1. Introduction
Micronutrient malnutrition or hidden hunger has become a major issue for people in developing countries, especially in Asia and Africa [
1,
2]. Marginal intake of micronutrients has been shown to contribute to increasing mortality rates, affect livelihoods, and result in adverse effects on millions of school-going children and pregnant women [
3]. About three billion people worldwide are estimated to be affected and more than 24,000 people die daily owing to micronutrient malnutrition or hidden hunger [
4]. Recently, the United Nations (UN) declared that tackling micronutrient deficiencies is one of the sustainable development goals (SDGs) and set a goal of SDG 2 to be achieved by 2035 (
https://sustainabledevelopment.un.org/sdgs (20 September 2022)). Among different micronutrients, zinc (Zn) and iron (Fe) are the most essential and are associated with the development of physical and mental health in humans and reducing the risk of micronutrient malnutrition [
5].
Microelements, such as iron (Fe), zinc (Zn), copper (Cu), and manganese (Mn), are found in many enzymes and have great importance for maintaining normal metabolic pathways in humans. Zn serves as a major co-factor for more than 300 enzymes and 2000 transcription factors and is involved in the metabolism of carbohydrates, lipids, proteins, and nucleic acids in humans [
6,
7]. It is the only metal present in all six enzyme classes (oxidoreductase, transferase, hydrolases, lyases, isomerases, and ligases) [
8]. One third of the human population, particularly children and women, suffer from Zn deficiency-associated health complications such as stunting, diarrhea, reduced immunity, poor cognitive development, and skin problems [
9]. According to World Health Organization (WHO), Zn deficiency is sixth among the top 10 major causes of illness in developing nations, affecting 27–30% of the global population. A large portion of the global population suffers from mineral malnutrition, as they depend on plant-based diets that have a low mineral/calorific density [
10].
Rice is a predominant and major staple food crop that provides the caloric needs of half of the global population [
11,
12]. A total of 483.3 million tons of milled rice is produced globally, according to a recent survey (ICRISAT., 2018). To meet the need, however, production must rise to 800 to 900 million tons by 2025 [
13]. Mega varieties under cultivation are a poor source of micronutrients, particularly Zn in polished form (12 ± 14 ppm), and provide only one fifth of daily recommended Zn requirements (15 mg/day) [
14,
15]. In other words, 11 to 15 mg of Zn must be present in 220 gm of rice, which is the per capita amount of rice identified for India. The present international target as well as that from the AICRIP biofortification trial is 28 ppm, which can only meet half of the RDA. Through the AICRIP rice biofortification trial, medium slender, long slender, and short bold grain type rice varieties were released to some of the states in the country. Therefore, there is a need to develop biofortified varieties suitable for rice consumers under aromatic grain and other grain types. To ameliorate this situation, researchers are working to enhance the grain Zn content in rice by different approaches. Biofortification has emerged as a cost-effective and sustainable strategy to tackle mineral deficiencies by enhancing micronutrient content without compromising the yield achieved through agronomic, conventional, and biotechnological approaches [
16,
17]. The availability of genetic variability in grain mineral nutrients in the rice germplasm can be employed to develop biofortified high-yielding rice varieties [
18,
19].
Understanding the genetic potential of the genotypes as well as interaction between genotype and environment is essential for developing successful biofortified rice varieties. Developing Zn-biofortified rice is challenging, as it involves complex genetics of the trait, genetic interactions such as epistasis, and environmental factors such as soil and water [
14,
20,
21,
22]. Several genetic studies have also been carried out to identify markers linked with quantitative trait loci (QTLs) for high Zn in grains, which would help in the development of biofortified rice varieties through marker-assisted selection (MAS) [
23]. QTL mapping provides information for identification of genomic regions associated with targeted traits of interest (Zn) by combining genetic information with phenotypic data [
24]. Several QTLs with moderate to high phenotypic variance were reported for grain Zn on all 12 chromosomes of rice [
25]. Recent studies on QTL mapping for mineral elements in rice using bi-parental and multi-parental mapping populations have identified multiple loci and demonstrated the genetic complexity of grain micronutrient traits [
26].
