The Maize Shoot Ionome: Its Interaction Partners, Predictive Power, and Genetic Determinants

Received: 4 May 2019 Revised: 5 April 2020 Accepted: 18 April 2020 DOI: 10.1111/pce.13823

ORIGINAL ARTICLE

The maize shoot ionome: Its interaction partners, predictive power, and genetic determinants

1Institute for Quantitative Genetics and Genomics of Plants, Heinrich Heine University, Abstract Düsseldorf, Germany An improved understanding of how to manipulate the accumulation and enrichment 2 Cluster of Excellence on Plant Sciences, of mineral elements in aboveground plant tissues holds promise for future resource Düsseldorf, Germany 3Max Planck Institute for Plant Breeding efficient and sustainable crop production. The objectives of this study were to Research, Köln, Germany (a) evaluate the influence of Fe regimes on mineral element concentrations and con- 4 Leibniz Institute of Plant Genetics and Crop tents in the maize shoot as well as their correlations, (b) examine the predictive ability Plant Research, Gatersleben, Germany of physiological and morphological traits of individual genotypes of the IBM popula- Correspondence tion from the concentration of mineral elements, and (c) identify genetic factors Benjamin Stich, Institute for Quantitative Genetics and Genomics of Plants, Heinrich influencing the mineral element composition within and across Fe regimes. We evalu- Heine University, 40225 Düsseldorf, Germany. ated the concentration and content of 12 mineral elements in shoots of the IBM pop- Email: [email protected] ulation grown in sufficient and deficient Fe regimes and found for almost all mineral Present address elements a significant (α = 0.05) genotypic variance. Across all mineral elements, the Andreas Benke, Strube Research GmbH & Co. KG, Hauptstraße 1, 38387 Söllingen, variance of genotype*Fe regime interactions was on average even more pronounced. Germany. High prediction abilities indicated that mineral elements are powerful predictors of Claude Urbany, KWS Saat SE, Grimsehlstr. 31, morphological and physiological traits. Furthermore, our results suggest that 37555 Einbeck, Germany. ZmHMA2/3 and ZmMOT1 are major players in the natural genetic variation of Cd and Mo concentrations and contents of maize shoots, respectively.

KEYWORDS genomic prediction, ionomic prediction, iron regime, maize, physiological and morphological traits, QTL mapping, shoot ionome

1 | INTRODUCTION controlling uptake, transport, and accumulation of mineral elements. Ionomic profiling is amenable to high-throughput phenotyping, which A better understanding of processes and genes regulating mineral ele- when coupled with quantitative genetic approaches such as quantita- ment uptake and how to manipulate the concentrations of mineral tive trait locus (QTL) mapping or genome wide association studies elements in aboveground plant tissues holds promise for future becomes a powerful tool for gene discovery (Baxter, Gustin, Settles, & resource-efficient and sustainable crop production (Stein et al., 2017) Hoekenga, 2013). as well as for bio-fortification towards alleviating global malnutrition The majority of such studies has been performed in model plants (http://www.harvestzinc.org/harvestplus). Studies on the composition (e.g. Bentsink, Yuan, Koornneef, & Vreugdenhil, 2003; Ghandilyan of plant mineral elements are referred to as ionomics (Lahner et al., 2009; Klein & Grusak, 2009; Salt, Baxter, & Lahner, 2008; et al., 2003). Their aim is to reveal knowledge about the networks Vreugdenhil, Aarts, Koornneef, Nelissen, & Ernst, 2004; Waters &

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2020 The Authors. Plant, Cell & Environment published by John Wiley & Sons Ltd.

Plant Cell Environ. 2020;43:2095–2111. wileyonlinelibrary.com/journal/pce 2095 2096 STICH ET AL.

Grusak, 2008). Only a few studies have performed genetic analyses In agricultural research (Bej & Basak, 2014) and especially plant of the ionome of crop plants such as bean (Blair, Astudillo, Grusak, breeding (Wallace, Rodgers-Melnick, & Buckler, 2018), a high interest Graham, & Beebe, 2009), rice (Stangoulis, Huynh, Welch, Choi, & exists in exploiting biomolecular signatures to predict complex pheno- Graham, 2007), Sorghum (Shakoor et al., 2016), or rapeseed (Bus types. Baxter et al. (2008) have demonstrated that shoot ionomic sig- et al., 2014). Two studies used a quantitative genetic approach to dis- natures in one Arabidopsis thaliana genotype can be used to identify sect the ionome of maize grains using the intermated B73 x Mo17 plants that experience an environmental cue. However, to the best of (IBM) population (Asaro et al., 2016; Baxter et al., 2013). Baxter our knowledge, the accuracy of such approaches has not been et al. (2013) described 25 QTL for grain concentrations of nine mineral reported for segregating populations, that is, for entries that differ in elements that were detected as significant in two or more locations their genetic make-up. Furthermore, only few studies have evaluated and, thus, were suggested as targets for biofortification programs. In up to now the predictive ability of mineral elements to estimate geno- the study of Asaro et al. (2016), which comprised additional environ- typic differences in other physiological or molecular traits. ments to those sampled by Baxter et al. (2013), a total of 79 QTL con- The objectives of this study were to (a) evaluate the influence of trolling seed elemental accumulation were detected when each Fe regimes on mineral element concentrations and contents in the environment was analysed separately. While a set of these QTL was maize shoot as well as their correlations, (b) examine the ability of found in multiple environments, the majority were specific to a single predicting an environmental perturbation from the ionomic response environment, illustrating the importance of genotype*environment across a segregating population, (c) examine the predictive ability of interactions. Similar results were obtained with segregating physiological and morphological traits of individual genotypes of the populations different from IBM (Gu et al., 2015; Simic et al., 2012). IBM population from 110 the concentration of mineral elements, and However, despite the important role of the mineral elements during (d) identify genetic factors influencing 111 the mineral element con- the vegetative development of plants, no earlier study examined the centrations or contents within and across Fe regimes. inheritance of the shoot ionome of a C4 species. Most previous studies examined the concentration of mineral elements. Mineral element concentrations reflect primarily the nutritional 2 | MATERIALS AND METHODS status of a plant or tissue and provide valuable information on differences in uptake, metabolism, or allocation among genotypes as long 2.1 | Plant material as biomass differences between genotypes are low. In our study, we considered additionally mineral element contents, that is, the product The recombinant inbred lines (RIL) of the intermated B73 x Mo17 of concentration and biomass. These shoot contents reflect the cumu- (IBM) syn4 population (Lee et al., 2002) were examined in the current lative nutrient uptake and translocation under consideration of the study. Due to the unavailability of seeds for the RIL MO040, MO043, growth response. MO048, MO057, MO062, MO063, MO076, MO079, and MO344, a The plant's ionomic state results from an interaction between the total of 85 RIL were evaluated. genotype and the environment, where the latter is strongly influenced by the soil (Baxter et al., 2012). However, our present knowledge of these complex genotype*environment interactions remains sketchy 2.2 | Culture conditions and measured mineral and limited in particular by the vast and discontinuous variation in elements soil composition at multiple scales in nature (Stein et al., 2017). Joint collections of both plant accessions and adjacent soil were Maize seeds were sterilized in a 3% NaClO solution for 3 min and suggested to overcome this bottleneck (Baxter et al., 2012). Apart then treated with 60C hot water for 5 min (Benke et al., 2014). After- from being laborious, disadvantages of such approaches are that in wards, seeds were placed between two filter paper sheets moistened most cases the experimental design does not allow to separate with saturated CaSO4 solution for germination in the dark at room genotype*environment interaction variance from error variance. Fur- temperature. After 6 days, the germinated seeds were transplanted to thermore, it is impossible to dissect genotype*environment interac- a continuously aerated nutrient solution with element concentrations tions into genotype*individual mineral element interactions. as described by von Wiren, Marschner, and Römheld (1996). The Therefore, such studies need to be complemented by experiments plants were supplied with 100 μM Fe(III)-EDTA for 7 days. From day with artificial growth media, in which nutrient supplies can be con- 14–28, plants were supplied with 300 (Fe sufficient) (Urbany trolled. However, such studies are lacking so far. et al., 2013) or 10 (Fe deficient) (Shi et al., 2018) μM Fe(III)-EDTA. The Iron is one essential mineral element, required for many important nutrient solution was renewed every third day. Plants were cultivated biochemical processes, including photosynthesis and respiration, from day 7 to day 28 in a growth chamber at a relative humidity of where it participates in electron transport (Marschner, 2012). There- 60%, a light intensity of 170 μmol m−2 s−1 in the leaf canopy, and a fore, we have chosen Fe exemplarily as the factor to be altered in a day-night temperature regime of 16 hr/24C and 8 hr/22C, respec- hydroponic culture to study a specific form of genotype*environment tively. Four plants of each RIL were grown in one shaded 5 L pot. Two interaction, the genotype*Fe regime interaction, and to identify the separate pots were used for the two examined Fe concentrations underlying genetic factors. (T = 2). All pots were arranged in a growth chamber following a THE MAIZE SHOOT IONOME 2097 split-plot design, where the two parental inbreds were included as Student's t test was used to examine the statistical significance of controls. The entire experiment was replicated R = 3 times. For each the mean difference between deficient and sufficient Fe regimes. RIL, the shoot of the four plants from one pot were pooled so that Pairwise Pearson's correlation coefficients were assessed between each RIL was represented by one sample for each of the three repli- the 12 mineral elements examined in the current study, 11 morpholog- cates in each of the two Fe regimes. Afterwards, the entire sample was ical and physiological traits evaluated by Benke et al. (2014), and nine ground and the concentrations of 12 mineral elements (Figure 1) were mineral elements measured in maize grains by Baxter et al. (2013). measured using inductively coupled plasma optical emission spectrom- The difference between pairs of correlations was tested using a t-test etry (ICP-OES, iCAP 6,400, Thermo Fisher) according to Shi et al. (2012). of the Z-transformed correlations. A principal component (PC) analysis In a previous study, the variation of morphological and physiological of the normalized and scaled concentrations of 12 mineral elements in traits of the same experiment has been reported (Benke et al., 2014). the shoot of the maize IBM population as well as the two parental inbred lines measured in sufficient and deficient iron regimes was performed. 2.3 | Statistical analyses All above described statistical analyses were not only performed for the concentration of mineral elements but also for the total con- Upon the removal of outliers with residuals larger than 2.5 times the tent accumulated in the shoots, that is, average content per plant. The standard deviation of the residuals, the concentrations of each mineral average content per plant, which was in the following designated as element were analysed across both Fe regimes using the following content was calculated from the product of concentration and the mixed model: shoot dry weight per plant described by Benke et al. (2014).

