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Variation in mineral elements in grains of 20 brown rice accessions in two environments
Accepted Manuscript Variation in Mineral Elements in Grains of 20 Brown Rice Accessions in Two Environments Yan Huang, Chuan Tong, Feifei Xu, Yaling Chen, Caiya Zhang, Jinsong Bao PII: DOI: Reference:
S0308-8146(15)01120-6 http://dx.doi.org/10.1016/j.foodchem.2015.07.087 FOCH 17883
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Food Chemistry
Received Date: Revised Date: Accepted Date:
29 April 2015 18 July 2015 20 July 2015
Please cite this article as: Huang, Y., Tong, C., Xu, F., Chen, Y., Zhang, C., Bao, J., Variation in Mineral Elements in Grains of 20 Brown Rice Accessions in Two Environments, Food Chemistry (2015), doi: http://dx.doi.org/ 10.1016/j.foodchem.2015.07.087
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Variation in Mineral Elements in Grains of 20 Brown Rice Accessions in Two Environments
3 Yan Huang1, Chuan Tong1, Feifei Xu1, Yaling Chen1, Caiya Zhang2*, Jinsong
4
Bao1*
5 6 7 8
1
Institute of Nuclear Agricultural Sciences, College of Agriculture and
Biotechnology, Zhejiang University, Hangzhou, 310029, China.
9
2
10
China
Department of Statistics, Zhejiang University City College, Hangzhou 310015,
11 12
*Corresponding author: phone +86-571-88284309, and email [email protected]
13
(C. Zhang); and phone +86-571-86971932; fax +86-571-86971421; and email:
14
[email protected] (J. Bao).
15 16
Running Title: Variation in mineral elements in grains of 20 brown rice
17 18
1
19
Abstract
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Twenty rice accessions were planted in Hainan province, China, for 2 years to
21
investigate the effects of genotype, environment, and their interactions on the Ca, Mg,
22
Na, K, Fe, Zn, and Cu contents in brown rice. Analysis of variance showed that the Ca,
23
Na and K were mainly affected by the genotypic variance, whereas the Fe, Zn and Cu
24
were mainly affected by the environment variance. The genotype × environment
25
interaction effects for Mg, Na, Zn, and Cu were highly significant (P＜0.001), though
26
it only accounted for a small proportion of the total variation (0.5-16.3%). The
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correlation analyses showed that Mg was significantly positively correlated with K,
28
Fe, and Zn. A total of 9 and 8 single nucleotide polymorphism (SNP) loci were
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identified in 2011 and 2012, respectively, which were strongly associated with for Ca,
30
Cu, K, Na, and Zn.
31 32
Keywords: Rice; mineral content; genotype and environment interaction, single
33
nucleotide polymorphism
34
2
35
1. Introduction
36
Rice (Oryza sativa) is the most important staple food crop in the world and
37
provides starch, protein, and other essential nutrients for over half of the global
38
population. At least 49 nutrients are required by humans for their normal growth and
39
development (Welch & Graham, 2004), and the demand for most nutrients is generally
40
supplied by cereals, particularly rice as a staple food. Along with an ever growing
41
number of global populations, especially in regions like Asia where rice is regarded as
42
the basic food source, nutritional quality of the grain plays an indispensable role upon
43
human health.
44
Among these nutrients, mineral elements play numerous beneficial roles due to
45
their direct or indirect effects in both plant and human metabolism, otherwise their
46
deficiencies or insufficient intakes may lead to several dysfunctions and diseases in
47
humans (Garcia-Oliveira, Tan, Fu, & Sun, 2009). It was suggested that relatively low
48
intake of Ca in most developing countries was accompanied by the increased risk of
49
osteoporosis (Nordin, 2000; Welch & Graham, 1999). The deficiency of Fe and Zn, two
50
of the major micronutrients, are affecting more than 2 billion people worldwide
51
(Sandstead, 1991); Fe deficiency results in suffering from anemia, while zinc
52
deficiency causes stunted growth and underdeveloped intelligence (Umeta, West,
53
Haidar, Deurenberg, & Hautvast, 2000). Although rice is not considered to be
54
mineral-rich, it still can be an important source for those who eat rice as staple food
55
since it provides human with caloric energy and minerals at the same time (Zhang,
56
Pinson, Tarpley, Huang, Lahner, & Yakubova, 2014). Therefore, even a small increase
57
in the mineral elements concentrations in rice grain, in regions especially in developing
58
countries would be highly significant. Biofortification of mineral nutrients in rice is
59
extremely necessary for rice breeders, which is considered an efficient way for
60
ameliorating mineral deficiency in humans.
61 62
Mineral elements accumulation in rice grains is a complex trait. Quantitative trait locus (QTL) mapping and association analysis are considered two powerful tools for 3
63
discovering the genes/QTLs underlying such complex traits.
In
recent
years,
64
tremendous efforts have been made on the genetic analysis of different mineral
65
elements in rice. Stangoulis, Huynh, Welch, Choi, and Graham (2007) mapped the
66
QTLs for inorganic phosphorus (P), total P, Fe, Zn, Cu and Mn concentrations. Norton,
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Deacon, Xiong, Huang, Meharg, & Price (2010) identified 41 QTLs for the
68
concentration of 17 elements in rice grain. Garcia-Oliveira et al. (2009) identified 31
69
putative QTLs for Fe, Zn, Mn, Cu, Ca, Mg, P, and K contents of 85 introgression lines
70
derived from a cross between an elite indica cultivar Teqing and the wild rice (Oryza
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rufipogon), and found that the wild rice contributed favorable alleles for most of the
72
QTLs.
73
In addition, many researchers have explored the impact of environment on the
74
accumulation of mineral elements in rice. Du, Zeng, Wang, Qian, Zheng, and Ling
75
(2013) determined the Ca, Fe, K, Mg, Mn, P, and Zn contents in brown rice and
76
identified 23 and 9 QTLs in two different ecological environments, respectively. Only
77
2 QTLs for Mg content have been detected in both environments, indicating that
78
mineral accumulation QTLs are greatly affected by the environment. Pinson, Tarpley,
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Yan, Yeater, Lahner, & Yakubova (2015) analyzed 16 mineral elements concentrations
80
in brown rice which were produced over 2 years in Beaumont, TX, under both flooded
81
and unfolded watering regimes, and found that environmental variance was large for
82
some elements.
83
However, it is still unclear how genotypes, environment, and genotypes ×
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environment interaction influence the mineral accumulation in rice grains. The
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objectives of this study were (1) to investigate the genetic diversity on the
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accumulation of mineral elements among 20 diverse rice accessions representing a
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wide geographical origins in two environments (years), and (2) to explore additional
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genetic loci (QTLs) for the mineral elements which may be useful for molecular
89
breeding for improving the mineral nutrients in rice grains.
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2. Materials and method 4
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2.1. Rice materials
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Twenty rice accessions (Oryza sativa L.) used for this study, including cultivars,
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germplasm lines, and landraces from 10 geographical areas, was introduced from the
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OryzaSNP project (http://www.oryzasnp.org/) (McNally, Childs, Bohnert, Davidson,
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Zhao, &Ulat, 2009) . All the 20 rice accessions were planted in a randomized block
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design with two replications in two years at the same site in Lingshui, Hainan
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province, China. Each accession in the fields was planted into ten rows with six plants
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per row. The seeds were sowed in early December 2010 and 2011, and harvested in
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April2011 and 2012, respectively. Hereafter two years (environments) are denoted
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2011 and 2012 for when the grain was harvested. Field management followed
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conventional practices including nutrient and pest control procedures to ensure the
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production in the field. The major environmental conditions, i.e. the mean temperature,
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total radiation hours and total rainfall for each month during rice growth periods were
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reported previously (Tong, Chen, Tang, Xu, Huang, Chen, et al. 2014).
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The grains were air-dried to a moisture content of about 12%, stored at room
107
temperature for two months and later at 4 ℃ in the dark until all the grain materials
108
were obtained. The samples were dehusked on a Satake Rice Machine (Satake Co.,
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Tokyo, Japan) and the dehusked rice samples were ground to flours, which were used
110
for further analyses.
111
2.2. Sample Treatment and Determination of Mineral content
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Sample preparation and determination of mineral contents were according to
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Jiang, Wu, Feng, Yang, & Shi (2007). Rice powder (0.5 g) was weighed and placed
114
into a crucible, and then carbonized at 250 °C on an electro thermal plate until the
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sample turned into black but not smoking completely. The crucibles with samples
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were dry-ashed by heating in a muffle furnace at 550 °C (about 10–12 h). After
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sample incineration, a white residue was obtained, which was carefully transferred
118
into a 50 mL volumetric flask, dissolved with 5 mL of 6 M HCl and then diluted to 50 5
119
mL with water. The diluted solutions were subjected to analysis for Ca, Mg, Na, K, Fe,
120
Zn, and Cu by inductively coupled plasma mass spectroscopy (ICP-MS) (Agilent
121
7500A; Agilent Technologies, Inc.).
