Article pubs.acs.org/JAFC
Association Mapping of Quantitative Trait Loci for Mineral Element Contents in Whole Grain Rice (Oryza sativa L.) Yan Huang,† Chengxiao Sun,‡ Jie Min,‡ Yaling Chen,† Chuan Tong,† and Jinsong Bao*,† †
Institute of Nuclear Agricultural Science, College of Agriculture and Biotechnology, Zhejiang University, Huajiachi Campus, Hangzhou 310029, China ‡ Rice Product Quality Inspection and Supervision Center, China National Rice Research Institute, Hangzhou 310006, China S Supporting Information *
ABSTRACT: Mineral elements in brown rice grain play an important role in human health. In this study, variations in the content of iron (Fe), zinc (Zn), selenium (Se), cadmium (Cd), and lead (Pb) in 378 accessions of brown rice were investigated, and association mapping was used to detect the quantitative trait loci (QTLs) responsible for the variation. Among seven subpopulations, the mean values of Zn and Cd in the japonica group were significantly higher than in the indica groups. The population structure accounted for from 5.7% (Se) to 22.1% (Pb) of the total variation. Correlation analyses showed that Pb was positively correlated with the other minerals (P < 0.001) except for Se. For the five mineral elements investigated, 20 QTLs, including some previously reported and new candidate loci, were identified. Particularly, three cases of QTL colocalization, i.e. Cd and Pb on chromosome 5, Zn and Pb on chromosome 7, and Se and Pb on chromosome 11, were observed. This study suggested that the identified markers could feasibly be used to enhance desired micronutrients while reducing the heavy metal content in whole rice grain by marker-assisted selection (MAS). KEYWORDS: rice, mineral accumulation, association mapping, molecular markers
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INTRODUCTION Rice (Oryza sativa) is one of the most important staple food crops in the world and provides starch, protein, and other essential nutrients for over half of the global population. It has been reported that as much as 75% of the daily calorie intake of the people, especially those in some Asian countries, is derived from rice.1 Normally, rice is not considered to be mineral-rich, but it can still be an essential source for those living on rice, since it can provide caloric energy, vitamins, and minerals simultaneously.2 Minerals have a direct or indirect effect on the metabolism and physiological processes of humans and plants as well. Deficiencies or insufficient intake of minerals may lead to several dysfunctions and diseases in humans.3 Approximately 60% of the world’s population have diets that are iron deficient, with more than 30% zinc deficient, and about 15% selenium deficient.4 The deficiency of Fe and Zn, two of the major micronutrient elements, affects more than two billion people worldwide.5 Fe deficiency causes anemia, while zinc deficiency results in growth retardation and mental retardation.6 However, rice is not a concentrated source of essential micronutrients such as Fe and Zn.7 Biofortification, including conventional plant breeding and genetic engineering, has emerged as one possible way to develop new cultivars with elevated concentration of Fe and Zn.8 Epidemiological studies revealed that selenium intake correlates inversely with death from various types of cancer.9 Enriching food with selenium appears to be an effective method of providing selenium to humans for cancer prevention.10 When the Se level of rice products is at 0.3−0.5 μg·g−1, Se-enriched rice products can increase daily Se intake on average by 100−200 μg if an average of 400 g of rice products is consumed.11 Thus, it is desirable to increase the © 2015 American Chemical Society
level of certain micronutrients in rice grain. On the other hand, some elements, especially heavy metals, such as As (Arsenic), Cd, and Pb, if contained in rice grain and other food products, can be harmful to human health.2 Pb and Cd have also been identified as having a negative effect on plant growth.12 Increasing the micronutrient content and decreasing the heavy metal contents in rice grain may provide health benefits to humans. Recently great effort has been paid to the improvement of the nutritional quality of rice through breeding and genetic engineering.13 Understanding the genetic control of mineral accumulation in rice is a prerequisite for biofortification.14 A wide range of genetic variation for grain mineral accumulation has been revealed among rice accessions, indicating that mineral contents are complex quantitative traits. Therefore, quantitative trait locus (QTL) mapping has been widely conducted to identify loci associated with the mineral contents. QTLs were mapped for total P, Fe, Zn, Cu, and Mn concentrations in a doubled haploid mapping population.15 Another study identified 41 QTLs for the concentration of 17 elements, including several heavy metals in rice grain.16 The contents of Cu, Ca, Zn, Mn, and Fe were controlled by 10 main-effect QTLs and 28 digenic interaction QTLs. 17 Identification of 31 putative QTLs for Fe, Zn, Mn, Cu, Ca, Mg, P, and K contents with introgression lines derived from a cross between an elite indica cultivar Teqing and the wild rice (Oryza ruf ipogon) was reported.18 It was found that wild rice Received: Revised: Accepted: Published: 10885
October 10, 2015 December 4, 2015 December 7, 2015 December 7, 2015 DOI: 10.1021/acs.jafc.5b04932 J. Agric. Food Chem. 2015, 63, 10885−10892
Article
Journal of Agricultural and Food Chemistry Table 1. Summary of Grain Element Contents in 378 Rice (Oryza sativa L.) Accessionsa
a
Minerals
Mean
SD
Min.
