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Article

Association Mapping for Quantitative Trait Loci Controlling Superoxide Dismutase, Flavonoids, Anthocyanins, Carotenoids, γ-Oryzanol and Antioxidant Activity in Rice

by
Ramakrushna Bastia
1,2,†,
Elssa Pandit
1,3,†,
Priyadarsini Sanghamitra
1,
Saumya Ranjan Barik
1,
Deepak Kumar Nayak
1,
Auromira Sahoo
1,
Arpita Moharana
1,
Jitendriya Meher
1,
Prasanta K. Dash
4,
Reshmi Raj
1,
Binod Kumar Jena
5,
Kartik Chandra Pradhan
2,
Devraj Lenka
2,
Nabaneeta Basak
1,
Srikanta Lenka
1 and
Sharat Kumar Pradhan
1,*
1
ICAR-National Rice Research Institute, Cuttack 753006, India
2
College of Agriculture, OUAT, Bhubaneswar 751003, India
3
Bioscience & Biotechnology Department, Fakir Mohan University, Balasore 756020, India
4
ICAR-National Institute for Plant Biotechnology, New Delhi 110012, India
5
KVK, OUAT, Rayagada 765022, India
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2022, 12(12), 3036; https://doi.org/10.3390/agronomy12123036
Submission received: 24 August 2022 / Revised: 24 September 2022 / Accepted: 30 September 2022 / Published: 30 November 2022
(This article belongs to the Special Issue Genetics, Genomics and Breeding of Cereals and Grain Legumes)
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Abstract

:
Antioxidant-rich rice is a cheaper way to solve stress-related disorders and other health benefits for the global rice-eating population. Five antioxidant traits, namely, superoxide dismutase, flavonoids, anthocyanins, γ-oryzanol and 2,2′-azino-bis-3-ethylbenzthiazoline-6-sulphonic acid (ABTS) activity were mapped using a representative panel population through association mapping. Potential landraces carrying multiple antioxidant compounds were identified from the population. The population represented four genetic groups and correspondence for presence of antioxidants traits in each group was noticed. The population showed linkage disequilibrium for the studied traits based on the Fst values. A total of 14 significant marker–trait associations were detected for these antioxidant traits. The study validated the QTLs, qANC3 and qPAC12-2 for anthocyanin content and qAC12 for ABTS activity will be useful in marker-assisted breeding. Eleven QTLs such as qTAC1.1 and qTAC5.1 controlling anthocyanin content, qSOD1.1, qSOD5.1 and qSOD10.1 for superoxide dismutase (SOD), qTFC6.1, qTFC11.1 and qTFC12.1 for total flavonoids content (TFC), qOZ8.1 and qOZ11.1 for γ-oryzanol (OZ) and qAC11.1 for ABTS activity were detected as novel loci. Chromosomal locations on 11 at 45.3 cM regulating GO, TFC and TAC, and on the chromosome 12 at 101.8 cM controlling TAC and ABTS activity, respectively, were detected as antioxidant hotspots.

