1

QTL mapping for mineral nutrients in rice grain using

introgression lines population

Ana Luisa Garcia-Oliveira, Lubin Tan, Yongcai Fu and Chuanqing Sun*

Department of Plant Genetics and Breeding and State Key Laboratory of Plant

Physiology and Biochemistry, China Agricultural University; National Evaluation

Central for Agricultural Wild Plant (Rice); Beijing Key Laboratory of Crop Genetic

Improvement; Key Laboratory of Crop Genetic Improvement and Genome of Ministry of

Agriculture, Beijing 100094, China

* Corresponding author

With 3 tables and 1 figure Address for Editorial Correspondence: Dr. Sun Chuangqing

State Key Laboratory of Plant Physiology and Biochemistry,

Department of Plant Genetics and Breeding

China Agricultural University

Yuanmingyuan West Road, Haidian, 100094

Beijing, China

Phone: 086-010-62731811

FAX: 086-010-62731811

E-mail: suncq@cau.edu.cn

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Abstract

In present study, Fe, Zn, Mn, Cu, Ca, Mg, P and K contents of 85 introgression lines (ILs)

derived from a cross between an elite indica cultivar Teqing and the wild rice (O.

rufipogon) were measured by Inductively Coupled Argon Plasma (ICP) Spectrometer.

Substantial variation was observed for all traits and most of mineral elements were

significantly positive correlated or independent except Fe with Cu. A total of 31 putative

quantitative trait loci (QTLs) were detected for these eight mineral elements by single

point analysis. Wild rice (O. rufipogon) contributed favorable alleles for most of QTLs

(26 QTLs), and chromosome 1, 9 and 12 exhibited 14 QTLs (45%) for these traits. One

major effect QTL for zinc content accounted for the largest proportion of phenotypic

variation (11-19%) was detected near the SSR marker RM152 on chromosome 8. The co-

locations of QTLs for some mineral elements observed in this mapping population

suggested the relationship at molecular level among these traits and could be helpful for

simultaneous improvement of these traits in rice grain by marker assisted selection

(MAS).

Key words: QTL, introgression lines, mineral elements, Oryza sativa, O. rufipogon

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Humans require at least 49 nutrients for their normal growth and development, and

the demand for most of nutrients are supplied by cereals especially rice due to its staple

role (Welch and Graham 2004). Among these nutrients, mineral elements play numerous

beneficial roles due to their direct or indirect impact in both plant and human metabolism

and the deficiencies or insufficient intakes of these nutrients led to several dysfunctions

and diseases in humans. Studies have indicated widespread occurrence of deficiencies for

mineral elements such as anemia for iron and osteoporoses for calcium in most of

developing as well as in developed countries (Welch and Graham 1999). The numbers

indicate that around two billion people suffer from iron deficiency, while prevalence of

zinc deficiency is much harder to quantify due to the lack of a reliable and easy clinical

assay (FAO 2004). In addition, other minerals deficiencies such as calcium, where data

suggests that roughly three million people after the 50s suffer from osteoporosis (van

Staa et al. 2001) are also associated with malnutrition and have reached at worrying

levels. Recent epidemiological studies found that whole-grain intake (such as brown rice),

is linked to disease prevention against cancer, cardiovascular disease, diabetes and

obesity (Slavin 2003).

In the past, much emphasis was placed on enhancement of yield to increase the

availability of food for resource poor peoples. In recent past, due to more awareness

regarding importance of mineral elements in human diets, breeders started to pay more

attention for improvement of nutrient qualities of major food grain crops especially

mineral elements (Zhang et al. 2004). Many researchers already studied genetic variation

for minerals elements in cereal grains such as rice (Gregorio et al. 2000; Zhang et al.

