2). As shown in Figures 4 and 5, the 75th percentiles of shoot and root dry weights in the -Zn/CK treatment in these two RIL populations were no more than 1, indicating that shoot and root biomass accumulation in most of the RILs were reduced in response to Zn deficiency. However, the 90th percentiles of R/S ratio in the -Zn/CK treatment were higher than 1 (Figures 4e,h and 5e,h), suggesting that the root-to-shoot ratios of the RILs responded positively to low Zn stress. In these two RIL populations, the Zn score was significantly correlated with plant height, shoot and root dry weights, and R/S ratio using Spearman correlation analysis (p < 0.01). Furthermore, significant correlation was found among any other two traits using Pearson correlation analysis (p < 0.01) ( Figure 6).   shoot and root biomass accumulation in most of the RILs were reduced in response to Zn deficiency. However, the 90th percentiles of R/S ratio in the -Zn/CK treatment were higher than 1 (Figures 4e,h and 5e,h), suggesting that the root-to-shoot ratios of the RILs responded positively to low Zn stress. In these two RIL populations, the Zn score was significantly correlated with plant height, shoot and root dry weights, and R/S ratio using Spearman correlation analysis (p < 0.01). Furthermore, significant correlation was found among any other two traits using Pearson correlation analysis (p < 0.01) ( Figure 6).
In summary, the number of QTLs per trait varied from three to eight in these two RIL populations. Support intervals determined by the 1-LOD method in the KB and DK population averaged 5.  (Tables S1 and S2). In these two RIL populations, 14.8% of 54 single loci had a PVE ≥ 15%, suggesting that quite a few QTLs with higher PVEs and a number of QTLs with lower PVEs mainly contribute to the genetic component of the traits associated with Zn deficiency tolerance. This also reflects the complex basis of the tolerance to Zn-deficiency stress in maize. Additionally, 71.4% (15/21) of the identified QTLs in the KB population had additive effects for increasing the values of the detected traits, and this value in the DK population was 60.6% (20/33). These results implied that parent lines BY815 and DAN340 have occupied abundant favorable alleles, which may be available for the improvement of Zn deficiency tolerance during maize breeding.

QTL Colocalization and Candidate Genes Identification
Genomic regions overlapped by the QTLs controlling the same traits and colocalizations of the QTLs detected by different the RIL populations are important for the identification of candidate genes. Evidence for identifying candidate genes by the QTLs colocalized by the loci detected in different populations have been recorded by multiple studies in maize [14,39,40]. Apart from the QTLs detected in the KB and DK populations in this study, we also used the QTLs identified in the Ye478×Wu312 (YW) RIL population to further determine the QTL colocalization among different populations [25]. Five QTL colocalization intervals were identified in eleven QTLs detected by different traits in the KB, DK, and YW RIL populations: two localizations on chromosome 1 and three localizations on chromosome 2 ( Figure 7, Table 3). Based on the annotated genes in the B73 reference genome Version 5, a total of 253 candidate genes was identified within these QTL colocalizations. According to the functional descriptions of 253 candidate genes in Arabidopsis and rice on the MaizeGDB Database (Available online: http://www.maizeGDB.org (accessed on 15 January 2022)) and the Gramene Database (Available online: https://www.gramene.org (accessed on 15 January 2022)) (Table S3), ZmIRT1 (Zm00001eb052440) and ZmNRAMP (Zm00001eb051790), which were considered to be associated with Zn deficiency tolerance, were identified within the overlapped interval of qKB-PH1-1 and qDK-PH1-1, which both controlled plant height under Zn deficiency. In addition, another seven genes which may be associated with the responses to Zn deficiency, containing ZmTOM3 (Zm00001eb093430), ZmHMA3 (Zm00001eb095020), ZmHMA4 (Zm00001eb095010), ZmCTR3 (Zm00001eb096080), ZmVIT (Zm00001eb248740), ZmIAA17 (Zm00001eb258220), and ZmHMA9 (Zm00001eb389830), were identified within other overlapped regions colocalized by the QTLs controlling different traits ( Table 4). Beyond that, according to previous studies on Zn deficiency in plants, eight genes associated with Zn uptake and translocation, stress sensing and signaling were identified within the intervals of five single loci, containing ZmDMAS1 (Zm00001eb010040), ZmNAS3 (Zm00001eb052890), ZmETR (Zm00001eb054170), ZmEIN2 (Zm00001eb054060), ZmEIN3 (Zm00001eb331080), ZmEBF1 (Zm00001eb011850), and ZmAFBs (Zm00001eb085030, Zm0-0001eb421340) ( Table 4).