Graphical Abstract

1. Introduction

Rice is a principal food for more than half of the global population. The crop is mostly produced and consumed in Asiatic countries. However, the majority of rice-consuming people are observed to suffer problems such as malnutrition, Fe and Zn deficiency, and oxidative stress-related health problems such as stroke, psoriasis, type II diabetes, heart diseases, obesity, cancers, dermatitis, and rheumatoid arthritis [1,2]. Antioxidants protect cells against free radicals, which may cause diseases in human. As the majority of the global population is dependent on rice, enriching the grains with Fe, Zn, and antioxidant compounds are the priority areas of rice research [3,4,5,6,7]. Consumption of rice rich in antioxidants is a better and cheaper option for combatting the stress-related disorders and gaining other health benefits [7]. Enhancing the nutraceutical value of the antioxidant compounds in rice though biofortification is the best and cheapest way of achieving health benefits for the people in a country [8]. In recent years, consumption of wholegrain pigmented rice enriched with antioxidant compounds has been gaining popularity in developed and developing countries due to its health benefits of reducing the risk of many chronic diseases [9,10,11]. Thus, rice breeding programs need to focus on the development of nutrient-dense rice for which improvement of the antioxidant traits is a necessity to meet the nutritional quality standards. Therefore, locating the genes and QTLs regulating these antioxidants in rice grain is very important research to conduct before starting an improvement program for these traits.
Antioxidants are present in plants both in enzymatic and non-enzymatic forms. Enzymatic antioxidants are catalases, peroxidases, superoxide dismutases, glutathione and other proteins and non-enzymatic antioxidants include phenolic defense compounds (vitamin E, flavonoids, phenolic acids and other phenols); nitrogen compounds (alkaloids, amino acids and amines), carotenoids, and chlorophyll derivatives [12,13]. The enzymatic antioxidants protect the plant cell from damage caused by reactive oxygen species and act as a defense system for maintaining the structural and functional integrity of a cell by inhibiting the oxidative deterioration to macromolecules such as lipid, protein, and nucleic acid [13,14,15]. Hence, improvement of these traits in rice will lead to the development of better-quality rice. Non-enzymatic antioxidants such as phenolic acids, flavonoids, anthocyanins and proanthocyanidins, tocopherols and tocotrienols (vitamin E), andγ-oryzanol have beenreported to be higher in pigmented rice (rice with red, black, and purple pericarp). The antioxidants show impressive health benefits such as reducing oxidative stress and cholesterol levels in human body, lowering the chances of type II diabetes, obesity, cancer, etc. [16,17,18]. These antioxidant traits are complex traits, polygenic in nature and quantitatively inherited [19]. Understanding the genetic bases of these complex antioxidant traits and identification of major QTLs are essential for the improvement of these phytochemicals through molecular breeding to ameliorate the increasing nutrition problems of the rice-eating population and seed quality, as well.
Identification of QTLs/genes for higher carotenoid content and development of functional markers is slow in rice as reports of carotenoids are not available in rice [20]. Wide genetic variation for carotenoid content exists in rice. White rice accumulates a very small quantity of carotenoid [21,22]. Color-providing pigments, anthocyanidin and proanthocyanidin, are present in the pericarp and aleurone layer of rice grain. The proanthocyanidin content in rice imparts red color to rice pericarp is controlled by the interaction of Rc and Rd genes [23,24,25]. Whereas, anthocyanidins impart purple-black pericarp to rice grain which is controlled by two loci, Pb and P [26]. Two genes, dihydroflavonolreductase (DFR) and anthocyanin synthase (ANS), present on chromosome 1 regulate the anthocyanin content in rice seeds [27]. However, a recent study reported that A1 (Kala1/Rd/OsDFR) and C1alleles (OsC1) determine the purple color of grain, and the pattern of anthocyanin pigmentation in grain is determined by the allelic status of A1, C1, and S1 (OsANS1) [28]. Kala 4/OsB2/Pb gene was mainly responsible for black pigmentation of rice pericarp [29,30].
The genetic analyses for identification and fine mapping of genes and QTLs for pericarp pigmentation in rice have been published by many workers using various mapping populations [10,19,25,30,31]. However, few reports on QTL mapping are available for γ-oryzanol, total phenolics content (TPC), total flavonoids content (TFC), ABTS (Azinobis 3-ethyl benzothiazoline-6-sulfonic acid), and SOD (Super oxide dismutase) traits in rice. Flavonoids are the major class of phenolic compounds responsible for color in rice. Rice bran contains seven flavonoids, of which tricin is the key compound. The QTLs, qPH-12, qFL-2-1 and qAC-1, control the phenolic content, flavonoid content, and antioxidant capacity, respectively, in rice [10,31,32,33]. A mapping study on γ-oryzanol content in rice was reported by Kato et al. [34]. In addition, recent reports indicated the possibility of marker–trait association for phenolics, carotenoids, anthocyanin, γ-oryzanol, and other antioxidant contents in rice [9,35]. In addition, the antioxidant traits are reported to be regulated by different pathways, viz. Phenylpropanoid biosynthesis pathway for flavonoids [36,37]; Methylerythritol 4-phosphate (MEP) pathway for carotenoids [38,39]; Mevalonate (MVA) pathway [40]; chromogen activator and tissue-specific regulator (CAP) regulatory pathway [41]; Phenyl propanoid metabolic pathway [42], or Phenyl alanine pathway [43] for anthocyanins; Esterification of hydroxyl sterols for Gamma-oryzanols [42] and Mitogen activated protein (MAP) kinase pathway for superoxide dismutase [44], from which insights into molecular mechanisms of the traits are possible.
Association mapping based on linkage disequilibrium has emerged as a powerful alternative strategy for identifying genes or quantitative trait loci (QTL) for various complex traits in plants by analyzing natural variable population. The genetic diversity and structure of the population will be helpful for detecting marker–trait association which could be useful for trait enhancement in molecular breeding programs. In order to avoid spurious association of marker-phenotype in a population, population structure (Q) with relative kinship (K) analyses are essential to check the adequacy of the panel population composition for linkage disequilibrium (LD) mapping analyses [45,46]. Thus, association estimates based on both the models of Generalized linear model and Mixed linear model are considered appropriate for mapping complex traits that have been shown to perform better than other model analyses. Although several genes for these antioxidant traits have been reported, more genes/loci are still to be identified to explain the complex regulation of carotenoids, SOD, total anthocyanins, γ-oryzanols, TFC, and ABTS in rice grains. In the present study, we have mapped these six antioxidant traits through association mapping in a highly variable representative set of 120 rice population representing the landraces and cultivars (67 white and 53 red grain) from an original population of 270 germplasm lines using 136 rice microsatellite markers.

