Running Head: Genetic determinants of seed element ... · 6 124 the ionome provides significant insights into the networks underlying ion homeostasis beyond 125 single element studies

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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 7 Fax: 314.587.1538 8 [email protected] 9 10 Research Area: Biochemistry and Metabolism 11 Secondary Research Area: Genes, Development and Evolution 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Plant Physiology Preview. Published on February 19, 2016, as DOI:10.1104/pp.15.01971

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Title: Integration of experiments across diverse environments identifies the genetic determinants 32 of variation in Sorghum bicolor seed element composition 33 34 Authors: Nadia Shakoor1, Greg Ziegler5, Brian P. Dilkes2, Zachary Brenton3, Richard Boyles3, 35 Erin L. Connolly4, Stephen Kresovich3 and Ivan Baxter5* 36 1Donald Danforth Plant Science Center, St. Louis, Missouri, 63132 (N.S.) 37 2Department of Biochemistry, Purdue 38 University, West Lafayette, Indiana 47907 (B.P.D.) 39 3Department of Genetics and Biochemistry, Clemson University, Clemson, South Carolina, 40 29631 (Z.B., R.B., S.K.) 41 4Department of Biological Sciences, University of South Carolina, Columbia, South Carolina, 42 29208 (E.C.) 43 5USDA-ARS, Donald Danforth Plant Science Center, St. Louis, Missouri, 63132 (G.Z., I.B.) 44 45 Email Addresses 46 Ivan Baxter ([email protected]) 47 Nadia Shakoor ([email protected]) 48 Greg Ziegler ([email protected]) 49 Brian P. Dilkes ([email protected]) 50 Zachary Brenton ([email protected]) 51 Richard Boyles ([email protected]) 52 Erin L. Connolly ([email protected]) 53 Stephen Kresovich ([email protected]) 54 55 One sentence summary: Genome-wide association analysis in Sorghum bicolor identifies novel 56 alleles controlling seed element accumulation. 57 58 This project was partially funded by the iHUB Visiting Scientist Program NSF DBI 0953433 to 59 David Salt, Chromatin, Inc., NSF EAGER 1450341 to IB and BPD, NSF IOS 1126950 to IB, 60 NSF IOS 0919739 to ELC, and BMGF OPP 1052924 to BPD. 61 62



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*Corresponding Author 63 Ivan Baxter 64 975 North Warson Road 65 Saint Louis, MO 63132 66 Phone: 314.587.1438 67 Fax: 314.587.1538 68 [email protected] 69 70 71



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Abstract: 72 Seedling establishment and seed nutritional quality requires the sequestration of sufficient 73 element nutrients. Identification of genes and alleles that modify element content in the grains of 74 cereals, including Sorghum bicolor, is fundamental to developing breeding and selection 75 methods aimed at increasing bioavailable element content and improving crop growth. We have 76 developed a high-throughput workflow for the simultaneous measurement of multiple elements 77 in sorghum seeds. We measured seed element levels in the genotyped Sorghum Association 78 Panel (SAP), representing all major cultivated sorghum races from diverse geographic and 79 climatic regions, and mapped alleles contributing to seed element variation across three 80 environments by genome-wide association. We observed significant phenotypic and genetic 81 correlation between several elements across multiple years and diverse environments. The power 82 of combining high-precision measurements with genome-wide association was demonstrated by 83 implementing rank transformation and a multilocus mixed model (MLMM) to map alleles 84 controlling 20 element traits, identifying 255 loci affecting the sorghum seed ionome. Sequence 85 similarity to genes characterized in previous studies identified likely causative genes for the 86 accumulation of zinc (Zn) manganese (Mn), nickel (Ni), calcium (Ca) and cadmium (Cd) in 87 sorghum seeds. In addition to strong candidates for these five elements, we provide a list of 88 candidate loci for several other elements. Our approach enabled identification of SNPs in strong 89 LD with causative polymorphisms that can be evaluated in targeted selection strategies for plant 90 breeding and improvement. 91 92 93



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Introduction: 94 Sorghum bicolor is a globally cultivated source of food, feed, and fiber. Contrasting 95

needs for elemental nutrient accumulation limit crop yield and quality for sorghum marketed to 96 different sectors. The seed bearing reproductive organs, or panicles, in sorghum represent up to 97 30% of the total dry matter yield (Amaducci et al., 2004). Plant-based diets, in which grains 98 compose the major food source, require the accumulation of bioavailable essential elements in 99 the plant seeds. Currently, iron (Fe) and zinc (Zn) deficiencies negatively affect the health of 100 over two billion people worldwide (World Health Organization, 2002). Increased bioavailable 101 elemental nutrient content in the edible portions of sorghum for human and animal nutrition 102 could ameliorate this nutritional crisis (Graham et al., 1999; World Health Organization, 2002). 103 Additional global health benefits could be achieved by increasing magnesium (Mg), selenium 104 (Se), calcium (Ca) and copper (Cu) (White and Broadley, 2005) while reducing the concentration 105 of toxic elements, including arsenic (As) and cadmium (Cd) (Ma et al., 2008). 106