2004), wheat (Ortiz-Monasterio and Graham 2000; Cakmak et al. 2000; Balint et al. 2001)

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and maize (Arnold and Bauman 1976; Arnold et al. 1977; Bänziger and Long 2000) and

reported the narrow genetic base for mineral elements especially for iron, zinc,

manganese and copper in cultivated rice (Banziger and Long 2000, Beebe et al. 2000,

Chavez et al. 2000, Gregorio et al. 2000, Ortiz-Monasterio and Graham 2000, Stangoulis

et al. 2007). But, at molecular level (QTL analysis) information in cereals is very limited.

Recently, Stangoulis et al. (2007) identified some QTLs under controlled environment for

Fe, Zn, Mn and P in rice grain using doubled-haploid population.

Several researchers have successfully exploited the wild related species to incorporate

the variability in cultivated rice for the improvement of qualitative and quantitative traits

of agronomic importance (Brar and Khush 1997). Thus, to overcome the bottleneck for

improvement of rice grain quality, utilization of wild relatives such as O. rufipogon can

be a candidate source for broadening the genetic base of rice cultivars.

In the study reported here we mapped quantitative trait loci responsible for

accumulation of micro (iron, zinc, manganese and copper) and macro (calcium,

magnesium, potassium and phosphorus) mineral elements in rice grain using

introgression lines (ILs) under field conditions.

1 Material and methods

1.1 Plant materials

The introgressed population used in the present investigation was derived by three

consecutive backcrosses with a recipient parent Teqing (O. sativa ssp. indica), an elite

indica cultivar whereas an accession of common wild rice (O. rufipogon Griff.)

introduced from Yunnan, China was used as donor parent. The development of

introgression lines (ILs) population and genomic contribution of O. rufipogon in the total

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genome of each line were explained by Tan et al. (2007). A total of 85 introgression lines

(ILs) with normal maturity and fertility, and having sufficient amount of seeds during

both years were included in this study.

1.2 Field evaluation

The 85 ILs along with the recurrent parent Teqing were evaluated in randomized

complete block design at Experiment Station of China Agricultural University, Beijing

(E116°28′, N39°54′) during summer 2005 and 2006. Each genotype was planted in a five

rows plot containing 11 plants per row with 15 cm apart plant to plant spacing. The field

management conditions were similar to that under normal rice production. At maturity,

ten plants per plot were randomly selected from central three rows and harvested

separately. To get the representative sample for trait measurements, seeds from individual

plants within plot were mixed. Eight mineral elements namely, iron (Fe), zinc (Zn),

Manganese (Mn), calcium (Ca), copper (Cu), magnesium (Mg), potassium (K) and

phosphorus (P) were measured.

1.3 Sample preparation and extraction of mineral elements from rice grains

A representative sample from each line was manually dehulled in a mini rice huller

(Kett TR-100) and washed with deionized double distilled water to remove small husks

and others possible contamination. The dehulled rice grain samples were covered in

individual paper bags and kept in oven at 75°C for overnight. About 15g air-dried sample

from each genotype was ground in a small rice miller and stored in plastic bags. Mineral

elements were extracted from a 0.5g representative ground sample using standard wet-

ashing digestion method (AOAC 1995): 5ml nitric acid (HNO3) of analytical grade was

added into each microwave vessels containing rice samples and kept the samples for

overnight at room temperature. After overnight digestion, 1ml hydrogen peroxide (H2O2)

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was added to each sample. For final digestion, the temperature was ramped up to 120°C

in first 5min, then raised up to 150°C in next 3min, and ended at 185°C in 4min and at

last each digested sample was cooled down for 20min in microwave accelerate reaction

system; model MARS (CEM Corporation, USA). Each sample was transferred into a

25ml volumetric flask and final volume was made up 25ml with MilliQ water. To avoid

possible contamination, each sample was stored in polypropylene flasks.