2. Materials and Methods

2.1. Seed Materials

The study material comprised of 270 genotypes (landraces and cultivars) of 121 white and 149 colored rice grains. The initial population was shortlisted on the basis of maturity duration (upto 135 days) and kernel color (red, black, purple, and white) from about 1000 germplasm lines. Seeds of these germplasm were collected from Gene bank, ICAR-National Rice Research Institute, Cuttack and were grown in the experimental plot of the Institute during wet season, 2019. The genotypes were grown in a randomized complete block design in three rows, each with a spacing of 20 × 15 cm, in two replications, by following recommended package and practices. Each replication is divided into 5 blocks by accommodating 54 germplasm lines in each block. Panicles from middle-row plants of each replication were harvested, sun dried for 4–5 days to reduce the moisture content to 11–12%, stored for three months to remove dormancy, and then used for estimation of superoxide dismutase, flavonoids, anthocyanins, carotenoids, γ-oryzanol and antioxidant activity. A representative panel population containing 120 germplasm lines was prepared from the original 270 germplasm lines (120 genotypes consisting of 67 white and 53 red grain rice). The panel population was raised during wet season, 2019 and 2020. The harvested seeds from both years were used for the estimation of antioxidant traits. The panel population (120) was used in the genotyping for association mapping of antioxidant traits (Table 1).

2.2. Phenotyping for the Antioxidant Traits

The seed samples were dehulled by the Satake rice huller, Japan and were ground into flour by a grinding machine (Glenmini grinder) and sieved through a 100-mesh-size sieve, and then stored at 4 °C. Analyses of all the traits were based on dry matter basis, except for carotenoid content, which was estimatedon a fresh-weight basis. Leaf samples from 10 days old seedling grown on aPetri dish at 30 °C were used for estimation of carotenoids (mg g−1) by following the protocol of Davis [47]. Seed enzymatic antioxidant, super oxide dismutase (SOD: unit g−1), was estimated as per the procedure of Madamanchi et al. [48]. Non-enzymatic antioxidant, total anthocyanin content (TAC: mg 100 g−1) was estimated by the procedure of Fuleki and Francis, [49]. Estimation of γ-Oryzanol (GO:mg 100 g−1) was performed according to Bucci et al. [50] with minor modifications. Total flavonoids content (TFC) was estimated as per the procedure of Eberhardt et al. [51] and expressed as catechin equivalent (mg CEt 100 g−1). Antioxidant activity, 2,2′-azino-bis-3-ethylbenzthiazoline-6-sulphonic acid (ABTS) radical scavenging was assayed by the modified protocol of Serpen et al. [52] and expressed as % inhibition.

2.3. Statistical Analysis

Cropstat software7.0 developed by IRRI was used for analysis of variance (ANOVA) for each trait and for the estimation of mean, range, and coefficient of variation (CV%). Pearson’s correlation coefficients were analyzed to identify the relationship among the various antioxidant traits, based on the mean values of the 120 genotypes and presented in a correlation matrix heatmap by using PAST 4.03 software (Oyvind Hammer). The germplasm lines were classified into five groups as very high, high, medium, low, and very low categories based on the mean values of the antioxidant traits.

2.4. Genomic DNA Isolation, PCR Analysis, and Selection of SSR Markers

The genomic DNA was isolated from 15-day-old seedlings of the germplasm lines by adopting CTAB method [53]. A total of 136 SSR (simple sequence repeat) markers were selected from the database (http://gramene.org/, accessed on 24 August 2022) available in the public domain and used for genotyping of the panel population (Supplementary Table S5). The DNA fragments were resolved in gel electrophoresis for quantification of the isolated DNA. PCR analysis was performed using the markers selected based on positions covering all the chromosomes to illustrate the diversity and to identify the polymorphic loci among the 120 rice germplasm lines (Table 1). The conditions of reaction were set to initial denaturation step (2 min, 95 °C), followed by 35 cycles of denaturation (30 s, 95 °C) and annealing/extension (30 min, 55 °C), extension (2 min, 72 °C), final extension (5 min, 72 °C) and store at 4 °C (infinity). The PCR products were electrophoresed using 3% agarose gel containing 0.80 g mL−1 ethidium bromide and 50 bp DNA ladder was used to determine the size of amplicons. The gel was run for 4 h at 2.5 V cm−1 and photographed using a Gel Documentation System (Syngene). Earlier publications of molecular analysis were followed for DNA isolation, electrophoresis, and imaging techniques [54,55,56,57].