Seed element accumulation results from interconnected biological processes, including 107 element uptake by the roots, translocation and remobilization within the plant and ultimately 108 import, deposition and assimilation/storage in the seeds. Element availability is further affected 109 by the accumulation of metabolites in seeds (Vreugdenhil et al., 2004). High-throughput ionomic 110 analysis, or concurrent measurement of multiple elements, allows for the quantitative and 111 simultaneous measurement of an organism’s elemental composition, providing a snapshot into 112 the functional state of an organism under different experimental conditions (Salt et al., 2008). 113 Most studies of the plant ionome utilize inductively-coupled plasma mass spectroscopy (ICP-114 MS). Briefly, ICP functions to ionize the analyte into atoms, which are then detected by mass 115 spectroscopy. Reference standards are used to identify and quantitate each element of interest in 116 the sample. ICP-MS analysis can be accomplished in as little as 1 minute per sample, which 117 allows for high-throughput processing of thousands of samples (Salt et al., 2008). Previous 118 studies have demonstrated that several elements, including Fe, manganese (Mn), Zn, cobalt (Co) 119 and Cd share mechanisms of accumulation (Yi and Guerinot, 1996; Vert et al., 2002; Connolly et 120 al., 2003). Ionomic signatures derived from multiple elements have also been shown to better 121 predict plant physiological status for some elements than the measure of the element’s 122 concentration, including essential nutrients like Fe (Baxter et al., 2008). Holistically examining 123



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the ionome provides significant insights into the networks underlying ion homeostasis beyond 124 single element studies (Baxter and Dilkes, 2012). 125

There are over 45,000 catalogued lines of sorghum at the United States Department of 126 Agriculture Germplasm Resource Information Network (USDA). This diverse collection of 127 sorghum germplasm contains genetic variation with undiscovered impact on seed element 128 composition (Das et al., 1997). Mapping quantitative trait loci (QTL) for seed element 129 concentration has been successful in a number of species including Arabidopsis (Vreugdenhil et 130 al., 2004; Waters and Grusak, 2008; Buescher et al., 2010), rice (Norton et al., 2010; Zhang et 131 al., 2014), wheat (Shi et al., 2008; Peleg et al., 2009) and maize (Šimić et al., 2012; Baxter et al., 132 2013; Baxter et al., 2014). Genome-wide association (GWA) mapping is well suited for 133 uncovering the genetic basis for complex traits, including seed element accumulation. One of the 134 key strengths of association mapping is that a priori knowledge is not necessary to identify new 135 loci associated with the trait of interest. Further, a GWA mapping population is comprised of 136 lines that have undergone numerous recombination events, allowing for a narrower mapping 137 interval. Previous GWA studies in maize (Tian et al., 2011), rice (Huang et al., 2010) and 138 sorghum (Morris et al., 2013) have been successful in identifying the genetic basis for various 139 agronomic traits. Here, we analyzed the seed ionome from a community-generated association 140 panel to identify potential loci underlying seed element accumulation in sorghum. 141

142 Results: 143 Phenotypic diversity for seed element concentrations in the Sorghum Association Panel 144

We grew 407 lines from the publicly available Sorghum Association Panel (SAP) 145 selected for genotypic diversity and phenotypic variation (Casa et al., 2008) (Supplemental table 146 1). These lines were previously genotyped by sequencing (GBS) (Morris et al., 2013). The SAP 147 lines were grown in three experiments: Lubbock, Texas in 2008 (SAP 2008), Puerto Vallarta, 148 Mexico in 2012 (SAP 2012), and two field replicates produced in Florence, SC in 2013 (SAP 149 2013-1 and SAP2013-2). 287 of the 407 SAP lines were present in all 4 growouts. 150

Seed samples were taken from each replicate and weighed before analysis. A simple 151 weight normalization and established methods to estimate weight from the element content were 152 attempted (Lahner et al., 2003). However, both methods created artifacts, particularly in 153 elements with concentrations near the level of detection for ICP-MS (Supplemental figure 1). To 154