1.4 Micro and macro-nutrients measurement

Inductively Coupled Argon Plasma (ICP) Spectrometer (model Iris Intrepid II XSP,

Thermo Electron Corporation, Canada) was performed to measure the content of mineral

elements in rice grain samples. To control the variation within sample, each sample was

measured thrice and mean of triplicate observation was used to represent the content of

mineral elements in each sample. For error control among samples, all samples were

measured in a set of 20 samples containing two blanks and 2 standard reference samples

with known concentration of mineral elements. Standard reference samples of rice grain

were used to compensate for possible matrix interference variations during microwave

digestion whereas external standards were used to construct the standard curve for

calibration of ICP. The content of each sample was converted into amount of mineral

elements (µg g-1) in each sample by using Microsoft excel sheet.

1.5 Genotypic data and genetic linkage map

Genomic DNA extraction and SSR analysis were reported earlier by Tan et al. 2004.

Initially, 120 polymorphic SSR markers were used to construct the genetic linkage map

with MAP MANAGER QTX (http://mapmgr.roswellpark.org/mmQTL.html) in BC3F1

mapping population. In subsequent generations, additional 59 polymorphic SSR markers

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were included. Finally, 179 SSR markers were used to generate the molecular linkage

map (Tan et al. 2007).

1.6 Trait analysis

Individual (2005 and 2006) as well as pooled analysis of variance (ANOVA) and

Pearson correlation coefficients were carried for all studied traits viz. micro and macro-

nutrients content (µg g-1 dry seed) using the “PROC GLM” procedure of SAS (SAS

Institute, version 8.0, 1999). Significance of correlation coefficients (r) at P = 0.05 or

0.01 is indicated by * or **, respectively. Heritability in broad-sense was estimated as

explained by Singh and Chaudhary (1985).

1.7 QTL analysis

QTL mapping was performed by single point analysis method using MAP MANAGER

QTX software. Model QTXb17 was used to detect association between phenotype and

the corresponding marker genotype (Manly et al. 2001). The statistical threshold for

single-point analysis was P < 0.05. For main QTL effects, positive and negative signs of

the estimates indicate that O. rufipogon and Teqing contributed toward higher value

alleles for the corresponding traits, respectively. QTL were named according to McCouch

et al. (1997), in which a two- or three-letter abbreviation is followed by the number of the

chromosome where the QTL is found and a terminal suffix, separated by a period,

providing a unique identifier to distinguish multiple QTL on a single chromosome.

2 Results

2.1 Trait segregation and field performances

The overall mean and range of each trait measured in grains of the 85 ILs grown

during both 2005 and 2006 are presented in Table 1. Analysis of variance showed the

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significant genotype by environment interactions for these mineral elements. A wide

range of variation was observed for levels of all the mineral elements studied in this set of

population (Table 1). Among micro-elements, Zn and Mg were observed in highest

amount with a combined mean value of 27.1 µg g-1 and 16.6 µg g-1, respectively whereas

Fe and Cu were found in least amount with mean performance of 9.6 and 5.3 µg g-1,

respectively. Fe and Mn showed high performance during 2005 whereas reverse trend

was observed in case of Zn and Cu (Table 1). Among macro-elements, P and K was

observed as predominant elements with mean value of 3010 µg g-1 and 2297 µg g-1,

respectively, whereas Mg and Ca was found in lower amount with mean value of 1132 µg

g-1 and 102.5 µg g-1, respectively. Among macro-elements, the contribution of P and K

was observed highest in 2006 whereas Mg and Ca showed opposite trends. Skewness and

Kurtosis were measured to describe nature of distribution (Table 1). All the eight traits

had platykurtic distribution (kurtosis value b2<3).

2.2 Heritability and correlation analysis

A wide range of heritability in broad-sense was observed for these elements (Table 2).