2.5. Molecular Data Analysis

Presence or absence of amplified products obtained on the basis genotype-primer combination was used to score the data. A binary data matrix was used as discrete variables for the entry of our result data. The parameters namely number of alleles (N), major allele frequency (A), polymorphic information content (PIC), observed heterozygosity (H), and gene diversity (GD) for each SSR locus were analyzed by using, ‘Power Marker Version3.25’ software [57]. A Bayesian model-based clustering approach STRUCTURE 2.3.6 software was used to analyze genetic data and obtain population structure [58]. STRUCTURE software was run with K-values varying from 1 to 10, with 10 iterations for each K value to derive the ideal number of groups. A high throughput parameter set of burn-in period of the 150,000 followed by 150,000 Markov Chain Monte Carlo (MCMC) replications was adapted during the running period. The highest value of ΔK was obtained from the Evanno table used to detect the subpopulation groups from the panel of populations in the next step. The maximal value of L (K) was identified using the exact number of sub-populations. The model choice criterion to detect the most probable value of K was ΔK, an ad-hoc quantity related to the second-order change of the log probability of data with respect to the number of clusters inferred by STRUCTURE [59]. For estimation of the ΔK-value as a function of K showing a clear peak, the optimal K-value Structure Harvester was used [60]. The principal coordinate analysis of all the genotypes and unweighted neighbor joining unrooted tree for NEI coefficient dissimilarity index [61] with bootstrap value of 1000 were obtained by using DARwin5 software [62]. Analysis of molecular variance (AMOVA) using GenAlEx 6.5 software was used to estimate the presence of molecular variance across the whole population, within a population and between the sub-population structures (FIT, FIS, FST) calculated by the deviation from Hardy–Weinberg expectation. The procedures followed in earlier publications were adopted for molecular data analysis [63,64].
To analyze the marker–trait association for mapping study of the seed antioxidant traits in rice, the software “TASSEL 5.0” was used. General linear model and Mixed linear model in TASSEL 5.0 were used to calculate the genetic association between the phenotypic traits, and molecular markers were adopted as per Bradbury et al. [65]. By considering the significant p-value and r2 value, convincing associated markers were identified. The associations of markers were further confirmed by the Q-Q plot generated by the software. Linkage disequilibrium plot was obtained using LD measured r2, between pairs of markers plotted against the distance between the pair. Additionally, the accuracy of the marker–trait association was established by estimating the FDR adjusted p-values (q-values) using R software as described in the earlier publications [9,35,46].

3. Results

3.1. Phenotyping of the Population for the Six Antioxidant Traits

A total of five antioxidant compounds and one antioxidant enzyme, viz., superoxide dismutase, flavonoids, anthocyanins, carotenoids, γ-oryzanol, and ABTS, were estimated from the 270 germplasm lines during wet season, 2019 (Supplementary Table S1). Wide genetic variation was observed for the six antioxidant traits in the germplasm lines. The genotypes were classified into five groups based on the phenotyping results of each compounds (Figure 1). The frequency distribution of germplasm lines showed various groups or populations for each compound and enzyme (Figure 1). A panel population was prepared by selecting 120 genotypes which represented each group and trait from the original population of 270 germplasm lines (Table 1; Figure 2). The mean estimates of six antioxidant traits obtained from the representative panel population showed wide variation among the genotypes for each trait (Table 1). Very high values of carotenoid content were found in germplasm lines Ac. 44598, Ac. 44597, and Ac. 9005. Additionally, very high TAC content was estimated from the lines Ac. 43670, Ac. 43660, and Ac. 43675. Germplasm lines namely Ac. 9063, Ac. 20371, and Ac. 20627 showed very high level of γ-oryzanol content in the seeds. Good donor lines were identified carrying very high TFC content, viz., Ac. 43670, Ac. 43660, Ac. 44646, Ac. 44592, Ac. 44595, Ac. 43737, Ac. 43738, and Ac. 43676. The SOD level was found very high in the seeds of germplasm lines such as Ac. 20317, Palinadhan-1, Ac. 20362, Ac. 20328, Gochi, Chatuimuchi, Ac. 20770, Ac. 20920, Ac. 20907, Magra, and Chinamal. The potential donors identified for exhibiting very high level of ABTS were Ac. 44592, Ac. 43670, Ac. 4460, Ac. 44595, Ac. 44588, Ac. 43660, Ac. 43738, and Ac. 43732. However, germplasm lines (Ac. 44592, Ac. 44646, Ac. 44595, Ac. 43660, Ac. 43738, Ac. 43660, and Ac. 43669) were identified for possessing a higher level of more than three antioxidant traits.

3.2. Genotype-by-Trait Biplot Analysis for the Six Antioxidant Traits in the Germplasm Lines

The scatter diagram was plotted using the first two principal components to generate genotype-by-trait biplot graph for the six antioxidant traits in the 120 germplasm lines present in the panel (Figure 3). The first and second principal components showed 68.3 and 19.8%of the total variability with Eigen values of 8064 and 2342, respectively (Supplementary Figure S1). The compound, γ-oryzanol content contributed maximum diversity, followed by TFC and ABTS, among the six antioxidant traits estimated from the genotypes present in the panel (Figure 3). The scattering pattern of the germplasm lines in the four quadrants indicated that genotypes containing high estimates of the studied antioxidants are placed in quadrants I (top right) and II (bottom right). Higher estimates of the antioxidant traits with multiple compounds containing genotypes have been encircled in the figure (Figure 3). The top right (quadrant I) and bottom right (quadrant II) accommodated the majority of the genotypes containing high estimates of the antioxidant traits. The quadrant III (bottom left) kept most of the germplasm lines as moderate in the studied antioxidant traits, while the 4thquadrant (top left) accommodated the majority of poor germplasm lines for the antioxidants (Figure 3).

