Abstract
Biofortification is a cost-effective method for increasing the availability of micronutrients. Rice breeding for high levels of micronutrients is one of the best approaches to solve the problem of malnutrition. In this study, we developed a doubled haploid (DH) population derived from a cross between the rice cultivars 93-11 and Milyang 352 and evaluated QTLs for grain micronutrients and grain shape. Two co-localized QTLs, qFe3-1 and qZn3-1, were identified in the interval between ah03002520 and cmb0336.5 on chromosome 3, which explained 17.6% and 10.5% of the phenotypic variation, respectively. Correlation analysis between agronomic and micronutrient traits showed positive correlations between grain Fe and Zn contents but a negative correlation between grain Fe content and length-to-width ratio. This indicated the possibility of simultaneously increasing both Fe and Zn content in rice grains for improving the micronutrient profile of rice. We selected some promising lines by recombinant selection using linked markers on chromosome 3. The co-localized QTLs qFe3-1 and qZn3-1 might be useful for the improvement of biofortified rice breeding by marker-assisted selection and gene pyramiding.
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Key words: Rice, Biofortification, Breeding, QTL, Micronutrient
INTRODUCTION
Fe and Zn are the most important micronutrients, the deficiencies of which constitute a major cause of malnutrition. More than half of the world’s population suffering from deficiencies of bioavailable nutrients is in developing countries (
Shahzad et al. 2014). Fe deficiency causes anemia, stunted growth, and poor cognitive development, whereas Zn deficiency causes stunting, reduced immunity, diarrhea, lesions on the eyes and skin, delayed healing of wounds, mental lethargy, etc. (
Harvest Plus 2014). Usually, indica rice contains less micronutrients than japonica rice. Thus, milled rice does is not a major source of mineral elements in significant quantities and cannot meet the recommended daily dietary intake (
Garcia-Oliveira et al. 2009). As a result, most people who usually eat indica rice in South and Southeast Asia, Africa, and Latin America often have chronic micronutrient malnutrition, which is known as “Hidden Hunger” (
Cakmak 2008).
The breeding of biofortified rice is a highly practicable way to solve the malnutrition problem in developing countries. To develop biofortified rice varieties, access to diverse germplasm collections rich in grain micronutrients and a thorough understanding of the molecular genetic basis of grain micronutrients are necessary.
Several studies have reported the evaluation of germplasm and advanced breeding lines for grain Fe and Zn concentration (
Gregorio et al. 2000). In an evaluation of around 1,038 samples of brown rice for Fe and Zn,
Gregorio et al. (2000) found that the micronutrient concentration was very low in brown rice: 6.3-24.4 mg/kg Fe and 13.53-58.4 mg/kg Zn. A few varieties with high grain Fe and Zn were found in aromatic rice varieties, including Jalmagna, Zuchem, Madhukar, and Xua Bue Nuo.
Owing to limited rice genetic resources that are rich in Fe and Zn, most QTL studies have been conducted by using general mapping populations of recombinant inbred lines (RILs), backcrossed inbred lines (BILs), and doubled haploids (DH)s.
Lu et al. (2008) and Norton
et al. (2014) identified six QTLs for Fe on chromosomes 1, 3, 6, 7, and 9 and seven QTLs for Zn on chromosomes 5, 6, 7, 10, and 11 using RIL populations derived from indica × indica or indica × japonica crosses. Swamy
et al. (2018) identified 18 QTLs for Fe concentration on chromosomes 1, 2, 3, 4, 6, 8, 11, and 12, and 12 QTLs for Zn concentration on chromosomes 1, 2, 3, 5, 6, 8, 9, 10, and 12 using BIL populations from the wild rice species
Oryza rufipogon and
Oryza nivara.
Stangoulis et al. (2007) identified three QTLs for Fe concentration on chromosomes 2, 8, and 12, and two QTLs for Zn on chromosomes 1 and 12 in a DH population derived from the cross ‘IR64’ × ‘Azucena’.