yijk = μ + gi + tj + gi tj + rk + eijk, 2.4 | Ionomic and genomic prediction where yijk was the observed elemental concentration of the ith genotype in the pot of the jth Fe regime and kth replication, μ the general The prediction of the presence of an environmental perturbation from mean, gi the effect of the ith genotype, tj the effect of the jth Fe the physiological response was performed by applying a logistic regime, gi*tj the interaction effect of the ith genotype and the jth Fe regression model with the Fe regime as dependent variable. Two sets regime, rk the effect of the kth replication, and eijk the residual error. of mineral elements were used as independent variables: (a) all mineral

To estimate adjusted entry means (AEM) for all genotypes, gi and tj elements except Fe and (b) all mineral elements except Fe and Zn. We were considered as fixed and all other effects as random. Further- applied fivefold cross-validation (CV) for validation of these predic- more, all effects, except tj, were considered as random to estimate the tions (Hjorth, 1994). For this purpose, the 85 RIL were randomly sub- 2 genotypic variance (σ g), the interaction variance between genotypes divided into five disjoint subsets. The AEM of one subset for each of 2 2 and Fe regime (σ g*t), and the error variance (σ e). Statistical signifi- the two Fe regimes were left out and used as validation set whereas 2 2 cance of σ g and σ g*t has been assessed according to Scheipl, Greven, the other four subsets were used as training set. This procedure was and Küchenhoff (2008). The broad sense heritability H2 across both replicated 20 times, yielding in total 100 CV runs. For each of the CV Fe regimes was calculated as: runs, the proportion of genotype*environment combinations from the validation set for which the Fe regime was correctly predicted, was σ2 H2 = g : estimated. The median of this proportion across all CV runs was desig- σ2t σ2 σ2 g e g + T + TR nated as predictive ability. The physiological and morphological traits assessed by Benke In addition, the concentrations of mineral elements collected from et al. (2014) for the same plants in each of the two Fe regimes were each Fe regime were analysed separately using the following mixed predicted by two predictors: (a) the mineral element concentrations model: and (b) the molecular marker information described in the next para- graph. W is a matrix of feature measurements for the respective pre- yik = μ + gi + rk + eik, dictor. The dimension of W is determined by the number of RIL and where yik was the observed elemental concentration of the ith geno- m, the number of features in the corresponding predictor (mionome = 12, type in the pot of the kth replication and eik the residual error. To esti- mmolecularmarkers = 1,212). The columns in W were centred and stan- mate AEM for all genotypes, gi was considered as fixed and rk as dardized to unit variance. For each predictor, an additive relationship random. Furthermore, gi and rk were considered as random to esti- matrix was calculated as. 2 2 2 mate σ g and σ e. The broad sense heritability H for each Fe regime was calculated in analogy to the method described above: G =1=m + WWT,

σ2 g 2 : T H = σ2 where W denotes the transpose of W (VanRaden et al., 2009). The σ2 + g g R additive relationship matrices were used for genomic best linear 2098 STICH ET AL. unbiased prediction (GBLUP, Meuwissen, Hayes, & Goddard, 2001). haplotype version 3 (Bukowski et al., 2018) after converting the GBLUP method was used as implemented in the R package sommer assembly to AGPv4 using CrossMap (Zhao et al., 2014). The annota- (Covarrubias-Pazaran, 2016). Fivefold CV was applied, as described tion of these polymorphisms was performed using the Ensembl Vari- above. We calculated the predictive ability [r(ypred,yobs)] as the Pear- ant Effect Predictor (McLaren et al., 2016). Furthermore, selected son correlation between the observed AEM and the predicted geno- polymorphisms that are predicted to have a high variant consequence typic values. were validated by Sanger sequencing. After converting the AGPv4 based positions of candidate genes to AGPv2 based, the v2.7 data from genotyping by sequencing experi- 2.5 | QTL analyses ments of maize inbreds of the USA national seed bank (Romay et al., 2013) was used to extract existing haplotypes based on poly- The publicly available genotypic data for the RIL as well as the genetic morphism information from the candidate genes. These 1,749 inbreds map positions of these markers on the IBM2 map were the basis of were considered in our study for which the information of the US our analyses. Markers that showed a highly significant (p<.001) dis- state of origin was available in the Germplasm Resources Information torted segregation were excluded. The remaining 1,212 markers were Network database. For haplotypes occurring in the entire sample with used for the QTL analyses. QTL analyses to detect genome regions a frequency >2.5%, a haplotype network was built using an infinite linked to the difference in the concentration and content of mineral site model (Hamming, 1950). elements across the two examined Fe regimes were conducted with If not stated differently, all statistical analyses were performed Rqtl (Broman, Wu, Sen, & Churchill, 2003). Standard interval mapping using statistical software R (R Development Core Team, 2016). using the Haley-Knott regression algorithm (Haley & Knott, 1992) and a model that included the Fe regime as additive covariate were used in a first step. 3 | RESULTS Forward selection was used in the following to determine the maximum number of QTL to include in the model selection procedure. We characterized the variability of the shoot concentrations of min- An automated forward and backward search algorithm was then eral elements across two Fe regimes using mixed model analyses to applied to perform multiple QTL mapping using a model that included quantify the influence of the various sources of variance. For the con- again the Fe regime as additive covariate. Model selection was based centrations of all mineral elements examined in our study, a significant 2 on the highest penalized LOD score with penalties determined (α = 0.05) genotypic variance (σ g) was observed except for K and Zn 2 through 1,000 permutations. In the next step, the presence of epi- (Figure 1). Similarly, σ g was significant (α = 0.05) for the content of all static interaction among all pairs of QTL with main effects were exam- mineral elements except for Cu and Mn. The variance of genotype*Fe 2 ined. Afterwards, a forward and backward search algorithm was regime interactions (σ g*t) was on average across the concentrations 2 applied to add significant QTL*Fe regime interactions to the model, of all mineral elements even more important than σ g and was signifi- where significance of all models tested was empirically determined cantly different from 0 (α = 0.05) for all mineral elements. In addition, 2 using 1,000 permutations. σ g*t of the concentrations of mineral elements was considerably more Confidence intervals for QTL were calculated using a 1.5 LOD pronounced compared to that of the contents of mineral elements. As drop technique (Manichaikul, Dupuis, Sen, & Broman, 2006). In order a consequence, the broad sense heritabilities (H2) of the concentra- to obtain unbiased estimates of the proportion of the explained phe- tions of mineral elements for the sufficient and deficient Fe regime notypic variance, fivefold CV accounting for genotypic sampling as were with a range from 0.39 to 0.95 slightly higher than H2 across applied as described earlier for QTL mapping (Utz, Melchinger, & both Fe regimes, whereas the opposite trend was observed for the Schön, 2000) and the mean proportion of the phenotypic variance mineral element contents. explained by one or multiple QTL (RCV) was calculated. The AEM for all mineral element concentrations except Fe was on average across all RIL significantly higher (α = 0.05) under the deficient Fe regime compared to the sufficient regime, while the opposite 2.6 | Characterization of genes in QTL confidence was true for Fe (Figure 2). In contrast, the AEM for the content of all intervals mineral elements except Cd, Cu, Mn, and Zn was on average across all RIL significantly higher (α = 0.05) under the sufficient Fe regime The molecular markers flanking the QTL confidence interval were compared to the deficient regime, while the opposite was true for identified and their physical position on the B73 AGPv4 sequence was Zn. A strong transgressive segregation was observed in the RIL for derived. For those flanking markers for which a physical map position the concentrations of all mineral elements which suggests that both could not be derived, it was estimated based on the nearest locus on parental inbreds contribute alleles to the progenies that increase the IBM2 2008 Neighbours map for which a physical positioning was the concentrations of the studied mineral elements. For the mineral available. The genes in the physical QTL confidence interval were element contents, transgressive segregation was less pronounced extracted from the B73 AGPv4 sequence. The polymorphisms within compared to that observed for the concentrations of mineral the physical confidence interval were extracted from the maize elements. THE MAIZE SHOOT IONOME 2099

FIGURE 1 Analysis of variance for shoot concentrations and contents of 12 mineral elements. Linear mixed models were used to describe 2 2 2 2 2 (a) the variance contributed from genotype (σ g), genotype*iron (Fe) regime interaction (σ g*t), and error (σ e). σ g and σ g*t estimates marked by *, **, and *** are significantly (α = 0.05, 0.01, and 0.001) different from 0. (b) Estimates for heritability (H2) across as well as for the individual Fe regimes were calculated 2100 STICH ET AL.