Physiological Mechanisms Underlying ZE
Zn efficiency, which is considered to characterize the tolerance to Zn deficiency, can be defined as the ability of a genotype to grow and yield well under Zn-deficient condition for a standard cultivar. A linear relationship between the reduction in shoot dry matter and the severity of leaf symptoms of Zn deficiency are observed in a range of cereals [41], and some reports further confirmed that the visual symptoms of Zn deficiency are significantly correlated with Zn efficiency [42,43]. Consistent with previous results mentioned above, Zn scores evaluating the symptoms of Zn deficiency in the linkage population for the present study were significantly correlated with R/S ratios, as well as the absolute and relative values of the dry matter weights of the shoots and roots (p < 0.01) ( Figure 6). Therefore, the visual scoring system has provided a rapid selection criterion to estimate Zn efficiency at the seedling stage.
Our results showed that the root-to-shoot ratios of most RILs were increased in response to low Zn stress, further confirming that the increase in R/S ratio occurs as an initial response to Zn deficiency in crops [44,45]. Zn-efficient and Zn-inefficient inbred lines displayed differential responses to Zn deficiency in shoot and root growth. Root growth can be increased in some cereals as a consequence of Zn deficiency [46]. For a Zn-efficient inbred line like BY815, which had the highest ZEs based on the shoot and root dry weights (116% and 111%), the root dry matter increased more than the shoot and may be attributed to the change in the partitioning of the dry matter, enabling higher nutrient uptake [47]. A Zn-efficient inbred line like DAN340, with higher ZEs (below 100%), reduced the shoot growth at the expense of the root growth, so as to decrease the metabolic demands of the shoots and the greater relative root surface area for ion absorption [46,48]. Furthermore, there was significant correlation among the R/S ratio, the shoot and root dry weights ( Figure 6). Therefore, the absolute and relative shoot and root biomass accumulation can be suitable indicators to characterize Zn efficiency in the linkage populations in maize.
In our work, Zn-efficient genotypes did not have higher Zn concentrations in the shoot or root at a low level of Zn supply. A number of studies have indicated that plant tissue Zn concentration is not a dependable parameter for evaluating differential Zn efficiency among genotypes [49,50]. Zn concentrations in tissues do not reflect how much is physiologically available for metabolic processes, or how much is inactivated or compartmented in nonmetabolic pools [51]. Zn efficiency in crop plants does not differ in shoot concentrations and may be due to internal biochemical utilization or the subcellular compartmentation of Zn in leaf cells [52,53]. Compartmental analysis of 65 Zn efflux from bean leaves provides further evidence in support of compartmentation in one of the ZE mechanisms, which indicates that the Zn-efficient genotype not only has moderately more Zn in the cytoplasm, and less Zn in the vacuole, but also exhibits a faster Zn exchange from vacuole when compared with the Zn-inefficient genotype [54].
Fe, Mn, and Cu concentrations in the shoots of Zn-deficient plants increased likely due to competitive interaction of Zn deficiency-inducible transport proteins in the transport of Fe, Mn, and Cu compared to Zn across the plasma membrane [34,55]. In addition, maize inbred lines accumulated high amounts of P in shoots on deficient Zn supply, resulting in a higher P/Zn ratio in the shoots. This is completely in accord with the general view of many researchers, that P has an antagonistic effect on Zn uptake by plants and the degree of the increase in the P/Zn ratios in plant tissue is indicative of the degree of Zn-deficiency stress in plants [56,57]. Moreover, this effect is possibly regulated by the expression of the genes encoding P transporters, which is induced by the plant's Zn nutritional status [58,59]. Zn deficiency not only results in increased expression of the genes encoding high-affinity P uptake transporters in the roots [60], but also increases the expression of P transporters involving in the transport of P to the xylem, leading to enhanced P transport to the shoot [61].