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address this concern, we included weight as a cofactor in a linear model that included other 155 sources of technical error and utilized the residuals of the model as the trait of interest for genetic 156 mapping. The residuals from this transformation were used for all further analyses and 157 outperformed any other method (data not shown). 158

We calculated broad-sense heritability for each trait to determine the proportion of the 159 phenotypic variation explained by the genetic variation present in the SAP across the three 160 environments (Table 1). Heritability estimates ranged from 1% (sodium, Na) to 45% (Cu). We 161 obtained moderate heritability (> 30%) for several elements including: Mg, phosphorus (P), 162 sulfur (S), potassium (K), Ca, Mn, Fe, Co, Zn, strontium (Sr) and molybdenum (Mo). Low 163 heritabilities were reported previously for seed accumulation of aluminum (Al) and As (Norton 164 et al., 2010) as well as for Se, Na, Al, and rubidium (Rb) in a similarly designed study in maize 165 seed kernels (Baxter et al., 2014). The relatively lower heritabilities for these elements, including 166 boron (B), Cd and Se could be explained by environmental differences between the experiments, 167 element accumulation near the limit of detection via ICP-MS, or the absence of genetic variation 168 affecting these element’s concentrations. Consistent with the hypothesis that field environment 169 was masking genetic variation, we calculated the heritability for two field replicates of the SAP 170 in 2013, and found higher heritabilities for 12 elements (Table 1). 171



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We detected significant effects of both genotype and environment on most of the 172 elements (Figure 1 and Supplemental table 2). The measured element concentrations of the 173 present study corroborate the broad range observed in the sorghum element literature (Mengesha, 174 1966; Neucere and Sumrell, 1980; Lestienne et al., 2005; Ragaee et al., 2006). Similar to a study 175 carried out in wild emmer wheat (Gomez-Becerra et al., 2010), grain Na and Ca showed large 176 variation (5 and 4 fold, respectively). Compared to micronutrients, the remaining macronutrients 177 (P, K, S and Mg) measured in the study exhibited less phenotypic variation overall (Table 1 and 178 Supplemental table 3) and ranged between 1.6 and 1.8 fold across the SAP. Of the 179 micronutrients, high variation was detected for Al and Ni (8 and 6 fold, respectively). With the 180 exception of these two elements, seed micronutrient concentration showed phenotypic variation 181 ranging between 2.4 to 5.6 fold. High variation in Ni and Al may indicate strong environmental 182 effects on seed Ni and Al concentration or contamination during handling and analysis of the 183 seeds as previously suggested (Baxter et al., 2014). The element traits were well distributed 184 across the sorghum subpopulations, with no specific subpopulations accumulating 185 disproportionate levels of any element (Supplemental figure 2). 186



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We used two different approaches to identify the shared regulation of element 187 accumulation. Pairwise correlations were calculated and graphed (Supplemental table 4 and 188 Figure 2a), and principal component analysis (PCA) was carried out (Figure 2b). Highly 189 correlated element pairs in our data included Mg/P, Mg/Mn, P/S and Mg/S. Divalent cations Ca/ 190 Sr and K/Rb are chemical analogs and strong correlation was observed between these pairs of 191 elements, consistent with previous reports in other species (Queen et al., 1963; Hutchin and 192 Vaughan, 1968; Ozgen et al., 2011; Broadley and White, 2012). In the SAP, the first two 193 principal components accounted for a large fraction of the phenotypic covariance (36%). PCA 194 clustering of elements reflected known elemental relationships, including the covariation of 195 Ca/Sr and K/Rb (Figure 2). A cluster of the essential micronutrient transition metals, Fe, Zn and 196 Cu is distinguishable, suggesting that their accumulation may be affected by a shared 197 mechanism. Similarly, clustering of Mg and P is consistent with previous studies in wheat (Peleg 198 et al., 2009). Seed P is predominately stored as the Mg2+ salt of phytic acid (inositol-199 hexaphosphate; IP6), which may explain the significant positive correlation of these elements 200 (Maathuis, 2009; Marschner and Marschner, 2012). 201 202 Genome-wide association mapping of seed element traits 203