(a) * *** *** *** K Zn Cd Mo 0.29** 0.68*** 0.09ns 0.94*** r= r= r= r= 6 5 4 3 2 1

80 60 40 50

0.4 0.3 0.2 0.1 0.0

120 100 DW mg/g 350 300 250 200 150 100

g/g DW g/g DW g/g DW g/g μ μ μ *** *** *** *** S Fe Ca Mn 0.42*** 0.44*** 0.43*** 0.31** r= r= r= r= 8 6

80 60 40 20 50 16 14 12 10

5.0 4.5 4.0 3.5 3.0 2.5 2.0

140 120 100 200 150 100

g/g DW g/g DW g/g mg/g DW mg/g DW mg/g μ μ *** *** *** *** P B Cu Mg 0.31** 0.31** 0.31** 0.46*** r= r= r= r=

5 7 6 5 4 3 2 8 6 4

30 25 20 15 10 20 15 10 16 14 12 10

mg/g DW mg/g DW mg/g

g/g DW g/g DW g/g μ μ

FIGURE 2 Frequency distribution of the adjusted entry means (AEM) of the (a) concentrations, (b) shoot contents of 12 mineral elements in the maize IBM population measured at sufficient (white) and deficient (grey) iron regimes. The AEM of the parental inbreds B73 and Mo17 are represented by a square and a triangle, respectively. The significance level of a t-test examining the difference of one mineral element examined in two different iron (Fe) regimes across the population are given above the horizontal line. Pearson's correlation coefficient (ρ) was calculated between the AEM of both Fe regimes. *, **, ***: p = .05, .01, and .001, respectively; ns, not significant

In the PC analysis of the concentrations of mineral elements, the shown). The heat map of Pearson's pairwise correlation coefficients first two principal components explained 19.2 and 13.9% of the vari- between all pairs of mineral elements (Figure 3) revealed that with the ance (Figure S1), where no obvious clustering of the RIL was observed exception of Mo and Cd all mineral elements showed higher correla- with respect to these two principal components. The same trend was tion coefficients within the same Fe regime than the same mineral ele- observed in the PC analysis of the mineral element contents (data not ments measured in samples from different Fe regimes (Figure S2) THE MAIZE SHOOT IONOME 2101

(b) ns *** *** *** K Zn Cd Mo 0.21ns 0.84*** 0.42*** 0.66*** r= r= r= r= 8 6 4 2 0

50 50 10

0.6 0.5 0.4 0.3 0.2 0.1 0.0

200 150 100 300 250 200 150 100

g/plant g/plant g/plant mg/plant μ μ μ ns *** *** *** S Fe Ca Mn 0.11ns 0.36*** 0.39*** 0.46*** r= r= r= r= 5 8 6 4 2

50 50 35 30 25 20 15 10 10

300 250 200 150 100 200 150 100

mg/plant g/plant mg/plant g/plant μ μ ns *** *** *** P B Cu Mg 0.23* 0.33** 0.27* 0.09ns r= r= r= r=

5 5

40 30 20 10 50 40 30 20 10 15 10 20 15 10

g/plant mg/plant mg/plant g/plant μ μ

FIGURE 2 (Continued)

regardless of whether the concentration or the content of mineral ele- Interestingly, correlations typically found in shoots, such as between ments was considered. Ca and Mg, were strong in the Fe sufficient regime but got lost The pair of mineral element concentrations with the highest under Fe deficiency. Other correlations, especially between Zn and correlation coefficient that was consistent across both Fe regimes Mn became more pronounced under Fe deficiency. The mineral ele- was P/Mn, while that with the lowest was Mo/Fe. In total, we ments that were most frequently involved in the correlations that observed for 10 pairwise correlations of mineral element concentra- differed significantly between the Fe sufficient and deficient regime tions that did not involve Fe significant (α = 0.05) differences were Ca (four times), Cu, and K (three times), and S, Mg, Mn, P (two between the Fe sufficient and the Fe deficient regime (Figure 4a). times). 2102 STICH ET AL.

(a)

(b)

FIGURE 3 Heat map of Pearson's correlation coefficients calculated for all pairs of mineral element (a) concentrations and (b) contents measured in the shoots of the maize IBM population grown under sufficient (above diagonal) and deficient (below diagonal) iron regimes. The cells marked with an asterisk * indicate that the corresponding correlation coefficient was significantly (α = 0.05) different from 0 [Colour figure can be viewed at wileyonlinelibrary.com]

The same general trends that were observed for the concentra- correlation coefficients calculated between the contents of two min- tions of mineral elements were also observed for the contents of mineral elements under the Fe sufficient and the Fe deficient regime was eral elements (Figure 3b). However, the relationship between the more tight than that observed for the concentrations of mineral THE MAIZE SHOOT IONOME 2103

FIGURE 4 Scatterplot of Pearson's correlation coefficient between 12 mineral element (a) concentrations and (b) contents determined in maize shoots harvested from an iron sufficient versus deficient regime. The pairs of correlations that are significantly (α = 0.05) different from each other are markerd by an asterisk *. The linear trendline corresponds to the bisectrix

elements. Nevertheless, also for the contents of mineral elements to 0.53 for the sufficient Fe regime (Table 1). For the deficient Fe 10 pairwise correlations not involving Fe differed significantly regime, the observed predictive abilities were higher and ranged from between the Fe sufficient and deficient regime (Figure 4b). The min- 0.23 to 0.57. The same trend was observed when using the mineral eral elements that were most frequently involved in these correlations elements as predictors. However, the such observed predictive abili- were P, Cd, K, and Mg (three times), as well as Mo and Mn (two ties were for the sufficient Fe regime for five physiological and mor- times). phological traits significantly (α = 0.05) higher than for predictions We examined the correlations between the mineral element con- made from molecular marker data. For the deficient Fe regime, this centrations in maize shoots and morphological and physiological traits trend was observed for nine of the 11 examined traits. of the same plants (Benke et al., 2014). In the sufficient Fe regime, we To unravel the genetic determinants of the shoot ionome of observed tight correlations between the shoot concentration of P and maize, a linkage mapping approach was used. For the concentration of root weight (RW), shoot dry weight (SDW), and lateral root formation all mineral elements except K, genome regions with significant effects (LAT), whereas under deficient Fe conditions, the tightest correlations were identified (Table S1). The cross validated proportion of pheno- were observed between P, Mn, and Zn concentrations and the shoot typic variance explained by the genome regions with significant main dry weight and different leaf greenness parameters (SP) (Figure S3). effect ranged from 0.9 to 66.6%. Seven of the detected 27 genome These correlations were in close agreement with earlier studies and regions showed a significant interaction with the Fe regime, while for confirmed here across a RIL population. two of the seven genome regions no significant main effect was From the ionomic profile of maize shoots, we evaluated the possi- observed. For none of the QTL with a significant main effect, signifi- bility to predict an environmental perturbation. This was done by cant epistatic interactions were observed. The LOD score (up to 5.9) predicting the Fe regime in which the RIL of the IBM population was as well as the proportion of the phenotypic variance (up to 2.6%) cultivated based on the measured concentrations of mineral elements. explained by the QTL*Fe regime interactions were considerably lower When using all mineral elements except Fe and Zn as independent than that of the main effect QTL. For the contents of all mineral ele- variables, the cross-validated predictive ability was very high with a ments except P, the same trends were observed for the 33 genome value of 0.90. This ability increased to 1.0, when Zn was included as regions (Table S2). A total of four genome regions that contributed independent variable. In a second approach, we examined here the significantly to the phenotypic variance of the concentration of min- prediction of physiological and morphological traits assessed previ- eral elements (C-Cd1, C-Cu2, C-Mo1, C-Mo5) contributed also to the ously by Benke et al. (2014) in the IBM population from molecular content of the same mineral element (A-Cd1, A-Cu3, A-Mo1, A-Mo4). marker data. The cross-validated predictive ability ranged from 0.13 In general, we observed a clustering of genome regions that 2104 STICH ET AL.

TABLE 1 Median and standard deviation of predictive ability [r(ypred, yobs)] of ionomic/genomic prediction in the IBM population for physiological and morphological traits (Benke et al., 2014) obtained across 100 fivefold cross-validation runs with the prediction method genomic best linear unbiased prediction

Sufficient Deficient

Mineral element Molecular Mineral element Molecular Trait concentrations markers Difference concentrations markers Difference SP3 0.31 ± 0.18 0.36 ± 0.18 Ns 0.61 ± 0.13 0.40 ± 0.18 * SP4 0.57 ± 0.14 0.25 ± 0.20 * 0.69 ± 0.12 0.50 ± 0.17 * SP5 0.39 ± 0.19 0.48 ± 0.17 * 0.74 ± 0.10 0.38 ± 0.16 * SP6 0.35 ± 0.19 0.50 ± 0.18 * 0.76 ± 0.09 0.37 ± 0.18 * RL 0.05 ± 0.21 0.13 ± 0.24 * 0.45 ± 0.16 0.24 ± 0.25 * RW 0.56 ± 0.19 0.28 ± 0.21 * 0.68 ± 0.18 0.37 ± 0.20 * SL 0.41 ± 0.19 0.26 ± 0.16 * 0.59 ± 0.15 0.36 ± 0.18 * SDW 0.62 ± 0.16 0.31 ± 0.20 * 0.77 ± 0.10 0.31 ± 0.21 * WC 0.37 ± 0.21 0.53 ± 0.17 * 0.36 ± 0.19 0.57 ± 0.17 * LAT 0.65 ± 0.15 0.17 ± 0.22 * 0.60 ± 0.17 0.31 ± 0.17 * NEC 0.23 ± 0.20 0.19 ± 0.22 Ns 0.22 ± 0.21 0.23 ± 0.18 Ns

Note: The considered traits were the relative chlorophyll content of leaf 3–6 (SP3, SP4, SP5, and SP6), root length (RL), root weight (RW), shoot length (SL), shoot dry weight (SDW), water content (H2O), lateral root formation (LAT), and leaf necrosis rating (NEC).