Heritability estimates for Fe, Zn, Mn, Cu, Ca, Mg, P and K were observed 72.8%, 40.6%,

55.9%, 85.6%, 70.6%, 54.2%, 46.4% and 19.1%, respectively. Micro-elements exhibited

medium to high level of heritability whereas a very low to high level of heritability was

observed for macro-elements. Micro elements exhibited a weak correlation with each

other. Only during 2005 and 2006, Fe content showed very weak correlation from

negative with Cu to positive with Zn and Mn, respectively whereas Zn showed a fragile

correlation in positive direction in positive direction with Cu and Mn in 2005 and 2006,

respectively. In contrast highly positive correlation was observed among macro-elements

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except Ca with K. However, no negative correlation was observed between micro and

macro elements (Table 2).

2.3 QTL detection for mineral-elements

A total of 31 putative QTLs associated with these mineral elements were detected on

all chromosomes except chromosome 7. Out of 31 putative QTLs, only 17 QTLs were

observed during both years (Table 3). Chromosome(s) 1 and 9 had highest number of

QTLs (5 QTLs each) for these minerals elements whereas lowest number of QTLs was

observed on chromosome(s) 6 and 11 (one QTL each) (Figure 1). At least one QTL and

as many as 7 QTLs were detected for different traits. The position of QTLs for micro and

macro elements along with linked marker, degree and direction of additive effect,

probability level and phenotypic variance explained by each QTL detected during 2005

and 2006 are given in Table 3.

Micro elements

A total of 10 QTLs were detected for micro elements covering all the chromosomes

except chromosomes 4, 7 and 11. Out of 10, 6 QTLs were observed during both years.

Iron For iron content, one QTL located on chromosome 2 accounted for 5% and 7%

phenotypic variation in 2005 and 2006, respectively. During 2006, a minor QTL was also

detected near marker RM296 on chromosome 9. The favorable allele for iron content at

chromosome 2 was contributed by O. rufipogon whereas recipient parent Teqing

accommodated at chromosome 9.

Zinc Three QTLs for Zn content were identified on chromosomes 5, 8 and 12. The QTL

near marker RM152 on chromosome 8 accounted for the largest proportion of phenotypic

variation (11-19%) for Zn content over both years whereas QTL located on chromosome

12 accounted for 9% phenotypic variation was detected only during 2005 and the O.

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rufipogon alleles enhanced the Zn content at these loci with a range of 3.86-6.91 µg g-1.

On the other hand a minor QTL for Zn content was contributed by Teqing at

chromosome 5 with an additive effect of 2.29-2.44.

Manganese Four chromosomal regions explaining 5-11% phenotypic variation for Mn

content were found on chromosome 1, 2, 3, and 10. Two of them located on

chromosomes 3 and 10 near markers RM5488 and RM590, respectively were found in

both years with opposing allelic effect. In contrast, QTLs located on chromosome 1 and 2

were found only during single year and accounted for 11% and 5% phenotypic variation

for Mn content, respectively. The O. rufipogon derived alleles enhanced Mn content at

these loci.

Copper Only one QTL accounting for 6% phenotypic variation for Cu content was

identified during both years near marker RM204 on chromosome 6 and the favorable

allele at this locus was accommodated by O. rufipogon.

Macro elements

A total of 21 putative QTLs associated with macro elements were identified on all

chromosomes except chromosome 2, 6 and 7. It is noteworthy that more than 50 per cent

QTLs (11 QTLs) were located only on chromosome(s) 1, 9 and 12, and the O. rufipogon

derived alleles enhanced macro elements at these loci in Teqing background.

Calcium Seven QTLs were observed for Ca content and O. rufipogon contributed the Ca

enhancing alleles at these QTLs except QTL qCa11-1 located near the marker RM202 on

chromosome 11. Ca QTLs located on chromosomes 1, 4, 5, 9, 10, 11 and 12 and

explained a range of 5% to 14% total phenotypic variation. Five QTLs, qCa1-1, qCa4-1,

qCa5-1, qCa9-1 and qCa12-1 were detected in both years. QTL, qCa1-1 located near

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marker RM6480 on chromosome 1 exhibited largest proportion of phenotypic variation

(9-14%) for Ca content.