There is a significant correlation between Fe and Zn concentration, and in general, their QTLs have been found to be closely linked. For example, the co-localized QTLs for Fe and Zn have been identified on chromosomes 1, 2, 3, 6, 7, 8, and 12 (
Stangoulis et al. 2007;
Anuradha et al. 2012;
Xu et al. 2015;
Swamy et al. 2018;
Dixit et al. 2019). However, only two stable co-localized QTLs for Fe and Zn have been identified on chromosomes 7 and 12.
To develop rice varieties with improved micronutrient concentrations, it is necessary to combine different genetic backgrounds for the QTL analysis for grain Fe and Zn concentrations using marker-assisted pyramiding. The main objective of this study was to identify QTLs for grain Fe and Zn concentrations from DH populations derived from the indica variety 93-11 and the japonica variety Milyang 352.
MATERIALS AND METHODS
Plant materials
We used a DH population consisting of 123 lines derived from a cross between 93-11 and Milyang 352. Each line comprised 24 plants. The DH population was transplanted at two different times (early and late) in a paddy field in Miryang, Korea, in 2018. 93-11 is a Chinese indica rice cultivar with long grains, and Milyang 352 is a Korean japonica cultivar with short grains and earlier maturity than that of 93-11. And 93-11 has lower concentrations of Fe and Zn compared to Milyang 352.
Phenotype evaluation of the DH populations
The populations were phenotyped for three grain shape traits (grain length [GL], grain width [GW], and length-to-width ratio [LWR]) and grain micronutrient traits (Fe and Zn content). GL and GW were measured using a Vernier caliper, and the LWR was calculated (RDA 2012). For Fe and Zn analysis of the grains, brown rice samples weighing at least 3 g each were subjected to X-ray fluorescence spectrometry (NEX-CG EDX-RF, Rigaku, Austin, TX, USA).
Molecular marker analysis
Total genomic DNA was extracted from fresh leaves of 4-week-old individual plants using the CTAB method with some modifications. DNA was quantified by using nano-drop spectrophotometry (ND1000 spectrophotometer, Mettler Toledo, Greifensee, Switzerland). For the construction of the molecular map, Kompetitive Allele-Specific PCR (KASP) marker amplification and allelic discrimination were performed using the Nexar system (LGC Douglas Scientific, Alexandria, VA, USA) at the Seed Industry Promotion Center (Gimje, Korea) of the Foundation of Agri. Tech. Commercialization & Transfer in Korea. An aliquot (0.8 mL) of 2X master mix (LGC Genomics, London, UK), 0.02 mL of 72 KASP assay mix (LGC Genomics), and 5 ng genomic DNA template were mixed in 1.6 mL KASP reaction mixture in a 384-well array tape. KASP amplification was performed as described in Cheon et al. (2018).
Fluidigm markers for SNP genotypes were determined using the BioMarkTM HD system (Fluidigm, San Francisco, CA, USA) and 96.96 Dynamic Array IFC (96.96 IFC) chip according to the manufacturer’s instructions, at the National Instrumentation Center for Environmental Management, Seoul National University (Pyeongchang, South Korea). The genotyping results were obtained using the Fluidigm SNP Genotyping Analysis software. All genotype calls were manually confirmed, and any errors in homozygous or heterozygous clusters were curated.
Linkage mapping and QTL analysis
Marker data sets were generated from KASP markers and Fluidigm markers. A genetic map of the DH populations was created based on the physical positions of each marker. QTLs were identified by inclusive composite interval mapping (ICIM) in QTL IciMapping ver. 4.1. using the Kosambi function. The average trait values for each line in each DH population were used for the QTL analysis. The LOD thresholds of the QTLs were determined by setting a LOD threshold of 3.0. The proportion of observed phenotypic variance explained by each QTL and the corresponding additive effects were also estimated. Histograms and correlations between pairs of traits were estimated through Pearson correlation coefficients using R software.