contribute significantly to the phenotypic variance on chromosomes found. For Zm00001d005189 and Zm00001d005190, B73 haplo- 1, 5 and 7 (Figure 5). Nevertheless, the high mapping resolution of the types occurred with high frequency in the studied set of maize IBM population allowed for all except four pairs of QTL for the con- inbreds (Supplementary Figure 4), whereas the Mo17 allele was centration of mineral elements a separation of the effects. For the underrepresented compared to genome-wide data. In addition, no mineral element contents, the number of overlapping pairs of QTL obvious clustering of the haplotypes with respect to the state of origin was with 10 considerably higher. The two most prominent QTL of the respective inbred was observed (data not shown). explaining in CV at least 39% of the phenotypic variance and that were detected for the concentration as well as the content of mineral elements were C-Cd1/A-Cd1 as well as C-Mo1/A-Mo1 (Figure 6). At all 4 | DISCUSSION of these four QTL, the Mo17 allele increased the concentration or content of the corresponding mineral element. 4.1 | Natural variation in the mineral element Two genes (Zm00001d005189 and Zm00001d005190) were profile of maize shoots and the interaction with the Fe located within the confidence interval of C-Cd1/A-Cd1 that both have regime an annotation as cadmium/zinc-transporting ATPase. The gene Zm00001d033053 that was located within the confidence interval of Across the RIL of the IBM population, we observed a high phenotypic C-Mo1/A-Mo1 has an annotation as molybdate transporter 1. Because variability in the concentration but also the content of all mineral ele- polymorphisms in these genes contribute to phenotypic variation of ments (Figure 2). The performed mixed-model analyses revealed that Cd and Mo in other plant species, we evaluated the polymorphisms in for most mineral elements concentrations as well as contents this vari- 2 these three genes in detail (Figure 7). Zm00001d005189 has an early ation was due to significant (α = 0.05) genotypic (σ g) variance stop codon in B73 that is absent in Mo17. For Zm00001d005190 (C- (Figure 1). These findings indicate that the examined population is Cd1/A-Cd1), Mo17 gained a stop codon compared to B73 at position appropriate to be used to identify the genetic factors that contribute 163,039,505 where it lost one at position 163,039,743. The SNP to the variation of mineral element concentrations in maize shoots. observed in the sequence of the genes Zm00001d033053 (C-Mo1/A- This holds especially true as we have studied an intermated RIL popu- Mo1) caused a change in a single amino acid. These polymorphisms, lation, in which additional rounds of intermating cause a rapid decay which were detected in the maize haplotype version 3, have been vali- of linkage disequilibrium. Thus, correlations between mineral elements dated using Sanger sequencing (data not shown). observed in our study are less likely caused by linkage but are rather We mined the data from genotyping by sequencing experiments due to pleiotropy. Therefore, the examined population is also particu- of 1,749 maize inbreds of the USA national seed bank and explored larly suitable to validate correlations among mineral elements. the existing haplotypes at the three genes as well as their frequency. The correlations between pairs of concentrations of mineral ele- For Zm00001d033053 only a reduced set of polymorphisms that did ments that were observed in our study under the Fe sufficient regime not allow any differentiation between B73 and Mo17 haplotypes was (Figure 3) were generally in good agreement with those observed in ofdneitrasidct T ihsgiiatmi n neato fet swl sQLwt nysgiiatitrcinefcs respecti QTL effects, blue interaction and significant Green only effects. with main QTL significant as only wileyonlinelibrary.com] well showed at as black, viewed effects in be interaction coloured can and is figure main interval [Colour significant the with which QTL for indicate QTL, intervals maize. confidence of map IBM2 the on elements 5 FIGURE IONOME SHOOT MAIZE THE

Rendered b cM y Linka g eMa 1000 800 600 400 200 0 p View 1 ofdneitraso h eetdqatttv ri oi(T)frsotcnetain C* n otns(-)o 2mineral 12 of (A-*) contents and (C-*) concentrations shoot for (QTL) loci trait quantitative detected the of intervals Confidence

umc1605 bnl6.32 phi064 umc2045 csu1089 umc1331 lim228 umc1630 umc86a umc84a umc1744 umc1118 npi241a umc1111 bnl8.29a umc1681 umc1421 umc1553 cdo87b npi238 phi265454 umc161a lim39 mmp87 uaz130a lim78 lim99a rz630a npi407 mmp172 bnlg1671 mmp83 umc2149 mmp141 nfa103a cdo122a umc107a umc1431 umc1082 csu554a umc197a msu2 csu222a bnlg1331 umc140a umc2047 chrom7 rz403 csu696 ufg53 cdj2 umc1383 umc1991 an1 umc1928 umc1446 csh4 umc1838 npi255 umc2181 umc1955 npi120 lim254 umc83a bnlg2228 mmp99 cdo98b umc37a umc128 umc1245 phi002 npi447a bcd386a umc1147 umc1128 bcd207a bnlg1025 php20661 mmp173 mmp189 lim442 umc23a bnlg1556 umc1358 bcd98a php20855 csu374b asg62 umc1925 csu1132 uaz147b ufg50 umc1924 php20644 mmp123 asg16b umc58 ntf1 umc2151 psr152a bnlg1615 umc1919 umc1748 umc1396 mmp156 umc1123 bnlg1598 bnlg2057 umc1811 asg58 php20654 umc1590 myb6 umc1972 umc67a hac101b umc1603 umc1395 mmp124 umc1906 cdo344c umc1703 mmp101 umc1076 csu1138 mmp143 uaz246c umc1515 mmp39 umc2025 lim497 mmp61 csu3 asg3 bnlg2295 bnl9.11b bnlg1811 ufg43 isu041b ufg77 ufg13b csu207 asg30b umc1169 asg75 umc1849 bnlg2238 cdo938a mmp56 mmp100 mmp151a lim432 umc1598 bnlg1866 ndp1 mmp23 ndp2 asg35b umc13 bnlg1203 umc1479 npi242b umc1397 mmp66 umc11a umc230 npi439a umc1403 umc1073 lim122 cdo1387b npi403b bnlg1953 bnlg1127 mmp135 umc1976 lim504 php20640 bnlg1429 umc1568 umc1166 csu1171 mmp68 npi579b umc1160 mmp93 umc1685 php20689 php20603 umc1977 umc1269 umc1071 umc1363 bnlg1014 ufg34 ufg32 ufg33 ufg31 php20537b npi415 asg31 mmp49 lim179 umc94a mmp102 bnl5.62a umc1566 umc1177 tub1 umc1354

A−S2 A−S1 A−Ca1 A−Mo1 A−Mg1

A−Zn1 C−Mo1 A−Fe1

C−Fe1 A−Mg2 C−P1

umc1696 phi101049 ufg55 nfa103b lim104 bnl17.14 mmp183 umc2184 umc36a bnlg1893 bnlg469b mmp195e umc1252 umc1256 umc1516 psr144c psr144a bnlg1940 bnlg1746 mmp188 mmc0381 npi610 mmp34 umc2085 npi210 bnl6.20 umc1604 umc1947 phi435417 umc137a mmp84 mmp116 umc1049 asg20 umc1560 php20017a umc36b phi251315 uaz194a rz474c umc1890 umc2129 php20005 bcd926b umc1108 umc1004 umc1080 bnlg1138 umc2178 umc1658 bnlg1036 umc1079 npi297 umc1028 mmc0401 csu1080b mmp119 psr666 umc131 umc1454 mmp89 php10012 umc2079 bnlg121 umc2088 npi242a umc1861 umc2030 umc1259 bnlg108 mmp91 bnlg1175 umc134b mmp167 umc1541 umc1465 umc1448 umc259b umc1326 npi607 bnlg381 umc34 bnlg1064 mmp42 psr901 mmp33 umc61 npi287a umc44b mmc0231 lim328 umc1422 umc1261 umc1262 myb5 bnlg1327 bnlg2277 eks1 mmc0111 umc1265 umc1980 bnlg1017 bnlg1297 umc1542 npi254a umc53a umc1165 php20568b isu144a

umc1580 npi208c C−S1

npi421a

bnlg1018

A−Cd1 C−S2

A−B1 C−Cd1 3 bnlg197 php15033 lim424 bnlg1160 csu191 ufg42 umc82c bnlg1951 csu1183 mmp5 umc1644 psr754a umc60 asg39 rz538b lim486 bnl10.24a umc1027 umc1730 umc1311 bnl5.37b umc1539 npi296 umc1973 csu636 umc26a cdo105 psr119a umc1501 umc1167 bnlg1035 umc1102 rz296b uaz288a mmp80 umc1600 umc1174 umc102 cdo344a umc2002 psr628 umc1773 umc1527 hac101a umc1449 mmp9 php20558a umc10a umc1920 umc1223 php10016c rz244b bnlg1816 rz382a mmp69 npi247 umc1908 mmc0312 mmp36 mmp144 bnlg1638 umc2117 umc1742 psr754b umc2033 umc1392 umc1495 umc2158 umc1608 umc2000 umc1425 npi446 npi276a bnl8.35a asg48 mmp186 mmp79 bnlg1447 lim66 csu324b bnlg1647 php20042a umc1886 bnlg1144 umc1458 asg30c mmp38 umc2049 phi104127 php20905 umc1892 mmp158a asg64 umc2071 umc1970 umc1394 bnl8.15 umc1780 phi404206 phi453121 umc1931 umc2118 umc1594 lim82 lim96 lim444 umc1641 npi420 npi425a npi457 bnlg1754 bnlg1536 umc1813 lim182 umc2152 sho89 csu845 csu303 umc63a umc1273 umc1320 php10080 umc2081 bnlg1108 umc1915 mmc0251 umc1140 umc17a umc1825 php20521 umc1404 umc1489 npi212b umc1528 umc1767 umc1135 umc2050 bnl6.16a asg7b umc1399 umc2020 cdo244d bnlg1452