3.3. Nature of Association among the Antioxidant Traits

The association study provides information for correlation among the traits in which the correlated complex traits are useful in improvement programs. The association among the six antioxidant traits revealed a strong positive correlation (r ≥ 0.7) of TAC with TFC and TFC with ABTS. Moderate positive correlation (r 0.5–0.7) of TAC with ABTS and a weak positive correlation (r < 0.5) were observed for carotenoid with γ-oryzanol content (Figure 4). These antioxidant traits positively or negatively correlated may be controlled by the closely linked genes or because they might be structurally related. Therefore, a variety that accumulates high concentrations of one antioxidant may also contain alarger quantity of other correlated antioxidants.

3.4. Genetic Diversity Parameters Analysis

The studied panel population exhibiting wide genetic variation in 120 germplasm lines for the six antioxidant traits was genotyped using 136 SSR markers. The genetic diversity parameters estimated from the panel population are depicted in Table 2. Genotyping results showed a total of 508 markers’ alleles from the population, exhibiting mean alleles of 3.74 per locus. The number of alleles per locus ranged from 2 to 7 per marker. The largest numbers of alleles were produced by the marker RM493 in the studied panel population. The measure for the variation by a marker in the population was analyzed by the availability of major allele frequency parameter. The average major allele frequency linked to the polymorphic markers was computed to be 0.561, which showed a range of 0.279 (RM8044) and 0.925 (RM6054) (Table 2). The informativeness of a genetic marker is estimated by the PIC value. It ranged from 0.137 (RM6045 and 6054) to 0.787 (RM493) with an average value of 0.496. In case of low predicted heterozygosity of alleles in a population, the population may be shifting towards inbreeding for that trait. If it is higher than the predicted heterozygosity, that may be the effect of mixing of two genetic populations. Here, the observed mean heterozygosity (Ho) in the population was 0.116 which varied from 0.00 to 0.958 (RM3735). Twenty marker loci showed 0.00 Ho value in the panel population. The gene diversity (He), which gives a measure of genetic diversity in the panel population, ranged from 0.142 (RM6054) to 0.813 (RM493) with a mean value of 0.555.

3.5. Population Genetic Structure Analysis

The diverse population for the studied antioxidant traits was genotyped for genetic structure and analyzed by adopting probable sub-populations (K) and selecting higher ∆K-value by applying the STRUCTURE 2.3.6 software. The rate of change in the log probability of data between successive K values is the delta K value used in the analysis. The panel population was categorized into two sub-populations by considering a high ∆K peak value of 362.4 at K = 2 among the assumed K (Supplementary Table S2; Supplementary Figure S2). The two subpopulations were in the proportion of 0.277 and 0.723 for population 1 and population 2, respectively. However, the subpopulations showed poor correspondence with the six antioxidant traits in the germplasm present in the studied population. Therefore, the next ∆K peak at K = 3 was compared in which the population was classified into three subpopulations. The three subpopulations showed genotypes in the proportion of 0.208, 0.689, and 0.103 in the inferred clusters for the sub-population 1, 2, and 3, respectively. The Fst1, Fst2, and Fst3 values were 0.3392, 0.1664, and 0.3701 for the sub-population 1, 2, and 3, respectively (Supplementary Table S2; Supplementary Figure S3). The ancestry value of ≥80% obtained in a genotype grouped the genotype into the particular subpopulation.
The assumed subpopulations at K = 3 differentiated the germplasm lines based on the six antioxidant traits, but did not clearly separate the SP2 and SP3 subpopulations. Hence, next ∆K peak at K = 4 was considered for the subpopulations in which the population was classified into four genetic groups. The six antioxidant traits in the studied population showed a fair degree of correspondence at K = 4 with inferred structure values in the subpopulations. The majority of the germplasm lines with high to very high antioxidant-carrying germplasms were present in subpopulation 4. The germplasm lines showing moderate value of the antioxidant estimates are present in subpopulation 2. Germplasm lines with poor and moderate levels of antioxidant estimates were in subpopulation 1, while very poor to poor types are in subpopulation 3 (Table 3; Figure 5). The alpha value of the panel showed a low value (α = 0.0578) estimated by the structure analysis at K = 4. Positively skewed leptokurtic distributions were observed for the mean alpha-value while normally skewed leptokurtic distributions detected for all the 4 Fst values for the panel population showing a distinct variation in the distribution among the Fst values (Supplementary Figure S4).

