Running Head: Genetic determinants of seed element ... · 63 (), Chromatin, Inc., NSF EAGER (1450341) to I.B. and BPD, NSF 64 IOS ... 159 Genome-wide ... . 287 of the 407 SAP lines

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Running Head: Genetic determinants of seed element composition 1 Corresponding Author: 2 Ivan Baxter 3 975 North Warson Road 4 Saint Louis, MO 63132 5 Phone: 314.587.1438 6 Fax: 314.587.1538 7 [email protected] 8 9 Research Area: Biochemistry and Metabolism 10 Secondary Research Area: Genes, Development and Evolution 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was not. http://dx.doi.org/10.1101/019083doi: bioRxiv preprint first posted online May. 8, 2015;

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Title: Integration of experiments across diverse environments identifies the genetic determinants 31 of variation in Sorghum bicolor seed element composition 32 33 Authors: Nadia Shakoor1, Greg Ziegler5, Brian P. Dilkes2, Zachary Brenton3, Richard Boyles3, 34 Erin L. Connolly4, Stephen Kresovich3 and Ivan Baxter5* 35 1Donald Danforth Plant Science Center, St. Louis, Missouri, 63132 (N.S.) 36 2Department of Horticulture and Landscape Architecture, Purdue 37 University, West Lafayette, Indiana 47907 (B.P.D.) 38 3Department of Genetics and Biochemistry, Clemson University, Clemson, South Carolina, 39 29631 (Z.B., R.B., S.K.) 40 4Department of Biological Sciences, University of South Carolina, Columbia, South Carolina, 41 29208 (E.C.) 42 5USDA-ARS, Donald Danforth Plant Science Center, St. Louis, Missouri, 63132 (G.Z., I.B.) 43 44 Email Addresses 45 Ivan Baxter ([email protected]) 46 Nadia Shakoor ([email protected]) 47 Greg Ziegler ([email protected]) 48 Brian P. Dilkes ([email protected]) 49 Zachary Brenton ([email protected]) 50 Richard Boyles ([email protected]) 51 Erin L. Connolly ([email protected]) 52 Stephen Kresovich ([email protected]) 53 54 One sentence summary: High-throughput measurements of element accumulation and genome-55 wide association analysis across multiple environments identified novel alleles controlling seed 56 element accumulation in Sorghum bicolor. 57 58 59 60 61


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This project was partially funded by the iHUB Visiting Scientist Program 62 (http://www.ionomicshub.org), Chromatin, Inc., NSF EAGER (1450341) to I.B. and BPD, NSF 63 IOS 1126950 to IB, NSF IOS-0919739 to EC, and BMGF (OPP 1052924) to B.P.D. 64 65 *Corresponding Author 66 Ivan Baxter 67 975 North Warson Road 68 Saint Louis, MO 63132 69 Phone: 314.587.1438 70 Fax: 314.587.1538 71 [email protected] 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92


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Abstract: 93 Seedling establishment and seed nutritional quality require the sequestration of sufficient 94 element nutrients. Identification of genes and alleles that modify element content in the grains of 95 cereals, including Sorghum bicolor, is fundamental to developing breeding and selection 96 methods aimed at increasing bioavailable element content and improving crop growth. We have 97 developed a high throughput workflow for the simultaneous measurement of multiple elements 98 in sorghum seeds. We measured seed element levels in the genotyped Sorghum Association 99 Panel (SAP), representing all major cultivated sorghum races from diverse geographic and 100 climatic regions, and mapped alleles contributing to seed element variation across three 101 environments by genome-wide association. We observed significant phenotypic and genetic 102 correlation between several elements across multiple years and diverse environments. The power 103 of combining high-precision measurements with genome wide association was demonstrated by 104 implementing rank transformation and a multilocus mixed model (MLMM) to map alleles 105 controlling 20 element traits, identifying 255 loci affecting the sorghum seed ionome. Sequence 106 similarity to genes characterized in previous studies identified likely causative genes for the 107 accumulation of zinc (Zn) manganese (Mn), nickel (Ni), calcium (Ca) and cadmium (Cd) in 108 sorghum seed. In addition to strong candidates for these four elements, we provide a list of 109 candidate loci for several other elements. Our approach enabled identification of SNPs in strong 110 LD with causative polymorphisms that can be evaluated in targeted selection strategies for plant 111 breeding and improvement. 112 113 114


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Introduction: 115 Sorghum bicolor is a globally cultivated source of food, feed, sugar and fiber. Classified 116

as a bioenergy feedstock, sorghum biomass also has unique advantages for sustainable biofuel 117 production (Kimber et al., 2013). The element composition of stems, leaves and reproductive 118 organs all contribute significantly to biomass quality. The seed bearing reproductive organs, or 119 panicles, in sorghum represent up to 30% of the total dry matter yield (Amaducci et al., 2004). 120 Inorganic elements, particularly alkali metals, influence the combustion process and can limit the 121 effectiveness of biomass conversion (Obernberger et al., 1997; Monti et al., 2008). Targeted 122 reduction of specific elements and compositional traits via transgenic and breeding approaches 123 can be implemented to improve biomass quality. 124