RESULTS
Phenotype analysis
Wide variation was observed for grain morphological traits and micronutrient elements in both early-transplanted and late-transplanted DH populations (
Fig. 1). Most traits showed the typical normal distribution, suggesting that they are controlled by multiple genes.
Fe and Zn concentrations in the brown rice of Milyang 352 (japonica rice) was higher than those of 93-11 (indica rice). The Fe and Zn concentrations of 93-11 were 23.0 mg/kg and 16.0 mg/kg, respectively, while those of Milyang 352 were 30.0 mg/kg and 23.0 mg/kg, respectively. In the early-transplanted DH population, the Fe concentration ranged from 18.5 mg/kg to 31.5 mg/kg (mean, 25.7 mg/kg), and Zn concentration ranged from 15.5 mg/kg to 33.5 mg/kg (mean, 23.0 mg/kg). The GL, GW, and LWR in the brown rice of 93-11 were 5.38 mm, 2.08 mm, and 2.59, respectively, while in Milyang 352, they were 5.04 mm, 2.46 mm, and 2.05, respectively. In the early-transplanted DH population, GL ranged from 4.32 mm to 5.94 mm (mean, 5.21 mm); GW ranged from 1.90 mm to 2.80 mm (mean, 2.23 mm); and LWR ranged from 1.67 to 2.80 (mean, 2.35). Correlation analysis between grain morphological traits and the micronutrients Fe and Zn showed significant positive correlations between Fe and Zn, while significant negative correlation was observed among Fe and LWR (
Fig. 2).
Fe and Zn concentrations in the brown rice of Milyang 352 (japonica rice) were higher than those of 93-11 (indica rice). Fe and Zn concentrations of 93-11 were 22.5 mg/kg and 15.0 mg/kg, respectively, while those of Milyang 352 were 23.0 mg/kg and 17.0 mg/kg, respectively. In the late-transplanted DH population, the Fe concentration ranged from 14.0 mg/kg to 26.5 mg/kg (mean, 21.5 mg/kg), and the Zn concentration ranged from 12.5 mg/kg to 28.0 mg/kg (mean, 19.3 mg/kg). The GL, GW, and LWR in the brown rice of 93-11 were 5.60 mm, 2.33 mm, and 2.41, respectively, while in Milyang 352, they were 5.48 mm, 2.50 mm, and 2.20, respectively. In the late-transplanted DH population, GL ranged from 5.15 mm to 6.65 mm (mean, 5.83 mm); GW ranged from 2.02 mm to 2.85 mm (mean, 2.41 mm); and LWR ranged from 1.93 to 3.02 (mean, 2.43). Correlation analysis between grain morphological traits and the micronutrients Fe and Zn showed significant positive correlations between Fe and Zn (
Fig. 2).
For the physical map construction and QTL analysis of the DH population, 507 KASP markers and 192 Fluidigm markers were used to genotype the parents and progenies. Among these markers, 110 KASP markers and 134 Fluidigm markers were used to construct the physical map.
Early-transplanted DH population
QTL analysis in the early-transplanted DH population identified 14 QTLs for five traits in the population (
Fig. 3). Most of the QTLs contributed about 6.4-26.0% of the phenotypic variance explained (PVE). The details of the QTLs identified are provided in
Table 1. Two QTLs for Fe and Zn concentrations were detected using the ICIM program. Details of QTLs with their marker position, LOD score, additive effect, and PVE are given in
Table 1. Two QTLs for Zn and Fe concentration were on chromosome 3. The PVE by QTLs was 10.7% for Fe and 18.0% for Zn. These QTLs were co-located between ad03013905 and KJ03_069 on chromosome 3, and their phenotypic variation was increased by Milyang 352. Twelve QTLs for GL, LW, and LWR were detected in the DH population. Six QTLs for GL were located on chromosomes 1, 3, 4, 7, and 12, with the PVE ranging from 6.4% to 17.6% and LOD value ranging from 3.8 to 10.4. Three QTLs for GW were observed on chromosomes 5 and 7, with the PVE ranging from 9.9% to 26.0% and LOD value ranging from 9.9 to 26.0. Three QTLs for LWR were located on chromosomes 3, 5, and 7. The highest PVE was explained by a QTL for
qLWR5-1 (PVE, 19.5%) on chromosome 5.
