Prospects for Selecting Wheat with Increased Zinc

Published June 9, 2015

RESEARCH

Prospects for Selecting Wheat with Increased Zinc and Decreased Cadmium Concentration in Grain Mary J. Guttieri,* P. Stephen Baenziger, Katherine Frels, Brett Carver, Brian Arnall, Shichen Wang, Eduard Akhunov, and Brian M. Waters

Abstract Wheat (Triticum aestivum L.) is a primary staple cereal and significant source of mineral nutrients in human diets. Therefore, increasing concentration of the essential mineral, Zn, and decreasing concentration of the toxic mineral, Cd, could significantly improve human health. Because plant mechanisms for uptake and translocation of Cd and Zn are related, we assessed both Cd and Zn concentration to evaluate their independence in hard winter wheat germplasm. Grain Cd concentrations of some genotypes grown in Nebraska trials were above the Codex guidance level (0.2 mg kg –1), and highly repeatable differences in grain Cd were found between pairs of low and moderate-Cd commercial cultivars. Grain Cd concentration was predicted by Cd concentration in aboveground plant tissues at anthesis. However, grain Zn concentration was not predicted by Zn concentration in aboveground plant tissues. Genome-wide association scans using high-density single nucleotide polymorphism (SNP) markers identified Cdassociated SNPs on 5AL in a region homoeologous to the Cdu1 locus on 5BL in durum wheat (Triticum turgidum L. var. durum Desf.). Genetic regulation of grain cadmium concentration in bread wheat may be more complex than in durum wheat because epistatic interactions between SNP markers were identified, and SNP marker haplotypes were imperfect predictors of grain Cd phenotype. The SNP marker associations with Zn concentration were weak and inconsistent across trials, and Zn concentration was independent of 5AL markers. The independent genetic regulation of grain Cd and Zn concentrations indicates that breeding low Cd hard winter wheat genotypes without reducing Zn concentration has high potential for success.

1712

M.J.Guttieri, P.S. Baenziger, K. Frels, and B.M. Waters, Dep. of Agronomy and Horticulture, Univ. of Nebraska, Lincoln, NE 68583-0915. B. Carver and B. Arnall, Dep. of Plant and Soil Sciences, Oklahoma State Univ., Stillwater, OK 74078. S. Wang and E. Akhunov, Dep. of Plant Pathology, Kansas State Univ., Manhattan, KS 66506-5502. Contribution of the Nebraska Agricultural Experiment Station. Received 18 Aug. 2014. Accepted 14 Jan. 2015. *Corresponding author ([email protected]). Abbreviations: GWAS, genome-wide association scans; LD, linkage disequilibrium; MAF, minor allele frequency; QTL, quantitative trait loci; SNP, single nucleotide polymorphism.

W

heat is a primary staple cereal for human consumption, with 2011–2012 world production of nearly 700 Tg. Global per capita food consumption of wheat (67.7 kg, FAO, 2012) is the highest of any grain crop. Approximately 68% of the world wheat crop is used directly for food, and 21% is fed to livestock (FAO, 2012). People who rely on cereals as a staple food receive a significant proportion of their mineral nutrients from grains. An estimated 17.3% of the global population is at risk of inadequate dietary Zn intake, and the prevalence of inadequate Zn intake has been correlated with the prevalence of stunting in children under age five (Bhutta et al., 2012). In India, sub-Saharan Africa, and Southeast Asia, the prevalence of Zn deficiency is >25%. Therefore, increased Zn concentration in cereal grains is an important goal of some international crop improvement programs (Velu et al., 2012). Zinc is in the same group in the periodic table as Cd, and Zn 2+ and Cd 2+ have similar electronic outer structures, however Cd is toxic (reviewed by Järup and Åkesson, 2009; Nordberg, 2009). Therefore, increasing concentrations of Zn and decreasing

Published in Crop Sci. 55:1712–1728 (2015). doi: 10.2135/cropsci2014.08.0559 © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher. www.crops.org

crop science, vol. 55, july– august 2015

concentrations of Cd in edible plant parts could significantly improve human health (Khan et al., 2014). Nearly 27% of Cd dietary exposure (European Food Safety Authority, 2012) was contributed by grain and grain products. People who eat a diet proportionally higher in whole grains and vegetables have greater dietary Cd exposure (Berglund et al., 1994). The Codex Alimentarius Commission (2011) of the Food and Agriculture Organization/World Health Organization guidance level for wheat grain is 0.2 mg kg–1, and the European Community regulates Cd concentration of wheat and rice (Oryza sativa L.) at this level (EC No 1881/2006 3.2.13). Australia, New Zealand, and China have established maximum permissible Cd concentration in wheat of 0.1 mg kg–1 (Wang et al., 2012). Grain Cd concentration is of particular concern as consumer trends favor increasing consumption of whole wheat. Minerals are largely accumulated in the germ and bran of the wheat kernel, fractions that are typically removed on milling of grain into white flour (Iskander et al., 1987). In a study of commercially milled grain samples (Zook et al., 1970), Cd concentration in grain of five hard wheat blends averaged 0.10 mg kg–1; mean Cd concentration in commercially milled patent flour from these five grain samples was 0.05 mg kg–1. Similar results were obtained for Australian wheat flour (Oliver et al., 1993), with grain Cd concentrations from 0.005 to 0.035 mg kg–1, and flour Cd concentrations from 0.003 to 0.023 mg kg–1. Therefore the variation in grain Cd concentration among cultivars has increased dietary significance in whole grain food. Uptake of Zn and Cd from soil and distribution within the plant is a complex and dynamic process facilitated by a number of transporters (Waters and Sankaran, 2011; Khan et al., 2014). Specificity is a concern in biofortification efforts for Zn because up-regulation of pathways to increase Zn transport could inadvertently increase Cd transport (Palmgren et al., 2008; Sebastian and Prasad, 2014). Cadmium uptake and distribution throughout plants involves proteins that also transport Fe, Zn, and Mn (Nakanishi et al., 2006; Sasaki et al., 2012). A class of ATPases, known as the heavy metal ATPases (HMAs) play a central role in Cd transport in plants (reviewed by Takahashi et al., 2012). Heavy metal ATPases vary in their metal substrate specificity and several have affinity for both Zn and Cd. In rice, OsHMA3 is localized to the root tonoplast and sequesters Cd in the vacuoles of root cells (Ueno et al., 2010). Phytochelatins, non-ribosomally synthesized glutathione-derived polypeptides, form complexes with Cd and other heavy metals that are transported into vacuoles where they are sequestered (Clemens, 2006). Phytochelatin synthase (PCS) assembles the heavy metal-phytochelatin complexes, and concerted action of both PCS and an ATPase-type transporter crop science, vol. 55, july– august 2015 

appears to be required for vacuolar transport of Cd in barley (Hordeum vulgare L., Song et al., 2014). Moreover, phytochelatins also participate in Zn homeostasis. Because of these underlying physiological relationships for Zn and Cd, it is important to assess whether decreasing grain Cd concentration also decreases Zn concentration. Grain Cd concentration is increased in soils with high soil-available Cd concentration (Norvell et al., 2000; Adams et al., 2004; Wang et al., 2012). Cadmium-contaminated soils are a significant concern in some wheat-producing countries. Grain Cd concentrations as high as 0.69 mg kg–1 have been reported in grain from regions of Iran with high bioavailable Cd in the soil ( Jafarnejadi et al., 2011). In a study of Chinese soils conducted from 2005 to 2013, the Ministry of Environmental Protection and the Ministry of Land Resources in China reported that more than 19% of Chinese farmland is polluted, predominantly (83%) with heavy metals, with Cd among the most common contaminants. Soil Cd concentrations in China have risen 50% in the Southwest and coastal areas, and 10 to 40% in other regions since 1986 to 1990 (Wall Street Journal, Apr. 17 2014). In wheat samples from the Yangtze River Delta (Wang et al., 2012), 14.3% of samples exceeded 0.1 mg kg–1. To feed a growing world population, agricultural land must remain in production, therefore, wheat genotypes with low grain Cd accumulation will become increasingly important to producing safe food. The problem of high grain Cd was thought to be restricted to durum wheats (e.g., Wiebe et al., 2010), based on a 1965 survey of 11 wheat samples (Zook et al., 1970) that included five hard bread wheat samples, and on a survey of U.S. commercial wheat production across all wheat classes (Wolnik et al., 1983) that reported an average wheat Cd concentration of 0.043 mg kg–1. However, a maximum Cd concentration of 0.207 mg kg–1 was reported in this survey. In durum, high-Cd genotypes have increased translocation of Cd from roots to shoots compared to low Cd genotypes (Greger and Löfstedt, 2004; Harris and Taylor, 2001, 2004, 2013). Our survey of grain mineral concentrations in 299 hexaploid winter wheat genotypes reported high grain Cd concentrations (Guttieri et al., 2015). Grain Cd concentrations in Nebraska trials averaged 0.230 mg kg–1 in 2012 and 0.154 mg kg–1 in 2013, and grain Cd concentrations were as high as 0.580 mg kg–1 in 2012 and 0.347 mg kg–1 in 2013. Therefore, grain Cd concentration of adapted U.S. winter wheat germplasm grown in some environments can exceed the Codex guidance level of 0.2 mg kg–1. The present paper expands on our previous work by evaluating differences in Cd concentration within locally adapted winter wheat germplasm across a broader range of Nebraska trial environments. Association mapping (AM) has been used for mapping important traits in cereals, including quantitative trait loci

www.crops.org 1713

(QTL) associated with variation in grain As, Cu, Mo, and Zn in rice (Norton et al., 2014). In bread wheat, association panels have been used to identify loci that control agronomic traits (Bordes et al., 2014; Edae et al., 2014; Crossa et al., 2007; Neumann et al., 2011; Le Gouis et al., 2012; Bordes et al., 2013) and quality-related traits (Breseghello and Sorrells, 2006; Bordes et al., 2011; Plessis et al., 2013). The power of association mapping depends on genetic variance for the trait, marker density, and coverage of the genome. Based on the high heritability (H 2 = 0.791) of grain Cd concentration (Guttieri et al., 2015), we used genome-wide association analysis with high-density SNP markers from the wheat 90K iSelect assay to identify genomic regions associated with grain Cd and Zn concentrations. Here, we address several objectives related to reducing grain Cd concentration in hard winter wheat through breeding. First, we measured grain Cd and Zn concentration in pairs of locally-adapted commercial wheat cultivars with differential grain Cd concentration in trials grown across Nebraska to determine the repeatability of differences in grain Cd and Zn concentration in locally adapted genotypes. Second, we tested whether grain Cd and Zn concentrations in bread wheat could be predicted from aboveground plant tissue concentration, which would give breeders an early selection tool. Third, we identified genomic region(s) associated with grain Cd and Zn concentrations in bread wheat using genomewide association analysis, which indicated that Cd and Zn accumulation are controlled by different regions of the genome, and could lead to new markers to select for low Cd bread wheat while not decreasing Zn.