Increasing the bioavailable elemental nutrient content in the edible portions of the crop 125 has the potential to increase the value of sorghum for human and animal nutrition. Plant-based 126 diets, in which grains compose the major food source, require the availability of essential 127 elements in the plant seed. Iron (Fe) and Zn deficiencies affect over 2 billion people worldwide 128 (Organization, 2002), and increases in the accumulation and bioavailability of these elements in 129 cereal grains, including sorghum, could potentially make a significant impact towards 130 ameliorating this nutritional crisis (Graham et al., 1999; Organization, 2002). Additional global 131 health benefits could be achieved by increasing magnesium (Mg), selenium (Se), Ca and copper 132 (Cu) (White and Broadley, 2005) and reducing the concentration of toxic elements, including 133 arsenic (As) and Cd (Ma et al., 2008). 134

Seed element content is determined by interconnected biological processes, including 135 element uptake by the roots, translocation and remobilization within the plant, and ultimately 136 import, deposition and assimilation/storage in the seeds. Element availability is further affected 137 by the accumulation of metabolites in seeds (Vreugdenhil et al., 2004). High-throughput ionomic 138 analysis, or concurrent measurement of multiple elements, allows for the quantitative and 139 simultaneous measurement of an organism’s elemental composition, providing a snapshot into 140 the functional state of an organism under different experimental conditions (Salt et al., 2008). 141 Most studies of the plant ionome utilize inductively-coupled plasma mass spectroscopy (ICP-142 MS). Briefly, the ICP functions to ionize the analyte into atoms, which are then detected by mass 143 spectroscopy. Reference standards are utilized to quantify each element of interest in the sample 144 analyte. ICP-MS analysis time is approximately 1-3 minutes per sample, which allows for a 145


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high-throughput processing of hundreds of samples (Salt et al., 2008). Previous studies have 146 demonstrated that several elements, including Fe, Mn, Zn, cobalt (Co) and Cd share mechanisms 147 of accumulation (Yi and Guerinot, 1996; Vert et al., 2002; Connolly et al., 2003). Ionomics 148 signatures derived from multiple elements have been shown to better predict plant physiological 149 status than the measurements of the elements themselves, including the essential nutrients 150 (Baxter et al., 2008). Holistically examining the ionome provides significant insights into the 151 networks underlying ion homeostasis beyond single element studies (Baxter and Dilkes, 2012). 152

With over 45,000 catalogued sorghum germplasm lines (USDA), there is significant 153 genetic variation (Das et al., 1997) with undiscovered impact on seed element composition. 154 Mapping quantitative trait loci (QTL) for seed element concentration has been successful in a 155 number of species including Arabidopsis (Vreugdenhil et al., 2004; Waters and Grusak, 2008; 156 Buescher et al., 2010), rice (Norton et al., 2010; Zhang et al., 2014), wheat (Shi et al., 2008; 157 Peleg et al., 2009) and maize (Šimić et al., 2012; Baxter et al., 2013; Baxter et al., 2014). 158 Genome-wide association (GWA) mapping is well suited for uncovering the genetic basis for 159 complex traits, including seed element accumulation. One of the key strengths of association 160 mapping is that a priori knowledge is not necessary to identify new loci associated with the trait 161 of interest. Further, a GWA mapping population is comprised of lines that have undergone 162 numerous recombination events, allowing for a narrower mapping interval. Previous GWA 163 studies in maize (Tian et al., 2011), rice (Huang et al., 2010) and sorghum (Morris et al., 2013) 164 have been successful in identifying the genetic basis for various agronomic traits. Here, we 165 analyzed the seed ionome from a community-generated association panel to identify potential 166 loci underlying seed element accumulation in sorghum. 167

168 Results: 169 Phenotypic diversity for seed element concentrations in the sorghum association panel 170

We grew 407 lines from the publicly available sorghum association panel (SAP) selected 171 for genotypic diversity and phenotypic variation (Casa et al., 2008) (Supplemental table 1). 172 These lines were previously genotyped by sequencing (GBS) (Morris et al., 2013). The SAP 173 lines were grown in three experiments: Lubbock, Texas in 2008 (SAP 2008), Puerto Vallarta, 174 Mexico in 2012 (SAP 2012), and two field replicates produced in Florence, SC in 2013 (SAP 175 2013-1 and SAP2013-2). 287 of the 407 SAP lines were present in all 4 growouts. 176


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Seed samples were taken from each replicate and weighed before analysis. A simple 177 weight normalization and established methods to estimate weight from the elemental content 178 were attempted (Lahner et al., 2003). However, both methods created artifacts, particularly in 179 elements with concentrations near the level of detection (Supplemental figure 1). To address this 180 concern, we included weight as a cofactor in a linear model that included other sources of 181 technical error and utilized the residuals of the model as the trait of interest for genetic mapping. 182 The residuals from this transformation were used for all further analyses and outperformed any 183 other method (data not shown). 184