mmc0132 bnlg1605 mmc0022 bnlg1113

C−S3 A−Mo2 4

umc2046 umc1101 npi593a cdo534a umc1328 mmp94 asg22 npi449b mmp134 php10025 umc2139 lim446 umc52 umc2188 mmp178 umc2135 rz596b umc1842 umc2187 umc15a npi444 php20071 npi270 ufg23 npi570 umc158 umc1899 bnlg2244 bnl10.05 gol1 bnlg1444 asg27a mmp3 umc1808 umc1667 umc1775 asg33 bnl5.24b mmp115 umc19 umc2038 mmp147 umc104a bnlg1137 rz273a rz567b mmp176 mmp97 umc2027 umc1945 mmc0371 php20597a mmp78 mmp149 umc1346 umc1142 bnlg1755 mmp140 psr128 umc1511 bnl15.45 umc42a umc1031 mmp125 csu509 psr152b umc1303 bnlg1265 umc191 umc2061 umc1969 agrr301 bnlg490 mmc0471 umc1652 umc1963 lim415 umc1117 wip2 pgd3 umc2039 mmp111 umc1902 umc2176 isu144b csu235 umc1926 umc87a psr144b umc1943 umc1669 bx4 mmp174 umc123 umc1228 csu221 msf1 rca1 ufg52 bnlg1434 ufg26 bnlg1890 umc1707 mmp182 umc169 umc1180 asg41 bnl15.07a lim471 umc1109 umc124b umc1532 bnlg589 php20608a

h255 umc1759 phi295450

A−Mn1 A−Cu1

C−Mn1

A− Cu2 5 bnlg1118 mmp169 php20566 rz567a bnlg609 npi458a php20531 umc1752 umc54 mmc0481 umc126a mmp104 mmp90 nbp35 umc1155 umc1264 mmp47 umc2026 umc1822 phi333597 mmc0081 bnl5.71a umc1482 csu308 umc1221 myb3 umc1349 csu302 npi449a bnlg2323 lim4 bnlg1208 umc1747 umc1990 umc1060 umc1591 rz87 mmp60 umc40 bnl4.36 bnlg1902 mmp58 ufg60 php15024 psr167 umc1389 mmp108a umc1355 umc1 umc1609 rz242b umc1935 ufg49 umc1315 lim175 umc1447 umc1048 bnl5.02a cdo98a umc2035 mmc0351 bcd207b psr544 csu340 bnl7.56 umc1597 bnlg1046 cdo795b mmp112 umc1686 rz474a csu164b bnlg1879 bcd1072a mmp130 cdo122b umc107b umc1587 psr922a bnlg565 asg73 tua4 rz630f npi282a umc2036 lim407 bnl7.21c umc1523 npi409 sca1 umc1260 umc1325 umc86b umc1901 bnl8.33 mmp6 umc1097 umc1445 umc1423 csu1087 umc1253 ufg36 umc1153 php10017 rz446b umc104b mmp175 umc1225 php20523b npi288a umc1792 mmp170 bnlg1597c mmp118 umc1072 bnlg118 rz273b umc1524 umc43 bnl6.10 cdo507b

bnl5.24a

umc1680

A−Cu3

A−Fe2 A−Mg3 A−Ca2

A−S3 C−Ca1 C−Mg1

A−Mo3

A−Zn2 C−Cu1

C−Mo2 C−Cu2 6

cdo202a cdo345c umc2059 agp2 mmp105 php20599 bnlg1136 npi561 umc62 bnlg1740 umc1350 bnlg1759a umc2165 mmp113 npi419a umc1490 phi299852 nfa102 umc132a asg6a umc2170 psr162 lim151 lim379 bcd738a umc2162 umc1762 umc1463 umc1859 umc1912 umc38a mmp150 rz444d bnlg1732 mmp145 uaz121a npi608 bnlg1702 bnlg1174 npi252 npi616a mmp62 umc1388 npi560 umc1379 umc2141 umc1413 umc1352 umc1826 csu481 umc1250 uaz280c ufg11 umc21 umc2006 umc1857 umc1979 umc1105 npi223a rz476d umc1796 umc65a csu923 psr129b umc1257 mmp51 mmp117 umc1656 rz242a mir1 umc1006 mmp108b mmp4 mmp10 mmp20 ufg69 mmp76 mmp160 uaz232b umc1133 uck1 php20854 cdo545 php20528 umc1229 bnlg1867 bnl6.29a bnlg1371 cdo1173c umc1606 umc85a rz143a umc1143 umc49f

ng6bbnlg238 bnlg161b A−Mg4 7

umc168 ufg39 umc1407 umc1406 cdo938d phi069 php20909b npi433 npi380 umc245 umc1412 ufg57 umc1295 bnlg2259 umc1768 csu8 umc1708 bcd349 umc1029 bnl8.29c npi352 npi263 npi240a bnlg1666 ufg17 asg32 umc1251 umc1710 umc254 psr135a tif1 isu150 bnlg1805 umc1888 umc1324 umc1112 bnlg2271 ndk1 psr371a umc1134 umc111b bnlg155 rz404 umc1837 umc110a umc56 npi389 umc1660 mmp152 npi394 bnlg434 bnlg1070 mmp46 umc1987 umc1450 mmp177c bnl15.21 bcd926a umc1713 mmc0411 mmp127 umc116a bnlg1808 cdo412b ufg54 umc5b umc1393 umc2092 umc1787 umc1929 umc1138 umc2142 umc1983 lim333 bnlg2203 bnlg1380 bnlg2233 bnlg1247 rz698d uaz187 psr371b bnlg1094 crt2 npi600 umc1978 umc1401 gta101a asg34a umc1270 mmp81 mmp18 umc1159 php20581a asg8 bnlg2132 umc1426 umc1694 umc1672 umc1241 csu582 umc2177 umc35a npi611a php20020 phi116

m10 umc1936 umc1301

A−K1 A−B2

A−Mg5 C−Mn2 C−Ca2

C−Zn1 C−Mo3 A−Fe3

C−Fe2

C−Mo4 A−Fe4

C−Fe3

C−P2 A−K2 C−Zn2 8

bnlg1131 umc1916 umc1638 phi233376 agrr21 csu146b npi112b npi107 umc1673 mmp146 umc1933 umc1005 php20793 mmp64 npi414a bnlg1828 lim301 umc1268 psy2 bnlg1823 umc1607 rz538a bnlg1065 npi268a bnlg1031 umc1905 umc1728 umc1149 bnl12.30a hda103 umc1340 umc1889 umc12a umc1263 mmp15 umc1130 bnl2.369 gta101d bnlg2046 umc1858 umc1460 umc2154 php20714 phi121 umc1457 umc1735 phi100175 umc1984 umc1470 umc1415 php3818 cdo202e mmp195f umc1910 cdo1160a npi260b umc1904 umc1157 umc1807 bnlg1834 rz244a bnlg2082 umc32b mmp158b mmp72 mmp120 umc1530 umc124a csu329 umc1913 cdo328 psr598 umc1974 npi585a mmp166 bcd1823a bnlg2235 umc1304 mmp57 cdo460 npi110a bnlg1194 mmp85 umc1483 umc1327 umc1414 bnl13.05a umc1592 mmp148 umc2042 umc1139 npi114a rz382b csu319 npi220a

C−S4 C−P3

A−Zn3 A−Mg6

A−Mo4 C−Mo5 9 bnl5.09a umc1366 mmp168 ufg75c csu93a mmp131 asg44 mmp132 npi439b mmp142 npi443 umc2134 ufg24 npi427a rz574b csu634 umc2095 ufg67 mmp151d mmp41 umc1231 php20554 ufg63 ufg64 ufg13a lim458 umc1078 umc140b umc95 umc38c umc2121 mmp37 mmp96 umc1120 umc1492 ufg68 npi580a ufg70 bnlg1012 gta101c bnlg1159b umc1107 psr129a psr547 bnlg1209 lim166 lim99b rz682 bnl5.04 umc114 umc1743 umc1700 umc1921 umc20 umc1688 umc1691 umc1271 asg63a mmp2 umc1191 csu623 bnl5.10 php20052 bcd1421 rz273c psr160c psr160d mmp170b ufg71 lim101 umc1586 umc1258 umc1634 lim286 umc1698 mmp30 mmp77 bnlg1401 bnlg244 mmp162 umc1636 isu111b csu471 umc1170 bz1 umc1596 umc1967 umc1588 sh1 umc1809 c1 ufg41 lim343 php10005 umc1867 bnlg2122 umc1370 npi253a bnlg1724 umc109 umc1957 umc1505 umc1137 mmp171a umc2131 umc2089 mmp136 bnlg619 umc1675 asg12 umc1789 bnl14.28a mmp110 umc81 bnlg1583 rz953

bnlg1810

C−B1

C−Cu3 A−Mo5 10

csu48 asg19b umc2126 umc2021 php20568a bnlg1450 npi254b bnlg1360 umc1176 bnlg1839 mmp181 npi208b bnlg1677 umc1084 umc1196 agrr37c bnl7.49a ufg62 bnlg2190 bnl17.02 bnlg1028 bnl10.13a umc1477 umc1506 csu745a ufg81 bnlg1250 bnlg1074 mmp12 umc1677 npi578 umc259a php20719a umc1930 umc1272 umc1115 php15013 umc1330 asg2 umc1911 umc1077 mmp16 umc1246 umc1995 umc64a fgp1 umc1336 umc155 bnlg1712 npi445a psr690 umc1239 ufg59 umc1345 bcd147 umc2016 umc1381 php20646 uaz116 umc1367 bnlg210 umc1962 php06005 bcd1072b lim2 gcsh1 umc18b npi105a csu625 umc130 umc2069 phi059 umc2034 umc1152 umc2018 umc2053 csu1061b php20075a phi041 php20753a bnl3.04 psr119c php20626 umc1380 mmp48b mmp48a

f2aufg37 ufg28a A−Mo6 2105 vely 2106 STICH ET AL.