Comparisons of QTLs Identified in this Study with Previous Reports
To our knowledge, very few Zn efficiency-associated loci have been identified in maize, but numerous QTLs were identified to be associated with Zn and other microelements concentrations in maize [22,24,[62][63][64][65]. In addition, there were lots of QTLs related to the traits we used in this study, such as plant height, shoot or root dry weight, and the R/S ratio under other conditions rather than Zn-deficient condition [66][67][68][69]. Thereby, these QTLs in previous studies were compared with current results based on their physical position.
In total, 41 of 54 QTLs for different traits in two linkage populations have been identified to be colocalized with the QTLs reported by other researchers (Tables S1 and  S2). Among these QTLs, 21 QTLs were also identified in the QTLs controlling mineral concentrations in grain, including Zn, Fe, Mn, and P concentrations, detected in previous studies [22,24,[62][63][64][65]. Especially, the major-effect QTL qKB-R/S7-2 in the KB population was colocalized with two QTLs for Zn and Fe concentrations of the kernel [62]. In addition, five QTLs with a PVE > 20% mapped on chromosome 2 in the DK population were colocalized with the loci controlling Fe and Mn concentrations of the grain detected by Gu et al. [22] (2015) and Zhang et al. [63] (2017). These results imply that these colocalized QTL regions may have pleiotropic effects on the mineral concentration of grains and seedlings in maize.
Moreover, 35 of 54 QTLs in the current study were identified in the QTLs associated with plant height, the shoot or root dry weight, the R/S ratio, and the seedling root traits in previous reports (Tables S1 and S2). Both qKB-PH6-1 and qKB-PH9-1, controlling plant height under Zn deficiency on chromosome 6 and 9, were overlapped with the loci related to plant height reported by Zhang et al. [69] (2018) and Luo et al. [68] (2017), respectively. This indicates that these regions may contain a single gene exerting a strong effect underlying the QTL colocalization for plant height under Zn-deficiency stress. Three QTLs (qKB-RDW1-1, qKB-RDW1-2, and qDK-RDW1-1) on chromosome 1, two QTLs (qDK-RDW2-1 and qDK-RDW2-2) on chromosome 2, one QTL on chromosome 5 (qDK-RDW5-1) and 6 (qDK-RDW6-1), and three QTLs (qKB-RDW9-1, qKB-RDW9-2 and qDK-RDW9-1) on chromosome 9, which were associated with root dry weight, were colocalized with the QTLs regulating the seedling root traits, including the root length, root surface area, and root volume under normal condition [64,70], the axial root number, and the primary root length under high-and low-nitrogen levels [67]. These findings suggest that these QTLs may harbor several genes with pleiotropic effects on root traits at the seedling stage.

Candidate Genes Associated with Zn Uptake and Transport
Zm00001eb052440, also known as ZmIRT1, which belongs to the ZIP family, was mapped in the QTL colocalization overlapped by qKB-PH1-1 and qDK-PH1-1, controlling plant height under Zn-deficient condition in two RIL populations (Figure 7, Tables S1 and S2). It is reported that the yeast mutants expressing ZmIRT1 showed the strongest propagation under both Zn-and Fe-limited conditions [35]. The ZmIRT1-overexpressing Arabidopsis plants not only exhibit increased levels of Zn and Fe in different tissues, but also show altered tolerance to various Fe and Zn conditions compared with wild-type plants [71].