We calculated broad-sense heritability for each trait to determine the proportion of the 185 phenotypic variation explained by the genetic variation present in the SAP across the three 186 environments (Table 1). Heritability estimates ranged from 1% (sodium, Na) to 45% (Cu). We 187 obtained moderate heritability (> 30%) for several elements including: Mg, P, sulfur (S), 188 potassium (K), Ca, Mn, Fe, Co, Zn, strontium (Sr) and molybdenum (Mo). Low heritabilities 189 were reported previously for seed accumulation of Al and As (Norton et al., 2010) as well as for 190 Se, Na, Al, and Rb in a similarly designed study in maize seed kernels (Baxter et al., 2014). The 191 relatively lower heritabilities for these elements, including boron (B), Cd and Se could be 192 explained by environmental differences between the experiments, element accumulation near the 193 limits of detection via ICP-MS, or the absence of genetic variation affecting these element’s 194 concentrations. Consistent with the hypothesis that field environment was masking genetic 195 variation, we calculated the heritability for two field replicates of the SAP in 2013, and found 196 higher heritabilities for 12 elements (Table 1). 197 We detected significant effects of both genotype and environment on most of the 198 elements (Figure 1 and Supplemental table 2). The measured element concentrations of the 199 present study corroborate the broad range observed in the sorghum element literature (Mengesha, 200 1966; Neucere and Sumrell, 1980; Lestienne et al., 2005; Ragaee et al., 2006). Similar to a study 201 carried out in wild emmer wheat (Gomez-Becerra et al., 2010), grain Na and Ca showed large 202 variation (5 and 4 fold, respectively). Compared to micronutrients, the remaining macronutrients 203 (P, K, S and Mg) measured in the study exhibited less phenotypic variation overall (Table 1 and 204 Supplemental table 3) ranging between 1.6 and 1.8 fold across the SAP. Of the micronutrients, 205 high variation was detected for Al and Ni (8 and 6 fold, respectively). With the exception of 206 these two elements, seed micronutrient concentration showed phenotypic variation ranging 207


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between 2.4 to 5.6 fold. High variation in Ni and Al may indicate strong environmental effects 208 on grain Ni and Al concentration or contamination during handling and analysis of the seeds, as 209 previously suggested (Baxter et al., 2014). The element traits were well distributed across the 210 sorghum subpopulations, with no specific subpopulations accumulating disproportionate levels 211 of any element (Supplemental figure 2). 212 We used two different approaches to identify the shared regulation of elemental 213 accumulation. Pairwise correlations were calculated and graphed (Supplemental table 4 and 214 Figure 2a), and principal component analysis (PCA) was carried out (Figure 2b). Highly 215 correlated element pairs in our data included Mg-P, Mg-Mn, P-S and Mg-S. Divalent cations 216 Ca2+ and Sr2+ are chemical analogs and strong correlation was observed between these two 217 elements, consistent with previous reports in other species (Queen et al., 1963; Hutchin and 218 Vaughan, 1968; Ozgen et al., 2011; Broadley and White, 2012). In the SAP, the first two 219 principal components accounted for a large fraction of the phenotypic covariance (36%). 220 Clustering of elements reflected known elemental relationships, including the covariation of Ca 221 and Sr (Figure 2). A cluster of the essential metal micronutrients, Fe, Zn and Cu is 222 distinguishable suggesting that their accumulation can be affected by a shared mechanism. 223


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Similarly clustering of Mg and P is consistent with previous studies in wheat (Peleg et al., 2009). 224 Seed P is predominately stored as the Mg2+ salt of phytic acid (inositol-hexaphosphate; IP6), 225 which may explain the significant positive correlation of these elements (Maathuis, 2009; 226 Marschner and Marschner, 2012). 227 228 Genome-wide association mapping of seed element traits 229

To dissect the genetic basis of natural variation for seed element concentration in 230 sorghum seed, GWA mapping was performed using both an optimal model obtained from the 231 multi-locus mixed model (MLMM) algorithm and a compressed mix linear model (CMLM) that 232 accounts for population structure. For the MLMM analysis we considered several models to 233 account for population structure as well as two different models to determine how many 234 cofactors to add into the analysis (see Methods and Supplemental figure 5). We decided to use 235 the kinship model to account for population structure and the most conservative mBonf model 236 for selecting cofactors. We also used the conservative, Bonferroni-corrected threshold (P = 0.05) 237 for CMLM, and identified overlapping SNPs significantly associated with seed element 238 concentration using both approaches (Supplemental tables 5 and 6). Compared to traditional 239


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single-locus approaches (e.g. CMLM), MLMM utilizes multiple loci in the model, which 240 contribute to a higher detection power and lower potential of false discoveries (Segura et al., 241 2012). MLMM also identified additional associations of interest. Significant SNPs identified 242 with the MLMM approach were prioritized for further analysis (Supplemental table 5). 243

In an effort to comprehensively identify significant SNPs associated with element 244 concentration, we created several datasets for GWA analysis. After averaging the two SAP 2013 245 growouts, each location was treated as an individual experiment. To link SAP experiments 246 across environments, we ranked the individual lines of each experiment by element 247 concentration and derived a robust statistic describing element accumulation for GWAS by using 248 the average of ranks across the four SAP environments. By utilizing rank-order, we eliminated 249 skewness and large variation in element concentration due to environmental differences 250 (Conover and Iman, 1981). GWA scans across individual experiments identified 270, 228, and 251 207 significant SNPs for all twenty element traits in the SAP2008, SAP2012 and SAP2013 252 panels, respectively. In total we identified 255 significant loci in the ranked dataset for the 253 twenty element traits (Supplemental table 5). The number of significant SNPs per element trait 254 ranged from two (B) to 33 (Ca) and roughly reflected their heritabilities. 255