Compared with other molecular markers (RFLP, RAPD, SSRs, etc.) single nucleotide polymorphisms (SNPs) are preferred because of their uniform distribution and wide occurrence across the genome (one marker every 100–500 bp, specific to species), thus making them the ideal choice for constructing high-density linkage maps and identification of markers closely associated with the trait of interest [
27,
28]. Next generation sequencing (NGS) technology with reduced cost provides abundant sequence information along with significant improvements in genome coverage and time [
29]. Genotyping by sequencing (GBS), an advancement in NGS technology based on genome target reduction and restriction enzyme use has gained popularity as a cost-effective method in the development of genome-wide markers for genetic studies [
30].
Numerous methods for reduced representation genome sequencing have been developed and include genotyping by sequencing (GBS) [
31], double digest restriction-site-associated DNA sequencing (ddRAD) [
32], and specific-locus amplified fragment sequencing (SLAF) [
33]. GBS is being deployed for genetic studies in various crops [
34,
35], particularly GWAS analysis, diversity studies, genomic selection (GS), marker and gene discovery, and high-resolution QTL mapping in rice [
36,
37,
38].
The present study aims to construct a high-density linkage map and identify QTLs for high grain Zn in polished rice and related agronomic traits using a recombinant inbred line (RIL) population derived from a MTU1010 X Ranbir Basmati cross. Putative candidate genes present in the QTLs identified for high grain Zn in polished rice were retrieved. The QTLs and putative candidate genes identified in the study can be deployed to advance genetic research and breeding applications for enhancing grain Zn content in rice.
3. Discussion
One third of the global population is reported to be suffering from a lack of sufficient Zn nutrition [
39]. Biofortification is considered as long-term sustainable strategy to address micronutrient malnutrition by enhancing the grain micronutrient density. Over the last decade, using conventional breeding approaches, a few biofortified rice varieties with high grain Zn have been developed and released globally, as Zn plays a critical role in several cellular and metabolic activities [
40,
41]. Though wide variability for grain Fe is available in brown rice, because of polishing, around 70 to 80% of grain Fe is lost [
42].
Marker-assisted breeding has a vast potential to achieve desirable phenotypic variations in less time through the deployment of molecular markers linked to QTLs for desirable traits [
43]. Large QTL regions identified with low-density SSR markers may have undesirable linkages, resulting in unsuccessful introgression [
44]. In some of the recent studies, undesirable linkage groups were successfully eliminated by employing NGS-generated markers such as SNPs by identifying the recombinants. Next generation sequencing (NGS) technologies have become potential tools for the discovery of millions of SNPs in a cost-effective manner. The genotyping-by-sequencing (GBS) technique has facilitated the identification of key genomic regions for both complex and simple traits and has accelerated the breeding process [
45,
46,
47,
48]. ddRAD sequencing facilitates the identification of SNPs by reducing genome complexity in rice and other crops [
49].
In the present study, we developed a high-density genetic linkage map from an RIL population MTU1010 X Ranbir Basmati using a GBS approach to identify QTLs for grain Zn and Fe using multi-seasonal phenotype data of the mapping population. Detailed analysis of phenotype data revealed a wide genetic variability within the RIL population for grain Zn and Fe along with agronomic and yield-related traits and showed normal distribution across four seasons. Previous studies indicated that complex inherited traits and micronutrient values exhibit wide variation, and these can be attributed to various genetic and environmental factors [
50,
51]. We examined data from the two wet and dry seasons in the current study and confirmed our results based on the previous variability studies in rice grain mineral content across the seasons (Kharif and Rabi) [
52,
53].