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To dissect the genetic basis of natural variation for seed element concentration in 204 sorghum seed, GWA mapping was performed using both an optimal model obtained from the 205 multi-locus mixed model (MLMM) algorithm and a compressed mixed linear model (CMLM) 206 that accounts for population structure. For the MLMM analysis we considered several models to 207 account for population structure as well as two different models to determine the number of 208 cofactors to add into the analyses (see Methods and Supplemental figure 5). We chose a kinship 209 model to account for population structure and the most conservative multiple-Bonferroni 210 criterion (mBonf) model for selecting cofactors. We also used a conservative Bonferroni-211 corrected threshold (p < 0.05) for CMLM. These two approaches identified overlapping SNPs 212 significantly associated with seed element concentration (Supplemental tables 5 and 6). 213 Compared to traditional single-locus approaches (e.g. CMLM), MLMM uses multiple loci in the 214 model, which contribute to a higher detection power and lower potential of false discoveries 215 (Segura et al., 2012). MLMM also identified additional associations of interest. Significant SNPs 216 identified with the MLMM approach were prioritized for further analyses (Supplemental table 217 5). 218

In an effort to comprehensively identify significant SNPs associated with element 219 concentration, we created several datasets for GWA analysis. After averaging the two SAP 2013 220 growouts, each location was treated as an individual experiment. To link SAP experiments 221



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across environments, we ranked the individual lines of each experiment by element 222 concentration and derived a robust statistic describing element accumulation for GWA by using 223 the average of ranks across the three SAP environments. By utilizing rank order, we eliminated 224 skewness and large variation in element concentration due to environmental differences 225 (Conover and Iman, 1981). GWA scans across individual experiments identified 270, 228, and 226 207 significant SNPs for all twenty element traits in the SAP2008, SAP2012 and SAP2013 227 panels, respectively. In total we identified 255 significant loci in the ranked dataset for the 228 twenty element traits (Supplemental table 5). The number of significant SNPs per element trait 229 ranged from two (B) to 33 (Ca) and roughly reflected their heritabilities. 230

We identified several SNPs common to multiple environments (Supplemental table 8). 231 For example, GWA for Ca concentration in all three of our SAP experiments identified 232 significant SNPs within 5kb of locus Sobic.001G094200 on chromosome 1. Sobic.001G094200 233 is a putative Ca homeostasis regulator (CHoR1) (Zhang et al., 2012). We also identified several 234 significant SNPs that colocalized for multiple element traits (Figure 3 and Supplemental table 9). 235 Several of these SNPs were detected as significantly associated with multiple elements that are 236 known to be coordinately regulated (Yi and Guerinot, 1996; Vert et al., 2002; Connolly et al., 237 2003; Lahner et al., 2003) and implicate candidate genes involved in the regulation of multiple 238 elements. For example, a SNP on chromosome 1 (S1_18898717) was a significant peak in both 239 Mg and Mn GWA analysis (Figure 3). This SNP peak is in LD with the Arabidopsis homolog of 240 AT3G15480. AT3G15480 is a protein of unknown function, however T-DNA knockout lines 241 display mutant phenotypes in both Mg and Mn accumulation (www.ionomicshub.org, 242 SALK_129213, Tray 449). T-DNA knockout lines in Arabidopsis also validated the significant 243 peak for Co accumulation in the present study (S2_8464347). This SNP is linked to the homolog 244 of AT5G63790, a NAC domain-containing protein that imparts a significantly decreased Co 245 phenotype in the T-DNA knockout line (www.ionomicshub.org, SALK_030702, Tray 1137). 246

We focused our interpretation efforts on the GWA results from the SAP rank average 247 dataset, as these are the most likely to provide the tools to manipulate seed element concentration 248 in multiple environments. The GWA results for each element trait obtained at the optimal step of 249 the MLMM model were compiled. The data for Cd using the SAP rank average dataset is 250 presented in Figure 4 as an example of the analysis procedure. GWA with the optimal MLMM 251 model (mBonf) across multiple environments identified one significant SNP (S2_8883293) 252



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associated with Cd levels. (Figure 4A). The distribution of expected vs. observed P values, QQ-253 plots (Figure 4B and Supplemental figure 5), suggests that population structure was well 254 controlled and false positive association signals were minimized using the kinship matrix plus 255 cofactors. Further, this SNP explained 18% of the phenotypic variation in Cd (Figure 4C), and 256 the allelic effects of each genotype were estimated (Figure 4D). This major-effect locus on 257 chromosome 2 is in LD with a homolog of a well-characterized Cd transporter in plants, heavy 258 metal ATPase 2 (HMA2). 259

260 Discussion: 261 Ionome profiling for improved sorghum seed quality 262



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Increasing the concentration of elements essential for human and animal nutrition (e.g. Fe 263 and Zn) while simultaneously minimizing and increasing tolerance to anti-nutrients and toxic 264 elements (e.g. As, Cd and Al) is a significant goal of fundamental research directed towards 265