FIGURE 6 The plots in the left column present the logarithm of odds (LOD) versus the position on the genetic map of chromosomes two, two, one, and one, respectively, for a selected set of quantitative trait loci (QTL) with significant main effects across two iron regimes for cadmium (Cd) and molybdenum (Mo) concentration (C-*) and content (A-*) in shoots of the maize IBM population. The plots in the right column are boxplots of the adjusted entry means of the genotypes carrying the B73 or Mo17 allele at the QTL position across the sufficient and deficient Fe regime [Colour figure can be viewed at wileyonlinelibrary.com] THE MAIZE SHOOT IONOME 2107

FIGURE 7 Schematic gene model of the first transcript of candidate genes for C-Cd1/ACd1 (first row) and C-Mo1/A-Mo1 (second row). Blue boxes indicate exons. Light blue boxes indicate untranslated regions (UTRs). Vertical lines indicate polymorphisms between B73 and Mo17, where grey is used for polymorphisms in 30 and 50 UTR, yellow for intron variants, green for synonymous, and red for non-synonymous polymorphisms in the coding sequence, respectively. The orange or red disk below the polymorphisms indicates a moderate or high severity of the variant consequence, respectively. The y-axes indicate the positions of the polymorphisms in bp on the corresponding chromosomes [Colour figure can be viewed at wileyonlinelibrary.com] earlier studies with maize (Baxter et al., 2013) but also with Brassica capacities in the cell wall of leaves (White, Broadley, El-Serehy, napus (Bus et al., 2014) or with 334 angiosperm species (Neugebauer, George, & Neugebauer, 2018). This and other correlations got lost Broadley, El-serehy, & George, 2018). In contrast to previous studies, under Fe deficiency, when more negative correlations prevailed we not only assessed the concentration of mineral elements but also (Figure 4a). A major determinant for this shift is the degradation of the shoot content. Shoot contents reflect the cumulative uptake and suberin in the root endodermis under Fe deficiency, which in Ara- translocation of mineral elements to the above-ground biomass of a bidopsis has been shown to form an apoplastic barrier that is regu- plant over the whole growth period. The proportion of pairs of min- lated by the nutritional status of the plant (Barberon et al., 2016). The eral element concentrations with a significant positive correlation degradation increases the endodermal passage and subsequent root- (Figures 3 and 4) was much lower than that for the contents of min- to-shoot translocation particularly of Ca, Mn and Zn (Baxter eral elements and this finding was independent of the considered Fe et al., 2009). Hence, correlations between these and other elements regime. This observation is explained by the variation in biomass which disappear under Fe deficiency, while new ones are formed between was in the Fe sufficient regime >threefold and even higher in the Fe- elements that profit in the same way, as Zn and Mn (Figure 4a). deficient regime (Benke et al., 2014). Thus, shoot contents correct for Earlier studies have reported the interaction of Fe with the con- differences in biomass, which were here mostly related to the geno- centration of several mineral elements, mostly with S, P or other type rather than the genotype*environment interaction. In our RIL, the metals (Astolfi, Zuchi, Passera, & Cesco, 2003; Kanai, Hirai, Yoshiba, latter made only a minor contribution to the overall variance in element Tadano, & Higuchi, 2009; Mori & Nishizawa, 1987; Zheng concentrations and even less in shoot element contents (Figure 1a). et al., 2009). Under Fe deficiency, all of these correlations turned Due to the design of our study that is, the assessment of the more negative (Figure 4a), which is not only related to endodermal shoot ionome of the IBM population in two contrasting Fe regimes, suberization but also to the Fe deficiency-induced expression of we were not only able to validate correlations between pairs of min- genes involved in Fe acquisition which favour also Zn and Mn acquisi- eral elements but to compare the correlation of two mineral elements tion (Eroglu, Meier, Wiren, & Peiter, 2016; Leskova, Giehl, Hartmann, across different Fe regimes. By that, we identified interactions among Fargasova, & von Wiren, 2017). In maize, this includes the release of Fe and two other mineral elements. We observed significant phytosiderophores and uptake of phytosiderophore-chelated metals, (α = 0.05) differences in 10 pairwise correlations of mineral element such as Zn, Cu and Ni (Schaaf et al., 2004; von Wiren et al., 1996). concentrations between the Fe sufficient and deficient regime that In addition, we observed a particularly strong difference between did not involve Fe (Figure 4a). the correlations of P and all mineral elements except Fe and B Maybe most prominent is the positive interaction between Ca assessed in the two Fe regimes. In the sufficient Fe regime, all these and Mg, which in angiosperms has been related to cation exchange correlations were positive while in the deficient Fe regime these 2108 STICH ET AL. correlations were negative (Figure 4a). This link between P and Fe is variation of mineral element concentrations are in direct functional in accordance with earlier studies. DeKock, Hall, and Inkson (1979) relationship to the physiological and morphological traits examined in and Zheng et al. (2009) have shown that elevated P supplies decrease the frame of our study. This is emphasized by the observation that the availability and mobility of Fe in soils as well as in plants and molecular marker-based prediction values responded less to Fe defi- therewith provoke Fe deficiency-induced chlorosis (Ajakaiye, 1979). ciency. We thus conclude that mineral element profiles are powerful The results of Shi et al. (2018) indicate that Fe retention in the hemi- predictors of physiological and morphological traits. Their use for the cellulose fraction of roots is an important determinant of the toler- prediction of further, for example, yield-related traits, should be exam- ance to Fe deficiency-induced chlorosis of graminaceous plant species ined in further studies. with low phytosiderophore release, like maize. Then, P-dependent Fe retention in the hemicellulose fraction may become a determining factor for Fe provision of the shoot and, thus, for chlorosis susceptibility. 4.3 | Genetics of the shoot ionome With the increasing knowledge on such mechanistic processes underlying nutrient correlations, the shoot ionome becomes more and In contrast to most previous studies examining the inheritance of the more informative (Huang & Salt, 2016) and paves the way to identify ionome of different species in single environments or in multiple envi- and assign anatomical changes or physiological processes to allelic ronments that differ with respect to multiple environmental factors variation. (e.g. Asaro et al., 2016; Bus et al., 2014; Shakoor et al., 2016), we analysed the inheritance under two different environmental conditions that differed only with respect to one environmental factor, in 4.2 | Predictive power of the shoot ionome our case the Fe regime. This allows to separate the genotype*Fe regime interaction from the genotype*environment interaction, where Baxter et al. (2008) showed that the impact of an environmental per- the former is biologically easier to interpret. turbation on a plant can be predicted by changes in its ionome. While In this study, we exploited the significant genotypic and this principle was demonstrated for the Col-0 accession in A. thaliana genotype*Fe regime interaction variance found in the IBM population when subjected to Fe deficiency, we first addressed the question (Figure 1a) and employed quantitative genetic analyses in order to dis- whether the shoot ionome is also a reliable predictor for the nutri- sect this variation in the underlying genetic factors. For all mineral ele- tional status in a RIL population subjected to the same nutritional per- ments examined in the frame of this study, except for the turbation. Using the IBM population for this purpose is especially concentration of K and the content of P, genomic regions were identi- meaningful, because it has been generated from a cross between two fied that explained a significant part of the genotypic or genotype*Fe parental lines opposing in P and Fe efficiency (Benke et al., 2014; regime interactions (Table S1 and S2). The proportion of variance Kaeppler et al., 2000; Shi et al., 2018). Indeed, our analysis revealed a explained for one mineral element by all QTL interacting with the cross-validated predictive ability of 0.90 when predicting the Fe- environment simultaneously was considerably lower than that of the regime, in which the RIL of the IBM population were cultivated. This main effect QTL. This finding suggests that albeit the variance of was based on the measured concentrations of mineral elements, genotype*Fe regime interactions for the concentration of mineral ele- except for Fe and Zn. Predictive ability increased to 1.0 when includ- ments was considerably higher than the genotypic variance (Figure 1) ing Zn. Likewise, a study with 19 different Arabidopsis accessions the QTL interacting with the environment were not detected in our showed that changes in the shoot ionome can be employed to distin- study. One major reason might be that each QTL explains only a small guish accessions according to their Zn nutritional status (Campos proportion of the genotype*Fe interaction. et al., 2017). These findings indicate that even across diverse genetic Due to the use of the IBM population, which has compared to materials, the profile of the mineral elements is a reliable proxy for the non intermated linkage mapping populations an increased mapping physiological state and allows identifying nutritional perturbations. resolution (Lee et al., 2002), the confidence intervals observed in our We then addressed the question whether the shoot ionome is study were small for most of the 60 QTL (Table S1 and S2). Out of the also a reliable predictor for morphological and physiological traits detected QTL, four QTL had overlapping confidence intervals for the perturbed by an altered Fe regime. In fact, when predicting from the concentrations and contents of the same mineral element. Two of concentration of mineral elements, physiological and morphological these pairs of QTL jutted out regarding the LOD score as well the pro- traits of individual RIL (cf. Benke et al., 2014), cross-validated predic- portion of the explained phenotypic variance and these will be dis- tive abilities up to 0.77 were observed (Table 1). An increasing predic- cussed in more detail. tion ability under Fe deficiency indicated that an imposed nutritional disorder even increases the predictive power of the ionome. We then compared the prediction of physiological and morpho- 4.3.1 | C-Cd1 and A-Cd1 logical traits by mineral elements with that by molecular markers. Unexpectedly, ionome-based predictive abilities were considerably We did not add Cd to the nutrient solution in which the IBM popula- higher compared to the predictive abilities based on molecular marker tion was cultivated. Nevertheless, we observed under both Fe regimes profiles using GBLUP (Table 1). A likely explanation is that the low Cd concentrations in maize shoots. To rule out the effect of Cd THE MAIZE SHOOT IONOME 2109 translocation from the seeds to the growing shoot tissue, we evalu- between the coding sequences of B73 and Mo17 one non- ated the Cd concentrations in the grains of a subset of the IBM popu- synonymous sequence variant (Figure 7) that is predicted to have only lation. The overall amount of Cd in the grain made up less than 4% of a medium effect on the protein. Instead, in accordance with findings the shoot content, that is, the total amount of Cd accumulated in the of Baxter et al. (2008) for A. thaliana, we consider differences in gene above-ground biomass. This indicated that the highly heritable pattern expression as more relevant, because in the deficient Fe regime of Cd concentrations that we observed (Figure 1) was caused by Cd mRNA levels of Zm00001d033053 were about 1,000-fold higher in traces in the chemicals or the technical devices used to prepare the Mo17 than in B73, whereas in the sufficient Fe regime this difference nutrient solution. As therefore all RIL were exposed systematically to was only sixfold (Urbany et al., 2013). This Fe dependence of the Mo the same Cd concentration in the nutrient solution, we interpreted accumulation was not only observed at the gene expression level but the observed variation in our study. also at the QTL level. C-Mo1 showed the strongest QTL*Fe regime We detected in our study two overlapping QTL on chromosome interaction of this study. However, further research is required to 2 for Cd concentration and content. These QTL were in very good unravel the molecular mechanism of the interaction between Fe accordance with the QTL detected for Cd content in maize kernels and Mo. observed in a B73*IL13H population by Baxter et al. (2014). This observation indicates that the mechanisms contributing to the accumulation and sequestration of this mineral element in maize shoots 5 | CONCLUSIONS and kernels are similar. However, for nine other mineral elements, correlation coefficients between the mineral element concentrations in Our results suggest that the shoot ionome is under tight genetic con- maize shoots from our study and in maize kernels from Baxter trol, and that strong interactions of a genotype with the environment, et al. (2013) were low (data not shown). Hence, a common regulation in our case the Fe regime, are driving the variation for the concentra- across tissues exists for some mineral elements but not for all. tion as well as the content of mineral elements. Furthermore, the high Across both Fe regimes, the trait-decreasing allele was provided predictive abilities found in this study indicate that mineral elements by B73 (Figure 6). This indicated that inbreds and hybrids carrying the are powerful predictors of morphological and physiological traits. B73 allele are suitable to produce maize on soils with increased Cd Their use for the prediction of further, for example, yield-related accumulations without increasing the Cd concentration in the harvest traits, merits to be examined in further studies. We propose that product. ZmHMA2/3 and ZmMOT1 are major players in the natural genetic var- Within the confidence interval of C-Cd1/A-Cd1 two genes, iation of Cd and Mo concentrations and contents in maize shoots. Zm00001d005189 and Zm00001d005190, were located directly Furthermore, our results indicate that even in elite breeding material adjacent to each other that are annotated as HMA genes. In the cod- non-functional alleles segregate at major genes for uptake and trans- ing sequence of both genes, non-synonymous polymorphisms have location of different mineral elements. been identified that are predicted (McLaren et al., 2016) to have moderate or even high variant consequence (Figure 7). Zm00001d005189 ACKNOWLEDGMENTS is based on its amino acid sequence not of particular similarity to any We would like to thank the Maize Genetics Cooperation Stock Center of the HMA genes of Arabidopsis or rice (data not shown). In contrast, (MGCSC) for providing seeds of the IBM population. We also thank Zm00001d005190 has a high homology to OsHMA3 indicating that Nicole Kliche-Kamphaus, Andrea Lossow, Nele Kaul, Isabel Scheibert, Zm00001d005190 could be the ortholog ZmHMA3 which is in accor- and Susanne Reiner for the excellent technical support. This work was dance with results of Zhao et al. (2018). supported by the Deutsche Forschungsgemeinschaft (DFG, German Our result indicated that the Mo17 allele at Zm00001d005190, Research Foundation) via individual research grants (STI596/4-1 and which is presumably non-functional, is present at medium frequency in WI1728/16-1) as well as the Deutsche Forschungsgemeinschaft the maize germplasm of the USDA genebank. This suggests that under (DFG, German Research Foundation) under Germany's Excellence regular field conditions the higher accumulation of Cd does not have a Strategy - EXC 2048/1 - Project ID: 390686111. negative agronomic side effect. Another explanation is that commer- cially used maize hybrids are typically crosses between B73-type and CONFLICT OF INTEREST Mo17-type inbreds. In this case, the hybrids do not show increased Cd The authors declare no conflicts of interest. contents, as the functional allele of B73 is expected to be at least par- tially dominant over the loss-of-function allele of Mo17 (cf. Kuromori, ORCID Takahashi, Kondou, Shinozaki, & Matsui, 2009). Benjamin Stich https://orcid.org/0000-0001-6791-8068