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We identified several SNPs common to multiple environments (Supplemental table 8). 256 For example, GWA for Ca concentration in all three of our SAP experiments identified 257 significant SNPs within 5kb of locus Sobic.001G094200 on chromosome 1. Sobic.001G094200 258 is a putative calcium homeostasis regulator (CHoR1) (Zhang et al., 2012). We also identified 259 several significant SNPs that colocalized for multiple element traits (Figure 3 and Supplemental 260 table 9). Several of these SNPs were detected as significantly associated with multiple elements 261 that are known to be coordinately regulated (Yi and Guerinot, 1996; Vert et al., 2002; Connolly 262 et al., 2003; Lahner et al., 2003), and implicate candidate genes involved in regulation of 263 multiple elements. For example, a SNP on chromosome 1 (S1_18898717) was a significant peak 264 in both Mg and Mn GWA analysis (Figure 3). This SNP peak is in LD with the Arabidopsis 265 homolog of AT3G15480. AT3G15480 is a protein of unknown function, however T-DNA 266 knockout lines display mutant phenotypes in both Mg and Mn accumulation 267 (www.ionomicshub.org, SALK_129213, Tray 449). T-DNA knockout lines in Arabidopsis also 268 validated the significant peak for Co accumulation in the present study (S2_8464347). This SNP 269 is linked to the homolog of AT5G63790, a NAC domain containing protein that imparts a 270 significantly decreased Co phenotype in the T-DNA knockout line (www.ionomicshub.org, 271 SALK_030702, Tray 1137). 272

We focused our interpretation efforts on the GWA results from the SAP ranked dataset, 273 as these are the most likely to provide the tools to manipulate seed element content across 274 multiple environments. The GWA results for each element trait obtained at the optimal step of 275 the MLMM model were compiled. The data for Cd using the SAP ranked dataset is presented in 276 Figure 4 as an example of the analysis procedure. GWA across multiple environments identified 277 one significant SNP (S2_8883293) associated with Cd levels. (Figure 4A). The distribution of 278 expected vs. observed P values, QQ-plots (Figure 4B and Supplemental figure 5), suggests that 279 population structure was well controlled and false positive association signals were minimized 280 using the kinship matrix plus cofactors. 281

The optimal MLMM model (mBonf) included one SNP on chromosome 2, S2_8883293, 282 that explained 18% of the phenotypic variation in cadmium (Figure 4C), and the allelic effects of 283 each genotype were estimated (Figure 4D). The major-effect locus on chromosome 2 is in LD 284 with a homolog of a well-characterized cadmium transporter in plants, heavy metal ATPase 2 285 (HMA2). 286


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287 Discussion: 288 Ionome profiling for improved sorghum seed quality 289

Increasing the concentration of elements essential for human and animal nutrition (e.g. Fe 290 and Zn) while simultaneously minimizing and increasing tolerance to anti-nutrients and toxic 291 elements (e.g. As, Cd and Al) is a significant goal of fundamental research directed towards 292 global crop improvement (Schroeder et al., 2013). Element homeostasis in plants, is affected by 293 genotype, environment, soil properties, and nutrient interactions (Gregorio et al., 2000). 294 While determining strategies to enhance or reduce element content for food or fuel, several 295 components of seed element traits must be considered. These include: the heritability of the 296 various element traits, genotype by environment interactions, and the availability of high-297 throughput element content screening tools (Ortiz-Monasterio et al., 2007). Differences in seed 298 organic composition can also have large effects on the element composition of seeds, as different 299 seed compartments will contain elements in different proportions. Variation in seed composition 300


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together with variation in sorghum seed sizes, violate the assumption of a uniform elemental 301 concentration inherent in simple weight normalizations. Our data were not well modeled by a 302 simple weight normalization (Supplemental figure 1), and we subsequently employed a rank 303 transformation of the phenotypic data and linear model in the analysis (Ayana and Bekele, 2000; 304 Baxter et al., 2014). 305

Our results demonstrate environmental effects on the range and means of element 306 concentrations are largely element specific. In general, seed element concentrations did not 307 exhibit large variation due to environmental effects. This contributed to high heritabilities for 308 several elements and homeostasis of individual element concentration across very diverse 309 environments (Figure 1 and Table 1). The high heritabilities for these traits demonstrate the 310 feasibility of breeding strategies for the improvement of sorghum for seed element accumulation. 311 Further, due to the known genetic contributors to trait covariation, selection strategies can 312 include alteration of multiple traits, phenotypic correlations between traits or counter selection 313 for undesirable traits (e.g. As accumulation). The high heritability and the relationships we report 314 between important element elements, including Fe and Zn are encouraging for the development 315 of breeding schema for improved element profiles for the alleviation of human malnutrition. 316 Observed correlations of several elements indicate that changes in one or more elements can 317


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simultaneously affect the concentration of other elements present in the seed (Figure 2A). 318 However, the individual effects of particular alleles can deviate from this pattern. 319

Trait correlations and covariation were used to uncover genetic associations for multiple 320 elements. Even without more complicated analyses, we detected colocalized effects on several 321 element traits (Supplemental table 4 and Supplemental table 8). For example, several significant 322 SNPs colocalized for the strongly correlated element pairs of Ca and Sr (r= 0.79) as well as Mg 323 and P (r= 0.71). Shared SNPs and colocalization of significant loci across multiple element traits 324 suggest the possibility of tightly-linked genes or genes with pleiotropic effects and has been 325 documented in recent GWA studies, including experiments in tomato (Sauvage et al., 2014) and 326 rice (Zhao et al., 2011). In the present analysis, we applied a conservative threshold in our 327 MLMM implementation and identified SNPs from the most complex model in which the P 328 values of cofactors were below a defined threshold of 0.05. We implemented stringent 329 parameters to eliminate false positives, but also risked the elimination of true positives. To 330 identify additional candidate SNPs for further investigation, these stringent parameters can be 331 relaxed to include association signals below the threshold. 332 333