QTL analysis in the late-transplanted DH population identified 16 QTLs for six traits in the population (
Fig. 3). Most of the QTLs contributed about 4.52-21.90% of the PVE. The details of the QTLs identified are provided in
Table 2. Three QTLs for Fe and Zn concentrations were detected using the ICIM program. Details of QTLs with their marker position, LOD score, additive effect, and PVE are given in
Table 2. Two QTLs for Zn and Fe concentration were on chromosome 3. The PVE by QTLs was 17.10% for Fe and 24.56%, and 9.89% for Zn. Thirteen QTLs for GL, LW, and LWR were detected in the DH population. Two QTLs for GL were located on chromosomes 4 and 7, with the PVE ranging from 10.95% to 20.20% and LOD value ranging from 5.04 to 8.28. Seven QTLs for GW were observed on chromosomes 1, 4, 5, 7 and 10, with the PVE ranging from 4.52% to 29.16% and LOD value ranging from 5.99 to 25.23. Four QTLs for LWR were located on chromosomes 1, 4 and 5. The highest PVE was explained by a QTL for
qLWR5-2 (PVE, 42.63%) on chromosome 5.
qFe3-1, qZn3-1, qGL4-1, qGL7-1, qGW5-1, qGW7-1-2, and
qLWR5-1 were repeatedly observed both in the early-transplanted DH population and the late-transplanted DH population (
Fig. 3).
DISCUSSION
Micronutrient enrichment of rice grains is highly essential to reduce malnutrition in developing countries. Dietary deficiency of Fe and Zn is a major cause for malnutrition (
Bouis and Saltzman 2017). The availability of molecular markers and advanced bioinformatics tools will assist breeders to accumulate targeted alleles of genes known to play a role in grain nutritional quality traits to ultimately develop biofortified rice varieties. In this study, QTL analysis for Fe and Zn concentrations was performed using DH populations derived from a cross between the indica cultivar 93-11 and japonica cultivar Milyang 352. Thus far, many QTLs for Fe and Zn in rice grains have been mapped in various mapping populations from different genetic resources or wild rice.
A high correlation between Fe and Zn concentrations has been reported, and many studies have identified several co-localized Fe and Zn QTLs on chromosomes 1, 2, 3, 7, 8, and 12.
Dixit et al. (2019) reported co-located Fe and Zn-related QTLs on chromosome 1 (RM294A-RM12276) and on chromosome 6 (RM8226-RM400). Swamy
et al. (2018) reported four co-located Fe and Zn QTLs: on chromosome 2 (RM6-RM535), chromosome 3 (RM517- RM16), chromosome 8 (RM337-RM223), and chromosome 12 (RM415-RM247), and
Anuradha et al. (2012) also reported QTLs on chromosome 7 (RM234-RM8007) and on chromosome 12 (RM17-RM260).
Stangoulis et al. (2007) reported co-located Fe and Zn QTLs on chromosome 12 (RM235-RM17). We also observed two adjacent QTLs,
qFe3-1 and
qZn3-1, which were identified in the marker interval between ad03013905 and KJ03_069 on chromosome 3 (between 26.02 Mb and 32.02 Mb on the long arm of this chromosome) and explained 10.71% (early-transplanted), 17.10% (late-transplanted), and 18.04% (early-transplanted) of phenotypic variation, respectively.