3.1. Correlation
Highly significant positive correlations were obtained among ZnBR, ZnPR, FeBR, and FePR, irrespective of season and location; however, there was a negative correlation with single plant yield (SPY). Several studies have reported positive correlation between Zn and Fe in rice [
54,
55,
56,
57]. Swamy et al. (2018b) [
58] attributed the positive correlation between Zn and Fe contents to commonalities in their pathways and genetic networks of Zn and Fe uptake, translocation, and loading in rice. Negative correlations between yield and Zn in rice were earlier reported by [
20,
59] (G3: Mean Zn and Mean SPY). A QTL (
qGZn9a) identified in Australian wild rice strain
O.meridionalis was reported to be associated with an increase in grain Zn levels also found to be concomitant with fertility reduction [
60]. The negative linkages between the yield and Zn must be eliminated for developing a successful biofortified variety by generating a greater number of recombinants per cross for identifying positive combinations for grain Zn and yield.
3.2. Identification of Promising RILs
Six stable RILs were identified for four mineral traits in the present study with >24 ppm (Zn) based on stability and G × E interaction analysis across four environments. From the study, we could also identify promising RILs with high grain Zn in polished rice (35.6 ppm) and promising RILs with single plant yield (28.4 g to 38.4 g), which could be further deployed in the breeding of biofortified rice varieties. For rice grain Zn and Fe, stability and G × E analyses are used for identification of stable donors [
47,
61,
62,
63].
Considering the wide variability observed for the breeding lines with high grain Zn and Fe, stability and G × E analyses are being applied for selecting promising RILs in cereals. The contribution of environmental variation to grain Zn and Fe along with other agronomic traits in a RIL population of sorghum was demonstrated through genotype · environment interaction, correlation, and GGE biplot analyses [
64]. Stable RILs with higher grain Zn and Fe content were also identified in the RILs of pearl millet using AMMI and GGE biplot analyses [
65]. Different stable breeding lines were identified for different environments among eight Zn biofortified lines through stability and G × E analyses [
51].
3.3. Identification of Quantitative Trait Loci (QTLs)
Out of 13 QTLs identified for grain Fe and 10 QTLs for grain Zn in brown and polished rice using ICIM, six QTLs were found to be stable/consistent across the seasons, namely
qDFF.2.2,
qDFF.2.3,
qZnPR.2.1,
qZnPR.2.2,
qZnBR.9.1, and
qZnBR.9.2 on Chr02 and Chr09. The lack of stability of QTL effects across the environments/genetic backgrounds has been one of the most limiting factors for successful deployment of QTLs through MAS breeding for various complex traits [
66,
67,
68,
69]. The consistent QTLs (
qZnPR.2.1,
qZnPR.2.2) identified in this study can reliably be deployed in marker-assisted breeding for high grain Zn.
Several researchers have mapped QTLs for grain Zn and Fe traits in rice using various mapping populations, such as RILs, ILs, F2, DH, and MAGIC populations, and have identified multiple loci and clearly demonstrated the genetic complexity of the grain micronutrient traits. The genomic region on Chr02 (SNP_24248265-SNP_25682947) has shown co-localization in the
qZPR.2.1 region [
42].
Co-localization of QTLs for different element contents in grain is common in rice [
70]. In the study, co-localization was observed: the genomic regions of Chr02, 05, and 06 of the DFF trait was co-localized with mineral trait QTLs FeBR, FePR, ZnBR, and ZnPR on Chr02. The Fe QTLs on Chr05 were co-located with ZnBR QTL, suggesting that there may be a possibility of selecting high grain Zn and Fe lines using molecular markers; Ref. [
71] reported co-location of Zn and Fe QTLs on Chr02, 03, 08, and 12 in RIL populations.