122
2.3. Statistical Analysis
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All concentrations of the mineral elements were measured at least in duplicate.
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Data were presented as mean value ± standard deviation. Data analyses were
125
performed with the SAS program version 9.1.3 (SAS Institute Inc., Cary, NC,U.S.A.).
126
Analysis of variance (ANOVA) was carried out to determine genotypic and
127
environmental variation among the parameters using the general linear model
128
procedure (PROC GLM). Means of different genotypes were determined using the
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PROC MEANS, followed by least significant difference (LSD) multiple comparison
130
tests at P < 0.05. Duncan’s new multiple-range test was performed to examine
131
significant differences among subpopulations at P < 0.05. The t-test was performed to
132
compare the mean values between two years. Correlation analysis among the different
133
mineral elements in (or between) the two environments was calculated by the PROC
134
CORR procedure.
135
2.4. Genotype data and association mapping
136
The publically available genotype data were downloaded from the Gramene Web
137
site (http://www.gramene.org/) on March 10, 2013. During downloading, we removed
138
the reference genome data firstly, and we then filtered sites with the following
139
parameters: minimum count was 16 out of 20 sequences, and minimum frequency
140
was set at 0.25. Finally, a total of 32 655 SNPs distributed in 12 chromosomes were
141
downloaded (Xu, Tang, Shao, Chen, Tong, & Bao, 2014). Association mapping was
142
performed with GAPIT (Lipka, Tian, Wang, Peiffer, Li, & Bradbury, 2012).
143
Comparison of models and selection of the best model was referred to Xu et al (2014).
144
The results with the best model were presented at the significance level of P < 0.005.
145 6
146
3. Results
147
3.1. Variation in mineral elements concentrations among rice accessions
148
The concentrations of mineral elements in brown rice among 20 rice accessions
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produced over two years (2011 and 2012) are summarized in Tables 1 and 2. Both
150
macro elements (Ca, Mg, Na, K) and micro-elements (Fe, Zn, Cu) varied in
151
concentrations among different rice accessions. In 2011, the variation for Cu among
152
different rice was largest with ratio of the maximum/minimum content of 5.6-fold
153
ranging from the minimum values of 0.217 mg/kg in rice G03 to the maximum value
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of 1.216 mg/kg in rice G15. The content of Mg showed the smallest variation with
155
1.35-fold (1.52-2.05 g/kg) (Table 1). In 2012, the mineral contents also varied among
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20 genotypes with ratios of the maximum/minimum content of 2.52-fold (73.5-185.1
157
mg/kg) for Ca, 1.63-fold (1.42-2.31 g/kg) for Mg, 5.07-fold (7.67-38.86 mg/kg) for Na,
158
1.47-fold (2.55-3.74 g/kg) for K, 3.95-fold (4.01-15.81 mg/kg) for Fe, 2.14-fold
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(17.98- 38.52 mg/kg) for Zn, 2.36-fold (1.20-2.84 mg/kg) for Cu(Table 2). Moreover,
160
the mean value of Mg, Fe, Zn and Cu were of significant difference between two years
161
(Table 2), suggesting influence of environment on these mineral elements.
162
Of the 20 rice genotypes studied, eight represented the japonica subspecies, eight
163
indica subspecies and four belonged to the aus group (Table 1; Table 2) (Xu et al.
164
2014). The concentrations of the 7 elements also varied among different subgroups in
165
both years. The mean values of Na, K, Fe and Zn in japonica group were higher than
166
those in indica and Aus groups. The aus group had higher mean Ca content than the
167
japonica and indica groups, while the highest mean value of Cu was found in the
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indica group (Table 3).
169
3.2. Correlation analysis
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The result of pair-wise correlation analysis among all mineral elements of the 20 rice
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accessions is listed in Table 4.The correlation coefficients for Ca, Mg, Na, K and Fe
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between 2 years ranged from 0.56-0.81 (P< 0.001), suggesting that these elements 7
173
were relatively stable in different planting years. The content of Mg was positively
174
correlated with those of K, Fe, and Zn. The content of Fe was positively correlated
175
with the content of Na and K. All these correlations were observed in two years. The
176
content of Cu was negatively correlated with Fe and positively correlated with Zn,
177
which was only observed in 2011.
178
3.3. Analysis of variance for mineral elements
179
Analysis of variance (ANOVA) for mineral elements indicated that the genotype
180
variance for 7 mineral elements were highly significant (P<0.001), but the environment
181
variance for some elements such as Mg, Fe, Zn and Cu were also significant (P<0.001)
182
(Table 5). Additionally, the genotype × environment interaction for most elements
183
except for Ca, K and Fe were significant (P< 0.001), though it only explained a small
184
proportion of the total variation (0.5-16.3%). The genotype effects accounted for more
185
than 70% of the total variation for the elements of Ca, Na and K, indicating that the
186
genotype was the primary factor determining the difference of the mineral elements. In
187
contrast, the environment effects accounted for more than 80% of total variation for the
188
elements of Fe, Zn and Cu (Table 5), indicating that these elements were highly
189
effected by the environment.
190 191
3.4. Association test for mineral elements
192
Association mapping was used to find the genetic factor affecting the genetic
193
diversity in the 7 mineral elements. Seventeen QTLs locating on chromosome 1, 2, 6, 8,
194
10 and 11 were identified for 5 of 7 mineral elements except for Mg and Fe using
195
preliminary association test (Table 6; Supplementary Fig. 1). The Manhattan plots of
196
association mapping results for K and Na were shown in Supplementary Fig. 2 in the
197
two years, respectively.
198
Nine of 17 QTLs were detected in 2011, while the others were identified in the
199
2012. Only one QTL for Cu and Zn content was detected in 2011, indicating that the
200
QTLs for Cu and Zn in rice grains were largely environment-dependent. Five QTLs 8
201
were identified for Ca in the two years, of which two QTLs on chromosome 1 and 6
202
with different positions could be detected in each year. A large number of QTLs for K
203
and Na were detected (Table 6; Supplementary Fig.1 and Fig.2), but none of them
204
could be detected simultaneously in both years.
205
4. Discussion
206
The 20 rice accessions introduced from the OryzaSNP project represent wide
207
genetic diversity in various phenotypes (McNally et al, 2009), such as agronomic traits
208
(Xu et al, 2014), biomass traits (Jahn, McKay, Mauleon, Stephens, McNally, Bush, et
209
al., 2011), bioactive compounds (Heuberger, Lewis, Chen, Brick, Leach, & Ryan, 2010;
210
Shao, Tang, Huang, Xu, Chen, Tong, et al., 2014), and grain quality traits (Tong et al,
211
2014). However, genetic and environmental effects on the mineral elements in 20 rice
212
accessions have not been reported before.
213
In this study, the concentrations of Ca, Mg, Na, K, Fe, Zn, and Cu in brown rice
214
of 20 genotypes were evaluated. Each of these nutrient are essential for the growth
215
and development of plants, animals, and humans. The results showed that 7 mineral of
216
the 20 rice accessions varied greatly among genotypes. For example, the largest
217
variation of mineral elements concentrations were 5.6-fold (0.217-1.216 mg/kg) for Cu
218
in 2011 and 5.07-fold (7.67-38.86 mg/kg) for Na. Genetic variation for mineral
219
contents has been reported by many research groups in different rice accessions
220
(Gregorio, 2002; Jiang et al, 2007; Garcia-Oliveira et al, 2009; Anuradha, Agarwal,
221
Rao, Rao, Viraktamath, & Sarla, 2012). Brar, Jain, Singh, and Jain (2011) showed
222
large variation in Fe (5.1–441.5 µg/g) and Zn (2.12–39.4 µg/g) contents in brown rice
223
in a collection of 220 rice genotypes and identified three rice genotypes with
224
exceptionally higher Fe content (~400µg/g). Jiang et al (2007) indicated that the
225
differences in mineral contents among genotypes might be due to different genetic
226
resources involved. In this study, the genotype effects accounted for more than 70% of
227
variance for the elements of Ca, Na and K, which implied that genotypic variations 9
228
might provide opportunities to select for higher mineral element contents (Gregorio,
229
2002).