Max.
Max./Min. Ratio
Skewness
Kurtosis
Fe (mg/kg) Zn (mg/kg) Se (μg/kg) Cd (μg/kg) Pb (μg/kg)
19.44 28.7 91.65 125.59 46.73
3.94 4.6 32.41 33.97 20.75
10.66 16.1 25.49 27.01 13.26
33.84 43.14 218.24 233.35 122.84
3.17 2.68 8.56 8.64 9.26
0.73 0.53 0.79 0.12 1.13
0.84 0.39 1.68 0.33 1.16
Mean, minimum (Min.), maximum (max.), max./min ratio, standard deviation (SD), skewness, and kurtosis were calculated. Statistical analyses of phenotypic data. ANOVA was carried out using the general linear model procedure (PROC GLM) in the SAS program (version 8.0, SAS Institute, Cary, NC). Duncan’s new multiple-range test was performed to examine significant differences among subpopulations. Correlation analysis was performed with PROC CORR procedure. Boxplot and histogram of each trait in different subpopulations was calculated by R.23 Association mapping. A total of 143 markers including 100 SSR markers, 41 markers for starch synthesis-related genes (SSRGs), one marker for Rc and one for fragrance were employed in association mapping.24 To avoid possible spurious associations, the Q (population structure) + K (kinship) model was used to account for population structure and relatedness of individuals among 416 rice accessions.25 The population structure (Q) among the 416 entries was previously estimated by 100 SSRs with the program STRUCTURE version 2.2.21,26 A total of seven subpopulations were revealed.21 The relative kinship (K) matrix was calculated on the basis of 100 SSR loci using the method proposed by Ritland,27 which is built into the program SPAGeDi.28,29 The association mapping based on the K + Q model was performed using MLM in TASSEL V2.0.1.25,30 The default run parameters of the convergence criterion set at 1.0 × 10−4 and the maximum number of iterations set at 200 were used. Markers with P < 0.01 were regarded as significant.
contributed favorable alleles for most of the QTLs for these traits.18 Some QTLs for different elements were found at the same locus; these clustered QTLs also provide favorable information useful for molecular breeding for simultaneous improvement of different mineral contents in rice grains. Zhang et al. mapped 134 QTLs associated with 16 elements in rice grain in two mapping populations which were clustered into 39 genomic regions.2 Anuradha et al. detected 14 QTLs for Fe and Zn, and found that the high priority candidate genes were OsYSL1 and OsMTP1 for Fe, and OsARD2, OsIRT1, OsNAS1, and OsNAS2 for Zn in rice seeds.19 Ishikawa et al. identified a major quantitative trait locus qGCd7 located on the short arm of chromosome 7 for increasing Cd concentration in rice grain.20 However, it is still unclear how many QTLs are responsible for the mineral accumulation in rice grains, since the favorable alleles of QTLs are distributed in different rice germplasm. Further search for new QTLs for mineral content is necessary to support work on biofortification of minerals in rice grain by means of breeding. In this study, we explored more QTLs for different minerals using an association panel previously developed.21 The objectives were (1) to investigate the genetic diversity in the mineral content in the rice germplasm, (2) to determine the correlations among the mineral contents, and (3) to identify the QTLs underlying the mineral content and to investigate the QTL colocalization between micronutrient elements and heavy metals. This study may provide insights into the mechanism of elemental distribution in rice and may accelerate biofortification of rice varieties by marker-assisted selection (MAS).
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RESULT AND DISCUSSION Phenotypic variation in different mineral contents. The mean, standard deviation (SD), and range of the mineral elements evaluated for a total of 378 rice accessions are presented in Table 1. The frequency distributions of content for each mineral in the whole panel are shown in Figure 1. Significant differences in mineral element contents were found. The means for Fe, Zn, and Se content were 19.44 mg/kg, 28.70 mg/kg, and 91.65 μg/kg. Fe content ranged from 3.94 to 10.66 mg/kg with a ratio of maximum/minimum (Max./Min.) content of 3.17, showing less diversity than in previous studies.18,31 The brown rice reported in the reports of GarciaOliveira et al.18 and Pinson et al.31 had the Max./Min. ratios of 4.1 and 287.6 for Fe content, respectively. Zn content ranged from 16.1 to 43.14 mg/kg with a Max./Min. ratio of 2.68, which was slightly smaller than in previous studies.31 The differences in mineral contents among genotypes might be due to different genetic resources involved.22 It could be expected that higher genetic diversity would be found for some minerals such as Fe and Zn in a worldwide collection of 1763 accessions.31 Se content ranged from 25.49 to 218.24 μg/kg, showing the largest phenotypic variation among the micronutrients studied and with a Max./Min. ratio of 8.56. There are few reports available on the genetic diversity of Se in rice accessions. Brown rice had an average of 121 μg/kg in a population for genetic mapping.16 Milled rice had the Se content from