In the present study, eight significant epistatic interactions were observed for PH, NT, FeBR, FePR, and ZnPR on Chr01, 02, 04, 05, and 07 and accounted for 11.20–26.83% PVE, suggesting the complex genetic variation of the traits. Two digenic interactions were detected for PH on Chr02, with PVE 8.74% and 24.49%, respectively. None of the identified digenic interactions in the present study were found to be involved with the main effect QTLs. Similar observations were earlier reported for epistatic interactions for grain Zn in rice [
52,
59]. Recent QTL mapping studies suggest that epistasis is considered to be a crucial genetic component underlying complex quantitative traits. Epistasis should be underscored in studying complex traits because it can account for hidden quantitative genetic variations in natural populations [
72].
Integration of genomics with conventional breeding efforts can decipher the molecular mechanisms underlying traits of interest. Using the rice genome sequence, it is now possible to identify the putative candidate genes for grain Zn within QTL regions. In the present study, major QTLs involved in grain mineral element content that show consistency across seasons merit further examination by fine mapping and candidate gene analysis for ultimate use in MAS. The identified five candidate genes Os02t0629200 on Chr02 with Zn binding and transport activity, two genes Os09t0268300 and Os09t0297300 on Chr09 with transporter activity, and two genes Os06t0705700 and Os06t0706100 on Chr06 with Zn and Fe transporter activity and metal ion transport function could be promising genes for further validation. The enhanced expression of Os06t0705700 and Os06t0706100 in the flag leaf samples of the donor parent (Ranbir Basmati) and some of the donors/RILs with grain Zn further supported the identified QTLs/candidate genes in the present study.
Out of 92 RILs evaluated, one promising RIL G3 showed the highest SPY and Zn content across 92 RILs. The G3 RIL can be used as donor in biofortification breeding programs. Likewise, three other RILs, G1, G4 and G6, with high Zn content donors, and RILs G73, G49, G70, G82, and G52, with promising yield, can be used for future breeding programs.
4. Materials and Methods
4.1. Development of RIL Population
A recombinant inbred line (RIL) population (
n = 306) was developed using the single seed descent (SSD) method by crossing MTU1010 (also known as Cotton Dora Sannalu, pedigree: Krishnaveni/IR-64; Zn: 16.6 ppm in brown rice; 12.9 ppm in polished rice; mean yield of 6.5 tons ha
−1; widely cultivated mega variety) as a recipient parent for grain zinc (Zn) and Ranbir Basmati (pure line selection from Basmati 370-90-95; mean yield of 2–2.5 tons ha
−1; Zn: 27.5 ppm in brown rice; 23.4 ppm in polished rice) as a donor parent. In this study, two parental lines and a set of selected 92 RILs (F
8) were used for genotyping and phenotypic analysis. All 306 lines’ Fe and Zn values are included in
Supplementary Table S4.
4.2. Field Experiment Details
A total of 92 RILs along with parents were evaluated during four seasons (Wet season (WS) 2017; Dry season (DS) 2018; Wet season (WS) 2020 and Dry Season (DS) 2020) at ICAR-Indian Institute of Rice Research (IIRR) farm, Hyderabad, India (17.53° N latitude and 78.27° E longitude with 545 mm mean rainfall). Each RIL was planted in four rows. with each row consisting of 15 plants with a spacing of 20 × 15 cm following randomized block design (RCBD). The crop was raised by following recommended field management practices of production and protection.
4.3. Phenotypic Evaluation of Agronomic and Mineral Traits
Across four environments (seasons), five uniform plants were tagged from the center of each row, and phenotype data was collected for the traits. Days to 50% flowering (DFF), plant height (PH) (cm), number of tillers per plant (NT), panicle length (PL) (cm) at heading/early filling stage, single plant yield (SPY) (g), grain zinc (ZnBR, ZnPR), and iron (FeBR, FePR) in brown and polished rice were estimated at the post-harvest stage.