MATERIALS and METHODS Nebraska Statewide Sampling To evaluate the repeatability of differences in grain Cd concentration among locally-adapted commercial wheat cultivars, paired grain samples of moderate-Cd (cultivars Freeman and Wesley) and low-Cd (cultivars Overland and Panhandle) (Peterson et al., 2001; Baenziger et al., 2008, 2014) were obtained from yield trials routinely conducted by the University of Nebraska small grains breeding program, the University of Nebraska State Variety Testing program, and the USDA-ARS-Lincoln wheat genetics program. Grain samples were collected from six trials grown in 2012 and five trials grown in 2013 in locations across Nebraska (trials listed in Table 1). Trials were grown in eastern Nebraska (near Ithaca and Lincoln), South-central Nebraska (near Clay Center, McCook, and North Platte, and in western Nebraska (near Sidney and Alliance). Plot size, seeding rate, and production practices reflected standard practice for winter wheat yield testing in these environments. Grain was harvested with small plot combines and cleaned with forced air cleaners. Grain samples from field replicates were obtained from three of the trials (Ithaca_A, Alliance_R, and Alliance_I) to provide an error estimate for testing genotype ´ environment interaction. 1714

Table 1. Grain cadmium and zinc concentration in Panhandle, Freeman, Overland, and Wesley from 11 Nebraska yield trials. Data are presented as least squares adjusted mean ± standard error. Grain concentration Factor †

Cd

Year

Zn

———————— mg kg –1———————— Trial Clay Center Ithaca_A Ithaca_B Lincoln Clay Center Lincoln McCook North Platte Sidney Alliance_R Alliance_I Genotype Panhandle Freeman Overland Wesley Contrasts Panhandle vs. Freeman Overland vs. Wesley

2013 2012 2012 2013 2012 2012 2013 2012 2012 2013 2013

0.225 ± 0.022 0.171 ± 0.023 0.161 ± 0.021 0.149 ± 0.021 0.113 ± 0.021 0.101 ± 0.021 0.110 ± 0.022 0.083 ± 0.021 0.080 ± 0.024 0.051 ± 0.021 0.046 ± 0.021

21.7 ± 1.8 36.1 ± 1.9 34.3 ± 1.7 30.8 ± 1.7 32.9 ± 1.7 26.6 ± 1.7 31.9 ± 1.8 21.8 ± 1.7 18.3 ± 2.1 13.1 ± 1.7 45.2 ± 1.7

0.084 ± 0.018 28.1 ± 3.2 0.155 ± 0.018 25.9 ± 3.2 0.092 ± 0.018 28.8 ± 3.2 0.137 ± 0.017 30.9 ± 3.2 ———————— F value ———————— 33.0*** 3.2 ns‡ 16.9***

3.4 ns

*** Significant at the 0.001 probability level. †

‡

Two trials, grown on separate fields, were sampled in Ithaca in 2012 and in Alliance in 2013. The Alliance_R trial was rain-fed, and the Alliance_I trial was irrigated. ns, nonsignificant at the 0.05 probability level.

Grain Cd and Zn concentrations were measured in duplicate in 2-g samples of dried whole kernels (approximately 60 seeds). Kernels were weighed, then washed under suction with 0.4 mol L –1 HCl, then with ultra-pure water (E-pure, Barnstead, Dubuque, IA) to minimize potential external contamination. Samples were predigested overnight in 3.5 mL concentrated nitric acid (Optima, Fisher Chemical, Thermo Fisher Scientific Inc., Waltham, MA) then incubated 1 h at 40°C, then 1 h at 100°C. An additional 3.5 mL of nitric acid was added, and samples were incubated 1 h at 100°C. Hydrogen peroxide (4 mL 30% H 2O2 in water, Fisher BioReagents, Thermo Fisher Scientific Inc., Waltham, MA) was added and samples were incubated at 125°C for 1.5 h. An additional 4 mL of hydrogen peroxide was added, and samples were incubated 1.5 h at 125°C. Digests were completed by incubating 2 h at 150 and at 165°C to dryness. The residue was resuspended in 20 mL 1% nitric acid. Each digestion set (50 samples) included a reagent blank and National Bureau of Standards (NBS) reference flour (Standard Reference Material 1567a, National Bureau of Standards, Gaithersburg, MD). Cadmium and Zn concentrations were measured in duplicate in resuspended digests using an Agilent 7500cx inductively coupled plasma-mass spectrometry (ICP–MS) (Agilent Technologies Inc., Santa Clara, CA) with Ar carrier and a He collision cell at the University of Nebraska Redox Biology Center Proteomics and Metabolomics Core (Seravalli, 2012). A

www.crops.org

crop science, vol. 55, july– august 2015

50 g kg–1 gallium internal standard was added to each digested sample before analysis. Mineral concentrations were averaged across the duplicate injections, and reagent background (reagent blanks) was subtracted. Concentrations were adjusted for dilution and sample mass, and concentration was expressed in mg kg–1, dry weight basis.

of the limited genotype ´ environment interaction (Guttieri et al. 2015). Correlation analyses of Cd and Zn concentrations were conducted using adjusted genotype means with the rcorr function in the Hmisc package (Harrell, 2014) of R (R Core Team, 2014) or using PROC CORR in SAS. Regression analyses also were conducted in R using the lm function.

Prediction of Grain Concentration from Tissue Concentration

Genome-wide Association Scan

To assess the relationship between Cd and Zn concentration in aboveground plant tissues at anthesis and in mature grain, we used a set of genotypes from the hard winter wheat diversity panel with high, moderate, and low grain Cd accumulation. Samples from four field plots of the six selected genotypes (Panhandle, Freeman, Overland, Wesley, OK1068026, and Mit) were evaluated from trials grown in Ithaca, NE, trials in 2012 and 2013 (Guttieri et al. 2015). Plant tissue samples were harvested approximately 2 cm above the soil surface from 30 cm of two rows in 2012 and from 30 cm of one row in 2013. Samples were dried in a forced air dryer, and ground in a Model 4 Wiley Mill (Thomas Scientific, Swedesboro, NJ) to pass a 2-mm screen. Plant tissue was dried again at 70°C. Samples (0.25 g) were digested in 3 mL concentrated nitric acid overnight at room temperature in glass tubes, then heated at 100°C for 1.5 h under refluxing conditions. Hydrogen peroxide (3 mL) was added, and samples were incubated under refluxing conditions at 125°C for 1.5 h and 150°C for 2 h, then evaporated to dryness at 165°C. The dried residue was resuspended in 20 mL 1% (w/w) nitric acid. Each digestion set included a reagent blank and a NBS reference sample of peach leaves (Standard Reference Material 1547, National Bureau of Standards, Gaithersburg, MD). Duplicate plant tissue samples were digested, and Cd and Zn concentrations were measured in the resuspended digests by ICP–MS as described above. Zinc and Cd concentrations also were measured as described above in grain harvested at physiological maturity from these plots.

Statistical Analysis Effects of environment and genotype on grain Cd and Zn concentration among a set of four genotypes (Freeman, Overland, Panhandle, and Wesley) grown in trials across Nebraska in 2012 and 2013 were analyzed by mixed effects analysis of variance using PROC MIXED in SAS 9.3 (SAS Institute Inc., Cary, NC). Single-degree of freedom contrasts in PROC MIXED and the Wilcoxon signed rank test in PROC UNIVARIATE in SAS were used to test the significance of cultivar differences in Cd and Zn across trials. Grain and plant tissue Cd and Zn concentrations of the subset of six genotypes from both replications of the Nebraska diversity panel experiment in both years (four samples per environment) were analyzed using PROC GLIMMIX in SAS 9.3. For the analyses of grain Cd and Zn concentration in the complete diversity panel, genotypes were evaluated as fixed effects, and the genotypic least squares means for Cd and Zn concentration, adjusted for inter-replicate and inter-block information, were calculated using PROC MIXED per Guttieri et al. (2015). The 2012 and 2013 Nebraska trials were analyzed separately because of the significant genotype ´ environment interaction, while the 2012 and 2013 Oklahoma trials were combined because crop science, vol. 55, july– august 2015 

To identify genomic regions associated with grain Cd and Zn concentration, association analysis was conducted by coupling grain mineral concentrations from diversity panel trials grown in Nebraska and Oklahoma and 2012 and 2013 (Guttieri et al., 2015) with high-density SNP marker data. Mineral concentration data are available online at http://triticeaetoolbox.org/ wheat/. The test genotypes, their programs of origin, approximate release date, and pedigree are listed in Supplemental Information A. Wheat 90K iSelect (Wang et al., 2014) SNP data were determined for 286 genotypes and 21,943 polymorphic SNPs at the USDA-ARS Small Grains Genotyping Laboratory in Fargo, ND. For clarity of presentation in the text and figures, SNP markers are identified by the abbreviated SNP IDs. The corresponding iSelect assay SNP names from Wang et al. (2014) are listed in Supplemental B. The SNPs were filtered for minor allele frequency (MAF) > 0.05 and for missing genotype frequency 0.001 in the multi-location analysis were used to identify 5AL scaffolds in the wheat genome sequence (IWGSP1.22.dna_rm.toplevel. gz downloaded from http://plants.ensemble.org) by BLASTN queries. Predicted genes from the MIPS gene models for these scaffolds were obtained by querying the T. aestivum genome using http://plants.ensemble.org. Homology of the SNP markers with these predicted genes was verified using BLASTN. The SNPassociated 5AL scaffolds were aligned with the barley assembly for chromosome 5 (Hordeum_vulgare.030312v2.22.dna.chromosome5.fa.gz downloaded from http://plants.ensemble.org). Expressed sequence markers (ESTs) mapped to the wheat 5BL deletion bin 0.76 to 0.79 were obtained from the GrainGenes database (www.pw.wheat.usda.gov). The EST markers were aligned with the barley chromosome 5 genomic sequence to identify the syntenic positions of the ESTs. The EST markers also were aligned with the wheat genomic sequence to identify homologous and homoeologous scaffolds. Sequences orthologous to the Cd-selective rice heavy metal accumulator 3 gene (OsHMA3, gi|311692279, and gi311692275) were identified by BLASTN analysis using the tetraploid wheat transcriptome dataset developed by Krasileva et al. (2013) in the GrainGenes interface (http://wheat.pw.usda. gov/GG2/WheatTranscriptome/) and aligned with the barley genomic and cDNA sequence in the EnsemblePlants interface (http://plants.ensembl.org/Hordeum_vulgare/blastview). Wheat Vrn1A (gi|157382684) and CbfIIId-12 (EF028762.1) sequences were aligned with the barley genome sequence in the EnsemblePlants interface. The predicted barley phytochelatin synthase 2 protein coding sequence (PCS-201,-202, -203, -204) was located within the EnsemblePlants interface.