230
The mineral contents among different rice accessions were not only influenced
231
by genotypes but also affected by the environment factors. Some previous studies
232
have demonstrated that environment plays a tremendous impact on the accumulation
233
of minerals in rice grain. Du et al (2013) found the mineral concentrations in grains of
234
the two parents grown in Hangzhou and Lingshui, China, were significantly different,
235
especially for Ca, Fe, and Zn. Variations in mineral concentrations in plants may
236
depend on variations in many factors, such as mineral mobilization, uptake,
237
trafficking, and sequestration, which are all relevant processes in the mineral transport
238
pathway from roots to shoots (Ghandilyan, Ilk, Hanhart, Mbengue, Barboza, Schat, et
239
al. 2009; Clemens, 2001). Moreover, the climate may also affect the elemental uptake
240
by modifying plant growth and metabolism (Du et al, 2013). It is straightforward to
241
ascribe the difference in the mean value of most mineral elements except Ca and Na
242
among 20 rice accessions between two years to the effects of environments (Table 2).
243
Under the same environment, different rice accessions also showed different
244
concentrations in 7 mineral elements, which may be ascribed to the genotypic
245
differences (Table 1 and 2). The mean values of most mineral elements among
246
different subgroups showed significant difference in two years, further supporting the
247
influence of genotypes on the mineral accumulation (Table 3). Moreover, ANOVA
248
indicated that the genotype variance for 7 mineral elements were highly significant
249
(P<0.001), while the environment variance for some elements such as Mg, Fe, Zn and
250
Cu were also significant (P<0.001) (Table 4), also suggesting that the mineral elements
251
were affected by hereditary as well as environment. Batten (2002) indicated that the
252
correlation between mineral elements concentrations might be affected by the
253
genotype ×environment, which was supported by our study. The Mg, Na, Zn, and
254
Cu were greatly affected by genotype × environment interaction, though it only
255
explained a small proportion of the total variation. So to improve the mineral nutrients, 10
256
breeders should not ignore the effect of genotype × environment interaction in their
257
breeding lines in different environments.
258
The correlation analyses between 7 mineral elements showed that Mg had a
259
significantly positive correlation with K, Fe, and Zn. Jiang et al, (2007) showed that
260
Mg was negatively correlated with Cu and positively correlated with K, Ca, Na, Fe,
261
Zn and Mn. Ca and was positively correlated Na, Mg, Fe, Zn and Mn. This might be
262
due to the interaction between ions in which chemical properties were equivalently
263
similar, which may compete for the site of absorption, transport, and function in
264
plant tissues (Gussarsson, Adalsteinsson, Jensen, & Asp, 1995; Robson et al, 1983).
265
These correlation results from present and previous studies could help improving the
266
efficiency of early generation selection for rice materials abundant in Mg, K, Fe, and
267
Zn in rice breeding program.
268
Many putative genomic regions associated with the phenotypes using this set of
269
materials have been identified. For example, Xu et al. (2014) identified 23 QTLs for 10
270
agronomic traits in the two years. Tong et al. (2014) identified 22 QTLs for starch
271
physicochemical properties in which some starch biosynthesis related genes such as
272
Wx, SSIIIb, ISA1, and ISA2 were revealed. Shao et al. (2014) found many potential loci
273
for polyphenol and antioxidant capacity traits. Therefore, it is possible to detect the
274
putative genomic regions or QTLs associated with different mineral elements using this
275
set of rice materials. In this study, a total of 17 QTLs for the mineral elements except
276
Mg and Fe were discovered which were located on chromosome 1, 2, 6, 8, 10 and 11.
277
We identified 5 loci for Ca content on chromosomes 2, 6, 8 which were not reported
278
before (Du et al, 2013；Garcia-Oliveira et al, 2009). Only one QTL (qCu6) was
279
identified on the chromosome 6, and it was not the same region as reported by
280
Garcia-Oliveira et al, (2009). Consistent with previous research, qK6.1 was close to
281
the QTLs identified by Du et al, (2013) on chromosome 6, suggesting that the locus
282
may be a hot region for K accumulation in rice grain and more further research on this
283
region is needed. Kumar, Jain, & Jain, (2014) detected one QTL for zinc content on 11
284
chromosome 2 which is close to the position of the qZn2 in the study. Few QTLs were
285
identified for Na content in rice grain, our study identified 5 new QTLs located at
286
different regions from the previous studies, and so further studies are needed to
287
confirm the results.
288
5. Conclusion
289
Wide genetic diversity in the 7 mineral elements in 20 brown rice accessions
290
was discovered in two years. These mineral elements were affected by genotype as
291
well as environment. The effects of genotype × environment interaction should also
292
be considered in different environments. Moreover, we discovered 17 QTLs involved
293
in the accumulation of 7 mineral elements. This study provided new information on
294
the understanding of the contributions of the genotype and environment interaction
295
effects to the complex traits such as mineral contents in the brown rice. The results
296
will also be useful for developing rice varieties to improve the mineral nutrients in
297
rice grain by molecular breeding.
298
Acknowledgement
299
This work was financially supported by the Special Fund for Agro-scientific
300
Research in the Public Interest (201103007) from the Ministry of Agriculture. We
301
thank Dr. Ruaraidh Sackville Hamilton in the International Rice Research Institute for
302
providing the rice materials used in this study.
303
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Chemistry, 62, 5361-5368.
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micronutrient content. Euphytica, 154, 289-294.
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diversity of amylose content and RVA pasting parameters in 20 rice accessions
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grown in Hainan, China. Food Chemistry, 161, 239-245.
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supplementation and stunted infants in Ethiopia: a randomised controlled trial.
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environment interactions for agronomic traits of rice revealed by association
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mapping. Rice Science, 21, 133-141.
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395
trait loci associated with concentrations of 16 elements in unmilled rice grain.
396
Theoretical and Applied Genetics, 127, 137-165.
397
16
398 399
Table 1 The mineral contents of 20 rice accessions grown in Hainan in 2011. Rice
Code
Subgroups Ca (mg/kg)
Mg (g/kg)
Na (mg/kg)
K (g/kg)
Fe (mg/kg)
Zn (mg/kg) Cu (mg/kg)
Accessions G01
Azucena
Japonica
111.5±8.6 1.567±0.083 27.68±1.03 3.025±0.151 11.38±1.55
15.68±0.63 0.436±0.046
G02
Dom Sufid
Japonica
113.5±8.0 2.052±0.138 23.37±2.16 3.565±0.196 16.09±2.47
25.77±0.99 0.646±0.016
G03
Dular
Aus
117.1±7.6 1.696±0.092
18.46±0.01 0.217±0.026
G04
FR13A
Aus
115.6±0.9 1.599±0.104 16.29±2.82 2.968±0.232
9.60±2.06
18.97±0.92 0.442±0.034
G05
IR64-21
Indica
71.4±9.0
1.539±0.089
7.84±1.89
13.76±0.35 0.489±0.114
G06
LTH
Japonica
92.2±1.5
1.643±0.085 26.98±1.23 3.308±0.158 12.47±1.34
15.05±0.39 0.513±0.027
G07
M202
Japonica
108.7±5.2 1.636±0.012 29.73±4.94 3.695±0.181 11.74±1.45
11.5±0.14 0.417±0.005
G08
Minghui63
Indica
80.0±7.8
16.2±4.72
7.96±1.74
3.355±0.169 11.06±4.60
2.638±0.173
1.633±0.067
10.51±1.1
3.702±0.201
8.24±1.38
19.22±0.5 0.944±0.082
G09 Moroberekan Japonica
137.9±11.7 1.521±0.112
20.49±0.2
2.871±0.23
11.48±2.38
14.82±0.78 0.302±0.021
G10
N22
Aus
135.0±5.7
1.834±0.09
16.33±1.37 3.195±0.222 13.35±0.16
17.3±0.76 0.221±0.008
G11
Nipponbare
Japonica
77.5±7.2
1.854±0.097 30.98±3.27 3.596±0.245 13.88±2.62
14.81±0.27 0.519±0.033
G12
Pokkali
Indica
111.1±5.3 1.835±0.131 14.67±1.04 3.569±0.319
6.53±2.17
G13
Sadu-Cho
Indica
173.5±16.9 1.889±0.157
21.13±3.5
3.502±0.338
9.58±1.89
G14
SHZ-2
Indica
136.2±1.5
1.573±0.05
8.71±0.7
2.398±0.083
6.96±0.98
14.79±0.34 0.687±0.031
G15
Swarna
Indica
92.1±0.6
1.734±0.082 13.35±0.37 2.707±0.115
7.86±0.32
21.32±0.29 1.216±0.034
G16
Tainung67
Japonica
106.5±12.9 1.578±0.037 31.92±3.58 3.289±0.109
9.55±0.09
19.77±1.08 0.791±0.073
G17 Zhenshan97B
Indica
124.3±5.1 1.788±0.028 13.19±0.22 3.423±0.044
8.58±0.16
14.31±0.94 0.546±0.051
G18
Aswina
Indica
163.7±59.4 1.836±0.126
G19
Cypress
Japonica
G20
Rayada
Aus Average 1
LSD
400 401 402 403 404 405 406 407 408 409 410 411
92.6±1.4
18.71±1.53 0.579±0.059 21.81±2
0.739±0.055
30.3±0.21
3.687±0.353
9.50±1.20
21.29±1.24 0.787±0.077
1.792±0.037 15.76±2.12
3.36±0.097
12.50±0.81
18.43±0.1 0.484±0.023
26.5±1.03
3.06±0.042
10.18±0.88
19.49±2.35 0.451±0.013
160.3±0.9 1.754±0.026 116.0
1.718
20.10
3.246
10.42
17.76
0.571
31.7
0.190
4.92
0.422
3.85
2.07
0.104
Data were presented as mean value ± standard deviation. 1 LSD: least significant difference (P < 0.05).