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global crop improvement (Schroeder et al., 2013). Element homeostasis in plants is affected by 266 genotype, environment, soil properties and nutrient interactions (Gregorio et al., 2000). 267



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While determining strategies to enhance or reduce element content for food or fuel, several 268 components of seed element traits must be considered. These include: the heritability of the 269 various element traits, genotype by environment interactions and the availability of high-270 throughput element content screening tools (Ortiz-Monasterio et al., 2007). Differences in seed 271 organic composition can also have large effects on the element composition of seeds, as different 272 seed compartments will contain elements in different proportions. Variation in seed composition, 273 together with variation in sorghum seed size, violates the assumption of a uniform elemental 274 concentration inherent in simple weight normalizations. Our data were not well modeled by a 275 simple weight normalization (Supplemental figure 1), and we subsequently employed a rank 276 transformation of the phenotypic data and linear model in the analysis (Ayana and Bekele, 2000; 277 Baxter et al., 2014). 278

Our results demonstrate that the environmental effects on the range and means of element 279 concentrations are largely element specific. In general, seed element concentrations did not 280 exhibit large variation due to environmental effects. This contributed to high heritabilities for 281 several elements and homeostasis of individual element concentrations across very diverse 282 environments (Figure 1 and Table 1). The high heritabilities for these traits demonstrate the 283 feasibility of breeding strategies for the improvement of sorghum for seed element accumulation. 284 Further, due to the known genetic contributors to trait covariation, selection strategies can 285 include alteration of multiple traits, enhancing phenotypic correlations between traits or counter 286 selection for undesirable traits (e.g. As accumulation). The high heritability and the relationships 287 we report between important elements, including Fe and Zn are encouraging for the development 288 of breeding schema for improved sorghum elemental profiles. 289

Trait correlations and covariation were used to uncover genetic associations for multiple 290 elements. Observed correlations of several elements indicate that changes in one or more 291 elements can simultaneously affect the concentration of other elements present in the seed 292 (Figure 2A). However, the individual effects of particular alleles can deviate from this pattern. 293 Even without more complicated analyses, we detected colocalized effects on several element 294 traits (Supplemental table 4 and Supplemental table 8). For example, several significant SNPs 295 colocalized for the strongly correlated element pairs of Ca and Sr (r= 0.79) as well as Mg and P 296 (r= 0.71). Shared SNPs and colocalization of significant loci across multiple element traits 297 suggest the possibility of tightly-linked genes or genes with pleiotropic effects and has been 298



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documented in recent GWA studies, including experiments in tomato (Sauvage et al., 2014) and 299 rice (Zhao et al., 2011). In the present analysis, we applied a conservative threshold in our 300 MLMM implementation and identified SNPs from the most complex model in which the p 301 values of cofactors were below a defined threshold of 0.05. We implemented stringent 302 parameters to eliminate false positives, but also risked the elimination of true positives. To 303 identify additional candidate SNPs for further investigation, these stringent parameters can be 304 relaxed to include association signals below the threshold. 305 306 307 Candidate genes 308

One of the primary goals of this study was to utilize GWA analyses to identify candidate 309 genes and novel loci implicated in the genetic regulation of sorghum seed element traits. We 310 identified numerous significant SNPs for all twenty element traits that currently do not associate 311 with known elemental accumulation genes. Although it is likely that a small fraction of these 312 SNPs are false positives, many more may be novel associations with as-yet undiscovered causal 313 genes and merit further investigation. We did, however, identify several significant SNPs that 314 fall directly within a characterized candidate gene or are in close proximity, or LD, with putative 315 candidates. 316 Zinc 317 Zn deficiency is a critical challenge for food crop production that results in decreased 318 yields and nutritional quality. Zn-enriched seeds result in better seedling vigor and higher stress 319 tolerance on Zn-deficient soils (Cakmak, 2008). Here we identify a strong candidate for genetic 320 improvement of Zn concentration in the sorghum seed, Sobic.007G064900, a homolog of 321 Arabidopsis ZIP5, Zn transporter precursor (AT1G05300) (Table 2). AT1G05300 is a member of 322 the ZIP family of metal transporter genes, and overexpression lines of this gene display increased 323 Zn accumulation in Arabidopsis (www.ionomicshub.org, 35SZip5_2 _Tray 700). 324 Manganese 325

Associated with amino acid, lipid and carbohydrate metabolism, Mn is one of the 326 essential elements critical to human and animal nutritional requirements (Aschner and Aschner, 327 2005). We identified significant genome-wide associations in the putative sorghum homolog for 328 a member of the metal transporter-encoding cation diffusion facilitator gene family, MTP11 329