4.4. Estimation of Grain Zn and Fe Content
For estimation of grain Zn and Fe in brown rice (ZnBR, FeBR) and polished rice (ZnPR, FePR), seed samples were dehusked using a JLGJ4.5 rice husker (Jingjian Huayuan International Trade Co., Ltd., Hangzhou, Zhejiang, China). The brown rice was polished using a polisher with non-ferrous and non-zinc components (Krishi International India Ltd., New Delhi, India). After a thorough cleaning, samples of brown and polished rice (5 g) were subjected to energy-dispersive XRF (ED-XRF) (OXFORD Instruments X-Supreme 8000, Abingdon, UK) as per the standardized protocol [
73].
4.5. Statistical Analysis
For each season, descriptive statistics such as mean, standard error of the mean (SEM), skewness, kurtosis, and coefficient of variations (CV%) were calculated for five plants using MS-Excel (2010). Trait-wise frequency distribution histograms were generated using R software [
74]. Pearson correlation analysis and principal component analysis were computed using R script among nine traits. Different R packages (R core Team, 2018, Vienna, Austria), namely ggplot2, gge, FactoMinerR, and factoextra, were used to generate frequency distribution plots and individual comparison of RILs; the ‘Metan’ package was used for ranking of RILs [
75].
4.6. Genotyping by Sequencing (ddRAD-Seq)
Genomic DNA was extracted from pooled young leaves (20–25 days) of both parents and RILs using the DNeasy plant kit (Qiagen, Hilden, Germany) following the manufacturer’s protocol. DNA quality of each sample was checked on 0.8% agarose gel. Furthermore, DNA quantification was done using a Qubit 2.0 fluorometer (Themo Fisher Scientific Inc., Waltham, MA, USA). ddRAD–Seq protocol (modified GBS) was followed in the present study.
To perform GBS, genomic DNA was double digested with SphI and MlucI restriction enzymes (NEB, UK) and fractionated in 2% agarose gel to check the product size. The digested fragments were cleaned (Agencourt AMpure XP beads, Invitrogen, Waltham, MA, USA) using standard protocols. The ligation enzyme, T4 DNA ligase (NEB, England) was used to ligate the unique barcode adapters (4–8 nt sequence) at 16 °C for 30 min and heat-inactivated at 65 °C for 10 min.
This was followed by indexing with the addition of index-1 and index-2 (6–8 nt long) for multiplexing sequencing libraries in NGS Illumina. These libraries were PCR amplified (8–12 cycles) using a PhusionTM polymerase kit (Fisher Scientific, Loughborough, UK) followed by AMPure bead cleanup for purification to remove excess adapters and were sequenced on the Illumina HiSeqX platform (Illumina Inc, San Diego, CA, USA).
4.6.1. Genotyping and Filtration
The sequence reads for the parents and RILs were obtained as FASTQ files, which are used for SNP discovery. Raw reads were de-multiplexed according to their barcodes, and the adapters/barcode sequences were removed using standard software [
76]. High-quality reads were aligned onto the rice reference genome of Nipponbare (MSU7) using bowtie2-2.2.6. The mapped reads were exported in the form of a Sequence Alignment Map (SAM) file by SAM tools, version 0.1.19 [
77]. The alignment file was then processed for SNP calling using a Bayesian approach at specific site. Potential SNPs were filtered using the following criteria: loci with >70% missing data and those that showed distorted segregation of the two parental genotypes were excluded. SNPs with minimum allele frequency (MAF) > 0.05 and 90% call rate were considered for further analysis. The variant annotation was performed based on rice gene models, using in-house pipeline software developed by Agri Genome Labs Pvt., Ltd., Kochi, India.
4.6.2. Genetic Linkage Map Construction
High-quality SNPs obtained after filtering were used for map construction using the linkage mapping function using IciMapping v4.2. [
78]. The grouping and ordering of 2746 SNP events between adjacent markers was performed at a minimum logarithm of odds (LOD) value of 2.5. ‘Rippling’ was done for fine-tuning of the ordered markers on their respective chromosomes. The Kosambi map function was used for the construction of the genetic map and to convert the recombination frequencies into map distances in centiMorgans (cM) [
79].