412 17
413
Table 2
414
The mineral contents of 20 rice accessions grown in Hainan in 2012. Rice
Code
Subgroups Ca (mg/kg)
Mg (g/kg)
Na (mg/kg)
K (g/kg)
Fe (mg/kg) Zn (mg/kg)
Cu (mg/kg)
Accessions G01
Azucena
Japonica
100.6±2.0
1.566±0.023 44.53±2.72 3.121±0.026 9.17±0.61
G02
Dom Sufid
Japonica
136.2±5.0
2.306±0.087
G03
Dular
Aus
90.9±8.0
1.441±0.083 11.39±3.65 2.953±0.153 9.13±0.06
G04
FR13A
Aus
G05
IR64-21
G06
31.49±1.45 2.837±0.153
27.37±7.4 3.736±0.052 15.81±0.84 38.52±0.12 2.398±0.284 25.09±0.98 1.285±0.072
168.7±87.1 1.573±0.004
17.86±1.9 2.917±0.025 7.65±0.97
Indica
73.5±10.3
1.511±0.145
7.87±0.73 2.654±0.233 6.17±1.25
LTH
Japonica
97.8±1.8
1.69±0.028
22.62±3.6 3.385±0.081 8.53±0.69
24.21±0.47 1.772±0.152
G07
M202
Japonica
111.7±13.5
1.53±0.016
19.29±0.28 3.75±0.109
21.67±0.25
G08
Minghui63
Indica
77.1±1.9
1.416±0.014
G09 Moroberekan Japonica
106.6±0.9
1.483±0.002 16.49±1.48 3.046±0.025 9.16±0.54
25.83±1.32
G10
N22
Aus
126.8±4.5
1.86±0.002
18.19±0.64 3.22±0.022
28.42±0.77 1.623±0.029
G11
Nipponbare
Japonica
84.0±3.3
1.889±0.015
18.58±1.5 3.785±0.085 11.37±0.51
26.01±1.4
G12
Pokkali
Indica
120.0±27.6
1.676±0.02
16.96±1.07 3.463±0.062 4.01±0.10
20.93±0.48 1.779±0.064
G13
Sadu-Cho
Indica
122.9±4.2
1.843±0.041 14.65±2.19 3.532±0.039 7.42±0.34
23.6±1.21
2.097±0.117
G14
SHZ-2
Indica
140.7±7.5
1.563±0.062 11.41±0.65 2.549±0.133 5.09±0.43
17.98±1.49
2.757±0.15
G15
Swarna
Indica
81.8±2.6
1.628±0.065
9.93±2.26
4.88±0.35
19.04±0.07 2.416±0.088
G16
Tainung67
Japonica
93.3±6.5
1.257±0.01
17.31±0.5 2.988±0.038 4.47±0.05
21.54±0.59 2.128±0.037
G17 Zhenshan97B
Indica
113.7±5.3
1.931±0.024
7.83±0.65 3.486±0.027 7.73±0.30
18.34±0.09
G18
Aswina
Indica
121.8±40.9 1.691±0.058 35.33±3.08 3.474±0.081 6.82±0.89
20.15±1.37 2.241±0.031
G19
Cypress
Japonica
93.9±10.5
20.17±0.03 1.949±0.021
G20
Rayada
Aus
415 416 417 418 419
1.59±0.027
7.67±1.2
7.56±0.28
3.533±0.007 5.15±0.12
2.811±0.09
8.44±0.17
17.39±2.55 3.286±0.038 9.61±0.59
185.1±76.6 1.616±0.013 38.86±0.98 2.952±0.019 7.23±0.36
22.25±0.89 1.351±0.001 18.33±2
1.844±0.249
1.2±0.03
18.06±0.25 1.375±0.019 1.76±0.02
1.929±0.11
2.36±0.014
22.23±1.5
1.615±0.06
Average
112.3
1.653*
19.08
3.232
7.77*
23.19*
1.936*
LSD1
60.0
0.107
5.27
0.181
1.19
2.13
0.240
Data were presented as mean value ± standard deviation. 1 LSD: least significant difference (P < 0.05). * in the average data indicates significant difference between mean of mineral contents in different years (P < 0.05).
18
420
Table 3
421
Mean of mineral contents among different rice subspecies in different years. Ca Na Subgroup Mg (g/kg) K (g/kg) Fe (mg/kg) Zn (mg/kg) Cu (mg/kg) (mg/kg) (mg/kg) 2011 Aus 132.0a 1.721a 18.83b 3.144a 11.048a 18.55a 0.333c Indica 119.0ab 1.729a 14.98b 3.203a 8.136b 18.15a 0.748a Japonica 105.0b 1.705a 25.86a 3.339a 12.388a 16.98a 0.514b 2012 Aus 142.9a 1.622a 21.58ab 3.011b 8.111a 24.49a 1.468b Indica 106.4b 1.657a 13.96b 3.188ab 5.908b 19.56b 2.109a Japonica 103.0b 1.664a 22.95a 3.387a 9.461a 26.18a 1.997a 422 Different letters in the same column indicate significant difference at P < 0.05. 423
19
Table 4 Correlation analysis of mineral contents in two years. Correlation
424 425 426 427 428
Ca
Mg
Na
K
Fe
Zn
Cu
Ca 0.222 0.200 0.013 -0.014 0.265 -0.119 0.562*** Mg 0.237 0.194 0.618*** 0.495** 0.593*** 0.131 0.713*** Na 0.337* 0.161 0.526*** 0.062 -0.094 0.658*** 0.467** K -0.062 0.543*** 0.147 0.425** 0.255 0.024 0.809*** Fe 0.074 0.661*** 0.318* 0.447** 0.150 -0.356* 0.786*** Zn 0.170 0.558*** 0.485** 0.335** 0.804*** 0.455** 0.295 Cu -0.021 0.300 0.223 -0.132 0.077 0.169 0.383* *,**,*** Indicate significance at P < 0.05, 0.01, and 0.001 levels, respectively. Correlation between different mineral elements above diagonal were for data in 2011, those below diagonal were for data in 2012, and those in diagonal in bold were for data between 2011 and 2012.
20
429
Table 5
430 Mean square values from analysis of variance for mineral contents. Source df Ca Mg Na K Fe Zn Cu Genotype (G) 19 2803.234*** 0.126*** 276.232*** 0.531*** 25.525*** 49.085*** 0.369*** Year (E) 1 268.975 0.084*** 21.061 0.004 140.383*** 589.763*** 37.23*** 19 549.653 0.019*** 58.049*** 0.027 1.447 26.941*** 0.188*** G × E 431 *** indicate significance at P <0.001 levels; df: degree of freedom. 432 433
21
434
Table 6
435 436
Significantly associated QTLs for Ca, Cu, K, Na and Zn detected in 2 years by association test. Trait
Ca
Cu K
Na
Zn
Year
QTL
2011 qCa2.1 qCa6.1 2012 qCa2.2 qCa6.2 qCa8 2011 qCu6 2011 qK1 qK6.1 qK6.2 2012 qK2 qK8 qK11 2011 qNa1 qNa11 2012 qNa6 qNa10 2011 qZn2
Chromosome Position (bp)
2 6 2 6 8 6 1 6 6 2 8 11 1 11 6 10 2
30592700 3450780 19703118 2500449 22222152 15357788 41972397 813231 10689396 24689470 27690577 3086965 43155830 4854990 21112917 21698303 23978212
437 438
22
Major
Miner
Miner allele
allele
allele
frequency
C G A C G C C C T C C C T T G T C
T T G T T T T T C A T A C G A A A
P value
0.0046 0.0048 0.0048 0.0043 0.0025 0.0049 0.0046 0.0038 0.0036 0.0042 0.0044 0.0048 0.0049 0.0048 0.0031 0.0031 0.0036
0.325 0.35 0.375 0.425 0.5 0.3 0.3 0.375 0.325 0.325 0.35 0.3 0.45 0.375 0.45 0.45 0.4
Highlights
Variation in mineral elements in whole grain of 20 rices was investigated.