RESULTS Nebraska Statewide Sampling Averaged across cultivars, grain Cd concentration ranged from 0.046 to 0.225 mg kg–1, and Zn concentration ranged from 13.1 to 45.2 mg kg–1 in the 11 trials (Table 1). Grain Cd concentration was greatest in Clay Center 2013 and in the two Ithaca trials in 2012 (Table 1). Grain Cd concentration was lowest in Alliance and Sidney, which are in the major 1716

wheat-producing area of the Nebraska panhandle. Grain Zn and Cd concentrations were uncorrelated across the 11 trials (r = 0.07, p = 0.68). The effect of trial explained 59%, effects of cultivar explained 27%, and cultivar ´ trial interaction explained 10% of the total variance for grain Cd concentration. In comparison, the effect of trial explained 90%, effects of cultivar explained 4%, and cultivar ´ trial interaction explained 2.5% of total variance for grain Zn concentration. Differences in grain Cd concentration between pairs of well adapted commercial cultivars were reliable across a range of Nebraska production environments, but differences in Zn concentration between these cultivars were not reliable. The cultivars Panhandle and Overland had lower grain Cd concentration than Freeman and Wesley across all trials (Table 1). In all nine trials where the cultivars Wesley and Overland were grown together, the grain Cd concentration in Overland was less than Wesley (sign test p = 0.004). In all seven trials where the cultivars Panhandle and Freeman were grown together, the grain Cd concentration of Panhandle was less than Freeman (sign test p = 0.016). Comparisons of Cd concentrations between these pairs using single-degree of freedom contrasts were highly significant (p < 0.001; Table 1). However, Zn grain concentrations were not significantly different between these two pairs of genotypes.

Relationship between Plant Tissue and Grain Concentrations of Cadmium and Zinc Cadmium concentration in aboveground tissues at anthesis was significantly greater (F = 12.4, p = 0.02) in 2012 than in 2013 (Table 2), as was grain Cd concentration (F = 21.1, p = 0.01). Differences in Cd concentration in the 2 yr are consistent with lower soil pH (5.2 in 2012 and 6.2 in 2013), higher organic matter (3.0% in 2012 and 2.7% in 2013), and higher DTPA-extractable Cd in the top 25 cm of soil (0.265 mg kg–1 in 2012 and 0.160 mg kg–1 in 2013). Zinc concentration in aboveground plant tissues at anthesis was significantly greater in 2012 than in 2013 (F = 33.4, p = 0.004), as was grain Zn concentration (F = 146.9, p = 0.001). Soil Zn concentrations were 0.83 in 2012 and

www.crops.org

crop science, vol. 55, july– august 2015

0.97 mg kg–1 in 2013. The lower soil pH in 2012 may have increased availability of Zn throughout the growing season. Rankings of genotypes for Cd in plant tissue at anthesis were similar in the 2 yr: (Panhandle, Overland) < (Freeman, Wesley) < (OK1068026, Mit), and the Spearman rank correlation coefficient for 2012 and 2013 was significant (r = 0.89, p = 0.02). Cadmium concentration in grain of the six genotypes followed the pattern observed in the aboveground tissue samples at anthesis, and the Spearman rank correlation coefficient was significant (r = 0.89, p = 0.02). The Cd concentration in grain could be predicted (F = 11.7, p = 0.003; R 2 = 0.72) from Cd concentration in plant tissue with a common slope (0.60 ± 0.15), if different intercepts were fit for 2012 and 2013. In contrast to the consistent rankings of genotypes for Cd concentrations, Zn concentrations in aboveground tissues varied among genotypes, and the genotype rankings were uncorrelated between the 2 yr of the trial (r = 0.37, p = 0.47), although Mit had the highest tissue Zn concentrations in both years. Rankings of Zn concentrations in grain were also uncorrelated between the 2 yr (r = 0.37, p = 0.47). Zinc concentration in grain could be predicted from Zn concentration in aboveground tissue at anthesis in 2012 (F = 54.0, p < 0.001; R 2 = 0.93), but not in 2013 (F = 0.65, p = 0.47). The significant regression in 2012 was due to the high plant tissue and grain Zn concentrations of Mit, which were outliers in the data (Table 2). When Mit was excluded from the 2012 analysis, the regression was nonsignificant (F = 0.35, p = 0.60). Rankings of Zn concentrations in both plant tissues at anthesis and in grain were uncorrelated with rankings of Cd concentrations in plant tissues at anthesis in both years. Therefore Zn and Cd concentrations in both aboveground tissues and grain were independent in these genotypes.

Genome-Wide Association Scan Distributions of grain Cd and Zn concentrations in the 286 genotypes used for association analysis are shown in Fig. 1. Mean grain Cd concentrations ranked 2012 Nebraska > 2013 Nebraska > > Oklahoma (Fig. 1). Mean grain Zn concentrations ranked 2012 Nebraska > 2013 Nebraska = Oklahoma. Grain Cd concentration in the 2012 and 2013 Nebraska trials had high genotypic correlation (r = 0.71***, Table 3). Grain concentrations of Cd and Zn within each trial were weakly to moderately correlated (r = 0.27*** to 0.49***). Grain Zn concentrations between trials were weakly correlated (r = 0.15**–0.28***). Genome-wide association scans identified six significant SNP associations (q value  0.05) for grain Cd concentration in the 2012 Nebraska trial (Table 4). Five of the six SNPs map (Wang et al., 2014) to Chr 5A; the other SNP has not been mapped. In the 2013 Nebraska trial, GWAS identified 13 significant SNP associations with grain Cd concentration. Twelve of these SNPs map to 5A; crop science, vol. 55, july– august 2015 

one SNP is the same unmapped SNP from 2012. All six of the SNPs identified in 2012 also were identified in 2013. No SNPs had q value < 0.05 in the lower-Cd Oklahoma trials; however, the six most significantly associated SNPs, based on p values, were identical to the SNPs identified in the 2012 and 2013 Nebraska trials. In the multi-location analysis, eight SNP associations had q value < 0.05: seven of these SNPs map to 5A; one SNP is unmapped (Table 4, Fig. 2). The SNPs identified in the multi-location analysis also were identified in the 2013 Nebraska trial. In contrast to the GWAS results for Cd, we did not discover any marker associations for grain Zn concentration with q value < 0.05. Two SNP associations in the multi-location analysis had q value < 0.1 and allele substitution effects of 1.0 and 1.1 mg kg–1 (Table 5, Fig. 2). These SNPs map to chromosome 4B (Wang et al., 2014). We relaxed the criteria for significance further, to p < 0.001, to identify additional putative marker associations for Zn. With this relaxed criteria, IWB31407 was associated with Zn in both years of the Nebraska trial. This relaxed criteria identified 19 SNP associations on 4B in 2013, seven SNPs on 1B in 2012, and seven SNPs on 3A in Oklahoma trials. All significant Cd-associated SNPs (Table 4) were not significant for Zn (p > 0.01) in all analyses. Moreover, among Zn-associated SNPs in Table 5, only the 1B Znassociated SNPs in 2012 Nebraska had p values < 0.001 in the multi-location Cd GWAS. These 1B Zn-associated SNPs had p < 0.001 in the 2012 Nebraska Cd GWAS and p < 0.01 in the 2013 Nebraska Cd GWAS. Thus, the marker associations identified for Cd are independent of the marker associations for Zn.

Grain Cadmium Quantitative Trait Loci The linkage disequilibrium (LD, a cumulative measure of genetic linkage, selection, recombination, mutation, genetic drift, assortative mating, and population structure) relationships (D’ and r 2) for Cd-associated SNPs are included in Supplemental Information C. Among the 286 genotypes in the diversity panel, four SNPs (IWA1439, IWA7579, IWA6681, and IWA1752) were consistently associated with grain Cd concentration (Table 4) and were in complete LD, in that no recombination was observed between the SNP alleles. These four SNPs were in nearly complete LD with a fifth SNP, IWB43741, and only one genotype in the panel, Garrison, exhibited recombination. The additive effect (a) of the minor allele, associated with this high-Cd QTL designated QCdu.bmw-5A.1a, ranged from 0.0008 to 0.0259 mg kg–1, depending on environment, and QCdu.bmw-5A.1a was present in 29% of the genotypes in the panel (Table 6). The SNP marker IWB38719 was associated with Cd (q value < 0.05) in 2013 and in the multi-location analysis. This SNP maps (Wang et al., 2014) proximal to IWA1439.

www.crops.org 1717

Figure 1. Distribution of grain mineral concentrations (Cd and Zn) within the 286 genotypes used for association analysis in the hard winter wheat association mapping panel grown in Nebraska and Oklahoma in 2012 and 2013. Genotypes with mineral concentrations > 2 standard deviations above the mean are annotated. Expected distribution under normality is indicated by the curve. Table 3. Correlations of grain Cd and Zn concentrations in 286 genotypes of the hard winter wheat association mapp­ing panel grown in Nebraska 2012, Nebraska 2013, and Oklahoma (2012 and 2013 combined). Pearson correlation coefficient (r) Mineral, Environment Cadmium

Zinc

Cadmium Nebraska 2012

Nebraska 2013

–

0.71*** –

0.49*** –0.02 ns 0.08 ns

0.26*** 0.27*** 0.14*

Nebraska 2012 Nebraska 2013 Oklahoma Nebraska 2012 Nebraska 2013 Oklahoma

Zinc Oklahoma 0.46*** 0.44*** – 0.21*** 0.16** 0.32***

Nebraska 2012

Nebraska 2013

0.49*** 0.26*** 0.21*** –

–0.02 ns† 0.27*** 0.16** 0.22*** –

Oklahoma 0.08 ns 0.14* 0.32*** 0.15** 0.28*** –

* Significant at the 0.05 probability level. ** Significant at the 0.01 probability level. *** Significant at the 0.001 probability level. †

ns, nonsignificant.