3.6. Molecular Variance (AMOVA) and LD Decay Plot Analysis

The closely related plants among themselves in a population are grouped into isolated subpopulations. The genetic variations obtained within and between the subpopulations at K = 4 were estimated by the analysis of molecular variance (AMOVA) (Table 4). The genetic variations estimated at K = 4 was computed to be 6% among the populations, nil among individuals, and there was 94% variation within individuals of the panel population. Wright’s F statistics was used to obtain the deviation from the Hardy–Weinberg prediction. The parameter FIS was used to analyze the uniformity of individuals within the subpopulation and FIT for individuals within the total population for differentiation of the population. The FIT and FIS of the total population and within population estimated on the basis of 136 marker loci showed−0.148 and 0.235, whereas the total population had a FST value of 0.071 between the four subpopulations. Fst is used to identify the subpopulations or population differentiation within the total population. A clear differentiation among the four subpopulations was observed for the Fst values from each other in their distribution pattern (Supplementary Figure S4).
The association of alleles by different loci in a nonrandom manner is utilized in the marker–trait association analysis. Existence of marker–trait association is dependent on the LD decay rate in a population over a time period. The LD decay rate will indicate the possibility of new genes or allelic variants controlling the antioxidant compounds associated with molecular markers for these traits. A syntenic r2 value was used to plot the linkage disequilibrium decay of the population versus the physical distance in million base pair (Figure 6A). Tightly linked markers had higher r2 values and the average r2 values rapidly decreases for increase in linkage distance. In the LD plot, it is observed that the LD decay in the beginning was delayed in the studied panel populations. However, a decline of LD decay was noticed in the curve for the associated markers at about 1–2 mega base pair and there, after a gradual and very slow decay, this can be noticed in the graph. The graph clearly indicates the continuance of linkage disequilibrium decay in the population for the studied antioxidant traits in the population. The limitation for LD decay depends on non-random mating, mutation, selection, migration or admixture, and genetic drift, which will influence the estimates of LD. This LD decay plot also provides a clue for the creation of genetic admixture groups for various antioxidants traits in the population. A similar trend was also noticed in the marker ‘P’ versus the marker ‘F’ and marker R2 (Figure 6B) curve. The detected markers from this study indicated the strength of the markers for the studied antioxidant compounds.

3.7. Principal Coordinates and Cluster Analyses for Genetic Relatedness among the Germplasm Lines

The two-dimensional plot for the principal coordinate analysis (PCoA) was constructed based on the genotyping data of 136 SSR markers which classified the 120 germplasm lines as per the genetic relatedness among the lines (Figure 7). The inertia showed by component 1 was 11.73%, while 7.49% exhibited by component 2. The germplasm lines were allotted different spots in the four quadrants forming 3 major groups (Figure 7). The biggest group accommodated all the germplasm lines of the subpopulation 2 and 3 together and clustered in the 2nd (bottom right) quadrant. The genotypes in the 1stquadrant are divided into 2 groups, of which one group on the top of the 1st quadrant forms the SP3 subpopulation which showed mostly low to very low estimates for the antioxidant traits in the seeds. The other group near to the axis1 is for all the admix types of the germplasm lines. Few germplasm lines of quadrant II and closer to the axis 1 are also admix genotypes. Then, admix genotypes present on both sides of axis 1 are depicted in black color (Figure 7).
The germplasm lines containing high to very high estimates of antioxidant traits are grouped together, forming subpopulation 4. This subpopulation is present on quadrant III (top left) and IV (bottom left) and encircled in red color. The germplasm lines rich in antioxidants are placed on both sides of the axis 1 on the quadrant III and IV (Figure 7). The PCoA distributed all the germplasm lines into the four quadrants classifying them into 4 clusters and a separate admixture group. The subpopulations clustered by PCoA showed correspondence with the population structure (Figure 7). Germplasm lines namely Ac. 44594, Ac. 43669, Ac. 44597, Ac. 44588, Ac. 43737, Ac. 44595, Ac. 43676, Ac. 44597, Ac. 44592, Ac. 43738, and Ac. 44646 are placed together in one structure group present in quadrants III and IV and are rich in antioxidants. The PCoA placed germplasm lines in quadrant II which were mostly average in the antioxidant traits. This quadrant formed the group by placing all the germplasm lines of subpopulations 1 and 2.
As per the Ward clustering, all the germplasm lines were broadly grouped into two major groups. The largest cluster, cluster 1, accommodated 111 germplasm lines in which most of the lines showed poor to average for the antioxidant estimates. The cluster II had nine germplasm lines only. The dendrogram placed all the germplasm lines in this cluster II which were rich for the antioxidant traits. This cluster again subdivided into 2 subgroups, which were further divided into six sub-subclusters. Cluster I was divided into two main sub clusters which finally divided into 32 small groups. All the clusters and small groups accommodated in the Ward clustering approach were based on the antioxidant traits estimates in the germplasm lines (Figure 8A). The cluster analysis discriminated the germplasm lines on the basis of markers data of 136 SSR markers and placed the genotypes into different clusters which corresponded with the studied antioxidant level in the germplasm. The unweighted-neighbor joining tree differentiated the genotypes into four different clusters (Figure 8B). The cluster for subpopulation 4 was differentiated from SP2 by the presence of germplasm lines containing high antioxidants in it, while moderate to high-containing genotypes were in subpopulation 2. The green-colored portion of the tree is designated as SP4 while blue for SP2. The very poor in antioxidant traits in the germplasm lines were in the subpopulation 3 those depicted in red color in the tree. The majority of the germplasm lines present in subpopulation 1 were poor to medium in antioxidant value and are shown in pink color. The germplasm lines with admix type of population are depicted in black color in the neighbor joining tree (Figure 8B).