Actually, IRT1 has been identified to be a metal transporter with a broad substrate specificity [26,72]. A novel yeast uptake assay based on an inductively coupled plasmamass spectrometry analysis of 31 different metal and metalloid ions proved that the HvIRT1 protein is able to transport Zn 2+ and Cd 2+ , in addition to Fe 2+ [73]. The broad substrate specificity of HvIRT1 is similar to that found for AtIRT1 [27,74]. For another homolog, OsIRT1, its transcripts are induced by Fe, Zn, Cu, and Mn deficiencies, and over-expressing plants are sensitive to excess Zn and Cd [75]. Moreover, OsIRT1-overexpressing plants accumulate more Fe and Zn in the shoots, roots, and mature seeds [75]. This indicates that OsIRT1 may be a transporter for Zn.
Additionally, ZmHMA3 and ZmHMA4 were also identified in the QTL colocalization detected by three QTLs (Table 4). Up to now, many HMA genes have been identified and studied in Arabidopsis and rice. ZmHMA3 and ZmHMA4 are the homologs of AtHMA3 and OsHMA3, and ZmHMA9 is the ortholog of OsHMA9 [38]. The overexpression of AtHMA3 leads to the enhancement of Zn accumulation in both shoots and roots [76]. A 67 Zn-labeling experiment and a mobility experiment indicate that OsHMA3 is important for Zn detoxification and storage by sequestration into the vacuoles in the roots [77,78].
These findings suggest that ZmHMA3 and ZmHMA4 may be involved in the transport of Zn ions. Zm00001eb051790 (ZmNRAMP6), a member of the NRAMP family in maize, was detected in the overlapped region colocalized by qKB-PH1-1 and qDK-PH1-1 in two different RIL populations. NRAMP proteins play important roles in Zn, Fe, Mn, and Cd transport across the cellular membranes in living organisms [79]. Additionally, the conserved metal-binding site methionine dictates substrate preference in Nramp family divalent metal transporters [80]. The high expression of NRAMP3 and NRAMP4 genes is found in the leaves of Zn/Cd hyperaccumulating Arabidopsis halleri [81]. AtNRAMP4 is an identified Zn transporter [82], and AtNRAMP4 expression modulates the concentrations of Zn 2+ , Cd 2+ , and Mn 2+ in roots [83]. In addition, the candidate gene Zm00001eb248740 encoding vacuolar iron transporter has been identified in this research (Figure 7). It is reported that rice VIT1 and VIT2 function to transport Fe 2+ , Zn 2+ , and Mn 2+ into the vacuoles in yeast [84]. OsVIT1 and OsVIT2 are suggested to play a role in Fe/Zn translocation between source and sink organs [84]. Beyond that, three candidate genes identified within a single locus were associated with chelation mechanisms in the uptake and translocation of Zn, containing ZmNAS3 (Zm00001eb052890), ZmDMAS1 (Zm00001eb010040), and Zm-TOM3 (Zm00001eb093430). On the one hand, nicotianamine synthase (NAS) catalyzes the biosynthesis of nicotianamine (NA), which can chelate Zn ions to form stable complexes Zn(II)-NA [85]. On the other hand, deoxymugineic acid (DMA) is synthesized from NA via nicotianamine aminotransferase (NAAT) and DMA synthase (DMAS) [86][87][88][89]. Phytosiderophores (PSs), including NA and DMA, secreted by the transporter of mugineic acid (TOM) proteins [90][91][92], can form Zn complexes that are as stable as Fe(III)-PS [93], including Zn(II)-DMA [94] and Zn(II)-NA [95]. These stable complexes, especially Zn(II)-DMA, which is the preferred form for the uptake and long-distance transport of Zn, display important roles in Zn absorption from the soil and the distribution within plants [96][97][98].

Candidate Genes Linked with Hormone Signaling
Reactive oxygen species (ROS), which should be scavenged to keep cellular turgor and structures actively functioned [99,100], accumulate under Zn-deficient condition, leading to the oxidative degradation of IAA, and then the repression in the shoot growth [101]. Zn plays an important role in the production of indole-3-acetic acid [102,103], and Zn deficiency adversely affects the synthesis of the growth regulating compounds such as auxins, resulting in decreases of production and activity of indole-3-acetic acid [104]. Zn deficiency signals may be linked with hormones that include auxin and ethylene, which would indirectly regulate downstream Zn-responsive genes, such as the genes responsible for Zn acquisition, uptake, and transport, and the genes controlling Zn chelator biosynthesis and release [105].