It is noteworthy that several QTLs for Fe and Zn concentration have been detected on chromosome 3. The co-localized QTLs
qFe3.2 and
qZn3.1 reported by Swamy
et al. (2018) were in the marker interval between RM517 and RM16 on chromosome 3, and
Anuradha et al. (2012) detected
qZn3.1 linked with RM514 on the same chromosome. Zhang
et al. (2014) detected a QTL for Fe in the 29-36 Mb interval on chromosome 3 in TeQing-into-Lemont BILs. Among them,
qFe3.2 was detected repeatedly in early-transplanted and late-transplanted DH populations. These results highlight the high frequency of QTLs for Fe and Zn concentration on chromosome 3. Several QTLs were detected repeatedly in early-transplanted and late- transplanted DH populations, including
qFe3.2 and
qZn3.1 (
Table 1,
2).
Correlation analysis for grain shape and micronutrient traits in the DH population revealed positive correlations between Fe and Zn. Several studies have reported similar results on the relationship between Fe and Zn (
Anuradha et al. 2012;
Zhang et al. 2014). These results indicated that increases in both Fe and Zn levels could allow simultaneous improvement in rice. However, Fe and LWR were negatively correlated in this study. The QTLs for LWR governing grain shape traits are clustered with
qFe3-1 and
qZn3-1 at the end of chromosome 3. Therefore, the allele from 93-11 increased grain length but decreased Zn and Fe content. Most indica rice lines, including 93-11, have long grain shape (i.e., high LWR). This negative linkage of high LWR with Fe and Zn must be dissected for successful biofortification by designing appropriate breeding strategies. This promising line can be used as a new breeding resource for biofortified rice breeding.
We developed DH populations and evaluated QTLs for micronutrients and grain shape in rice. The QTLs qFe3-1 and qZn3-1 were co-located on chromosome 3, suggesting that traits governing high Zn and Fe concentrations may occur simultaneously in rice. These QTLs for Zn and Fe on chromosome 3 will accelerate breeding work to accumulate QTLs of genes related with micronutrient traits in rice grains and help develop desired biofortified rice cultivars for alleviating malnourishment in a large part of the world.
ACKNOWLEDGEMENTS
This work was supported by the “Cooperative Research Program for Agriculture Science & Technology Development (Project No. PJ01428202)” of the Rural Development Administration, Republic of Korea.
Fig. 1Frequency distribution of grain morphological and micronutrient traits. (A) Early-transplanted doubled haploid (DH) population. (B) Late-transplanted doubled haploid (DH) population.
Fig. 2A heatmap depicting correlation coefficients between grain morphological and micronutrient traits. Numbers in circles indicate significant correlations using a two-paired t-test (n = 123 DH lines). (A) Early-transplanted doubled haploid (DH) population. (B) Late-transplanted doubled haploid (DH) population. GL: Grain length, GW: grain width, LWR: length-to-width ratio.
Fig. 3Physical locations of QTLs for grain morphological and micronutrient traits. (A) Early-transplanted doubled haploid (DH) population. (B) Late-transplanted doubled haploid (DH) population.
Table 1QTLs for grain morphological and micronutrient traits detected in the early-transplanted DH population derived from 93-11/Milyang352 populations.