Ca, Na and K were mainly affected by the genotypic variance.
Fe, Zn and Cu were mainly affected by the environment variance.
17 genetic loci were identified to control the mineral contents.
S0308-8146(15)01120-6 http://dx.doi.org/10.1016/j.foodchem.2015.07.087 FOCH 17883
To appear in:
Food Chemistry
Received Date: Revised Date: Accepted Date:
29 April 2015 18 July 2015 20 July 2015
Please cite this article as: Huang, Y., Tong, C., Xu, F., Chen, Y., Zhang, C., Bao, J., Variation in Mineral Elements in Grains of 20 Brown Rice Accessions in Two Environments, Food Chemistry (2015), doi: http://dx.doi.org/ 10.1016/j.foodchem.2015.07.087
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1 2
Variation in Mineral Elements in Grains of 20 Brown Rice Accessions in Two Environments
3 Yan Huang1, Chuan Tong1, Feifei Xu1, Yaling Chen1, Caiya Zhang2*, Jinsong
4
Bao1*
5 6 7 8
1
Institute of Nuclear Agricultural Sciences, College of Agriculture and
Biotechnology, Zhejiang University, Hangzhou, 310029, China.
9
2
10
China
Department of Statistics, Zhejiang University City College, Hangzhou 310015,
11 12
*Corresponding author: phone +86-571-88284309, and email [email protected]
13
(C. Zhang); and phone +86-571-86971932; fax +86-571-86971421; and email:
14
[email protected] (J. Bao).
15 16
Running Title: Variation in mineral elements in grains of 20 brown rice
17 18
1
19
Abstract
20
Twenty rice accessions were planted in Hainan province, China, for 2 years to
21
investigate the effects of genotype, environment, and their interactions on the Ca, Mg,
22
Na, K, Fe, Zn, and Cu contents in brown rice. Analysis of variance showed that the Ca,
23
Na and K were mainly affected by the genotypic variance, whereas the Fe, Zn and Cu
24
were mainly affected by the environment variance. The genotype × environment
25
interaction effects for Mg, Na, Zn, and Cu were highly significant (P＜0.001), though
26
it only accounted for a small proportion of the total variation (0.5-16.3%). The
27
correlation analyses showed that Mg was significantly positively correlated with K,
28
Fe, and Zn. A total of 9 and 8 single nucleotide polymorphism (SNP) loci were
29
identified in 2011 and 2012, respectively, which were strongly associated with for Ca,
30
Cu, K, Na, and Zn.
31 32
Keywords: Rice; mineral content; genotype and environment interaction, single
33
nucleotide polymorphism
34
2
35
1. Introduction
36
Rice (Oryza sativa) is the most important staple food crop in the world and
37
provides starch, protein, and other essential nutrients for over half of the global
38
population. At least 49 nutrients are required by humans for their normal growth and
39
development (Welch & Graham, 2004), and the demand for most nutrients is generally
40
supplied by cereals, particularly rice as a staple food. Along with an ever growing
41
number of global populations, especially in regions like Asia where rice is regarded as
42
the basic food source, nutritional quality of the grain plays an indispensable role upon
43
human health.
44
Among these nutrients, mineral elements play numerous beneficial roles due to
45
their direct or indirect effects in both plant and human metabolism, otherwise their
46
deficiencies or insufficient intakes may lead to several dysfunctions and diseases in
47
humans (Garcia-Oliveira, Tan, Fu, & Sun, 2009). It was suggested that relatively low
48
intake of Ca in most developing countries was accompanied by the increased risk of
49
osteoporosis (Nordin, 2000; Welch & Graham, 1999). The deficiency of Fe and Zn, two
50
of the major micronutrients, are affecting more than 2 billion people worldwide
51
(Sandstead, 1991); Fe deficiency results in suffering from anemia, while zinc
52
deficiency causes stunted growth and underdeveloped intelligence (Umeta, West,
53
Haidar, Deurenberg, & Hautvast, 2000). Although rice is not considered to be
54
mineral-rich, it still can be an important source for those who eat rice as staple food
55
since it provides human with caloric energy and minerals at the same time (Zhang,
56
Pinson, Tarpley, Huang, Lahner, & Yakubova, 2014). Therefore, even a small increase
57
in the mineral elements concentrations in rice grain, in regions especially in developing
58
countries would be highly significant. Biofortification of mineral nutrients in rice is
59
extremely necessary for rice breeders, which is considered an efficient way for
60
ameliorating mineral deficiency in humans.
61 62
Mineral elements accumulation in rice grains is a complex trait. Quantitative trait locus (QTL) mapping and association analysis are considered two powerful tools for 3
63
discovering the genes/QTLs underlying such complex traits.
In
recent
years,
64
tremendous efforts have been made on the genetic analysis of different mineral
65
elements in rice. Stangoulis, Huynh, Welch, Choi, and Graham (2007) mapped the
66
QTLs for inorganic phosphorus (P), total P, Fe, Zn, Cu and Mn concentrations. Norton,
67
Deacon, Xiong, Huang, Meharg, & Price (2010) identified 41 QTLs for the
68
concentration of 17 elements in rice grain. Garcia-Oliveira et al. (2009) identified 31
69
putative QTLs for Fe, Zn, Mn, Cu, Ca, Mg, P, and K contents of 85 introgression lines
70
derived from a cross between an elite indica cultivar Teqing and the wild rice (Oryza
71
rufipogon), and found that the wild rice contributed favorable alleles for most of the
72
QTLs.
73
In addition, many researchers have explored the impact of environment on the
74
accumulation of mineral elements in rice. Du, Zeng, Wang, Qian, Zheng, and Ling
75
(2013) determined the Ca, Fe, K, Mg, Mn, P, and Zn contents in brown rice and
76
identified 23 and 9 QTLs in two different ecological environments, respectively. Only
77
2 QTLs for Mg content have been detected in both environments, indicating that
78
mineral accumulation QTLs are greatly affected by the environment. Pinson, Tarpley,
79
Yan, Yeater, Lahner, & Yakubova (2015) analyzed 16 mineral elements concentrations
80
in brown rice which were produced over 2 years in Beaumont, TX, under both flooded
81
and unfolded watering regimes, and found that environmental variance was large for
82
some elements.
83
However, it is still unclear how genotypes, environment, and genotypes ×
84
environment interaction influence the mineral accumulation in rice grains. The
85
objectives of this study were (1) to investigate the genetic diversity on the
86
accumulation of mineral elements among 20 diverse rice accessions representing a
87
wide geographical origins in two environments (years), and (2) to explore additional
88
genetic loci (QTLs) for the mineral elements which may be useful for molecular
89
breeding for improving the mineral nutrients in rice grains.
90
2. Materials and method 4
91
2.1. Rice materials
92
Twenty rice accessions (Oryza sativa L.) used for this study, including cultivars,
93
germplasm lines, and landraces from 10 geographical areas, was introduced from the
94
OryzaSNP project (http://www.oryzasnp.org/) (McNally, Childs, Bohnert, Davidson,
95
Zhao, &Ulat, 2009) . All the 20 rice accessions were planted in a randomized block
96
design with two replications in two years at the same site in Lingshui, Hainan
97
province, China. Each accession in the fields was planted into ten rows with six plants
98
per row. The seeds were sowed in early December 2010 and 2011, and harvested in
99
April2011 and 2012, respectively. Hereafter two years (environments) are denoted
100
2011 and 2012 for when the grain was harvested. Field management followed
101
conventional practices including nutrient and pest control procedures to ensure the
102
production in the field. The major environmental conditions, i.e. the mean temperature,
103
total radiation hours and total rainfall for each month during rice growth periods were
104
reported previously (Tong, Chen, Tang, Xu, Huang, Chen, et al. 2014).
105 106
The grains were air-dried to a moisture content of about 12%, stored at room
107
temperature for two months and later at 4 ℃ in the dark until all the grain materials
108
were obtained. The samples were dehusked on a Satake Rice Machine (Satake Co.,
109
Tokyo, Japan) and the dehusked rice samples were ground to flours, which were used
110
for further analyses.