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(Sobic.003G349200) (Table 2). The Arabidopsis homolog, AtMTP11, has been reported to 330 confer Mn tolerance and transport Mn2+ via a proton-antiport mechanism in Saccharomyces 331 cerevisiae (Delhaize et al., 2007). 332 Cadmium 333

The seeds are a major source of essential nutrients but can also be a source of toxic heavy 334 metals, including Cd. Contamination of ground water and subsequent uptake and absorption by 335 the plant can result in high levels of Cd contamination in the seed (Arao and Ae, 2003). GWA 336 analysis identified significant SNPs associated with a paralogous set of cation-transporting 337 ATPases (Figure 4), Sobic.002G083000 and Sobic.002G083100. These are sorghum homologs 338 of Arabidopsis HMA genes in the heavy metal–transporting subfamily of the P-type ATPases. 339 AtHMA3 participates in the vacuolar storage of Cd in Arabidopsis, and a recent study revealed 340 that HMA3 is a major-effect locus controlling natural variation in leaf Cd (Morel et al., 2009; 341 Chao et al., 2012). These SNP alleles could be used immediately to potentially produce sorghum 342 seed with lowered Cd2+ accumulation. 343 Nickel 344

Nickel (Ni) is an essential nutrient required for plant growth. However, similar to Cd, 345 high Ni concentrations in the soil can be toxic to the plant, resulting in reduced biomass and crop 346 yield. The most significant SNP for Ni concentration in the SAP 2008 environment (and present 347 in SAP 2012 and ranked dataset) was S6_53175238. This SNP is in LD with the candidate gene 348 Sobic.006G164300, a homolog of the Yellow Stripe-Like 3 (YSL) family of proteins (Table 2). 349 Originally identified in maize, the YSL proteins are a subfamily of transporters involved in 350 metal-chelate uptake, metal homeostasis and long-distance transport (Curie et al., 2009). YSL3 is 351 demonstrated to transport metals bound to nicotianamine (Curie et al., 2001) and, in the metal 352 hyperaccumulator Thlaspi caerulescens, YSL3 functions as a Ni–nicotianamine influx 353 transporter (Gendre et al., 2007). 354 355 Conclusion: 356

In the present study, we utilized GWA mapping and rank transformation of phenotypic 357 data to scale GxE interactions and identify a number of genetic loci and candidate gene 358 associations for immediate study and application to breeding strategies. The use of a multi-359 element, or ionomic approach, in the analysis allowed for the identification of SNPs that confer 360



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multiple advantageous traits that can be selected for in breeding programs. We identified co-361 localization of significant SNPs for different elements, indicating potential coregulation through 362 physiological processes of elemental uptake, transport, traffic and sequestration. Our results 363 suggest that combining elemental profiling with GWA approaches can be useful for 364 understanding the genetic loci underlying elemental accumulation and for improving the 365 nutritional content of sorghum. The data and analysis scripts used for this publication can be 366 found at www.ionomicshub.org. 367

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Materials and Methods: 370 Plant material 371 The SAP has been previously described (Casa et al., 2008). Seeds harvested from 407 lines that 372 comprise the SAP were utilized for this study. The SAP 2008 seeds were obtained from 373 Germplasm Resources Information Network (GRIN) and were produced in Lubbock, Texas by 374 the USDA-ARS Cropping Systems Research Laboratory in 2008 and 2009. The SAP 2012 seeds 375 were produced in Puerto Vallarta, Mexico in 2012. The SAP 2013 seeds were produced in 376 Florence, SC in 2013. 377 378 Phenotypic Elemental Analysis 379 Four seeds per replicate were weighed from each individual and a minimum of two replicates 380 from each line of the SAP 2008 and SAP 2013 panels were analyzed by ICP-MS. Each sample 381 was digested with 2.5 mL of concentrated nitric acid at 95°C for 3 hours. Elemental analysis 382 was performed with an ICP-MS for B, Na, Mg, Al, P, S, K, Ca, Mn, Fe, Co, Ni, Cu, Zn, As, Se, 383 Rb, Sr, Mo and Cd following established protocols (Baxter et al., 2010). A reference sample 384 derived from a pool of sorghum seed samples was generated and run after every 9th sample to 385 correct for ICP-MS run-to-run variation and within-run drift. 386 387 Data Processing and Analysis 388 Phenotype data were generated for 407 SAP lines. GBS SNP markers for the SAP lines used in 389 this study have been previously described (Morris et al., 2013). After removing SNPs with more 390 than 20% missing data and minor allele frequencies below 0.05, genotype data for 78,012 SNPs 391