1718

www.crops.org

crop science, vol. 55, july– august 2015

Table 4. SNP marker associations for grain Cd concentration identified by genome-wide association scan for 286 wheat genotypes in individual environments and across all environments. Chromosome assignments and map distances are from Wang et al. (2014). p Value† SNP

Chr

Map distance

IWB38719 IWA1439 IWA7579 IWA6681 IWA1752 IWB43741 IWB29981 IWB34418 IWA1829 IWA1797 IWB72425 IWB36340 IWB38760

5A 5A 5A 5A 5A 5A 5A 5A 5A 5A 5A 5A

88.70 89.02 89.02 89.02 89.02 89.02 89.56 89.56 89.56 89.56 89.56 89.56 –

†

‡

‡

2012 NE

2013 NE

Oklahoma

Multi-location

1.30 ´ 10 3.11 ´ 10 –6 2.03 ´ 10 –6 3.87 ´ 10 –6 7.69 ´ 10 –6

3.25 ´ 10 4.55 ´ 10 –6 7.90 ´ 10 –6 8.30 ´ 10 –6 1.63 ´ 10 –5 2.44 ´ 10 –5

8.77 ´ 10 1.10 ´ 10 –4 1.30 ´ 10 –4 1.07 ´ 10 –4 6.77 ´ 10 –5

9.70 ´ 10 –6 2.70 ´ 10 –7 5.94 ´ 10 –7 4.48 ´ 10 –7 1.04 ´ 10 –6 1.89 ´ 10 –6

3.83 ´ 10 –6

5.55 ´ 10 –6 5.05 ´ 10 –6 5.62 ´ 10 –6 7.58 ´ 10 –6 2.58 ´ 10 –5 1.49 ´ 10 –5 1.60 ´ 10 –7

–6

–6

–5

7.62 ´ 10 –6

4.77 ´ 10 –5

1.24 ´ 10 –7

p values are provided for SNPs significant for Cd with a q value  0.05, except for the Oklahoma trials in which no markers passed the false discovery test; p values are provided for the six most significant Cd markers in the Oklahoma trials. SNP not yet mapped to chromosome.

Figure 2. Manhattan plots of genome wide association scans for grain Cd and Zn concentration in a multi-environment analysis incorporating trials grown in Nebraska and Oklahoma in 2012 and 2013. For Cd, SNP positions with q value < 0.05 are indicated in green and the red significance line was established for this q value. For Zn, SNP positions with q value < 0.1 are indicated in green and the red significance line was established for this q value. UNK indicates markers not yet mapped. crop science, vol. 55, july– august 2015 

www.crops.org 1719

Table 5. Zinc SNP marker associations identified by genome-wide association scan for 286 wheat genotypes in individual environments and across all environments. The five most significant markers are presented in each column. Chromosome assignments and map distances are from Wang et al. (2014). p Value SNP IWB31407 IWA7566 IWB69938 IWB801 IWB7124 IWB18255 IWB3103 IWB11958 IWB36732 IWB6808 IWB35890 IWB11835 IWB5794 IWB11155 IWB65332 IWA4851 †

Chr

Map distane

4B 4B 4B 1B 1B

62.92 80.61 62.56 60.62 60.62 – – 106.55 106.55 106.55 106.55 101.00 101.00 101.00 – 105.67

§ §

1B 1B 1B 1B 3A 3A 3A §

3A

2012 NE

MAF†

2.80 ´ 10

0.20 0.14 0.17 0.10 0.10 0.15 0.35 0.08 0.08 0.08 0.08 0.19 0.20 0.20 0.20 0.20

–4

2013 NE

Oklahoma

1.65E-05 1.05 ´ 10 –4

Multi-Location 3.24E-06‡ 7.59E-06‡ 9.19E-05 2.14 ´ 10 –4 2.29 ´ 10 –4

4.23E-05

1.81 ´ 10 –4 1.83 ´ 10 –4 1.30 ´ 10 –4 1.34 ´ 10 –4 1.92 ´ 10 –4 3.44 ´ 10 –4 2.28 ´ 10 –4 4.25 ´ 10 –4 5.36 ´ 10 –4 5.69 ´ 10 –4 8.19 ´ 10 –4

MAF, minor allele frequency.

‡

q values  0.1.

§

Marker not yet mapped to chromosome.

Table 6. Substitution effects of SNP marker minor alleles on grain Cd concentration in each environment and in multi-environment genome-wide association scans. Map distances from Wang et al. (2014). SNP

MAF

†

p value

‡

IWB45593

0.09

8.07 ´ 10 –4

IWB38719 IWA1439 IWB34418 IWA1829 IWA1797 IWB36340 IWB9678 IWA2282 IWB38760

0.14 0.29 0.18 0.17 0.17 0.28 0.22 0.09 0.48

9.70 ´ 10 –6 2.70 ´ 10 –7 9.14 ´ 10 –5 6.89 ´ 10 –5 7.62 ´ 10 –6 2.38 ´ 10 –4 1.30 ´ 10 –4 4.38 ´ 10 –5 1.24 ´ 10 –7

†

MAF = minor allele frequency.

‡

Multi-location genome-wide association scan (GWAS).

§

Marker not yet mapped to chromosome.

5A Map distance

2012 NE

2013 NE

Oklahoma

Multi-Location

–––––––––––––––––––––––––mg Cd kg –1––––––––––––––––––––––––– –0.024 –0.018 –0.0006 –0.014 0.022 0.019 0.0006 0.014 0.024 0.015 0.0007 0.013 0.017 0.018 0.0005 0.011 0.018 0.018 0.0005 0.012 0.022 0.017 0.0006 0.013 0.013 0.014 0.0003 0.009 0.019 0.012 0.0006 0.010 0.030 0.021 0.0007 0.016 –0.023 –0.018 –0.0008 –0.014

49.03 88.70 89.02 89.56 89.56 89.56 89.56 89.96 105.99 §

IWB38719 and IWA1439 were in moderate LD (D’ = 0.83, r 2 = 0.29). The minor allele (MAF = 0.14) of IWB38719 had an effect (a) of 0.0008 to 0.0192 mg kg–1, depending on environment (Table 6). Across all environments, the high-Cd allele of IWB38719 did not affect the Cd concentration of genotypes in combination with the high-Cd allele of IWA1439, but the high-Cd allele of IWB38719 was associated with significantly increased grain Cd in combination with the low-Cd allele of IWA1439 (Table 7), which is an indication of epistasis. Four SNP markers (IWA1797, IWA1829, IWB34418, and IWB29981) were associated with Cd in 2013, and 1720

Allele substitution effect

IWA1797 was associated with Cd in the multi-location analysis (Table 4). These SNPs map distal to IWA1439 (Wang et al., 2014) and align to a single scaffold that contains two predicted genes (Traes_5AL_9C03C779C.1 and Traes_5AL_60CB3D93A.1). The minor allele of IWA1797 (MAF = 0.17) had an effect of 0.0006 to 0.022 mg kg–1, depending on environment (Table 6). Across all environments, alleles of IWA1797 did not affect the Cd concentration of genotypes in combination with either the high- or low-Cd allele of IWA1439 (Table 7). Therefore the significance of this set of SNPs likely results from their LD with the IWA1439 group of SNPs.

www.crops.org

crop science, vol. 55, july– august 2015

Table 7. Allele substitution effects of high-Cd SNP marker alleles in lines with low- and high-Cd marker genotypes of IWA1439 across all trial environments. Allele substitution effect High-Cd marker allele IWB38719 IWA1797 IWB36340 IWB9678 IWB45593 IWA2282 IWB38760

Low-Cd genotype IWA1439

High-Cd genotype IWA1439

–––––––––––––––––– mg Cd kg –1 –––––––––––––––––– 0.013* 0.001 ns† 0.006 ns 0.002 ns 0.002 ns 0.011*** –0.004 ns 0.005**

0.004 ns 0.001 ns 0.001 ns 0.008** 0.015 *** na‡

* Significant at the 0.05 probability level. ** Significant at the 0.01 probability level. *** Significant at the 0.001 probability level. †

ns, nonsignificant.

‡

na, not applicable.

The SNP markers IWB72425 and IWB36340 were associated with Cd (q value < 0.05) only in 2013 (Table 4). These SNPs did not recombine within the 286 genotypes in the panel. Both SNPs align to the same predicted gene (Traes_5AL_6C096F6E1.1). IWB36340 alleles had no effect in combination with either allele of IWA1439 (Table 7). Therefore the significance of this pair of SNPs likely results from their LD with the IWA1439 group of SNPs. Based on the important role of 5AL in grain Cd accumulation, we evaluated additional 5AL SNP markers that did not have significant q values (q < 0.05), but had p values < 0.001 in the multi-location GWAS. The SNP markers IWB9678 and IWA3085 align to separate scaffolds in the wheat genome assembly, but no recombination occurred between these SNPs within this panel. IWB9678 alleles had no effect in combination with either allele of IWA1439 (Table 7). Therefore the significance of this pair of SNP likely results from their LD with the IWA1439 group of SNPs. Three SNPs with p value < 0.001 in the multi-location analysis, IWB45593, IWB35587, IWB61122 (GWAS p = 4.7 × 10 –4 – 1.0 × 10 –3), align to the same predicted gene (Traes_5AL_1CCCD089A.2), and no recombination has occurred between these SNPs in this panel. These three SNPs map (Wang et al., 2014) proximal to IWB38719 and IWA1439. IWB45593 was in very weak LD (D’ = 0.16) and was uncorrelated (r 2 = 0.006) with IWA1439. Based on the independence of these three SNPs with QCdu.bmw-5A.1-associated SNPs, the QTL is designated QCdu.bmw-5A.2. The minor allele (MAF = 0.09) at QCdu.bmw-5A.2 was associated with lower grain Cd concentration (Table 6). The high-Cd major allele at QCdu. bmw-5A.2 was associated with greater grain Cd in combination with either allele of IWA1439 (Table 7). Therefore the rare, minor allele had a favorable association with reduced grain Cd concentration. crop science, vol. 55, july– august 2015 

The SNP marker IWA2282 had the greatest effect among the 5AL SNPs (Table 6), but because of the low frequency (MAF = 0.09) of the high-Cd allele, its statistical significance was less than other SNPs. This SNP did not have significant q values in any analysis, but had GWAS p value = 4.4 × 10 –5 in the multi-environment analysis. This SNP maps distal to IWB9678 (Wang et al., 2014), and was in moderate LD (D’ = 0.55) but was uncorrelated (r 2 = 0.07) with IWA1439. Based on the weak correlation of these three SNPs with QCdu.bmw-5A.1 and QCdu.bmw-5A.2-associated SNPs, the QTL is designated QCdu.bmw-5A.3. Alleles of IWA2282 had no effect in combination with the low-Cd allele of IWA1439, but the high-Cd allele of IWA2282 had substantial allele substitution effect (0.015 ± 0.003) in combination with the high-Cd allele of IWA1439 (Table 7), which is evidence of epistasis. Only 17 of the 271 genotypes scored for both SNPs had the high allele/high allele combination; but three of the eight highest grain Cd genotypes in each environment had the high allele/high allele combination. In Nebraska trials, the 17 high allele/high allele genotypes had a mean Cd concentration of 0.325 ± 0.030 mg kg–1 in 2012 and 0.193 ± 0.017 mg kg–1 in 2013, while the low allele/low allele genotypes had a mean Cd concentration of 0.216 ± 0.004 mg kg–1 in 2012 and 0.144 ± 0.003 mg kg–1 in 2013. The unmapped SNP that was consistently associated with Cd, IWB38760, aligns to scaffolds on 2DL, 4AL, 7AL, and two scaffolds on 5AL with e values < 1 × 10 –11. IWB38760 aligned with >75% identity with predicted cDNAs on 4AL, 6BL, 2DL, and 4AS. Additional data will be required to assign this SNP to a wheat chromosome. The minor allele of IWB38760 (MAF = 0.48) had an effect of –0.0006 to –0.0183, depending on environment. This allele affect may be due to the non-random association of IWB38760 alleles with IWA1439 alleles: all genotypes with the high-Cd allele of IWA1439 also had the high-Cd allele of IWB38760. Across all environments, mean Cd concentration in the 134 low allele/low allele genotypes was 0.121 ± 0.002 mg kg–1, while the mean concentration in 64 high IWB38760/low IWA1439 genotypes was 0.131 ± 0.003 mg kg–1, and mean Cd concentration in 79 high allele/high allele genotypes was 0.153 ± 0.003 mg kg–1. Multiple linear regression models that best predicted grain Cd concentration in each environment from the set of representative significant SNPs discussed above (IWA1439, IWB38719, IWA1797, IWB36340, IWB9678, IWB45593, IWA2282, and IWB38760) explained 19% of the variation in the two Nebraska environments and 12% of the variation in the Oklahoma environment (Table 8).