111
2.2. Sample Treatment and Determination of Mineral content
112
Sample preparation and determination of mineral contents were according to
113
Jiang, Wu, Feng, Yang, & Shi (2007). Rice powder (0.5 g) was weighed and placed
114
into a crucible, and then carbonized at 250 °C on an electro thermal plate until the
115
sample turned into black but not smoking completely. The crucibles with samples
116
were dry-ashed by heating in a muffle furnace at 550 °C (about 10–12 h). After
117
sample incineration, a white residue was obtained, which was carefully transferred
118
into a 50 mL volumetric flask, dissolved with 5 mL of 6 M HCl and then diluted to 50 5
119
mL with water. The diluted solutions were subjected to analysis for Ca, Mg, Na, K, Fe,
120
Zn, and Cu by inductively coupled plasma mass spectroscopy (ICP-MS) (Agilent
121
7500A; Agilent Technologies, Inc.).
122
2.3. Statistical Analysis
123
All concentrations of the mineral elements were measured at least in duplicate.
124
Data were presented as mean value ± standard deviation. Data analyses were
125
performed with the SAS program version 9.1.3 (SAS Institute Inc., Cary, NC,U.S.A.).
126
Analysis of variance (ANOVA) was carried out to determine genotypic and
127
environmental variation among the parameters using the general linear model
128
procedure (PROC GLM). Means of different genotypes were determined using the
129
PROC MEANS, followed by least significant difference (LSD) multiple comparison
130
tests at P < 0.05. Duncan’s new multiple-range test was performed to examine
131
significant differences among subpopulations at P < 0.05. The t-test was performed to
132
compare the mean values between two years. Correlation analysis among the different
133
mineral elements in (or between) the two environments was calculated by the PROC
134
CORR procedure.
135
2.4. Genotype data and association mapping
136
The publically available genotype data were downloaded from the Gramene Web
137
site (http://www.gramene.org/) on March 10, 2013. During downloading, we removed
138
the reference genome data firstly, and we then filtered sites with the following
139
parameters: minimum count was 16 out of 20 sequences, and minimum frequency
140
was set at 0.25. Finally, a total of 32 655 SNPs distributed in 12 chromosomes were
141
downloaded (Xu, Tang, Shao, Chen, Tong, & Bao, 2014). Association mapping was
142
performed with GAPIT (Lipka, Tian, Wang, Peiffer, Li, & Bradbury, 2012).
143
Comparison of models and selection of the best model was referred to Xu et al (2014).
144
The results with the best model were presented at the significance level of P < 0.005.
145 6
146
3. Results
147
3.1. Variation in mineral elements concentrations among rice accessions
148
The concentrations of mineral elements in brown rice among 20 rice accessions
149
produced over two years (2011 and 2012) are summarized in Tables 1 and 2. Both
150
macro elements (Ca, Mg, Na, K) and micro-elements (Fe, Zn, Cu) varied in
151
concentrations among different rice accessions. In 2011, the variation for Cu among
152
different rice was largest with ratio of the maximum/minimum content of 5.6-fold
153
ranging from the minimum values of 0.217 mg/kg in rice G03 to the maximum value
154
of 1.216 mg/kg in rice G15. The content of Mg showed the smallest variation with
155
1.35-fold (1.52-2.05 g/kg) (Table 1). In 2012, the mineral contents also varied among
156
20 genotypes with ratios of the maximum/minimum content of 2.52-fold (73.5-185.1
157
mg/kg) for Ca, 1.63-fold (1.42-2.31 g/kg) for Mg, 5.07-fold (7.67-38.86 mg/kg) for Na,
158
1.47-fold (2.55-3.74 g/kg) for K, 3.95-fold (4.01-15.81 mg/kg) for Fe, 2.14-fold
159
(17.98- 38.52 mg/kg) for Zn, 2.36-fold (1.20-2.84 mg/kg) for Cu(Table 2). Moreover,
160
the mean value of Mg, Fe, Zn and Cu were of significant difference between two years
161
(Table 2), suggesting influence of environment on these mineral elements.
162
Of the 20 rice genotypes studied, eight represented the japonica subspecies, eight
163
indica subspecies and four belonged to the aus group (Table 1; Table 2) (Xu et al.
164
2014). The concentrations of the 7 elements also varied among different subgroups in
165
both years. The mean values of Na, K, Fe and Zn in japonica group were higher than
166
those in indica and Aus groups. The aus group had higher mean Ca content than the
167
japonica and indica groups, while the highest mean value of Cu was found in the
168
indica group (Table 3).
169
3.2. Correlation analysis
170
The result of pair-wise correlation analysis among all mineral elements of the 20 rice
171
accessions is listed in Table 4.The correlation coefficients for Ca, Mg, Na, K and Fe
172
between 2 years ranged from 0.56-0.81 (P< 0.001), suggesting that these elements 7
173
were relatively stable in different planting years. The content of Mg was positively
174
correlated with those of K, Fe, and Zn. The content of Fe was positively correlated
175
with the content of Na and K. All these correlations were observed in two years. The
176
content of Cu was negatively correlated with Fe and positively correlated with Zn,
177
which was only observed in 2011.
178
3.3. Analysis of variance for mineral elements
179
Analysis of variance (ANOVA) for mineral elements indicated that the genotype
180
variance for 7 mineral elements were highly significant (P<0.001), but the environment
181
variance for some elements such as Mg, Fe, Zn and Cu were also significant (P<0.001)
182
(Table 5). Additionally, the genotype × environment interaction for most elements
183
except for Ca, K and Fe were significant (P< 0.001), though it only explained a small
184
proportion of the total variation (0.5-16.3%). The genotype effects accounted for more
185
than 70% of the total variation for the elements of Ca, Na and K, indicating that the
186
genotype was the primary factor determining the difference of the mineral elements. In
187
contrast, the environment effects accounted for more than 80% of total variation for the
188
elements of Fe, Zn and Cu (Table 5), indicating that these elements were highly
189
effected by the environment.
190 191
3.4. Association test for mineral elements
192
Association mapping was used to find the genetic factor affecting the genetic
193
diversity in the 7 mineral elements. Seventeen QTLs locating on chromosome 1, 2, 6, 8,
194
10 and 11 were identified for 5 of 7 mineral elements except for Mg and Fe using
195
preliminary association test (Table 6; Supplementary Fig. 1). The Manhattan plots of
196
association mapping results for K and Na were shown in Supplementary Fig. 2 in the
197
two years, respectively.
198
Nine of 17 QTLs were detected in 2011, while the others were identified in the
199
2012. Only one QTL for Cu and Zn content was detected in 2011, indicating that the
200
QTLs for Cu and Zn in rice grains were largely environment-dependent. Five QTLs 8
201
were identified for Ca in the two years, of which two QTLs on chromosome 1 and 6
202
with different positions could be detected in each year. A large number of QTLs for K
203
and Na were detected (Table 6; Supplementary Fig.1 and Fig.2), but none of them
204
could be detected simultaneously in both years.
205
4. Discussion
206
The 20 rice accessions introduced from the OryzaSNP project represent wide
207
genetic diversity in various phenotypes (McNally et al, 2009), such as agronomic traits
208
(Xu et al, 2014), biomass traits (Jahn, McKay, Mauleon, Stephens, McNally, Bush, et
209
al., 2011), bioactive compounds (Heuberger, Lewis, Chen, Brick, Leach, & Ryan, 2010;
210
Shao, Tang, Huang, Xu, Chen, Tong, et al., 2014), and grain quality traits (Tong et al,
211
2014). However, genetic and environmental effects on the mineral elements in 20 rice
212
accessions have not been reported before.
213
In this study, the concentrations of Ca, Mg, Na, K, Fe, Zn, and Cu in brown rice
214
of 20 genotypes were evaluated. Each of these nutrient are essential for the growth
215
and development of plants, animals, and humans. The results showed that 7 mineral of
216
the 20 rice accessions varied greatly among genotypes. For example, the largest
217
variation of mineral elements concentrations were 5.6-fold (0.217-1.216 mg/kg) for Cu
218
in 2011 and 5.07-fold (7.67-38.86 mg/kg) for Na. Genetic variation for mineral
219
contents has been reported by many research groups in different rice accessions
220
(Gregorio, 2002; Jiang et al, 2007; Garcia-Oliveira et al, 2009; Anuradha, Agarwal,
221
Rao, Rao, Viraktamath, & Sarla, 2012). Brar, Jain, Singh, and Jain (2011) showed
222
large variation in Fe (5.1–441.5 µg/g) and Zn (2.12–39.4 µg/g) contents in brown rice
223
in a collection of 220 rice genotypes and identified three rice genotypes with
224
exceptionally higher Fe content (~400µg/g). Jiang et al (2007) indicated that the
225
differences in mineral contents among genotypes might be due to different genetic
226
resources involved. In this study, the genotype effects accounted for more than 70% of
227
variance for the elements of Ca, Na and K, which implied that genotypic variations 9
228
might provide opportunities to select for higher mineral element contents (Gregorio,
229
2002).