4.6.3. QTL Analysis
The main effect QTL analysis was carried out using IciMapping. Deploying the BIP function in IciMapping, additive QTLs were identified by inclusive composite interval mapping (ICIM) based on a 1000—permutation test at a 95% confidence level. The QTLs with >3.0 LOD and phenotypic variance explained (PVE) > 10% were considered as major effect QTLs, those with PVE <10% were considered as minor effect QTLs for a particular trait. Epistatic interactions with the logarithm of odds (LOD) threshold value at 5.0 were considered as significant epistatic QTLs. The QTLs were visualized using MapChart V2.3 [
80].
4.7. Mining of Candidate Genes
To identify the candidate genes within the identified QTL region, the genomic position of the flanking markers was used. Using the rice genome annotation project ((
http://rice.plantbiology.msu.edu/ (22 August 2022)), RAP-DB genome browser (
https://rapdb.dna.affrc.go.jp (22 August 2022)), and Q-TARO (QTL Annotation Rice Online) database (Yonemaru et al., 2010) [
81] (
http://qtaro.abr.affrc.go.jp/ (22 August 2022)), the candidate genes within the QTL regions were retrieved. Genes were functionally characterized into various categories using WEGO [
82]. Network analysis of genes present in the major QTL genomic regions was derived using the Knetminer program (
http://knetminer.rothamsted.ac.uk/Oryza_sativa/ (24 August 2022)). The temporal and spatial expression of the identified candidate genes were studied using RiceXpro (
www.ricexpro.dna.affrc.go.jp (24 August 2022)).
4.8. Confirmation of Identified QTLs and Markers
Out of mined candidates from QTLs, primers were designed for two candidate genes with known function of Zn transport, namely
Os06g0705700 (CS528–F primer: GTGACGGCTTCGGATGAG and R primer: CCCGGTGTAGAAGAAGGTAATG),
Os06g0706100 (CS533–F primer: CCAATGCCTGGCCTACTT and R primer: CCACGTGGACACGTTCTT) using the Primer Quest tool (
https://eu.idtdna.com/pages/tools/primerquest (2 September 2022)) with qPCR parameters (amplicon size < 150 bp; GC content > 50%; melting temp ~60 °C; no secondary structures) and were synthesized at Integrated DNA Technologies (USA). Based on the RiceXpro data, flag leaf samples during the anthesis stage (after 10 days of flowering) from 11 genotypes (with differential grain Zn) were collected in RNALater™ (Sigma Aldrich, St. Louis, MO, USA). The published protocols for RNA isolation, cDNA synthesis, and checking of integrity of RNA and cDNA and their concentration were followed [
83]. To investigate the expression of
Os06g0705700 and
Os06g0706100 genes, qPCR was performed with three biological replicates. Each qPCR reaction was performed in three technical replicates containing 5 µL SYBR
® qPCR Master Mix (Promega Corporation, Madison, WI, USA), 0.6 µM forward and reverse gene-specific primers, and 100 ng template cDNA. The qPCR was performed using QuantStudio5 Applied Biosystems (Thermo Fisher Scientific, Waltham, MA, USA) Real-Time PCR Detection System with the following conditions: 40 PCR cycles of denaturation at 95 °C for 15 s, amplification at 58 °C for 15 s, followed by extension at 72 °C for 10 s. The melting curve (melting—95 °C for 10 s, 65 °C for 1 min, 97 °C for 1 s) was analyzed for checking the amplicon specificity. Two internal control genes, Membrane Protein (Memp) (LOC_Os12g32950.1-F Primer: GAGCGC AAAGTTCCAGAAGAA and R Primer: CGCCACTAGTTGCCGTCCTGAT) and Tumor Protein Homolog (TPH) (LOC_Os11g43900.1-F Primer: CATTGGTGCCAACCCATC and R Primer: AAGGAGGTTGCTCCTGAAGA) were used for the normalization [
84]. Relative fold change was calculated using the 2(−ΔΔC(T)) method as described by [
85].