REFERENCES 4.3.2 | C-Mo1 and A-Mo1 Ajakaiye, C. O. (1979). Effect of phosphorous on growth and iron nutrition of millet and sorghum. Plant and Soil, 51, 551–561. Asaro, A., Ziegler, G., Ziyomo, C., Hoekenga, O. A., Dilkes, B. P., & Baxter, I. In the confidence interval of these QTL, a homolog of A. thaliana's Mo (2016). The interaction of genotype and environment determines vari- transporter (MOT1) (Tomatsu et al., 2007) was detected. We observed ation in the maize kernel ionome. G3, 6(12), 4175–4183. 2110 STICH ET AL.

Astolfi, S., Zuchi, S., Passera, C., & Cesco, S. (2003). Does the sulfur assimi- for grain mineral concentration, content, and yield in maize (Zea mays lation pathway play a role in the response to Fe deficiency in maize L.). Theoretical and Applied Genetics, 128(9), 1777–1789. (Zea mays L.) plants? Journal of Plant Nutrition, 26, 2111–2121. Haley, C. S., & Knott, S. A. (1992). A simple regression method for mapping Barberon, M., Bellis, D. D., Takano, J., Salt, D. E., Geldner, N., Barberon, M., quantitative trait loci in line crosses using flanking markers. Heredity, … Naseer, S. (2016). Adaptation of root function by nutrient-induced 69(4), 315–324. plasticity of endodermal differentiation. Cell, 164(3), 447–459. Hamming, R. (1950). Error detecting and error correcting codes. Bell Sys- Baxter, I., Hermans, C., Lahner, B., Yakubova, E., Tikhonova, M., tem Technical Journal, 29, 147–160. Verbruggen, N., … Salt, D. (2012). Biodiversity of mineral nutrient and Hjorth, J. (1994). Computer intensive statistical methods. Validation, model trace element accumulation in Arabidopsis thaliana. PLoS One, 7, selection and bootstrap. London, England: Chapman & Hall. e35121. Huang, X. Y., & Salt, D. E. E. (2016). Plant ionomics: From elemental profil- Baxter, I., Hosmani, P. S., Rus, A., Lahner, B., Borevitz, J. O., ing to environmental adaptation. Molecular Plant, 9(6), 787–797. Mickelbart, M. V., … Salt, D. E. (2009). Root suberin forms an extracel- Kaeppler, S. M. S., Parke, J. L., Mueller, S. M., Senior, L., Stuber, C., & lular barrier that affects water relations and mineral nutrition in Ara- Tracy, W. F. (2000). Variation among maize inbred lines and detection bidopsis. PLoS Genetics, 5, e1000492. of quantitative trait loci for growth at low P and responsiveness to Baxter, I. R., Gustin, J. L., Settles, A. M., & Hoekenga, O. A. (2013). Ionomic arbuscular mycorrhizal fungi. Crop Science, 40, 358–364. characterization of maize kernels in the intermated B73 x Mo17 popu- Kanai, M., Hirai, M., Yoshiba, M., Tadano, T., & Higuchi, K. (2009). Iron lation. Crop Science, 53, 208–220. deficiency causes zinc excess in Zea mays. Soil Science & Plant Nutri- Baxter, I. R., Vitek, O., Lahner, B., Muthukumar, B., Borghi, M., tion, 55(2), 271–276. Morrissey, J., … Salt, D. E. (2008). The leaf ionome as a multivariable Klein, M. A., & Grusak, M. A. (2009). Identification of nutrient and physical system to detect a plant's physiological status. Proceedings of the seed trait QTL in the model legume Lotus japonicus. Genome, 52, National Academy of Sciences, 105(33), 12081–12086. 677–691. Baxter, I. R., Ziegler, G., Lahner, B., Mickelbart, M. V., Foley, R., Danku, J., Kuromori, T., Takahashi, S., Kondou, Y., Shinozaki, K., & Matsui, M. (2009). … Hoekenga, O. A. (2014). Single-kernel ionomic profiles are highly Phenome analysis in plant species using loss-of-function and gain-of- heritable indicators of genetic and environmental influences on ele- function mutants. Plant and Cell Physiology, 50(7), 1215–1231. mental accumulation in maize grain (Zea mays). PLoS One, 9(1), Lahner, B., Gong, J., Mahmoudian, M., Smith, E. L., Abid, K. B., e87628. Rogers, E. E., … Salt, D. E. (2003). Genomic scale profiling of nutrient Bej, S., & Basak, J. (2014). MicroRNAs: The potential biomarkers in plant and trace elements in Arabidopsis thaliana. Nature Biotechnology, 21 stress response. American Journal of Plant Sciences, 5, 748–759. (10), 1215–1221. Benke, A., Urbany, C., Marsian, J., Shi, R., Wiren, N. V., & Stich, B. (2014). Lee, M., Sharopova, N., Beavis, W. D. G. D., Katt, M., Blaie, D., & The genetic basis of natural variation for iron homeostasis in the maize Hallauer, A. (2002). Expanding the genetic map of maize with the IBM population. BMC Plant Biology, 14, 12. intemated B73*Mo17 (IBM) population. Plant Molecular Biology, 48, Bentsink, L., Yuan, K., Koornneef, M., & Vreugdenhil, D. (2003). The genet- 453–461. ics of phytate and phosphate accumulation in seeds and leaves of Ara- Leskova, A., Giehl, R. F., Hartmann, A., Fargasova, A., & von Wiren, N. bidopsis thaliana, using natural variation. Theoretical and Applied (2017). Heavy metals induce iron deficiency responses at different Genetics, 106(7), 1234–1243. hierarchic and regulatory levels. Plant Physiology, 174(3), 1648–1668. Blair, M. W., Astudillo, C., Grusak, M. A., Graham, R., & Beebe, S. E. (2009). Manichaikul, A., Dupuis, J., Sen, S., & Broman, K. W. (2006). Poor perfor- Inheritance of seed iron and zinc concentrations in common bean mance of bootstrap confidence intervals for the location of a quantita- (Phaseolus vulgaris L.). Molecular Breeding, 23(2), 197–207. tive trait locus. Genetics, 174(1), 481–489. Broman, K. W., Wu, H., Sen, S., & Churchill, G. A. (2003). R/QTL: QTL map- Marschner, H. (2012). Mineral nutrition of higher plants (3rd ed.). England: ping in experimental crosses. Bioinformatics, 19(7), 889–890. Elsevier. Bukowski, R., Guo, X., Lu, Y., Zou, C., He, B., Rong, Z., … Xu, Y. (2018). Con- McLaren, W., Gil, L., Hunt, S. E., Riat, H. S., Ritchie, G. R. S., Thormann, A., struction of the third-generation Zea mays haplotype map. GigaScience, … Cunningham, F. (2016). The ensembl variant effect predictor. 7(4), 1–12. Genome Biology, 17, 122. Bus, A., Körber, N., Parkin, I. A. P., Samans, B., Snowdon, R. J., Li, J., & Meuwissen, T. H. E., Hayes, B. J., & Goddard, M. E. (2001). Prediction of Stich, B. (2014). Species- and genome-wide dissection of the shoot total genetic value using genome-wide dense marker maps. Genetics, ionome in Brassica napus and its relationship to seedling development. 157(4), 1819–1829. Frontiers in Plant Science, 5(485), 485. Mori, S., & Nishizawa, N. (1987). Methionine as a dominant precursor of Campos, A. C. A., Kruijer, W., Alexander, R., Akkers, R. C., Danku, J., phytosiderophores in graminaceae plants. The Japanese Society of Plant Salt, D. E., & Aarts, M. G. (2017). Natural variation in Arabidopsis Physiologists, 28(6), 1081–1092. thaliana reveals shoot ionome, biomass, and gene expression changes Neugebauer, K., Broadley, M. R., El-serehy, H. A., & George, T. S. (2018). as biomarkers for zinc deficiency tolerance. Journal of Experimental Variation in the angiosperm ionome. Physiologia Plantarum, 163, Botany, 68(13), 3643–3656. 306–322. Covarrubias-Pazaran, G. (2016). Genome-assisted prediction of quantita- R Development Core Team. (2016) R: A language and environment for sta- tive traits using the R package sommer. PLoS One, 11(6), 1–15. tistical computing. DeKock, P., Hall, A., & Inkson, R. (1979). Active iron in plant leaves. Annals Romay, M. C., Millard, M. J., Glaubitz, J. C., Peiffer, J. A., Swarts, K. L., of Botany, 43(6), 737–740. Casstevens, T. M., … Gardner, C. A. (2013). Comprehensive genotyping Eroglu, S., Meier, B., Wiren, N. V., & Peiter, E. (2016). The vacuolar manga- of the USA national maize inbred seed bank. Genome Biology, 14, R55. nese transporter MTP8 determines tolerance to iron deficiency- Salt, D. E., Baxter, I., & Lahner, B. (2008). Ionomics and the study of the induced chlorosis in Arabidopsis. Plant Physiology, 170, 1030–1045. plant ionome. Annual Review of Plant Biology, 59, 709–733. Ghandilyan, A., Barboza, L., Tisne, S., Granier, C., Reymond, M., Schaaf, G., Ludewig, U., Erenoglu, B. E., Mori, S., Kitahara, T., & Von Koornneef, M., … Aarts, M. G. M. (2009). Genetic analysis identifies Wiren, N. (2004). ZmYS1 functions as a proton-coupled symporter for quantitative trait loci controlling rosette mineral concentrations in Ara- phytosiderophore- and nicotianamine-chelated metals. Journal of Bio- bidopsis thaliana under drought. New Phytologist, 184(1), 180–192. logical Chemistry, 279, 9091–9096. Gu, R., Chen, F., Liu, B., Wang, X., Liu, J., Li, P., … Yuan, L. (2015). Compre- Scheipl, F., Greven, S., & Küchenhoff, H. (2008). Size and power of tests hensive phenotypic analysis and quantitative trait locus identification for a zero random effect variance or polynomial regression in additive THE MAIZE SHOOT IONOME 2111