230
The mineral contents among different rice accessions were not only influenced
231
by genotypes but also affected by the environment factors. Some previous studies
232
have demonstrated that environment plays a tremendous impact on the accumulation
233
of minerals in rice grain. Du et al (2013) found the mineral concentrations in grains of
234
the two parents grown in Hangzhou and Lingshui, China, were significantly different,
235
especially for Ca, Fe, and Zn. Variations in mineral concentrations in plants may
236
depend on variations in many factors, such as mineral mobilization, uptake,
237
trafficking, and sequestration, which are all relevant processes in the mineral transport
238
pathway from roots to shoots (Ghandilyan, Ilk, Hanhart, Mbengue, Barboza, Schat, et
239
al. 2009; Clemens, 2001). Moreover, the climate may also affect the elemental uptake
240
by modifying plant growth and metabolism (Du et al, 2013). It is straightforward to
241
ascribe the difference in the mean value of most mineral elements except Ca and Na
242
among 20 rice accessions between two years to the effects of environments (Table 2).
243
Under the same environment, different rice accessions also showed different
244
concentrations in 7 mineral elements, which may be ascribed to the genotypic
245
differences (Table 1 and 2). The mean values of most mineral elements among
246
different subgroups showed significant difference in two years, further supporting the
247
influence of genotypes on the mineral accumulation (Table 3). Moreover, ANOVA
248
indicated that the genotype variance for 7 mineral elements were highly significant
249
(P<0.001), while the environment variance for some elements such as Mg, Fe, Zn and
250
Cu were also significant (P<0.001) (Table 4), also suggesting that the mineral elements
251
were affected by hereditary as well as environment. Batten (2002) indicated that the
252
correlation between mineral elements concentrations might be affected by the
253
genotype ×environment, which was supported by our study. The Mg, Na, Zn, and
254
Cu were greatly affected by genotype × environment interaction, though it only
255
explained a small proportion of the total variation. So to improve the mineral nutrients, 10
256
breeders should not ignore the effect of genotype × environment interaction in their
257
breeding lines in different environments.
258
The correlation analyses between 7 mineral elements showed that Mg had a
259
significantly positive correlation with K, Fe, and Zn. Jiang et al, (2007) showed that
260
Mg was negatively correlated with Cu and positively correlated with K, Ca, Na, Fe,
261
Zn and Mn. Ca and was positively correlated Na, Mg, Fe, Zn and Mn. This might be
262
due to the interaction between ions in which chemical properties were equivalently
263
similar, which may compete for the site of absorption, transport, and function in
264
plant tissues (Gussarsson, Adalsteinsson, Jensen, & Asp, 1995; Robson et al, 1983).
265
These correlation results from present and previous studies could help improving the
266
efficiency of early generation selection for rice materials abundant in Mg, K, Fe, and
267
Zn in rice breeding program.
268
Many putative genomic regions associated with the phenotypes using this set of
269
materials have been identified. For example, Xu et al. (2014) identified 23 QTLs for 10
270
agronomic traits in the two years. Tong et al. (2014) identified 22 QTLs for starch
271
physicochemical properties in which some starch biosynthesis related genes such as
272
Wx, SSIIIb, ISA1, and ISA2 were revealed. Shao et al. (2014) found many potential loci
273
for polyphenol and antioxidant capacity traits. Therefore, it is possible to detect the
274
putative genomic regions or QTLs associated with different mineral elements using this
275
set of rice materials. In this study, a total of 17 QTLs for the mineral elements except
276
Mg and Fe were discovered which were located on chromosome 1, 2, 6, 8, 10 and 11.
277
We identified 5 loci for Ca content on chromosomes 2, 6, 8 which were not reported
278
before (Du et al, 2013；Garcia-Oliveira et al, 2009). Only one QTL (qCu6) was
279
identified on the chromosome 6, and it was not the same region as reported by
280
Garcia-Oliveira et al, (2009). Consistent with previous research, qK6.1 was close to
281
the QTLs identified by Du et al, (2013) on chromosome 6, suggesting that the locus
282
may be a hot region for K accumulation in rice grain and more further research on this
283
region is needed. Kumar, Jain, & Jain, (2014) detected one QTL for zinc content on 11
284
chromosome 2 which is close to the position of the qZn2 in the study. Few QTLs were
285
identified for Na content in rice grain, our study identified 5 new QTLs located at
286
different regions from the previous studies, and so further studies are needed to
287
confirm the results.
288
5. Conclusion
289
Wide genetic diversity in the 7 mineral elements in 20 brown rice accessions
290
was discovered in two years. These mineral elements were affected by genotype as
291
well as environment. The effects of genotype × environment interaction should also
292
be considered in different environments. Moreover, we discovered 17 QTLs involved
293
in the accumulation of 7 mineral elements. This study provided new information on
294
the understanding of the contributions of the genotype and environment interaction
295
effects to the complex traits such as mineral contents in the brown rice. The results
296
will also be useful for developing rice varieties to improve the mineral nutrients in
297
rice grain by molecular breeding.
298
Acknowledgement
299
This work was financially supported by the Special Fund for Agro-scientific
300
Research in the Public Interest (201103007) from the Ministry of Agriculture. We
301
thank Dr. Ruaraidh Sackville Hamilton in the International Rice Research Institute for
302
providing the rice materials used in this study.
303
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398 399
Table 1 The mineral contents of 20 rice accessions grown in Hainan in 2011. Rice
Code
Subgroups Ca (mg/kg)
Mg (g/kg)
Na (mg/kg)
K (g/kg)
Fe (mg/kg)
Zn (mg/kg) Cu (mg/kg)
Accessions G01
Azucena
Japonica
111.5±8.6 1.567±0.083 27.68±1.03 3.025±0.151 11.38±1.55
15.68±0.63 0.436±0.046
G02
Dom Sufid
Japonica
113.5±8.0 2.052±0.138 23.37±2.16 3.565±0.196 16.09±2.47
25.77±0.99 0.646±0.016
G03
Dular
Aus
117.1±7.6 1.696±0.092
18.46±0.01 0.217±0.026
G04
FR13A
Aus
115.6±0.9 1.599±0.104 16.29±2.82 2.968±0.232
9.60±2.06
18.97±0.92 0.442±0.034
G05
IR64-21
Indica
71.4±9.0
1.539±0.089
7.84±1.89
13.76±0.35 0.489±0.114
G06
LTH
Japonica
92.2±1.5
1.643±0.085 26.98±1.23 3.308±0.158 12.47±1.34
15.05±0.39 0.513±0.027
G07
M202
Japonica
108.7±5.2 1.636±0.012 29.73±4.94 3.695±0.181 11.74±1.45
11.5±0.14 0.417±0.005
G08
Minghui63
Indica
80.0±7.8
16.2±4.72
7.96±1.74
3.355±0.169 11.06±4.60
2.638±0.173
1.633±0.067
10.51±1.1
3.702±0.201
8.24±1.38
19.22±0.5 0.944±0.082
G09 Moroberekan Japonica
137.9±11.7 1.521±0.112
20.49±0.2
2.871±0.23
11.48±2.38
14.82±0.78 0.302±0.021
G10
N22
Aus
135.0±5.7
1.834±0.09
16.33±1.37 3.195±0.222 13.35±0.16
17.3±0.76 0.221±0.008
G11
Nipponbare
Japonica
77.5±7.2
1.854±0.097 30.98±3.27 3.596±0.245 13.88±2.62
14.81±0.27 0.519±0.033
G12
Pokkali
Indica
111.1±5.3 1.835±0.131 14.67±1.04 3.569±0.319
6.53±2.17
G13
Sadu-Cho
Indica
173.5±16.9 1.889±0.157
21.13±3.5
3.502±0.338
9.58±1.89
G14
SHZ-2
Indica
136.2±1.5
1.573±0.05
8.71±0.7
2.398±0.083
6.96±0.98
14.79±0.34 0.687±0.031
G15
Swarna
Indica
92.1±0.6
1.734±0.082 13.35±0.37 2.707±0.115
7.86±0.32
21.32±0.29 1.216±0.034
G16
Tainung67
Japonica
106.5±12.9 1.578±0.037 31.92±3.58 3.289±0.109
9.55±0.09
19.77±1.08 0.791±0.073
G17 Zhenshan97B
Indica
124.3±5.1 1.788±0.028 13.19±0.22 3.423±0.044
8.58±0.16
14.31±0.94 0.546±0.051
G18
Aswina
Indica
163.7±59.4 1.836±0.126
G19
Cypress
Japonica
G20
Rayada
Aus Average 1
LSD
400 401 402 403 404 405 406 407 408 409 410 411
92.6±1.4
18.71±1.53 0.579±0.059 21.81±2
0.739±0.055
30.3±0.21
3.687±0.353
9.50±1.20
21.29±1.24 0.787±0.077
1.792±0.037 15.76±2.12
3.36±0.097
12.50±0.81
18.43±0.1 0.484±0.023
26.5±1.03
3.06±0.042
10.18±0.88
19.49±2.35 0.451±0.013
160.3±0.9 1.754±0.026 116.0
1.718
20.10
3.246
10.42
17.76
0.571
31.7
0.190
4.92
0.422
3.85
2.07
0.104
Data were presented as mean value ± standard deviation. 1 LSD: least significant difference (P < 0.05).