Zm00001eb421340, also known as ZmAFB, is identified to encode an auxin signaling F-box protein (AFB) and is involved in auxin-dependent regulation via the interaction between TIR1/AFB with Aux/IAA proteins in plants. Zm00001eb258220, which was mapped within qKB-R/S5-1 and qZEAL-RDW5-1 on chromosome 5, encodes a member of the Aux/IAA gene family (ZmIAA17). Exogenous auxin influences plant root growth by regulating the expression of early auxin-responsive genes of auxin/indole-3-acetic acid (Aux/IAA), which may be quickly activated and transcribed after being processed by auxin [106][107][108][109]. OsIAA9, an ortholog of ZmIAA17, is greatly induced by exogenously applied auxin [110]. Ectopic overexpression of OsIAA9 results in fewer crown and lateral roots and reduces the inhibition of root elongation by auxin, suggesting that OsIAA9 is a negative regulator of auxin-regulated root growth [111].

Plant Culture in Hydroponics
Maize seeds were sterilized for 30 min in a 10% solution of H 2 O 2 , washed with distilled water, and soaked in saturated CaSO 4 for 10 h, and then germinated on moist filter paper in the dark at room temperature. Two days later, the germinated seeds were wrapped in moist filter paper roll and grown. At the stage of two visible leaves, the seedlings were selected and transferred into a 40 L black tank (665 mm × 410 mm × 160 mm, length × width × height). Plants were cultured in hydroponics under Zn-deficient (-Zn: 3 × 10 −4 mmol L −1 Zn-EDTA) and Zn-sufficient conditions (CK: 1 × 10 −2 mmol L −1 Zn-EDTA). Each treatment contained three replicates. For each treatment, 53 RILs and two seedlings of each parent were grown in each tank, as shown in Figure 3a, and four tanks which contained all RILs were considered as a replication in each treatment. For each treatment, a total of twelve tanks was used as three replicates in each RIL population. Two experiments were conducted for two RIL populations randomized in incomplete blocks. NiCl. Solution pH was set at 5.5-6.0. Nutrient solution was renewed every three days and aerated by a pump. Maize seedlings were cultured in hydroponics in a growth chamber with strictly controlled conditions: 28 • C during 14 h light period from 8:00 to 22:00, 22 • C during 10 h dark period, average light intensity with 350 µmol m −2 s −1 that were measured at canopy.

Phenotyping Methods
Zn-deficient symptoms appeared since the 9-12th day after transplanting, and the Zn score (ZnSc) for each plant was visually recorded three times since 15th day after transplanting. Six scales (0-5) were designed to assess the tolerance to Zn deficiency. Zn deficiency tolerance scoring mainly depends on the suppression of the growth and development for the whole plant, as well as the areas of chlorosis and necrotic patches distributed on middle and young leaves.
Score 0: plant develops about three leaves with one sprout, plant growth is stunted, and leaf elongation is severely suppressed. All leaves show wrinkled leaf margins and small malformed leaves, and more than 50% of areas on middle and young leaves appear chlorosis and turn pale yellow. The whole leaf with obvious Zn-deficient symptoms tends to be dead, with declines in physiological functions (Figure 2b). Score 1: four leaves and one sprout have been developed, and plant height and leaf elongation are strongly suppressed, as the score-0 plant is. Clustering and malformed leaves are prominent symptoms, and 30-50% of areas on middle and young leaves show Zn-deficient chlorosis with necrotic patches distributed on the margins and tips of leaves (Figure 2c). Score 2: plant has 5 leaves and 1 sprout, exhibiting similar malformations in plant development and distorted leaf growth as with the score-1 plant. All leaves are clustering and small leaves, and 20-30% of areas on middle and young appear brown patches. Moreover, Zn-deficient chlorosis has been improved compared with plants in score-0 and 1 (Figure 2d). Score 3: suppression in plant height and internode length is markedly decreased when compared with the score-2 plant. However, the stretch for all leaves is strongly inhibited and the necrotic spots are shown on 10-20% of areas in middle and young leaves (Figure 2e). Score 4: there are no significant differences in plant growth and development between the -Zn (Zn-deficient condition) and CK treatment (Zn-sufficient condition). However, banded chlorosis is still shown on the leaf margins of the middle and young leaves (Figure 2f). Score 5: plants are green and healthy (Figure 2g).