Magnesium Five QTLs were detected one each on chromosome 1, 3, 5, 9 and 12

explaining phenotypic variation varied from 5% to 16% for Mg content. Three of these

QTLs detected only in single year (qMg1-1 and qMg12-1 in 2005, and qMg9-1 in 2006)

and the Mg content increasing alleles came from O. rufipogon at these loci. Only two

QTLs located near markers RM5488 and RM598 exhibited stable performance across

years with opposing allelic effects of Teqing and O. rufipogon, respectively.

Phosphorus Five QTLs enhancing P content were found on chromosomes 1, 3, 8, 9 and

12. Only two of them explained >10% phenotypic variation for P in both years, located

near markers RM212 and RM201 on chromosome(s) 1 and 9, respectively. However,

three QTLs accounting for 8% to 9% phenotypic variation for P content were also

observed during 2005, but these QTLs were not detected in 2006. All the alleles

controlling levels of P were derived from donor parent (O. rufipogon).

Potassium Four QTLs one each on chromosome 1, 4, 8 and 9 were associated with

quantitative variation for K content in present study. Two QTLs (qk4-1 and qk9-1) of

them located near markers RM1113 and RM3787 on chromosome 4 and 9, respectively

were found in both years whereas QTLs (qK1-1 and qK8-1) detected near markers RM5

and RM3572 on chromosome 1 and 8 were observed only during 2006 and 2005,

respectively. These QTLs explained a range of 4% to 14% of total phenotypic variation

for levels of K and the favorable alleles at these loci were contributed by O. rufipogon.

3 Discussion

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The presence of mineral deficiency in humans are manifested by a wide spectrum of

complex systems resulted in less attention on systematic breeding efforts for mineral

elements in major food crops. Several new functions of mineral elements identified over

the past few decades created a new interest for biofortification of major food crops with

enhanced level of mineral elements. Several reports indicated the narrow genetic

variability for mineral elements in cultivated rice whereas higher level of mineral

elements was observed in wild rice O. rufipogon (Cheng et al 2005). Therefore, the

exploration of new source of genetic variability for the improvement of rice grain quality

such as wild relatives, common wild rice (O. rufipogon) is highly relevant in present

context.

3.1 Trait variation and interrelationships

The mean performances of introgression lines (ILs) population was observed

significantly superior over elite cultivar Teqing for most of the traits except for K content,

which had higher levels in Teqing. Thus, the results obtained in present investigation

clearly exhibit the favorable effect of introgressed genetic variability and suggested that

inter-specific crosses especially O. rufipogon have enormous genetic potential for

improving the nutritional values of rice cultivars. The information regarding heritable

portion of a trait in any breeding program is highly relevant. Although, heritability

estimates are limited to experimental material and setup, and may differ widely in the

same crop and same trait (Hill et al. 1998; Holland et al. 2002). Heritability estimates

observed for these nutritional traits are in good agreement with results from previous

studies in rice (Zhang et al. 2004) and maize (Arnold and Bauman 1976; Arnold et al.

1977). In addition to ANOVA, the lower level of heritability estimates also indicated the

strong environmental effect on these traits.

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In order to develop a breeding selection scheme for simultaneous improvement of

multiple traits, the results obtained in the present investigation from interrelationship

study among these nutritionally important mineral elements are encouraging. Most of

traits studied in this experiment were significantly positive correlated or independent

except Fe with Cu (Table 2). This may point to common molecular mechanisms

controlling uptake and metabolism of these minerals in grains (Vreugdenhil et al. 2004).