412 17
413
Table 2
414
The mineral contents of 20 rice accessions grown in Hainan in 2012. Rice
Code
Subgroups Ca (mg/kg)
Mg (g/kg)
Na (mg/kg)
K (g/kg)
Fe (mg/kg) Zn (mg/kg)
Cu (mg/kg)
Accessions G01
Azucena
Japonica
100.6±2.0
1.566±0.023 44.53±2.72 3.121±0.026 9.17±0.61
G02
Dom Sufid
Japonica
136.2±5.0
2.306±0.087
G03
Dular
Aus
90.9±8.0
1.441±0.083 11.39±3.65 2.953±0.153 9.13±0.06
G04
FR13A
Aus
G05
IR64-21
G06
31.49±1.45 2.837±0.153
27.37±7.4 3.736±0.052 15.81±0.84 38.52±0.12 2.398±0.284 25.09±0.98 1.285±0.072
168.7±87.1 1.573±0.004
17.86±1.9 2.917±0.025 7.65±0.97
Indica
73.5±10.3
1.511±0.145
7.87±0.73 2.654±0.233 6.17±1.25
LTH
Japonica
97.8±1.8
1.69±0.028
22.62±3.6 3.385±0.081 8.53±0.69
24.21±0.47 1.772±0.152
G07
M202
Japonica
111.7±13.5
1.53±0.016
19.29±0.28 3.75±0.109
21.67±0.25
G08
Minghui63
Indica
77.1±1.9
1.416±0.014
G09 Moroberekan Japonica
106.6±0.9
1.483±0.002 16.49±1.48 3.046±0.025 9.16±0.54
25.83±1.32
G10
N22
Aus
126.8±4.5
1.86±0.002
18.19±0.64 3.22±0.022
28.42±0.77 1.623±0.029
G11
Nipponbare
Japonica
84.0±3.3
1.889±0.015
18.58±1.5 3.785±0.085 11.37±0.51
26.01±1.4
G12
Pokkali
Indica
120.0±27.6
1.676±0.02
16.96±1.07 3.463±0.062 4.01±0.10
20.93±0.48 1.779±0.064
G13
Sadu-Cho
Indica
122.9±4.2
1.843±0.041 14.65±2.19 3.532±0.039 7.42±0.34
23.6±1.21
2.097±0.117
G14
SHZ-2
Indica
140.7±7.5
1.563±0.062 11.41±0.65 2.549±0.133 5.09±0.43
17.98±1.49
2.757±0.15
G15
Swarna
Indica
81.8±2.6
1.628±0.065
9.93±2.26
4.88±0.35
19.04±0.07 2.416±0.088
G16
Tainung67
Japonica
93.3±6.5
1.257±0.01
17.31±0.5 2.988±0.038 4.47±0.05
21.54±0.59 2.128±0.037
G17 Zhenshan97B
Indica
113.7±5.3
1.931±0.024
7.83±0.65 3.486±0.027 7.73±0.30
18.34±0.09
G18
Aswina
Indica
121.8±40.9 1.691±0.058 35.33±3.08 3.474±0.081 6.82±0.89
20.15±1.37 2.241±0.031
G19
Cypress
Japonica
93.9±10.5
20.17±0.03 1.949±0.021
G20
Rayada
Aus
415 416 417 418 419
1.59±0.027
7.67±1.2
7.56±0.28
3.533±0.007 5.15±0.12
2.811±0.09
8.44±0.17
17.39±2.55 3.286±0.038 9.61±0.59
185.1±76.6 1.616±0.013 38.86±0.98 2.952±0.019 7.23±0.36
22.25±0.89 1.351±0.001 18.33±2
1.844±0.249
1.2±0.03
18.06±0.25 1.375±0.019 1.76±0.02
1.929±0.11
2.36±0.014
22.23±1.5
1.615±0.06
Average
112.3
1.653*
19.08
3.232
7.77*
23.19*
1.936*
LSD1
60.0
0.107
5.27
0.181
1.19
2.13
0.240
Data were presented as mean value ± standard deviation. 1 LSD: least significant difference (P < 0.05). * in the average data indicates significant difference between mean of mineral contents in different years (P < 0.05).
18
420
Table 3
421
Mean of mineral contents among different rice subspecies in different years. Ca Na Subgroup Mg (g/kg) K (g/kg) Fe (mg/kg) Zn (mg/kg) Cu (mg/kg) (mg/kg) (mg/kg) 2011 Aus 132.0a 1.721a 18.83b 3.144a 11.048a 18.55a 0.333c Indica 119.0ab 1.729a 14.98b 3.203a 8.136b 18.15a 0.748a Japonica 105.0b 1.705a 25.86a 3.339a 12.388a 16.98a 0.514b 2012 Aus 142.9a 1.622a 21.58ab 3.011b 8.111a 24.49a 1.468b Indica 106.4b 1.657a 13.96b 3.188ab 5.908b 19.56b 2.109a Japonica 103.0b 1.664a 22.95a 3.387a 9.461a 26.18a 1.997a 422 Different letters in the same column indicate significant difference at P < 0.05. 423
19
Table 4 Correlation analysis of mineral contents in two years. Correlation
424 425 426 427 428
Ca
Mg
Na
K
Fe
Zn
Cu
Ca 0.222 0.200 0.013 -0.014 0.265 -0.119 0.562*** Mg 0.237 0.194 0.618*** 0.495** 0.593*** 0.131 0.713*** Na 0.337* 0.161 0.526*** 0.062 -0.094 0.658*** 0.467** K -0.062 0.543*** 0.147 0.425** 0.255 0.024 0.809*** Fe 0.074 0.661*** 0.318* 0.447** 0.150 -0.356* 0.786*** Zn 0.170 0.558*** 0.485** 0.335** 0.804*** 0.455** 0.295 Cu -0.021 0.300 0.223 -0.132 0.077 0.169 0.383* *,**,*** Indicate significance at P < 0.05, 0.01, and 0.001 levels, respectively. Correlation between different mineral elements above diagonal were for data in 2011, those below diagonal were for data in 2012, and those in diagonal in bold were for data between 2011 and 2012.
20
429
Table 5
430 Mean square values from analysis of variance for mineral contents. Source df Ca Mg Na K Fe Zn Cu Genotype (G) 19 2803.234*** 0.126*** 276.232*** 0.531*** 25.525*** 49.085*** 0.369*** Year (E) 1 268.975 0.084*** 21.061 0.004 140.383*** 589.763*** 37.23*** 19 549.653 0.019*** 58.049*** 0.027 1.447 26.941*** 0.188*** G × E 431 *** indicate significance at P <0.001 levels; df: degree of freedom. 432 433
21
434
Table 6
435 436
Significantly associated QTLs for Ca, Cu, K, Na and Zn detected in 2 years by association test. Trait
Ca
Cu K
Na
Zn
Year
QTL
2011 qCa2.1 qCa6.1 2012 qCa2.2 qCa6.2 qCa8 2011 qCu6 2011 qK1 qK6.1 qK6.2 2012 qK2 qK8 qK11 2011 qNa1 qNa11 2012 qNa6 qNa10 2011 qZn2
Chromosome Position (bp)
2 6 2 6 8 6 1 6 6 2 8 11 1 11 6 10 2
30592700 3450780 19703118 2500449 22222152 15357788 41972397 813231 10689396 24689470 27690577 3086965 43155830 4854990 21112917 21698303 23978212
437 438
22
Major
Miner
Miner allele
allele
allele
frequency
C G A C G C C C T C C C T T G T C
T T G T T T T T C A T A C G A A A
P value
0.0046 0.0048 0.0048 0.0043 0.0025 0.0049 0.0046 0.0038 0.0036 0.0042 0.0044 0.0048 0.0049 0.0048 0.0031 0.0031 0.0036
0.325 0.35 0.375 0.425 0.5 0.3 0.3 0.375 0.325 0.325 0.35 0.3 0.45 0.375 0.45 0.45 0.4
Highlights
Variation in mineral elements in whole grain of 20 rices was investigated.
Ca, Na and K were mainly affected by the genotypic variance.
Fe, Zn and Cu were mainly affected by the environment variance.
17 genetic loci were identified to control the mineral contents.