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remained. Broad-sense heritability was calculated using the lmer function in the lme4 package to 392 perform an analysis of variance with the experimental replicates of the SAP using previously 393 described methods (Van Poecke et al., 2007; Bates et al., 2014). To ensure normality in the data 394 distribution of the phenotype, the Box-Cox procedure was carried out on the phenotype scores to 395 identify the best transformation method (Box and Cox, 1964). The ‘boxcox’ function in the 396 MASS package was utilized in R to carry out the transformations (R Development Core Team, 397 2014; Ripley et al., 2014). To address potential confounding factors in the GWA analysis, 398 specifically ICP run-to-run variation and the weight correction calculation, we used linear 399 regression to compute residuals adjusted for ICP run and sample weight. These residuals were 400 used to test for association with qualifying SNPs in the GWA analysis. 401 402 Genome-wide Association 403

GWA was executed in R with Genomic Association and Prediction Integrated Tool 404 (GAPIT) using CMLM (Zhang et al., 2010; Lipka et al., 2012). Significant associations were 405 determined by estimates of false discovery rate (FDR) (p = 0.05) (Benjamini and Hochberg, 406 1995). The CMLM uses a VanRaden kinship matrix and the first three principal components as 407 covariates to account for population structure. MLMM is based on EMMA (Kang et al., 2008) 408 and relies on the iterative use of a simple K, or Q+K, mixed model algorithm. The kinship term, 409 K, provides a fine-grained estimate of familial relatedness between lines. In addition, GWA 410 models often include a more granular measurement of population membership for each line, Q. 411 To determine the necessity of using the more complex Q+K model to control for spurious allele 412 associations, we analyzed QQ-plots generated from MLMM GWA using a simple K model plus 413 cofactors (Supplemental figure 5) and phenotypic distributions across known sorghum 414 subpopulations (Supplemental figure 2). Phenotypic distributions across subpopulations were 415 similar, suggesting that population structure does not play a strong role in elemental 416 accumulation. The QQ-plots show that after the addition of major-effect loci to the model as 417 cofactors, the p value distribution does not deviate drastically from the expected uniform 418 distribution. These results indicate that the mixed model containing only the kinship matrix, K, 419 plus cofactors is sufficient to control for spurious allele associations due to population structure 420 and cryptic relatedness. 421



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At each step of the MLMM, the phenotypic variance is divided into genetic, random and 422 explained variance. The most significant marker is included as a cofactor, and the variance 423 components of the model are recalculated. With each successive iteration, the remaining genetic 424 variance approaches zero, and an optimal model including cofactors that explains the genetic 425 fraction of the phenotypic variance is determined. The MLMM method selects two models using 426 stop criteria determined by two test statistics termed the mBonf and the extended Bayesian 427 information criterion (extBIC). The mBonf criterion selects a model wherein all cofactors have a 428 p value below a Bonferroni-corrected threshold (Segura et al., 2012) and, in our experiments, this 429 was the more stringent of the two model selection criteria (i.e. it favored less complex models) 430 and was used for all reported GWA analyses. We utilized a genome-wide significance threshold 431 of p < 0.05 for the Bonferroni correction. A kinship matrix was constructed to correct for 432 population structure and cryptic relatedness (Supplemental table 10). The kinship matrix was 433 estimated from all of the SNPs in the dataset using the VanRaden method (VanRaden, 2008) in 434 GAPIT (Lipka et al., 2012). Kinship was included as a random effect in the MLMM model. In 435 addition, the genetic variance partition described above provides an estimate of heritability, 436 termed pseudoheritability (Kang et al., 2010; Segura et al., 2012), explained by the model at each 437 step. The missing heritability can be calculated from the model at the optimal step (mBonf). The 438 percent variance explained by the model is the difference between the genetic variance at step 0 439 and the optimal step (Supplemental table 7). 440 441 Availability of supporting data 442 The datasets supporting the results of this article are available through Purdue Ionomics 443 Information Management System (PiiMS) at 444 http://www.ionomicshub.org/home/PiiMS/fileDownload?file=41 445 446 Supplemental Material 447 Supplemental figure 1: Seed weight and element concentration correlation 448 Supplemental figure 2: Element distribution across sorghum subpopulations 449 Supplemental figure 3: Correlation networks 450 Supplemental figure 4: PCA plots 451 Supplemental figure 5: Multiple Bonferroni QQ-plots 452