Table 1
|
Traits |
QTL name |
Chr. |
Position (Mb) |
Left_marker |
Right_marker |
LOD |
PVE (%) |
Additive effect |
|
Grain length |
qGL1-1
|
1 |
42.4 |
KJ01_125 |
KJ01_127 |
5.1 |
9.0 |
‒0.09 |
|
qGL3-1
|
3 |
4.0 |
ah03000403 |
KJ03_017 |
8.0 |
13.6 |
0.11 |
|
qGL4-1
|
4 |
22.5 |
cmb0420.7 |
cmb0422.7 |
3.8 |
6.4 |
‒0.08 |
|
qGL7-1
|
7 |
21.1 |
id7003072 |
KJ07_067 |
6.3 |
10.8 |
0.10 |
|
qGL12-1-1
|
12 |
2.3 |
id12000076 |
cmb1202.4 |
10.4 |
17.6 |
0.12 |
|
qGL12-1-2
|
12 |
24.3 |
cmb1224.0 |
cmb1226.0 |
6.0 |
9.3 |
0.09 |
|
Grain width |
qGW5-1
|
5 |
4.8 |
KJ05_013 |
KJ05_017 |
9.8 |
26.0 |
‒0.09 |
|
qGW7-1-1
|
7 |
3.1 |
KJ07_013 |
cmb0703.2 |
5.4 |
14.2 |
‒0.07 |
|
qGW7-1-2
|
7 |
29.1 |
cmb0728.5 |
KJ07_085 |
3.4 |
9.9 |
‒0.06 |
|
Grain length/width ratio |
qLWR3-1
|
3 |
33.0 |
id3015453 |
ah03002520 |
4.6 |
10.0 |
0.08 |
|
qLWR5-1
|
5 |
10.8 |
id5004086 |
cmb0511.1 |
8.2 |
19.5 |
0.11 |
|
qLWR7-1
|
7 |
6.1 |
KJ07_021 |
id7001155 |
5.6 |
13.3 |
0.09 |
|
Zn content |
qZn3-1
|
3 |
26.0 |
ad03013905 |
ad03014175 |
5.8 |
18.0 |
‒0.10 |
|
Fe content |
qFe3-1
|
3 |
32.0 |
ad03014175 |
KJ03_069 |
2.7 |
10.7 |
‒0.10 |
Table 2QTLs for grain morphological and micronutrient traits detected in the late-transplanted DH population derived from 93-11/Milyang352 populations.
Table 2
|
Trait |
QTL name |
Chr. |
Position (Mb) |
Left_marker |
Right_marker |
LOD |
PVE (%) |
Additive effect |
|
Grain length |
qGL4-2
|
4 |
20.7 |
cmb0420.7 |
cmb0422.7 |
5.04 |
10.95 |
‒0.0971 |
|
qGL7-2
|
7 |
20.3 |
id7003072 |
KJ07_067 |
8.28 |
20.2 |
0.1293 |
|
Grain width |
qGW1-2-1
|
1 |
0.4 |
id1000223 |
KJ01_001 |
6.19 |
4.75 |
0.0403 |
|
qGW1-2-2
|
1 |
19.3 |
id1010652 |
KJ01_065 |
5.99 |
4.52 |
0.0454 |
|
qGW4-2
|
4 |
30 |
id4009823 |
cmb0432.2 |
6.36 |
5.36 |
0.0459 |
|
qGW5-2
|
5 |
4.7 |
id5002497 |
KJ05_013 |
25.23 |
29.16 |
‒0.0998 |
|
qGW7-2-1
|
7 |
23 |
cmb0723.0 |
KJ07_069 |
17.17 |
16.84 |
0.0788 |
|
qGW7-2-2
|
7 |
24.2 |
KJ07_071 |
SLG7-GC |
6.78 |
5.4 |
‒0.0493 |
|
qGW10-2
|
10 |
21.4 |
KJ10_043 |
ah10001182 |
10.86 |
9.21 |
‒0.0579 |
|
Length/widgh ratio |
qLWR1-2-1
|
1 |
2.1 |
KJ01_005 |
id1009557 |
4.61 |
7.53 |
‒0.0622 |
|
qLWR1-2-2
|
1 |
33.1 |
id1018870 |
KJ01_097 |
7.51 |
9.41 |
0.0744 |
|
qLWR4-2
|
4 |
30 |
id4009823 |
cmb0432.2 |
11.68 |
15.04 |
‒0.0926 |
|
qLWR5-2
|
5 |
4.7 |
id5002497 |
KJ05_013 |
25.1 |
42.63 |
0.1473 |
|
Zn content |
qZn3-2
|
3 |
33 |
id3015453 |
ah03002520 |
9.16 |
24.56 |
‒0.0002 |
|
qZn4-2
|
4 |
26.4 |
ad04009559 |
ah04001252 |
4.11 |
9.89 |
‒0.0001 |
|
Fe content |
qFe3-2
|
3 |
27.1 |
ad03014175 |
KJ03_069 |
4.67 |
17.1 |
‒0.0001 |
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