3.8. Marker–Trait Association for Antioxidant Traits in the Rice Panel Population

Marker–trait associations were computed for the six antioxidant traits by using Generalized Linear Model (GLM) and Mixed Linear Model (MLM/K + Q model)) in the TASSEL 5 software. The marker–trait association values were compared at less than 1% error i.e., 99% confidence (p < 0.01). A total of 57 and 23 significant marker–trait associations were detected for five antioxidant traits by GLM and MLM, respectively, at p < 0.01. The range for marker R2 values was from 0.0477 to 0.159 by GLM while 0.0607 to 0.1169 detected by Mixed Linear Model (Supplementary Table S3; Supplementary Table S4). A total of 14 significant marker–trait associations were detected by both the models for five antioxidant traits present in the seed at p < 0.01 (Figure 9A). Significant association of 5 SSR markers with TAC; 3 with SOD, TFC; 2 with GO, and ABTS were detected. Five antioxidant compounds present in the studied germplasm lines presented a higher marker R2 (>0.1) with low p-values (<0.01) in the associations study includes SOD with RM405 and GO with RM3701 (Table 5; Figure 9A). The Q-Q plot also confirmed the association of these markers with the associated antioxidant traits in rice (Figure 9B).
Four markers, namely, RM440, RM5638, RM253, and RM5626, showed significant associations with compound, TAC detected by GLM and MLM models at p < 0.01, showing >0.05 marker R2 value. The QTLs controlling anthocyanin content in these genotypes are detected to be located near the markers present at RM440, RM5638, RM253, and RM5626 at 92.7, 86, 37, and 99 cM on the chromosome 5, 1, 6, and 3, respectively. Three markers, namely, RM582, RM467, and RM405, located at 66.4, 46.8, and 28.6 cM positions on chromosome 1, 10, and 5, respectively, were associated with the compound SOD. TFC content was detected to be associated with markers RM 3701, RM235, and RM494 present at 45.3, 101.8, and 124.4 cM on chromosome 1, 11, and 12, respectively. The QTLs for ABTS activity showed significant associations with RM3701 and RM235 on chromosomes 1 and 11, respectively. The marker RM216 showed association with SOD at very low p-value and high marker R2 value of >0.10618 analyzed by the GLM only. The QTLs for antioxidant compound, OZ, showed significant associations with RM3701 and RM502 on chromosomes 1 and 8, respectively (Table 5; Figure 9A). The Q-Q plot also confirmed the associations of these markers with the estimated antioxidant compounds in rice (Figure 9).
Association mapping studies for the antioxidant traits in seeds identified co-localization of QTLs controlling the antioxidant traits in rice. It is observed that the same marker showed significant associations with different antioxidant traits in rice by both models (Table 5). Significant associations of marker RM3701 with the antioxidant traits GO, TFC, and ABTS estimated from the germplasm lines were detected. In addition, it was also detected the association of RM235 with the traits TAC, TFC, and ABTS by both the models at <1% error and p < 0.01 (Table 5). While considering the marker association analyzed by GLM, the marker RM494 showed association with both carotenoids and TFC. In addition, RM494 was associated with both the traits, SOD and TFC analyzed by the model, MLM.