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Supplemental table 1: SAP lines 453 Supplemental table 2: Summary of one-way ANOVA 454 Supplemental table 3: Standard statistics for all SAP datasets 455 Supplemental table 4: Element trait correlations 456 Supplemental table 5: Significant SNPs 457 Supplemental table 6: CMLM Bonferroni SNPS 458 Supplemental table 7: Pseudo-heritability estimates for MLMM 459 Supplemental table 8: Shared significant SNPs across SAP datasets 460 Supplemental table 9: Significant SNPs shared for multiple elements 461 Supplemental table 10: SAP VanRaden kinship matrix 462 463 Acknowledgments 464 The authors would like to thank Todd Mockler for his support of NS during the final stages of 465 manuscript writing. For their contributions to sample preparation in the ionomics pipeline, we 466 also thank Kimberly Green and Janna Hutchinson. This project was partially funded by the 467 iHUB Visiting Scientist Program NSF DBI 0953433 to David Salt, Chromatin, Inc., NSF 468 EAGER 1450341 to IB and BPD, NSF IOS 1126950 to IB, NSF IOS 0919739 to ELC, and 469 BMGF OPP 1052924 to BPD. 470 471 Competing interests 472 The authors declare that they have no competing interests. 473 474 Authors’ contributions 475 NS, GZ and IB wrote the manuscript, carried out ionomics assays, data analysis and 476 interpretation of the results. ZB and RB contributed to experimental design and participated in 477 tissue sampling. BPD, ELC, and SK participated in data analysis and interpretation of the results. 478 All authors read, revised and approved the final manuscript. 479 480 Figure legends 481



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Figure 1 Box plots with median, minimum and maximum values, and interquartile ranges for the 482 20 elements in three SAP experimental populations. The raw concentration values for each of 483 the elements were log transformed to obtain normally distributed phenotypes. 484 485 Figure 2A Correlation network of seed element concentrations using rank average data 486 calculated across replicates from SAP association panels. Green solid lines represent positive 487 correlation values. Red dashed lines represent negative correlation values. Intensity and 488 thickness of lines indicate degree of correlation. Element correlation values can be found in 489 Supplemental table 4. Correlation networks for SAP 2008, SAP 2012, and SAP 2013 can be 490 found in Supplemental figure 3. 491 492 Figure 2B Principal component analysis applied to the rank average seed concentrations for 20 493 elements in the SAP lines across experiments. Each symbol represents a single element. PCA 494 analysis for SAP 2008, SAP 2012 and SAP 2013 can be found in Supplemental figure 4. 495 Outlined elements reflect clustering of known elemental relationships. 496 497 Figure 3 Heatmap displaying the Log P values of shared significant SNPs across 20 elements in 498 the rank average dataset. Significance values below 2 are white and the ranges from 2.01 to 9.01 499 are shown in green (light to dark green color). Outlined in red are biologically relevant SNPs that 500 colocalized for multiple elements. 501 502 Figure 4A Manhattan plot displaying Cd GWAS results (−log10(P)) for the 10 sorghum 503 chromosomes (x-axis) and associated p values for each marker (y-axis). The red lines indicate a 504 Bonferroni-corrected threshold of 0.05. 505 506 Figure 4B Quantile-quantile (QQ) of observed p values against the expected p values from the 507 GWAS analysis for Cd element concentration. The MLMM mixed model includes cofactors that 508 reduce inflation of p values (green line). The null model that does not consider significant 509 cofactors, indicating the presence of p value inflation (blue line). The red line indicates the 510 expected p value distribution under the null hypothesis. 511 512



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Figure 4C Evolution of genetic variance at each step of the MLMM (blue, genetic variance 513 explained; green, total genetic variance; red, error). The yellow line indicates the variance with 514 the inclusion of S2_8883293. The orange line indicates the optimal model selected by the 515 multiple bonferroni criterion (mBonf). 516 517 Figure 4D Allelic effect for the significant SNP marker on chromosome 2. 518 519 520 521 522 523 Literature Cited: 524 Amaducci S, Monti A, Venturi G (2004) Non-structural carbohydrates and fibre components in 525

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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 www.plantphysiol.orgon May 31, 2018 - Published by Downloaded from Copyright © 2016 American Society of Plant Biologists. All rights reserved.

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Casa AM, Pressoir G, Brown PJ, Mitchell SE, Rooney WL, Tuinstra MR, Franks CD, Kresovich S (2008) Community resources andstrategies for association mapping in sorghum. Crop science 48: 30-40


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Running Head: Genetic determinants of seed element ... · 6 124 the ionome provides significant insights into the networks underlying ion homeostasis beyond 125 single element studies