and linear mixed models. Computational Statistics and Data Analysis, for north American Holstein bulls. Journal of Dairy Science, 92(1), 52(7), 3283–3299. 16–24. Shakoor, N., Ziegler, G., Dilkes, B. P., Brenton, Z., Boyles, R., von Wiren, N., Marschner, H., & Römheld, V. (1996). Roots of iron- Connolly, E. L., … Baxter, I. R. (2016). Integration of experiments across efficient maize also absorb phytosiderophore-chelated zinc. Plant diverse environments identifies the genetic determinants of variation Physiology, 111(4), 1119–1125. in Sorghum bicolor seed element composition. Plant Physiology, 170, Vreugdenhil, D., Aarts, M. G. M., Koornneef, M., Nelissen, H., & 01971. Ernst, W. H. O. (2004). Natural variation and QTL analysis for cationic Shi, R., Melzer, M., Zheng, S., Benke, A., Stich, B., & Von Wiren, N. (2018). mineral content in seeds of Arabidopsis thaliana. Plant, Cell and Environ- Iron retention in root hemicelluloses causes genotypic variability in the ment, 27, 828–839. tolerance to iron deficiency-induced chlorosis in maize. Frontiers in Wallace, J. G., Rodgers-Melnick, E., & Buckler, E. S. (2018). On the road to Plant Science, 9, 557. breeding 4.0: Unraveling the good, the bad, and the boring of crop Shi, R., Weber, G., Köster, J., Reza-Hajirezaei, M., Zou, C., Zhang, F., & von quantitative genomics. Annual Review of Genetics, 52(1), 421–444. Wiren, N. (2012). Senescence-induced iron mobilization in source Waters, B. M., & Grusak, M. A. (2008). Quantitative trait locus mapping for leaves of barley (Hordeum vulgare) plants. The New Phytologist, 195(2), seed mineral concentration in two Arabidopsis thaliana recombinant 372–383. inbred populations. New Phytologist, 179(4), 1033–1047. Simic, D., Mladenovic Drinic, S., Zdunic, Z., Jambrovic, A., Ledenan, T., White, P. J., Broadley, M. R., El-Serehy, H. A., George, T. S., & Brkic, J., … Brkic, I. (2012). Quantitative trait loci for biofortification Neugebauer, K. (2018). Linear relationships between shoot magnesium traits in maize grain. Journal of Heredity, 103(1), 47–54. and calcium concentrations among angiosperm species are associated Stangoulis, J. C. R., Huynh, B. L., Welch, R. M., Choi, E. Y., & Graham, R. D. with cell wall chemistry. Annals of Botany, 122, 221–226. (2007). Quantitative trait loci for phytate in rice grain and their rela- Zhao, H., Sun, Z., Wang, J., Huang, H., Kocher, J. P., & Wang, L. (2014). tionship with grain micronutrient content. Euphytica, 154(3), 289–294. CrossMap: A versatile tool for coordinate conversion between Stein, R. J., Höreth, S., de Melo, J. R. F., Syllwasschy, L., Lee, G., genome assemblies. Bioinformatics, 30(7), 1006–1007. Garbin, M. L., … Krämer, U. (2017). Relationships between soil and leaf Zhao, X., Luo, L., Cao, Y., Liu, Y., Li, Y., Wu, W., … Lin, H. (2018). Genome- mineral composition are element-specific, environment-dependent wide association analysis and QTL mapping reveal the genetic control and geographically structured in the emerging model Arabidopsis hal- of cadmium accumulation in maize leaf. BMC Genomics, 19(1), 1–13. leri. New Phytologist, 213, 1274–1286. Zheng, L., Huang, F., Narsai, R., Wu, J., Giraud, E., He, F., … Shou, H. Tomatsu, H., Takano, J., Takahashi, H., Watanabe-Takahashi, A., (2009). Physiological and transcriptome analysis of iron and phospho- Shibagaki, N., & Fujiwara, T. (2007). An Arabidopsis thaliana high- rus interaction in rice seedlings. Plant Physiology, 151(1), 262–274. affinity molybdate transporter required for efficient uptake of molybdate from soil. Proceedings of the National Academy of Sciences, 104 SUPPORTING INFORMATION (47), 18807–18812. Additional supporting information may be found online in the Urbany, C., Benke, A., Marsian, J., Huettel, B., Reinhardt, R., & Stich, B. (2013). Ups and downs of a transcriptional landscape shape iron defi- Supporting Information section at the end of this article. ciency associated chlorosis of the maize inbreds B73 and Mo17. BMC Plant Biology, 13, 213. Utz, H. F., Melchinger, A. E., & Schön, C. C. (2000). Bias and sampling error How to cite this article: Stich B, Benke A, Schmidt M, of the estimated proportion of genotypic variance explained by quan- Urbany C, Shi R, von Wirén N. The maize shoot ionome: Its titative trait loci determined from experimental data in maize using interaction partners, predictive power, and genetic cross validation and validation with independent samples. Genetics, determinants. Plant Cell Environ. 2020;43:2095–2111. https:// 154(4), 1839–1849. VanRaden, P., Van Tassell, C., Wiggans, G., Sonstegard, T., Schnabel, R., doi.org/10.1111/pce.13823 Taylor, J., & 756 Schenkel, F. (2009). Reliability of genomic predictions