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Candidate genes 334 One of the primary goals of this study was to utilize GWA analyses to identify candidate 335

genes and novel loci implicated in the genetic regulation of sorghum seed element traits. We 336 identified numerous significant SNPs for all twenty element traits that currently do not associate 337 with known elemental accumulation genes. Although it is likely that a small fraction of these 338 SNPs are false positives, many more may be novel associations with as-yet undiscovered causal 339 genes and merit further investigation. We did, however, identify several significant SNPs that 340 fall directly within a characterized candidate gene or are in close proximity, or LD, with putative 341 candidates. 342 Zinc 343 Zinc deficiency is a critical challenge for food crop production that results in decreased 344 yields and nutritional quality. Zinc-enriched seeds result in better seedling vigor and higher stress 345 tolerance on Zn-deficient soils (Cakmak, 2008). Here we identify a strong candidate for genetic 346 improvement of zinc concentration in the in sorghum seed, Sobic.007G064900, an ortholog of 347 Arabidopsis ZIP5, zinc transporter precursor (AT1G05300) (Table 2). AT1G05300 is a member 348 of the ZIP family of metal transporter genes, and overexpression lines of this gene display 349 increased Zn accumulation in Arabidopsis (www.ionomicshub.org, 35SZip5_2 _Tray 700). 350 Manganese 351


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Associated with amino acid, lipid and carbohydrate metabolism, Mn is one of the 352 essential elements critical to human and animal nutritional requirements (Aschner and Aschner, 353 2005). We identified significant GWAS associations in the putative sorghum homolog for 354 member of the metal transporter encoding cation diffusion facilitator gene family MTP11 355 (Sobic.003G349200) (Table 2). The Arabidopsis ortholog, AtMTP11, confers Mn tolerance and 356 transports Mn2+ via a proton-antiport mechanism in Saccharomyces cerevisiae (Delhaize et al., 357 2007). 358 Cadmium 359

The seeds are a major source of essential nutrients, but can also be a source of toxic 360 heavy metals, including cadmium. Contamination of ground water and subsequent uptake and 361 absorption by the plant can result in high levels of Cd contamination in the seed (Arao and Ae, 362 2003). GWA analysis identified significant SNPs associated with a paralogous set of cation-363 transporting ATPases (Figure 4), Sobic.002G083000 and Sobic.002G083100. These are sorghum 364 orthologs of Arabidopsis HMA genes in the heavy metal–transporting subfamily of the P-type 365 ATPases. AtHMA3 participates in the vacuolar storage of Cd in Arabidopsis, and a recent study 366


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revealed that HMA3 is a major-effect locus controlling natural variation in leaf cadmium (Morel 367 et al., 2009; Chao et al., 2012). The SNP alleles could be used immediately to potentially 368 produce sorghum seed with lowered Cd2+ accumulation. 369 Nickel 370

Ni is an essential nutrient required for plant growth. However, similar to Cd, high Ni 371 concentrations in soil can be toxic to the plant, resulting in reduced biomass and crop yield. The 372 most significant SNP for Ni concentration in the SAP 2008 environment (and present in SAP 373 2012 and the ranked dataset) was S6_53175238. This SNP is in LD with the candidate gene 374 Sobic.006G164300, a homolog of the Yellow Stripe-Like 3 (YSL) family of proteins (Table 2). 375 Originally identified in maize, the YSL proteins are a subfamily of oligopeptide transporters 376 involved in metal uptake, homeostasis and long-distance transport (Curie et al., 2009). YSL3 is 377 suggested to transport metals bound to nicotianamine (NA)(Curie et al., 2001) and in the metal 378 hyperaccumulator Thlaspi caerulescens YSL3 functions as Ni–NA influx transporter (Gendre et 379 al., 2007). 380 381 Summary/Conclusion: 382

In the present study, we utilized GWA mapping and rank transformation of the 383 phenotypic data to scale GxE interactions and identify a number of genetic loci and candidate 384 gene associations for immediate study and application to breeding strategies. The use of a multi-385 element, or ionomic approach, to the analysis allows for the identification of SNPs that confer 386 multiple advantageous traits that can be selected for in breeding programs. We identify co-387 localization of significant SNPs for different elements, indicating potential coregulation through 388 physiological processes of elemental uptake, transport, traffic and sequestration. Our results 389 suggest that combining elemental profiling with GWA approaches can be useful for 390 understanding the genetic loci underlying elemental accumulation and for improving nutritional 391 content of sorghum. The data and analysis scripts used for this publication can be found at 392 www.ionomicshub.org. 393 394 Materials and Methods: 395 Plant material 396


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The Sorghum Association Panel has been previously described (Casa et al., 2008). Seeds 397 harvested from 407 lines that comprise the Sorghum Association Panel (SAP) were utilized for 398 this study. The SAP 2008 seeds were obtained from Germplasm Resources Information Network 399 (GRIN) and were produced in Lubbock, Texas by the USDA-ARS Cropping Systems Research 400 Laboratory in 2008. The SAP 2012 seeds were produced in Puerto Vallarta, Mexico in 2012. The 401 SAP 2013 seeds were produced in Florence, SC in 2013. 402 403 Phenotypic Elemental Analysis 404 Four seeds per replicate were weighed from each individual and a minimum of two replicates 405 from each line of the SAP 2008 and SAP 2013 panels were analyzed by ICP-MS. Each sample 406 was digested with 2.5 mL of concentrated nitric acid at 95°C for 3 hours. Elemental analysis 407 was performed with an ICP-MS for B, Na, Mg, Al, P, S, K, Ca, Mn, Fe, Co, Ni, Cu, Zn, As, Se, 408 Rb, Sr, Mo and Cd following established protocols (Baxter et al., 2010). A reference sample 409 derived from a pool of sorghum seed samples was generated and run after every 9th sample to 410 correct for ICP-MS run-to-run variation and within-run drift. 411 412 Data Processing and Analysis 413 Phenotype data were generated for 407 SAP lines. GBS SNP markers for the SAP lines used in 414 this study have been previously described (Morris et al., 2013). After removing SNPs with more 415 than 20% missing data and minor allele frequencies below 0.05, genotype data for 78,012 SNPs 416 remained. Broad sense heritability was calculated using the lmer function in the lme4 package to 417 perform an analysis of variance with the experimental replicates of the SAP using previously 418 described methods (Van Poecke et al., 2007; Bates et al., 2014). To ensure normality in the data 419 distribution of the phenotype, the Box-Cox procedure was carried out on the phenotype scores to 420 identify the best transformation method (Box and Cox, 1964). The ‘boxcox’ function in the 421 MASS package in R was utilized to carry out the transformations (R Development Core Team, 422 2014; Ripley et al., 2014). In order to address potential confounding factors in the GWA 423 analysis, specifically ICP run-to-run variation and the weight correction calculation, we used 424 linear regression to compute residuals adjusted for ICP run and sample weight. These residuals 425 were used to test for association with qualifying SNPs in the GWA analysis. 426 427