Synteny of Cadmium Quantitative Trait Loci with Barley The wheat 5AL scaffolds that contain the Cd-associated SNP markers align to barley chromosome 5 (5H, Fig. 3). The wheat SNP markers discussed above spanned the

www.crops.org 1721

Table 8. Optimal multiple marker regression models for grain Cd concentration in each environment. Estimates Model parameter Intercept SNP Marker† IWA1439 IWB45593 IWA2282 IWB38760 Adjusted R2 Model F

Nebraska 2012

Nebraska 2013

Oklahoma

———————————— mg kg –1 ———————————— 0.243 ± 0.008 0.148 ± 0.004 0.0145 ± 0.0003 0.025 ± 0.008 0.018 ± 0.006 0.021 ± 0.006 0.19 21.4***

0.014 ± 0.003 0.014 ± 0.004 0.007 ± 0.003 0.19 21.2***

0.0008 ± 0.0002 0.0007 ± 0.0002 0.0005 ± 0.0002 0.12 13.0***

*** Significant at the 0.001 probability level. †

Models developed with high- and low-Cd alleles for each SNP marker scored as 1 and –1, respectively.

region from 407.5 to 528.5 Mbp on 5H. The five scaffolds containing the IWA1439 group of five SNPs (QCdu.bmw5A.1) spanned a 310 Kbp region from 495.4 to 495.7 Mbp on 5H. The scaffold containing the IWB45593 group of three SNPs (QCdu.bmw-5A.2) aligned to 407.5 Mbp on 5H. The scaffold containing IWA2282 (QCdu.bmw-5A.3) aligned to 5H at 528.5 Mbp. The order of alignment of the SNP markers to 5H was consistent with the map data (Wang et al., 2014) and the LD observed in this panel of genotypes (Supplemental C).

Grain Zinc Quantitative Trait Loci Four of the five most significant SNPs associated with grain Zn concentration in 2012 map to chromosome 1B (Wang et al., 2014). These SNPs did not recombine among the genotypes in this panel and had a MAF of 8% and allele substitution effects of 2.6 to 2.7 mg kg–1 in Nebraska 2012. Three of the five most significant SNPs in the multilocation analysis and 2013 map to 4B (Wang et al., 2014). These SNPs had MAF of 14 to 20% and allele substitution effects of 1.3 to 1.4 mg kg–1 in 2013. Recombination was observed among all pairs of these SNPs. The two unmapped SNPs identified in 2013 were uncorrelated (r2 < 0.15) with all other mapped Zn-associated SNPs and had MAF of 15 and 35%, and allele substitution effects of 0.78 and 0.66 mg kg–1. Among the five most significant SNPs in the Oklahoma trials, four map to 3A (Wang et al., 2014), and one, IWB65332, is unmapped. Only one recombinant genotype (TX05A001188) was identified in the panel for this set of SNPs, which had a MAF of 0.2. The allele substitution effects of the five most significant Oklahoma SNPs ranged from 0.56 to 0.62 mg kg–1. The 1B SNPs among the five most significant SNPs in the multi-location analysis (IWB801 and IWB7124) did not recombine within the panel and had a minor allele frequency of 10% with allele substitution effects of 1.47 to 1.50 mg kg–1.

1722

DISCUSSION In North America, elevated Cd in wheat grain previously was thought to be a problem only in durum wheat, but we have demonstrated that bread wheat also can accumulate Cd above the Codex guidance level in some environments. In this work, we identified markers that are highly associated with grain Cd, but not with grain Zn. We also showed that Cd accumulation in paired genotypes was consistent across environments, but Zn accumulation was not, and that environment plays a major role in plant and grain Cd and Zn accumulation. We also determined that plant tissue Cd at anthesis is a reliable predictor of final grain Cd, giving breeders a tool that can be used for early selection. Importantly, we determined that grain Cd and Zn are not highly correlated, and that breeders can select for low Cd bread wheat without adversely affecting Zn concentration.

Genome-Wide Association Scan We applied a relatively conservative approach to the GWAS for Cd concentration, a highly heritable trait, by incorporating both population structure and kinship relationships in the analysis and by using q value < 0.05 as the test of significance. This conservative approach will have limited power to detect rare alleles and alleles with weak effects, and this approach will have limited power to detect multiple QTLs (Bradbury et al., 2011). Using this conservative structure + kinship approach, we identified a region of 5AL associated with grain Cd concentration. The sets of significant Cd-associated SNPs on chromosome 5AL were consistent across the individual environments and multi-location analysis, which was anticipated from the high heritability of grain Cd (Guttieri et al., 2015). To assess the potential for genetic structure within this group of genotypes that may have led to artificial marker association in the GWAS, we examined coefficients of the additive relationship matrix for the top 17 genotypes for grain Cd concentration in Nebraska (genotypes that were in the top 10% of the trial for Cd concentration in both the 2012 and 2013). Because only 5 of the 136 relationships between the 17 highest Cd genotypes shared greater than half-sib co-ancestry (Supplemental Information D), we conclude that there is not an unusual degree of genetic structure associated with the high-Cd phenotype. In durum wheat, grain Cd accumulation was associated with the Cdu1 locus, mapped to 5BL (Knox et al., 2009). The Cd-associated SNPs identified in our work are in a region of 5AL that is homoeologous to the region of 5BL containing Cdu1 in durum. To determine this homoeology, we aligned durum Cdu1-linked sequences with barley 5H. Durum Cdu1-linked SNPs mapped to deletion bin 5BL9 0.76 to 0.79 (Wiebe et al., 2010). The sequences for co-segregating ESTs for Cdu1, BF293297 and BF47090, aligned to 5H at 496.7 and 496.8 Mbp, amid the SNPs associated with grain Cd accumulation in our analyses

www.crops.org

crop science, vol. 55, july– august 2015

Figure 3. Alignment of SNP markers (in black) to chromosome 5 of barley. EST markers co-segregating with Cdu1 in durum (Wiebe et al. 2010) are shown in red. Barley phytochelatin synthase 2 (PCS2), rice heavy metal accumulator 3 (OsHMA3), and wheat Vrn1A and CbfIIId-12 are shown in blue. p values of selected SNPs in the multi-environment analysis are shown in green.

(Fig. 3). In durum, phytochrome-C and phytochelatin synthase 2 (PCS2) mapped 1.8 cM distal to Cdu1. Consistent with previous work in durum wheat, the predicted barley PCS2 aligned to 5H, distal to the Cdu1-linked EST markers and distal to the most strongly associated grain Cd SNPs in our analyses (Fig. 3). Triticum aestivum Xwg644, which encodes a half-height ABC transporter and is tightly linked to the Vrn-B1 locus, also mapped 1.5 cM distal to Cdu1. The T. aestivum VrnA1 sequence also aligned to 5H distal to the Cd-associated SNPs. In durum, a second, minor and additive QTL (QCdu.usw-B2) was identified 67 cM proximal to Cdu1, marked by CbfIIId-12 (Wiebe et al., 2010). The CbfIIId-12 coding sequence aligned to 5H at 464.8 Mbp. Additional Cd-associated SNPs we have identified, IWB38719 and IWB45593, align to 5H flanking this second 5BL durum QTL. Therefore, the 5AL SNP associations we have identified in this GWAS are likely homoeologous to the 5BL Cdu1 QTLs in durum. Research in durum has suggested that candidate genes could encode a metal chelator, phytochelatin synthase 2 (PCS2), or a metal transporter, heavy metal accumulator 3 (HMA3), or an ABC transporter (Wiebe et al., 2010; Harris and Taylor, 2013). A causal gene for the high-Cd phenotype in durum has yet to be identified, and regions of the rice crop science, vol. 55, july– august 2015 

and Brachypodium genomes that are collinear with the Cdu1 region of durum do not include obvious candidate genes (Wiebe et al., 2010). PCS2 is an unlikely candidate gene: synthesis of phytochelatin in roots of low and high-Cd accumulating isolines of durum was similar (Hart et al., 2006), and recombination between PCS2 and Cdu1 was observed (Wiebe et al., 2010). The ABC transporter tightly linked to VrnA1 also is an unlikely candidate gene because it also recombined with Cdu1 (Wiebe et al., 2010). The HMA3 from rice aligns to 5H at 560.16 Mbp, substantially distal to the scaffolds aligned with significant Cd accumulation SNPs. A set of five HMA3-homologous transcripts from the T. turgidum transcriptome (Krasileva et al., 2013) aligned well with T. aestivum scaffolds that have been assigned to 5BL, but not to scaffolds assigned to 5AL. While these results do not support the presence of an HMA3 gene in the 5AL QTL region of hard winter wheat, the 5AL assembly is not yet complete and the possibility of rearrangements in bread wheat relative to barley cannot be excluded. Ongoing work toward assembly and annotation of wheat genome (The International Wheat Genome Sequencing Consortium, 2014) will provide important understanding about the regulation of grain Cd concentration.

www.crops.org 1723

While our GWAS identified strong QTL, it did not explain all variation for grain Cd concentration. Among the 17 genotypes that were in the top 10% of the trial for Cd concentration in both the 2012 and 2013 Nebraska trials, five lack the high-Cd allele of IWA1439 (cultivars Ripper, TAM 112, Cossack, Wichita, and OK Bullet). Among the 13 genotypes that carried the high-Cd alleles at the four SNPs in the multiple regression models, two genotypes had grain Cd concentrations below the trial mean in both Nebraska trials (cultivars Judith and breeding line TX05A001188). Therefore, additional loci may influence the high-Cd phenotype. Studies in biparental mapping populations are needed to further characterize the genetics of high-Cd bread wheat. The conservative approach to GWAS did not identify any significant (q < 0.05) marker associations for grain Zn concentration, which had lower heritability (0.374) than Cd concentration (0.791, Guttieri et al., 2015). Power to detect QTL in GWAS is strongly affected by heritability and by the number of QTL controlling a trait (Bradbury et al., 2011). When we relaxed the criteria for significance and examined the five most significant SNPs in each environment, weak putative associations were identified on 1B, 3A, and 4B. The 4B associations we have identified may be related to previously reported 4B Zn QTLs (Genc et al., 2009; Xu et al., 2012). The GWAS results for Zn concentration varied in each environment, similar to the results from an association mapping study in rice (Norton et al., 2014). Because we did not identify consistent, significant marker associations for grain Zn concentration within Great Plains hard winter wheat, marker assisted selection for grain Zn concentration likely will be ineffective in this germplasm.