3.2 QTLs for mineral elements from wild rice

Advances in molecular marker technology resulted in numerous QTL analysis studies

for simple agronomic traits in rice, which led to identification of harboring genes under

these loci. Yet, mineral accumulation in seeds continues to be a perplexing phenomenon

that seems to be controlled by poly-genes (Grusak and DellaPenna 1999). However,

despite substantial genetic variability for these mineral elements in germplasm of major

food crops, the information at molecular level is limited and till date, only few QTL

analysis studies have been reported in Arabidopsis thaliana (Bentsink et al. 2003;

Vreugdenhil et al. 2004, 2005;), beans (Guzman-Maldonado et al. 2003), and more

recently in rice (Stangoulis et al. 2007). To resolve the complexity of quantitative traits,

introgression lines have key advantages in reducing the polygenic traits by dissecting

them into a set of monogenic loci (Peleman and Van-der-Voort 2003). Presently,

introgression lines become a useful experimental material for large-scale identification,

fine-mapping, cloning and molecular characterization of QTL in rice (Li et al. 2005; Tian

et al. 2006). Furthermore, utilization of wild relatives of cultivated plant species such as

wild rice (O. rufipogon) not only provides unique genetic resources to study the genetics

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and molecular biology, but also may help to identify the rare alleles for these mineral

elements.

A notable aspect of this study is that out of 31 putative QTLs for mineral elements, 26

QTLs (83.9%) had trait improving alleles derived from the wild rice (O. rufipogon) and

only three chromosomes (chromosome 1, 9 and 12) contributed 45% chromosomal

regions (14 QTLs) for these traits. The co-locations of QTL for some mineral elements

observed in this mapping population also indicated the relationship at molecular level

among these traits. Vreugdenhil et al. (2004) also reported co-location of QTLs for levels

of mineral elements in Arabidopsis thaliana. Although, if the QTL clusters identified in

this study are further confirmed, particularly located on chromosome 9 and 12, they could

be used in an efficient marker-assisted selection program to facilitate increasing levels of

mineral elements in rice grain. The co-locations of QTLs suggested the importance of

these regions for mineral accumulation in rice grain and may be due to physiological

coupling of the accumulation of certain minerals or tight linkage of different genes.

QTL detected for Zn content in this study on chromosome 12 near the SSR marker

RM235 was also previously reported in this region (Stangoulis et al. 2007). However, this

QTL accounted for 9% phenotypic variation for levels of Zn and was detected only

during 2005. One of the reasons for differences in power of QTL detection in both studies

may be due to strong environmental effect on these traits. Stangoulis et al. (2007)

conducted the experiment in controlled conditions (glass house) whereas we grew the

experimental material in open field conditions. However, both studies suggested the

stability of this QTL across environments.

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Due to the wide prevalence of anemia, the biofortification of rice grain with enhanced

amount of Fe content is becoming a hot issue in rice growing as well as consuming

countries. In the present study we detected a QTL for Fe content near the SSR marker

RM6641 on chromosome 2. Furthermore, Stangoulis et al. (2007) also reported a major

QTL explaining 16.5% of phenotypic variation for Fe content on chromosome 2 using a

double haploid population derived from a cross between an elite indica variety IR64 and

an upland japonica variety Azucena. The favorable allele at this QTL was contributed by

Azucena, whereas in present investigation the Fe enhancing allele came from wild rice (O.

rufipogon). The results obtained by Stangoulis et al. (2007) and in present study indicated

that indica subspecies may have inferior alleles for Fe content at this region on

chromosome 2. Gregorio et al. (2000) also previously reported three loci explaining 19-

30% variation for Fe content on chromosome 7, 8, and 9 in rice. In present study we did

not detect any QTL for Fe content on chromosome 7 and 8, but a minor QTL observed on

chromosome 9 also indicating the consensus region for Fe content on chromosome 9 in

rice.

The number of QTL detected in each study depends upon genetic diversity among

parents, population size and the number of markers tested (Brondani et al. 2002). It is

noteworthy that QTLs detected in present investigation for K on chromosome 1 and 4

(Wu et al. 1998) for P on chromosome 1 and 12 (Ni et al. 1998; Wissuwa et al. 1998;

Ming et al. 2001; Wissuwa and Ae 2001a, b) and for Mn on chromosome 10 (Wang et al.