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GWAS 428 GWAS was executed in R using Genomic Association and Prediction Integrated Tool 429

(GAPIT) using CMLM (Zhang et al., 2010; Lipka et al., 2012). Significant associations were 430 determined by estimates of false discovery rate (FDR) (P = 0.05) (Benjamini and Hochberg, 431 1995). The CMLM uses a VanRaden kinship matrix and the first three principal components as 432 covariates to account for population structure. MLMM is based on EMMA (Kang et al., 2008) 433 and relies on the iterative use of a simple K, or Q+K, mixed-model algorithm. The kinship term, 434 K, provides a fine-grained estimate of familial relatedness between lines. In addition, GWAS 435 models often include a more granular measurement of population membership for each line, Q. 436 To determine the necessity of using the more complex Q+K model to control for spurious allele 437 associations, we analyzed QQ-plots generated from MLMM GWAS using a simple K model plus 438 cofactors (Supplemental figure 5) and phenotypic distributions across known subpopulations 439 (Supplemental figure 2). Phenotypic distributions across subpopulations were similar, indicating 440 that population structure does not play a strong role in elemental accumulation. The QQ-plots 441 indicate that after the addition of major effect loci to the model as cofactors, the p value 442 distribution does not deviate drastically from the expected uniform distribution. These results 443 indicate that for MLMM the mixed model containing only the kinship matrix, K, plus cofactors 444 is sufficient to control for spurious allele associations due to population structure and cryptic 445 relatedness. 446

At each step of the MLMM, the phenotypic variance is divided into genetic, random and 447 explained variance. The most significant marker is included as a cofactor, and the variance 448 components of the model are recalculated. With each successive iteration, the remaining genetic 449 variance approaches zero, and an optimal model including cofactors that explains the genetic 450 fraction of the phenotypic variance is determined. The MLMM method selects two models using 451 stop criteria determined by two test statistics termed the multiple-Bonferroni criterion (mBonf) 452 and the extended Bayesian information criterion (extBIC). The mBonf criteria selects a model 453 wherein all cofactors have a p value below a Bonferroni-corrected threshold (Segura et al., 2012) 454 and, in our experiments, this was the more stringent of the two model selection criteria (i.e. it 455 favored less complex models) and was used for all further analyses. In addition, the genetic 456 variance partition, described above, provides an estimate of heritability, termed 457 pseudoheritability (Kang et al., 2010; Segura et al., 2012), explained by the model at each step. 458


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The missing heritability can be calculated from the model at the optimal step (mBonf). The 459 percent variance explained by the model is the difference between the genetic variance at step 0 460 and the optimal step (Supplemental table 7). 461

The MLMM method utilized the multiple-Bonferroni criterion (mBonf) which selects a 462 model wherein all cofactors have a p value below a Bonferroni-corrected threshold (Segura et al., 463 2012). We utilized a genome-wide significance threshold of p < 0.05 for the Bonferroni 464 correction. A kinship matrix was constructed to correct for population structure and cryptic 465 relatedness (Supplemental table 10). The kinship matrix was estimated from all of the SNPs in 466 the dataset using the VanRaden method (VanRaden, 2008) in GAPIT (Lipka et al., 2012). 467 Kinship was included as a random effect in the MLMM model. 468 469 Supplemental Material 470 Supplemental figure 1: Correlation between seed weight and elemental concentration 471 Supplemental figure 2: Element distribution across sorghum subpopulations 472 Supplemental figure 3: Correlation network of seed element concentrations 473 Supplemental figure 4: PCA applied to the average seed concentrations for 20 elements 474 Supplemental figure 5: QQ Plots comparing MLMM models 475 Supplemental table 1: SAP lines 476 Supplemental table 2: Summary of one-way ANOVA for each evaluated trait and environment 477 Supplemental table 3: Means and standard deviations of seed element concentrations 478 Supplemental table 4: Correlation coefficients among seed element concentrations 479 Supplemental table 5: Significant SNPS identified by MLMM 480 Supplemental table 6: Significant SNPS identified by CMLM 481 Supplemental table 7: Calculations from MLMM-Pseudoheritability 482 Supplemental table 8: Shared significant SNPs across SAP datasets 483 Supplemental table 9: Significant SNPs shared for multiple elements 484 Supplemental table 10: Kinship matrix estimated from all of the SNPs 485 486 Acknowledgments 487 This project was partially funded by the iHUB Visiting Scientist Program 488 (http://www.ionomicshub.org), Chromatin, Inc., NSF EAGER (1450341) to I.B. and B.P.D., 489