Nebraska Statewide Sampling Differences in grain Cd concentration between locally developed winter wheat cultivars were consistent across a range of Nebraska environments, consistent with the high heritability (H2 = 0.791) of Cd in the diversity panel (Guttieri et al., 2015). Trial environment was the most important source of variation for grain Cd and Zn concentration, with higher Cd soils producing higher Cd grain. Previous work in Great Britain (Adams et al., 2004) predicted grain Cd concentration as a function of increasing soil Cd concentration and decreasing soil pH, because soil adsorption of Cd increases with increased soil pH, reducing the solution concentration of Cd 2+ (Eriksson et al., 1996; Grant et al., 1998; Singh et al., 1995). Together, these results suggest that the accumulation of Cd in grain is amenable to selection in breeding programs using high-Cd trial environments. Moreover, among these locally adapted genotypes, grain Cd concentration was independent of grain Zn concentration, consistent with independent genetic regulation. Therefore low Cd wheat genotypes can be selected without reducing the concentration of Zn. 1724

To place the soils of the high-Cd Ithaca, NE, trial site into a global perspective, the DTPA-extractable Cd in these soils was comparable to the concentrations reported in the most highly polluted regions of the Khuzestan Province of Iran, along the Iraq border ( Jafarnejadi et al., 2013). The University of Nebraska farms at both Clay Center and Ithaca are located on land once used extensively by the U.S. military for munitions production and storage (Adams County Historical Society, 2008; EPA, 2009). In the Great Plains of the United States, elevated grain Cd may be of localized concern arising from WWII-era munitions manufacturing and related activities. The association of high-Cd grain with site history should be explored in subsequent surveys that incorporate soil analyses. The commodity grain merchandising system in the United States would effectively dilute high-Cd by aggregating grain at terminals and flour mills. Moreover, the dominant wheat producing area of the Nebraska panhandle produced the lowest Cd grain samples, a favorable finding for the wheat industry and for consumers.

Relationship between Plant Tissue Concentration and Grain Concentration of Cadmium and Zinc Because grain Cd concentration was predicted by above­ ground plant tissue concentration at anthesis in both years, wheat breeders could screen vegetative tissues of segregating populations to select for reduced grain Cd. Breeders also could accelerate breeding cycles by using vegetative tissue concentration in recurrent selection strategies for reduced grain Cd. The pathways of Cd translocation to grain are not well defined. In our results, concentration of Cd in plant tissues at anthesis averaged 160% of the final concentration in grain. In contrast, concentration of Zn in plant tissues at anthesis averaged 50% of the concentration in grain. This result is consistent with high remobilization of Zn from vegetative tissues in wheat (Waters et al., 2009; Guttieri et al., 2013) and lower phloem mobility for Cd than Zn in maturing wheat shoots (Riesen and Feller, 2005), and suggests that the translocation of these two metals to grain uses different mechanisms. Differences in grain Cd accumulation among durum genotypes has been related primarily to differences in translocation from root to shoot (Greger and Löfstedt, 2004; Harris and Taylor, 2013). Because durum roots sustain Cd uptake into roots during grain filling (Chan and Hale, 2004) and have little net remobilization from leaves, newly taken up Cd, rather than remobilized Cd, is the likely source of grain Cd (Harris and Taylor, 2013). This hypothesis is consistent with detection of radioactive Cd from hydroponic solution in rice panicles in 7 h, but not in leaves until 36 h (Fujimaki et al., 2010). Our results are consistent with previous work in durum wheat that has demonstrated that

www.crops.org

crop science, vol. 55, july– august 2015

high grain Cd is associated with high Cd concentration in aboveground plant tissues (Greger and Löfstedt, 2004; Chan and Hale, 2004; Harris and Taylor, 2013, Hart et al., 2005). Whether the genotypic differences in aboveground plant Cd concentration at anthesis are due to differential uptake, root sequestration, or translocation remains to be experimentally determined.

Implications for Breeding Our results provide some basis for future selection of breeding germplasm within the Great Plains germplasm pool. TAM 111, for example, consistently produced highCd grain, and TAM 111 was a parent of some of the highest Cd genotypes in the diversity panel. TAM 111 was the most widely grown wheat variety in Nebraska in 2012 (NASS, 2012) and was either the first or second most widely grown wheat variety in Kansas in 2010 to 2014 (NASS, 2014), and the most widely grown wheat variety in Texas in 2014 (TAMU, 2014). TAM 111 has been identified as a parent of entries in the Southern Regional Performance Nursery each year since 2010. The regional wheat industry may consider incorporating grain Cd evaluation from high-Cd trial sites into the regional variety testing system to enable breeders to identify and eliminate high-Cd genotypes. Grain Cd and Zn concentration appear to be independently regulated within Great Plains winter wheat. All significant Cd-associated SNPs (Table 4) had p > 0.01 for Zn in all analyses, and only one weak association of grain Cd concentration with a Zn-associated SNP was identified. The GWAS results support the hypothesis that grain Cd and Zn concentrations are controlled by different regions of the genome. Therefore breeding for reduced grain Cd concentration should not affect grain Zn concentration. Both Cd and Zn are measured simultaneously in ICP–MS, which will facilitate joint selection. Within the hard winter wheat diversity panel, several examples point to the importance of joint selection for both Cd and Zn. Desirable phenotypes were identified: Siouxland, for example, had grain Zn concentration in the top 5% of the panel in both Nebraska environments, but grain Cd concentration in both Nebraska environments was similar to the panel mean. Undesirable phenotypes also were identified: TAM 111 was ranked in the top 10 lines for Cd in each environment, but only ranked as high as 90th for Zn; OK1068026, ranked third and first for Cd concentration in 2012 and 2013 in Nebraska, only ranked 279th and 98th for Zn concentration in these trials. And two consistently highZn genotypes also had high grain Cd concentrations: the only genotype in the panel to have Zn concentration in the top 10% of the panel in all three environments, TX05V7259, also had grain Cd concentration in the top 10% of the panel in all three environments; and TAMW-101 had grain Zn concentration in the top 10% of the panel in both Nebraska crop science, vol. 55, july– august 2015 

environments, and had grain Cd concentration in the top 10% of the panel in these environments as well. Crop improvement programs with focused efforts toward breeding high Zn wheat genotypes for these regions should be able to produce high-Zn/low-Cd genotypes. However, possible pleiotropic effects of introgression of elevated Zn traits from T. spelta, T. dicoccon ´ Ae. Tauschii-derived synthetics and other alien sources (Velu et al., 2012; Neelam et al., 2011, 2012; Rawat et al., 2011; Tiwari et al., 2010) may not be well predicted by the germplasm used in our study, which did not include those introgressions. Therefore the effects of these introgressions on grain Cd concentration should be measured in wheat grown on high-Cd trial sites.

CONCLUSIONS The SNP marker associations identified in the hard winter wheat AM panel provide a starting point for selecting low Cd bread wheat. The most common QTL for elevated Cd was present in 29% of the genotypes in the AM panel. Based on the syntenic relationships with barley, these 5AL SNPs identify a region homoeologous to the region of 5BL containing two QTLs associated with the important Cdu1 locus in durum wheat. Epistatic interactions among QTLs on 5AL suggest that the trait may be complex. Although the 92K iSelect assay from which the SNP data were generated would be cost-prohibitive for marker assisted selection in breeding programs, key SNP assays could developed from our data and included in smaller, less costly arrays or converted to other more cost-effective marker platforms. This marker conversion is underway in our laboratory. Identification of the causal gene within this 5AL QTL region will give a better biological understanding of Cd accumulation in seeds. Mapping grain Cd concentration in biparental populations also will provide important information on the additional genes that contribute to the genetic architecture of grain Cd in hexaploid bread wheat.

Supplemental Information Available Supplemental information is available with the online version of this manuscript. Acknowledgments This research was supported by the USDA-NIFA Triticeae Coordinated Agricultural Project, 2011-68002-30029. We are grateful for the cooperation of Dr. Javier Seravalli and the University of Nebraska Redox Biology Center Proteomics and Metabolomics Core for the ICP-MS analyses, and to Dr. Shiaoman Chao of the USDA-ARS Regional Genotyping Laboratory for the iSelect assays. This research utilized the high performance computing cluster at the Holland Computing Center at the University of Nebraska. We also acknowledge the technical assistance of Hazal Canisag, Lega Dolicho, Amy Hauver, Madison Hergenrader, Melinda Knuth, and Carter Westerhold.