2002) were also previously reported in different parts (roots and shoots) of rice plant.

Thus the association of common chromosomal regions indicates the same physiological

process in sink to source.

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However, little information is available regarding the candidate genes that play a

critical role for the transport of these metals. Because, most of functional studies for these

candidate genes have been carried out in yeast, but their actual functions in plants is still

in question, especially from source to sink (Narayanan et al. 2007). Clemens (2001)

suggested that maintaining cation homeostasis in plant requires coordinated network of

several genes associated with metal uptake, transportation, trafficking and sequestration

mechanisms. In contrast to sequence homology, further fine mapping and analysis of

NILs could provide more precise role of co-localization of QTL for these traits. Thus, it

is difficult to conclude from these suggestive co-localizations of QTL for mineral

elements and it indicates coincidental or may be results of closely linked genes. Finally

the information reported from present investigation may help to solve the complexity of

mineral accumulations in rice grain.

Acknowledgements

This work was funded by the Protection and Utilization Project of Agricultural Wild

Plants of the Ministry of Agriculture of China, and a grant from China National High-

Tech Research and Development (“863”) Program (No. 2006AA100101).

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Figure 1. Chromosomal locations of putative quantitative trait loci (QTLs) for mineral traits detected in introgressed lines (ILs) population derived from a cross between indica cultivar Teqing and the wild rice O. rufipogon. Note: Symbols indicates the positions of putative QTL for corresponding minerals.

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Table 1. Mean (standard deviation), range, skewness and kurtosis for nutritional traits in 85 rice introgression lines and recipient parent grown during summer 2005 and 2006.

Mean

Teqing Introgression Lines Range Trait

(µg g-1 dw) 2005 2006 Pooled 2005 2006 Pooled Min Max

Skewness Kurtosis

Fe 8.5 6.6 7.5 10.2 ± 2.3 9.1 ± 2.1 9.6 ± 2.3 4.9 20.0 1.19 1.62

Zn 14.4 18.7 16.6 21.7 ± 7.1 32.4 ± 7.3 27.1 ± 8.9 13.3 60.1 1.10 1.22

Mn 16.5 16.7 16.6 17.5 ± 2.8 15.7 ± 2.6 16.6 ± 2.9 8.4 28.2 0.64 0.29

Micro- element

Cu 3.9 6.8 5.3 4.6 ± 2.6 5.9 ± 3.1 5.3 ± 2.9 1.3 19.3 1.46 1.91

Ca 96.9 100.1 98.5 105.5 ± 13.8 99.5 ± 13.9 102.5 ± 14.1 56.6 145.3 0.70 -0.09

Mg 1118.7 1122.8 1120.7 1138.0 ± 97.8 1126.0 ± 88.6 1132.0 ± 93.4 896.0 1480.0 0.69 0.72

P 2754.9 2912.8 2833.9 2907.0 ± 246.8 3112.0 ± 245.2 3010.0 ± 266.3 2405.0 3767.0 0.27 -0.24

Macro- element

K 2279.8 2538.2 2409.0 2123.0 ± 189.3 2471.0 ± 278.2 2297.0 ± 294.8 1503.0 3201.0 0.05 0.24

Note: Fe-Iron, Zn-Zinc, Mn-Manganese, Cu-Copper, Ca-Calcium, Mg-Magnesium, P-Phosphorus, K-Potassium.

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Table 2. Pearson correlation coefficients and heritability in broad-sense (h2B%) among mineral elements measured in 85 ILs derived from Teqing x O. rufipogon during 2005 (above diagonal) and 2006 (below diagonal).