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NSF IOS 1126950 to I.B., NSF IOS-0919739 to E.C., and BMGF (OPP 1052924) to B.P.D. 490 Availability of supporting data 491 The datasets supporting the results of this article are available through Purdue Ionomics 492 Information Management System (PiiMS) at http://www.ionomicshub.org 493 494 Competing interests 495 The authors declare that they have no competing interests. 496 497 Authors’ contributions 498 NS, GZ and IB wrote the manuscript, carried out ionomics assays, data analysis and 499 interpretation of the results. ZB and RB contributed to experimental design and participated in 500 tissue sampling. BD, EC, and SK participated in data analysis and interpretation of the results. 501 All authors read, revised and approved the final manuscript. 502 503 504 505 506 507 508 509 510 511 512 513 514 515


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516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 Table 1 Mean, standard deviation, and broad sense heritability of seed element 545 concentrations from the Sorghum Association Panel averaged across 3 environments. 546 Element concentration values are presented as mg kg-1 and broad sense heritability (H2) 547 was calculated as described in the methods section. Data represents an average of 548 individual samples (n=287) analyzed in 4 separate experiments. *Element concentration 549 presented in µg kg-1. 550

Trait Sorghum Association Panel All SAP Replicates

H2 H2

Mean Standard Deviation

All SAPReps

2013 Field Reps

B Na Mg Al P S K Ca Mn Fe Co Ni Cu Zn As Se Rb Sr

Mo Cd

12.8 0.572 1580 0.277 3350 890 3850 25.3 13.5 24.9 6.11* 0.18 2.54 19.9

0.0796 1.32 1.76 0.573 0.603 0.0683

5.88 0.316 246

0.269 623 127 795 18.8 3.33 5.7

4.48* 0.187 1.21 5.71

0.0361 0.454 0.848 0.586 0.306 0.0739

0.02 0.01 0.42 0.06 0.38 0.36 0.36 0.41 0.36 0.40 0.32 0.22 0.45 0.35 0.08 0.03 0.10 0.32 0.33 0.16

0.05 0.00 0.38 0.17 0.35 0.33 0.46 0.57 0.43 0.46 0.28 0.10 0.47 0.38 0.19 0.00 0.12 0.27 0.37 0.23


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Phenotype SNP Locus Name Chromosome P value Soybean Defline Annotation Arabidopsis thaliana Homolog

Cd

S2_8883293

Sobic.002G083000

2

9.67E-10

Cation-transporting ATPase

AtHMA2; Heavy Metal ATPase2

Mo

S3_64823106

Sobic.003G320600

3

1.65E-08

Membrane protein-like

Sulfite exporter TauE/SafE family protein

Ni

S6_53175238

Sobic.006G164300

6

1.84E-07

Iron transport protein 2

AtYSL3; YELLOW STRIPE like 3

Mg

S1_64935466

Sobic.001G443900

1

3.20E-07

Peptide transporter PTR2, putative, expressed

AtPTR2; Peptide transporter 2

Fe

S1_19766414

Sobic.001G213400

1

4.92E-07

HD domain containing protein, expressed

Metal-dependent phosphohydrolase

K

S6_45971634

Sobic.006G082200

6

4.25E-06

OSIGBa0160I14.4 protein

MGT4, MRS2-3; Magnesium transporter 4

B

S4_52068874

Sobic.004G174600

4

5.45E-06

Putative multidrug resistance protein

ABC transporter family protein

Rb

S8_6186108

Sobic.008G058700

8

7.06E-06

Putative uncharacterized protein OSJNBa0065C11.1

ZIP metal ion transporter family

P

S1_64935466

Sobic.001G443900

1

7.14E-06

Peptide transporter PTR2, putative, expressed

AtPTR2; Peptide transporter 2

Zn

S7_6880986

Sobic.007G064900

7

8.11E-06

Zinc transporter

ZIP5; Zinc transporter 5 precursor

Sr

S4_5986126

Sobic.004G073500

4

8.96E-06

Putative multidrug resistance protein

ABC transporter family protein

Mn

S3_66960028

Sobic.003G349200

3

6.37E-05

Cation efflux family protein

MTP11; ABC transporter family protein 551 552

Table II Detailed information for selected significant associations detected within the 20 element traits analyzed using the MLMM. 553 554 555 556 557

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http://dx.doi.org/10.1101/019083

Parsed CitationsAmaducci S, Monti A, Venturi G (2004) Non-structural carbohydrates and fibre components in sweet and fibre sorghum as affectedby low and normal input techniques. Industrial Crops and Products 20: 111-118

Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title

Arao T, Ae N (2003) Genotypic variations in cadmium levels of rice grain. Soil Science and Plant Nutrition 49: 473-479Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title

Aschner JL, Aschner M (2005) Nutritional aspects of manganese homeostasis. Molecular aspects of medicine 26: 353-362Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title

Ayana A, Bekele E (2000) Geographical patterns of morphological variation in sorghum (Sorghum bicolor (L.) Moench) germplasmfrom Ethiopia and Eritrea: quantitative characters. Euphytica 115: 91-104


Bates D, Mächler M, Bolker B, Walker S (2014) Fitting linear mixed-effects models using lme4. arXiv preprint arXiv:1406.5823Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title