www.crops.org 1725

References Adams County Historical Society. 2008. WWII Naval ammunitions depot. Adams County Historical Soc. http://incolor.inetnebr.com/achs/NAD/home.html (accessed 4 May 2014. Adams, M., F. Zhao, S. McGrath, F. Nicholson, and B. Chambers. 2004. Predicting cadmium concentrations in wheat and barley grain using soil properties. J. Environ. Qual. 33:532–541. doi:10.2134/jeq2004.5320 Baenziger, P.S., B. Beecher, R.A. Graybosch, A.M.H. Ibrahim, D.D. Baltensperger, L.A. Nelson et al. 2008. Registration of ‘NE01643’ wheat. J. Plant Reg. 2:36–42. doi:10.3198/ jpr2007.06.0327crc Baenziger, P.S., R.A. Graybosch, T. Regassa, R.N. Klein, G.R. Kruger, D.K. Santra et al. 2014. Registration of ‘NE06545’ (Husker Genetics Brand Freeman) Hard Red Winter Wheat. J. Plant Reg. 8:279–284. doi:10.3198/jpr2014.02.0009crc Beal, D.J. 2005. SA01-05. SAS code to select the best multiple linear regression model for multivariate data using information criteria. In: SESUG 2005: The Proceedings of the SouthEast SAS Users Group, Portsmouth, VA. 23–25 October. Inst. for Advanced Analytics. http://analytics.ncsu.edu/?page_id=208 (accessed 23 May 2014). Berglund, M., A. Akesson, B. Nermell, and M. Vahter. 1994. Intestinal-absorption of dietary-cadmium in women depends on body iron stores and fiber intake. Environ. Health Perspect. 102:1058–1066. doi:10.1289/ehp.941021058 Bhutta, Z.A., K.R. Wessells, and K.H. Brown. 2012. Estimating the global prevalence of zinc deficiency: Results based on zinc availability in national food supplies and the prevalence of stunting. PLoS ONE 7:e50568. doi:10.1371/journal.pone.0050568 Bordes, J., E. Goudemand, L. Duchalais, L. Chevarin, F.X. Oury, E. Heumez et al. 2014. Genome-wide association mapping of three important traits using bread wheat elite breeding populations. Mol. Breed. 33:755–768. doi:10.1007/s11032-0130004-0 Bordes, J., C. Ravel, J.P. Jaubertie, B. Duperrier, O. Gardet, E. Heumez et al. 2013. Genomic regions associated with the nitrogen limitation response revealed in a global wheat core collection. Theor. Appl. Genet. 126:805–822. doi:10.1007/s00122-0122019-z Bordes, J., C. Ravel, J. Le Gouis, A. Lapierre, G. Charmet, and F. Balfourier. 2011. Use of a global wheat core collection for association analysis of flour and dough quality traits. J. Cereal Sci. 54:137–147. doi:10.1016/j.jcs.2011.03.004 Bradbury, P., T. Parker, M.T. Hamblin, and J. Jannink. 2011. Assessment of power and false discovery rate in genome-wide association studies using the BarleyCAP germplasm. Crop Sci. 51:52–59. doi:10.2135/cropsci2010.02.0064 Bradbury, P.J., Z. Zhang, D.E. Kroon, T.M. Casstevens, Y. Ramdoss, and E.S. Buckler. 2007. TASSEL: Software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633–2635. doi:10.1093/bioinformatics/btm308 Breseghello, F., and M. Sorrells. 2006. Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars. Genetics 172:1165–1177. doi:10.1534/genetics.105.044586 Chan, D., and B. Hale. 2004. Differential accumulation of Cd in durum wheat cultivars: Uptake and retranslocation as sources of variation. J. Exp. Bot. 55:2571–2579. doi:10.1093/jxb/erh255 Clemens, S. 2006. Evolution and function of phytochelatin synthases. J. Plant Physiol. 163:319–332. doi:10.1016/j.jplph.2005.11.010

1726

Codex Alimentarus Commission. 2011. Joint FAO/WHO Food Standards Program: Codex Committee on Contaminants in Food. Fifth Session, 21–25 Mar. 2011. Codex Alimentarius Commission, The Hague, the Netherlands. Crossa, J., J. Burgueno, S. Dreisigacker, M. Vargas, S.A. HerreraFoessel, M. Lillemo et al. 2007. Association analysis of historical bread wheat germplasm using additive genetic covariance of relatives and population structure. Genetics 177:1889–1913. doi:10.1534/genetics.107.078659 Dabney, A., and J.D. Storey. 2014. qvalue: Q-value estimation for false discovery rate control. R package version 1.38.0. Fred Hutchinson Cancer Res. Ctr. http://www.bioconductor.org/ packages/release/bioc/html/qvalue.html (accessed 3 May 2014). Edae, E.A., P.F. Byrne, S.D. Haley, M.S. Lopes, and M.P. Reynolds. 2014. Genome-wide association mapping of yield and yield components of spring wheat under contrasting moisture regimes. Theor. Appl. Genet. 127:791–807. doi:10.1007/ s00122-013-2257-8 Endelman, J.B. 2011. Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Gen. 4:250– 255. doi:10.3835/plantgenome2011.08.0024 Endelman, J.B., and J.-L. Jannink. 2012. Shrinkage estimation of the realized relationship matrix. G3:Genes, Genomes. Genetics 2:1405–1413. doi:10.1534/g3.112.004259. EPA. 2009. Former Nebraska ordnance plant. Nebraska ID# NE6211890011. USEPA. http://www.epa.gov/region7/ cleanup/npl_files/ne6211890011.pdf (accessed 4 May 2014. Eriksson, J., I. Oborn, G. Jansson, and A. Andersson. 1996. Factors influencing cd-content in crops- results from Swedish field investigations. Swed. J. Agric. Res. 26:125–133. European Food Safety Authority. 2012. Cadmium dietary exposure in the European population. EFSA J. 10(1):2551. FAO. 2012. Food outlook: Global market analysis. November 2012. FAO of the United Nations. www.fao.org/docrep/016/al993e/ al993e00.pdf (accessed 4 May 2014).. Fujimaki, S., N. Suzui, N.S. Ishioka, N. Kawachi, S. Ito, M. Chino, and S. Nakamura. 2010. Tracing cadmium from culture to spikelet: Noninvasive imaging and quantitative characterization of absorption, transport, and accumulation of cadmium in an intact rice plant. Plant Physiol. 152:1796–1806. doi:10.1104/ pp.109.151035 Genc, Y., A.P. Verbyla, A.A. Torun, I. Cakmak, K. Willsmore, H. Wallwork, and G.K. McDonald. 2009. Quantitative trait loci analysis of zinc efficiency and grain zinc concentration in wheat using whole genome average interval mapping. Plant Soil 314:49–66. doi:10.1007/s11104-008-9704-3 Grant, C., W. Buckley, L. Bailey, and F. Selles. 1998. Cadmium accumulation in crops. Can. J. Plant Sci. 78:1–17. doi:10.4141/ P96-100 Greger, M., and M. Löfstedt. 2004. Comparison of uptake and distribution of cadmium in different cultivars of bread and durum wheat. Crop Sci. 44:501–507. doi:10.2135/cropsci2004.5010 Guttieri, M.J., P.S. Baenziger, K. Frels, B. Carver, B. Arnall, and B.M. Waters. 2015. Variation for Grain Mineral Concentration in a Diversity Panel of Current and Historical Great Plains Hard Winter Wheat Germplasm. Crop Sci. 55: (in press). Guttieri, M.J., R.J. Stein, and B.M. Waters. 2013. Nutrient partitioning and grain yield of Ta-NAM-RNAi wheat under abiotic stress. Plant Soil 371:573–591. doi:10.1007/s11104-013-1713-1 Harrell, F.E., Jr. with contributions from Charles Dupont and many others. 2014. Hmisc: Harrell Miscellaneous. R package version

www.crops.org

crop science, vol. 55, july– august 2015

3.14-4. The Comprehensive R Archive Network. http:// CRAN.R-project.org/package=Hmisc (accessed 14 Apr. 2014). Harris, N., and G. Taylor. 2001. Remobilization of cadmium in maturing shoots of near isogenic lines of durum wheat that differ in grain cadmium accumulation. J. Exp. Bot. 52:1473–1481. doi:10.1093/jexbot/52.360.1473 Harris, N.S., and G.J. Taylor. 2004. Cadmium uptake and translocation in seedlings of near isogenic lines of durum wheat that differ in grain cadmium accumulation. BMC Plant Biol. 4:4. doi:10.1186/1471-2229-4-4 Harris, N.S., and G.J. Taylor. 2013. Cadmium uptake and partitioning in durum wheat during grain filling. BMC Plant Biol. 13:103. doi:10.1186/1471-2229-13-103 Hart, J.J., R.M. Welch, W.A. Norvell, J.M. Clarke, and L.V. Kochian. 2005. Zinc effects on cadmium accumulation and partitioning in near-isogenic lines of durum wheat that differ in grain cadmium concentration. New Phytol. 167:391–401. Hart, J.J., R.M. Welch, W.A. Norvell, and L.V. Kochian. 2006. Characterization of cadmium uptake, translocation, and storage in near-isogenic lines of durum wheat that differ in grain cadmium concentration. New Phytol. 172:261–271. International Wheat Genome Sequencing Consortium. 2014. A chromosome-based draft sequence of the hexaploid bread wheat (Triticum aestivum) genome. Science (Washington, DC) 345(6194):1251788. doi:10.1126/science.1251788 Iskander, F., M. Morad, D. Klein, and T. Bauer. 1987. Determination of protein and 11 elements in 6-milling fractions of 2-wheat varieties. Cereal Chem. 64:285–287. Jafarnejadi, A.R., M. Homaee, G. Sayyad, and M. Bybordi. 2011. Large scale spatial variability of accumulated cadmium in the wheat farm grains. Soil Sed. Contam. 20:98–113. Jafarnejadi, A.R., G. Sayyad, M. Homaee, and A.H. Davamei. 2013. Spatial variability of soil total and DTPA-extractable cadmium caused by long-term application of phosphate fertilizers, crop rotation, and soil characteristics. Environ. Monit. Assess. 185:4087–4096. doi:10.1007/s10661-012-2851-2 Järup, L., and A. Åkesson. 2009. Current status of cadmium as an environmental health problem. Toxicol. Appl. Pharmacol. 238:201–208. doi:10.1016/j.taap.2009.04.020 Kang, H.M., N.A. Zaitlen, C.M. Wade, A. Kirby, D. Heckerman, M.J. Daly, and E. Eskin. 2008. Efficient control of population structure in model organism association mapping. Genetics 178:1709–1723. doi:10.1534/genetics.107.080101 Khan, M.A., N. Castro-Guerrero, and D.G. Mendoza-Cozatl. 2014. Moving toward a precise nutrition: Preferential loading of seeds with essential nutrients over non-essential toxic elements. Front. Plant Sci. 5:51. doi:10.1016/j.plantsci.2014.01.004 Knox, R.E., C.J. Pozniak, F.R. Clarke, J.M. Clarke, S. Houshmand, and A.K. Singh. 2009. Chromosomal location of the cadmium uptake gene (Cdu1) in durum wheat. Genome 52:741–747. doi:10.1139/G09-042 Krasileva, K.V., V. Buffalo, P. Bailey, S. Pearce, M. Soria, F. Tabbita, C. Uauy, International Wheat Genome Sequencing Consortium, and J. Dubcovsky. 2013. Separating homeologs by phasing in the tetraploid wheat transcriptome. Genome Biol. 14(6):R66. doi:10.1186/gb-2013-14-6-r66 Le Gouis, J., J. Bordes, C. Ravel, E. Heumez, S. Faure, S. Praud, N. Galic et al. 2012. Genome-wide association analysis to identify chromosomal regions determining components of earliness in wheat. Theor. Appl. Genet. 124:597–611. doi:10.1007/s00122011-1732-3