Micro-element Macro-element Traits

Fe Zn Mn Cu Ca Mg P K h2B (%)

Fe 1 -0.067 0.106 -0.184* 0.259** 0.223** 0.207** 0.165* 72.8

Zn 0.215* 1 -0.066 0.149* -0.048 -0.081 0.079 0.145 40.6

Mn 0.155* 0.187* 1 0.032 0.437** 0.241** 0.238** 0.262** 55.9 Micro- element

Cu 0.019 0.119 -0.067 1 -0.046 0.004 0.019 -0.071 85.6

Ca 0.123 0.130 0.324** -0.051 1 0.357** 0.269** 0.147 70.6

Mg 0.132 0.298** 0.346** 0.061 0.428** 1 0.772** 0.585** 54.2

P 0.181* 0.323** 0.240** 0.111 0.408** 0.764** 1 0.663** 46.4 Macro- element

K 0.103 0.403** 0.175* 0.031 0.188* 0.632** 0.642** 1 19.1

Note: Fe-Iron, Zn-Zinc, Mn-Manganese, Cu-Copper, Ca-Calcium, Mg-Magnesium, P-Phosphorus, K-Potassium.

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Table 3. Quantitative trait loci (QTLs) for micro and macro-elements detected by single point analysis.

2005 2006 Traits Locus Marker Chr.

P PV (%) Adda) P PV (%) Add a)

qFe2-1 RM6641 2 0.0383 5 0.72 0.01409 7 1.15 Fe

qFe9-1 RM296 9 0.04043 5 -0.78

qZn5-1 RM1089 5 0.04064 5 -2.29 0.03214 5 -2.44

qZn8-1 RM152 8 0.00002 19 5.02 0.00158 11 3.86

Zn

qZn12-1 RM3331 12 0.00485 9 6.91

qMn1-1 RM3475 1 0.002 11 1.29

qMn2-1 RM6367 2 0.04761 5 0.85

qMn3-1 RM5488 3 0.03714 5 -1.32 0.03112 5 -1.25

Mn

qMn10-1 RM590 10 0.04695 5 0.92 0.0126 7 1.06

Micro-

element

Cu qCu6-1 RM204 6 0.02799 6 1.16 0.01929 6 1.48

qCa1-1 RM6480 1 0.00538 9 12.29 0.0004 14 15.79

qCa4-1 RM317 4 0.02486 6 7.54 0.00172 11 14.87

qCa5-1 RM598 5 0.0051 9 8.41 0.00173 11 9.57

qCa9-1 RM296 9 0.03019 5 5.29 0.02444 6 3.87

qCa10-1 RM258 10 0.00901 8 5.26

Macro-

element

Ca

qCa11-1 RM202 11 0.01765 6 -5.48

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qCa12-1 RM5479 12 0.01879 6 9.05 0.03271 5 4.64

qMg1-1 RM499 1 0.00157 11 85.11

qMg3-1 RM5488 3 0.00326 10 -62.36 0.04397 5 -39.64

qMg5-1 RM598 5 0.00042 14 73.94 0.01718 6 46.67

qMg9-1 RM3919 9 0.00782 8 26.49

Mg

qMg12-1 RM5479 12 0.00014 16 101.00

qP1-1 RM212 1 0.00192 11 119.31 0.00347 10 111.70

qP3-1 RM3766 3 0.00866 8 97.58

qP8-1 RM3572 8 0.00471 9 173.09

qP9-1 RM201 9 0.00164 11 147.70 0.0176 11 156.00

P

qP12-1 RM5479 12 0.00803 8 186.82

qK1-1 RM5 1 0.00175 11 130.40

qK4-1 RM1113 4 0.01476 7 88.22 0.01353 7 134.2.0

qK8-1 RM3572 8 0.0003 14 145.10

K

qK9-1 RM3787 9 0.00044 14 117.00 0.04858 4 60.91

Note: Fe-Iron, Zn-Zinc, Mn-Manganese, Cu-Copper, Ca-Calcium, Mg-Magnesium, P-Phosphorus, K-

Potassium. a) Positive and negative signs of the estimates indicate that O. rufipogon and Teqing contributed toward

higher value alleles for the corresponding traits, respectively.