The experiment was terminated at the 21st day after transplanting, and the plant heights (PH) were measured first, then the shoots (SDW) and roots (RDW) were stored in the envelopes separately. All samples were dried at 75 • C until a constant weight, then shoot and root dry weights were recorded separately, and the R/S ratios (R/S) were calculated. Additionally, the values of SDW, RDW, and R/S in the -Zn/CK treatment refer the ratios of the values under Zn-deficient condition (-Zn) to the values under Zn-sufficient condition (CK). Dried shoots and roots for parent samples were separately ground into fine powder, and 0.3000 g powder was digested with HNO 3 -H 2 O 2 in a microwave accelerated reaction system (CEM, Matthews, NC, USA). The concentrations of Fe, Mn, Cu, Zn and P in the digested solutions were determined by inductively coupled plasma atomic emission spectroscopy (ICPAES, OPTIMA 3300 DV, Perkin-Elmer, Waltham, MA, USA). The ratio of shoot P content to shoot Zn content for each plant was calculated as P/Zn ratio.

Statistical Analysis
Means of different inbred lines were compared using one-way ANOVA at a 0.05 level of probability by SPSS 20.0 (SPSS, Chicago, IL, USA). The phenotypic difference between two parents for each RIL population was calculated by Student's t-test. The linear mixed effect function lmer in the lme4 package of R was fitted to each RIL in the RIL population to obtain the BLUP (Best Linear Unbiased Prediction) value for each trait: y ijk = µ + G i + E j + G i × E j + R(E) jk + ε i , where y ijk is the phenotypic value of ith inbred line in the jth environment and kth replication, µ is the overall mean, G i is the genetic effect of the ith inbred line, E i is the environmental effect of jth environment, Gi × Ej is the interaction between genotype and environment for the ith inbred line and jth environment, R(E) jk is the kth replication within the jth environment, and ε ijk is the residual error. The broad-sense heritability for each trait was estimated using the formula: H 2 = σ G 2 /(σ G 2 + σ GE 2 /e +σ E 2 /re), where σ G 2 is genetic variance, σ GE 2 is the interaction of genotype and environment, σ E 2 is residual error, while e and r are the number of environments and replications, respectively.

QTL Mapping
In this study, the BLUP values under Zn-deficient condition (-Zn) and the ratios (-Zn/CK) of the BLUP values under Zn-deficient condition to the values under Zn-sufficient condition were used to perform the QTL analysis. Here, the ratio of -Zn to CK for each trait was calculated using the BLUPs for each trait from the -Zn treatment and CK, respectively. The identification of QTL was performed using composite interval mapping (CIM) [118], implemented in the Windows QTL Cartographer version 2.5 (N.C. State University, Bioinformatics Research Center, Raleigh, NC, USA). The scanning interval between markers was set at 0.5 cM, and the window size was set at 10 cM. Model 6 of the Zmapqtl module was selected for detecting position and additive effects of QTL. The threshold logarithm of odds (LOD) values in this study were estimated by permutation tests with minimum of 1000 replicates at a significance level of p = 0.05. The support interval of the QTL position was determined using the 1-LOD interval method (1 LOD away from the peak LOD value). Moreover, the QTL having the same support intervals were defined as an identical QTL.

Annotation of Candidate Genes
According to the physical distance of peak bins, genes within the refined QTL peak and their functional descriptions were identified using the maize B73 reference genome assembly Version 5, available on the MaizeGDB Database (Available online: http://www. maizeGDB.org/ (accessed on 15 January 2022)) and the Gramene Database (Available online: https://www.gramene.org/ (accessed on 15 January 2022)). The functions of candidate genes were further confirmed by the annotations of the orthologs in Arabidopsis and rice.