Baxter I, Brazelton JN, Yu D, Huang YS, Lahner B, Yakubova E, Li Y, Bergelson J, Borevitz JO, Nordborg M (2010) A coastal clinein sodium accumulation in Arabidopsis thaliana is driven by natural variation of the sodium transporter AtHKT1; 1. PLoS genetics 6:e1001193


Baxter IR, Gustin JL, Settles AM, Hoekenga OA (2013) Ionomic characterization of maize kernels in the intermated B73× Mo17population. Crop Science 53: 208-220


Baxter IR, Vitek O, Lahner B, Muthukumar B, Borghi M, Morrissey J, Guerinot ML, Salt DE (2008) The leaf ionome as amultivariable system to detect a plant's physiological status. Proceedings of the National Academy of Sciences 105: 12081-12086


Baxter IR, Ziegler G, Lahner B, Mickelbart MV, Foley R, Danku J, Armstrong P, Salt DE, Hoekenga OA (2014) Single-Kernel IonomicProfiles Are Highly Heritable Indicators of Genetic and Environmental Influences on Elemental Accumulation in Maize Grain (Zeamays). PLoS One 9: e87628


Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journalof the Royal Statistical Society. Series B (Methodological): 289-300


Box GE, Cox DR (1964) An analysis of transformations. Journal of the Royal Statistical Society. Series B (Methodological): 211-252Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title

Broadley MR, White PJ (2012) Some elements are more equal than others: soil-to-plant transfer of radiocaesium andradiostrontium, revisited. Plant and soil 355: 23-27


Buescher E, Achberger T, Amusan I, Giannini A, Ochsenfeld C, Rus A, Lahner B, Hoekenga O, Yakubova E, Harper JF (2010)Natural genetic variation in selected populations of Arabidopsis thaliana is associated with ionomic differences. PLoS One 5:e11081


Cakmak I (2008) Enrichment of cereal grains with zinc: agronomic or genetic biofortification? Plant and Soil 302: 1-17


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Hutchin ME, Vaughan BE (1968) Relation between simultaneous Ca and Sr transport rates in isolated segments of vetch, barley,and pine roots. Plant physiology 43: 1913-1918


Kang HM, Sul JH, Service SK, Zaitlen NA, Kong S-y, Freimer NB, Sabatti C, Eskin E (2010) Variance component model to accountfor sample structure in genome-wide association studies. Nature genetics 42: 348-354


Kang HM, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ, Eskin E (2008) Efficient control of population structure in modelorganism association mapping. Genetics 178: 1709-1723


Kimber CT, Dahlberg JA, Kresovich S (2013) The gene pool of Sorghum bicolor and its improvement. In Genomics of theSaccharinae. Springer, pp 23-41


Lahner B, Gong J, Mahmoudian M, Smith EL, Abid KB, Rogers EE, Guerinot ML, Harper JF, Ward JM, McIntyre L (2003) Genomicscale profiling of nutrient and trace elements in Arabidopsis thaliana. Nature biotechnology 21: 1215-1221


Lestienne I, Icard-Vernière C, Mouquet C, Picq C, Trèche S (2005) Effects of soaking whole cereal and legume seeds on iron, zincand phytate contents. Food Chemistry 89: 421-425


Lipka AE, Tian F, Wang Q, Peiffer J, Li M, Bradbury PJ, Gore MA, Buckler ES, Zhang Z (2012) GAPIT: genome association andprediction integrated tool. Bioinformatics 28: 2397-2399


Ma JF, Yamaji N, Mitani N, Xu X-Y, Su Y-H, McGrath SP, Zhao F-J (2008) Transporters of arsenite in rice and their role in arsenicaccumulation in rice grain. Proceedings of the National Academy of Sciences 105: 9931-9935


Maathuis FJ (2009) Physiological functions of mineral macronutrients. Current opinion in plant biology 12: 250-258Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title

Marschner H, Marschner P (2012) Marschner's mineral nutrition of higher plants, Vol 89. Academic pressPubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title

Mengesha MH (1966) Chemical composition of teff (Eragrostis tef) compared with that of wheat, barley and grain sorghum.Economic Botany 20: 268-273


Monti A, Di Virgilio N, Venturi G (2008) Mineral composition and ash content of six major energy crops. Biomass and Bioenergy 32:216-223


Morel M, Crouzet J, Gravot A, Auroy P, Leonhardt N, Vavasseur A, Richaud P (2009) AtHMA3, a P1B-ATPase allowing Cd/Zn/Co/Pbvacuolar storage in Arabidopsis. Plant Physiology 149: 894-904


Morris GP, Ramu P, Deshpande SP, Hash CT, Shah T, Upadhyaya HD, Riera-Lizarazu O, Brown PJ, Acharya CB, Mitchell SE (2013)Population genomic and genome-wide association studies of agroclimatic traits in sorghum. Proceedings of the National Academy


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http://dx.doi.org/10.1101/019083


Zhang Z, Ersoz E, Lai C-Q, Todhunter RJ, Tiwari HK, Gore MA, Bradbury PJ, Yu J, Arnett DK, Ordovas JM (2010) Mixed linearmodel approach adapted for genome-wide association studies. Nature genetics 42: 355-360


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http://scholar.google.com/scholar?as_q=Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa.&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Zhao&as_ylo=2011&as_allsubj=all&hl=en&c2coff=1

http://dx.doi.org/10.1101/019083

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Running Head: Genetic determinants of seed element ... · 63 (), Chromatin, Inc., NSF EAGER (1450341) to I.B. and BPD, NSF 64 IOS ... 159 Genome-wide ... . 287 of the 407 SAP lines