crop science, vol. 55, july– august 2015 

Nakanishi, H., I. Ogawa, Y. Ishimaru, S. Mori, and N.K. Nishizawa. 2006. Iron deficiency enhances cadmium uptake and translocation mediated by the Fe2+ transporters OsIRT1 and OsIRT2 in rice. Soil Sci. Plant Nutr. 52:464–469. doi:10.1111/j.17470765.2006.00055.x NASS. 2012. Nebraska wheat varieties. Released March 2012. National Agric. Statistics Serv., Agric. Statistics Board, USDA. http://www.nass.usda.gov/Statistics_by_State/Nebraska/Publications/Crop_Variety_Reports/whet2012.pdf. (accessed 15 Dec. 2014). NASS. 2014. Wheat varieties. Released 20 Feb. 2014. National Agric. Statistics Serv., Agric. Statistics Board, USDA. http:// www.nass.usda.gov/Statistics_by_State/Kansas/Publications/ Crops/Whtvar/whtvar14.pdf (accessed 15 Dec. 2014). Neelam, K., N. Rawat, V.K. Tiwari, S. Kumar, P. Chhuneja, K. Singh, G.S. Randhawa, and H.S. Dhaliwal. 2011. Introgression of group 4 and 7 chromosomes of Ae. peregrina in wheat enhances grain iron and zinc density. Mol. Breed. 28:623–634. doi:10.1007/s11032-010-9514-1 Neelam, K., N. Rawat, V.K. Tiwari, R. Prasad, S.K. Tripathi, G.S. Randhawa, and H.S. Dhaliwal. 2012. Evaluation and identification of wheat-Aegilops addition lines controlling high grain iron and zinc concentration and mugineic acid production. Cereal Res. Commun. 40:53–61. doi:10.1556/CRC.40.2012.1.7 Neumann, K., B. Kobiljski, S. Dencic, R.K. Varshney, and A. Boerner. 2011. Genome-wide association mapping: A case study in bread wheat (Triticum aestivum L.). Mol. Breed. 27:37– 58. doi:10.1007/s11032-010-9411-7 Nordberg, G.F. 2009. Historical perspectives on cadmium toxicology. Toxicol. Appl. Pharmacol. 238:192–200. doi:10.1016/j. taap.2009.03.015 Norton, G.J., A. Douglas, B. Lahner, E. Yakubova, M.L. Guerinot, S.R.M. Pinson et al. 2014. Genome wide association mapping of grain arsenic, copper, molybdenum and zinc in rice (Oryza sativa L.) grown at four international field sites. PLoS ONE 9:e89685. doi:10.1371/journal.pone.0089685 Norvell, W., J. Wu, D. Hopkins, and R. Welch. 2000. Association of cadmium in durum wheat grain with soil chloride and chelateextractable soil cadmium. Soil Sci. Soc. Am. J. 64:2162–2168. doi:10.2136/sssaj2000.6462162x Oliver, D., P. Gore, H. Moss, and K. Tiller. 1993. Cadmium in wheat-grain and milling products from some Australian flour mills. Aust. J. Agric. Res. 44:1–11. doi:10.1071/AR9930001 Palmgren, M.G., S. Clemens, L.E. Williams, U. Kraemer, S. Borg, J.K. Schjorring, and D. Sanders. 2008. Zinc biofortification of cereals: Problems and solutions. Trends Plant Sci. 13:464–473. doi:10.1016/j.tplants.2008.06.005 Peterson, C., D. Shelton, P. Baenziger, D. Baltensperger, R. Graybosch, W. Worrall et al. 2001. Registration of ‘wesley’ wheat. Crop Sci. 41:260–261. doi:10.2135/cropsci2001.411260-ax Plessis, A., C. Ravel, J. Bordes, F. Balfourier, and P. Martre. 2013. Association study of wheat grain protein composition reveals that gliadin and glutenin composition are trans-regulated by different chromosome regions. J. Exp. Bot. 64:3627–3644. doi:10.1093/jxb/ert188 R Core Team. 2014. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/ (accessed 14 Apr. 2014). Rawat, N., K. Neelam, V.K. Tiwari, G.S. Randhawa, B. Friebe, B.S. Gill, and H.S. Dhaliwal. 2011. Development and molecular

www.crops.org 1727

characterization of wheat- Aegilops kotschyi addition and substitution lines with high grain protein, iron, and zinc. Genome 54:943–953. doi:10.1139/g11-059 Riesen, O., and U. Feller. 2005. Redistribution of nickel, cobalt, manganese, zinc, and cadmium via the phloem in young and maturing wheat. J. Plant Nutr. 28:421–430. doi:10.1081/PLN200049153 Sasaki, A., N. Yamaji, K. Yokosho, and J.F. Ma. 2012. Nramp5 is a major transporter responsible for manganese and cadmium uptake in rice. Plant Cell 24:2155–2167. doi:10.1105/ tpc.112.096925 Sebastian, A., and M.N.V. Prasad. 2014. Cadmium minimization in rice. A review. Agron. Sustain. Dev. 34:155–173. doi:10.1007/ s13593-013-0152-y Seravalli, J. 2012. Inductively coupled plasma-mass spectrometry as a tool for high-throughput analysis of plants. In: J. Nomanly, editor, High throughput phenotyping in plants: Methods and protocols. Methods in Molecular Biology 918:269–288. doi: 10.1007/978-1-61779-995-2_14. Singh, B., R. Narwal, A. Jeng, and A. Almas. 1995. Crop uptake and extractability of cadmium in soils naturally high in metals at different ph levels. Commun. Soil Sci. Plant Anal. 26:2123– 2142. doi:10.1080/00103629509369434 Song, W., D.G. Mendoza-Cozatl, Y. Lee, J.I. Schroeder, S. Ahn, H. Lee, T. Wicker, and E. Martinoia. 2014. Phytochelatinmetal(loid) transport into vacuoles shows different substrate preferences in barley and arabidopsis. Plant Cell Environ. 37:1192–1201. doi:10.1111/pce.12227 Srinivasa, J., B. Arun, V.K. Mishra, G.P. Singh, G. Velu, R. Babu, N.K. Vasistha, and A.K. Joshi. 2014. Zinc and iron concentration QTL mapped in a triticum spelta x T-aestivum cross. Theor. Appl. Genet. 127:1643–1651. doi:10.1007/s00122-014-2327-6 Storey, J.D., and R. Tibshirani. 2003. Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA 100:9440– 9445. doi:10.1073/pnas.1530509100 Takahashi, R., K. Bashir, Y. Ishimaru, N.K. Nishizawa, and H. Nakanishi. 2012. The role of heavy-metal ATPases, HMAs, in zinc and cadmium transport in rice. Plant Signal. Behav. 7:1605–1607. doi:10.4161/psb.22454 TAMU. 2014. Percent of wheat acres seeded for 2014. Variety Testing Information. Texas A&M Univ. http://varietytesting.tamu. edu/wheat/otherpublications/Texas%20Wheat%20Variety%20 2014%20-%20Publication.pdf (accessed December 2014). Tiwari, V.K., N. Rawat, K. Neelam, S. Kumar, G.S. Randhawa, and H.S. Dhaliwal. 2010. Substitutions of 2S and 7U chromosomes of Aegilops kotschyi in wheat enhance grain iron and zinc concentration. Theor. Appl. Genet. 121:259–269. doi:10.1007/ s00122-010-1307-8 Ueno, D., N. Yamaji, I. Kono, C.F. Huang, T. Ando, M. Yano, and J.F. Ma. 2010. Gene limiting cadmium accumulation in rice. Proc. Natl. Acad. Sci. USA 107:16500–16505. doi:10.1073/ pnas.1005396107

1728

Velu, G., R.P. Singh, J. Huerta-Espino, R.J. Pena, B. Arun, A. Mahendru-Singh et al. 2012. Performance of biofortified spring wheat genotypes in target environments for grain zinc and iron concentrations. Field Crops Res. 137:261–267. doi:10.1016/j. fcr.2012.07.018 Wang, S., D. Wong, K. Forrest, A. Allen, S. Chao, B.E. Huang et al. 2014. Characterization of polyploid wheat genomic diversity using a high-density 90 000 single nucleotide polymorphism array. Plant Biotechnol. J. doi:10.1111/pbi.12183. Wang, C., J. Ji, Z. Yang, L. Chen, P. Browne, and R. Yu. 2012. Effects of soil properties on the transfer of cadmium from soil to wheat in the Yangtze river delta region, China-a typical industry-agriculture transition area. Biol. Trace Elem. Res. 148:264–274. doi:10.1007/s12011-012-9367-z Warnes, G., G. Gorjanc, F. Leisch, and M. Man. 2013. Genetics: Population genetics. R package version 1.3.8.1. The Comprehensive R Archive Network. http://CRAN.R-project.org/ package=genetics (accessed 14 Apr. 2014). Waters, B.M., and R.P. Sankaran. 2011. Moving micronutrients from the soil to the seeds: Genes and physiological processes from a biofortification perspective. Plant Sci. 180:562–574. doi:10.1016/j.plantsci.2010.12.003 Waters, B.M., C. Uauy, J. Dubcovsky, and M.A. Grusak. 2009. Wheat (Triticum aestivum) NAM proteins regulate the translocation of iron, zinc, and nitrogen compounds from vegetative tissues to grain. J. Exp. Bot. 60:4263–4274. doi:10.1093/jxb/erp257 Wiebe, K., N.S. Harris, J.D. Faris, J.M. Clarke, R.E. Knox, G.J. Taylor, and C.J. Pozniak. 2010. Targeted mapping of Cdu1, a major locus regulating grain cadmium concentration in durum wheat (Triticum turgidum L. var durum). Theor. Appl. Genet. 121:1047–1058. doi:10.1007/s00122-010-1370-1 Wolnik, K.A., F.L. Fricke, S.G. Capar, G.L. Braude, M.W. Meyer, R.D. Satzger, and E. Bonnin. 1983. Elements in major raw agricultural crops in the United States. 1. Cadmium and lead in lettuce, peanuts, potatoes, soybeans, sweet corn, and wheat. J. Agric. Food Chem. 31:1240–1244. doi:10.1021/jf00120a024 Xu, Y., D. An, D. Liu, A. Zhang, H. Xu, and B. Li. 2012. Molecular mapping of QTLs for grain zinc, iron and protein concentration of wheat across two environments. Field Crops Res. 138:57–62. doi:10.1016/j.fcr.2012.09.017 Yu, J., G. Pressoir, W. Briggs, I. Bi, M. Yamasaki, J. Doebley, M. McMullen et al. 2006. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet. 38:203–208. doi:10.1038/ng1702 Zhang, Z., E. Ersoz, C. Lai, R.J. Todhunter, H.K. Tiwari, M.A. Gore et al. 2010. Mixed linear model approach adapted for genome-wide association studies. Nat. Genet. 42:355–360. doi:10.1038/ng.546 Zook, E.G., F.E. Greene, and E.R. Morris. 1970. Nutrient composition of selected wheats and wheat products. VI. Distribution of manganese, copper, nickel, zinc, magnesium, lead, tin, cadmium, chromium, and selenium as determined by atomic absorption spectroscopy and colorimetry. Cereal Chem. 47:720–731.

www.crops.org

crop science, vol. 55, july– august 2015