4. Discussion

The genotypes shortlisted for the six antioxidant traits mapping exhibited wide genetic variation among themselves (Supplementary Table S1; Table 1). In addition, significant correlation was observed between few antioxidant traits viz., TAC with TFC, TFC with ABTS, and TAC with ABTS. Existence of genetic variation and correlation for these traits provide enough insight about the possibility for improvement of the antioxidant traits in rice (Table 1; Table 4). Earlier reports of high variations for antioxidant traits were also published by few researchers [17,35,45,66,67,68]. The available diversity in the population based on 136 markers data for the six antioxidant traits represented clear-cut groups in the studied population (Table 2). A moderate to high PIC value coupled with better informative markers in the studied population will be useful for improvement of the antioxidant breeding program. The Jeypore tract of Odisha is known for being a secondary center of origin of rice, and germplasms from this tract were also included in this study. Additionally, the shortlisted germplasm lines used as materials in this study were collected from states known for their rich rice genetic diversity [35,45,69]. The genotypes rich in multiple antioxidant traits were estimated from the germplasm lines Ac. 44592, Ac. 44646, Ac. 44595, Ac. 43660, Ac. 43738, Ac. 43660, and Ac. 43669. These germplasm lines will be good source materials in the antioxidant improvement programs (Table 1; Supplementary Table S2). Therefore, it is expected that the breeding program with inclusion of parental lines from this population will be effective in terms of antioxidants’ improvement in rice. The assumed subpopulations at K = 3 differentiated the members different subpopulations for the 6 antioxidant traits but did not clearly separate the SP2 and SP3 subpopulations. Therefore, the next ∆K peak at K = 4 was considered for the subpopulations in which the population was classified into four genetic groups. The six antioxidant traits in the studied population showed a fair degree of correspondence at K = 4 with inferred structure values in the subpopulations. Structure analysis categorized the population into four subpopulations (K = 4), showing different Fst values, supporting the availability of the linkage disequilibrium groups in the population. The detection of a low alpha value and the existence of many genetic admix-type germplasm lines in the population indicated that the antioxidant traits evolved from a single source initially during evolution of the trait. Different antioxidant compounds were subsequently formed by admix genotypes with different ancestry value during evolutionary process. A similar view of the evolution of complex traits was reported by earlier publications based on the admix genotypes [5,8,9,70]. Population genetic structure group and its correspondence with the traits in each group are important for obtaining a marker–trait association. A good correspondence of genetic structure and different traits was previously published by many researchers [36,61,71]. Additionally, publications on the phenotype of various traits and structure correlation have been published by many workers [45,46,66,67,72].
Five antioxidant compounds were found to be associated with 12 SSR markers analyzed by both GLM and MLM approaches (Table 5). The markers’ association detected by both the models at p < 0.01 and low p-value are considered to be very robust and useful markers for improvement program. The strongly associated SSR markers, namely, RM440, RM235, RM5638, RM253 and RM5626 for TAC; RM582 and RM467 for SOD; RM 3701, RM235 and RM494 for TFC; RM3701 and RM235 for ABTS; RM3701 and RM502 for GO, will be useful markers for selection of antioxidant carrying plants (Table 5). The Q-Q plot also confirmed the associations of these markers with the antioxidant compounds in rice (Figure 9B).
The QTLs for anthocyanin and proanthocyanin content in rice were reported by earlier researchers [19,23,30]. In the present investigation, the QTLs for total anthocyanin content were detected on chromosomes 1, 3, 5, and 12. The QTLs on chromosomes 1 and 3 were at position of 86 cM and 99 cM, respectively. The genes qANC3 and qPAC12-2 reported by Xu et al. [19] were at the same position as in the present investigation. Therefore, these two QTLs were validated in our study using the present mapping population. However, another two QTLs located on chromosome 1 and 5 detected in this investigation were not reported by earlier researchers. These two QTLs may be new loci which affect TAC in rice and are designated as qTAC1.1 and qTAC5.1. Three markers, namely, RM582, RM405, and RM467, showed an association with SOD and were located on chromosomes 1, 5 and 10 at 66.4, 28.6, and 46.8 cM, respectively. The QTLs reported by [23,27] for anthocyanin content in rice were at different position than the locations detected by us on chromosomes 1,3, and 6. Saini et al. [73] reported 23 QTLs located on chromosome 3,5,6,7, and 9. We detected QTLs for the trait on chromosome 1, 5, and 10. The detected QTLs by Saini et al. [73] on chromosome 5 were quite away from the QTLs detected by us. In addition, no report of QTL from the earlier studies on chromosome 1 and 10 which were detected by us at 15.34 Mb and 13.48 Mb positions, respectively. Therefore, all the 3 QTLs were not reported in earlier studies. These QTLs designated as qSOD1.1, qSOD5.1, and qSOD10.1 may be new loci controlling the SOD activities in rice seeds. The total flavonoids content (TFC) is detected to be associated with three regions on chromosomes 6, 11, and 12. The earlier publication of Shao et al. [10] showed the presence of QTLs on chromosome 4, 7, 8, 9, and 10 [10]. The main flavonoids structural genes located on chromosome 11 for CHS [74]; on chromosome 3 for CHI [36]; on chromosome 4 for F3H [75]; on Chromosome 1 for DFR [75] and ANS [72]. The gene CHS on chromosome 11 was at 3.3 cM. We detected it at 45.3 cM. Therefore, all these three detected QTLs which affect total flavonoids are new loci and are designated as qTFC6.1, qTFC11.1, and qTFC12.1. Zhang [76] reported four QTLs controlling flavonoid content in rice grain located on chromosome 4. However, we detected three QTLs on chromosomes 6, 11, and 12. Therefore, the detected QTLs by us regulating flavonoid content in rice were not reported in earlier studies.
Food containing γ-oryzanol (OZ) is well recognized for its health benefits. This is a mixture of several compounds present in the rice bran layer. The γ-oryzanol content in this study showed significant association with markers on the chromosomes 8 and 11. However, QTLs previously reported by earlier workers reported on the chromosome 1, 5, and 9 in Asominori/IR24 RILs [34]. However, they detected another 5 QTLs for OZ in the backcross lines of Sasanishiki/Habataki/Sasanishiki. These two new loci detected in this investigation are new loci controlling γ-oryzanol, and are designated as qOZ8.1 and qOZ11.1. The QTLs for ABTS activities showed significant associations with RM3701 and RM235 on chromosomes 11 and 12, respectively. The candidate gene controlling ABTS and present on the chromosome 11 is not reported by earlier researchers. Hence, the detected QTL for ABTS on chromosome 11 at 45.3 cM position is a new locus controlling the trait, and it is designated as qAC11.1. However, the other detected association for the trait on chromosome 12 is located in the 26.1 Mb position. An earlier mapping publication reported the gene on the chromosome 12 at 25.2 Mp position [33]. As our detected QTL position for ABTS activity is close to the reported QTL qAC12, this QTL is validated in our mapping population and can be useful in the marker-assisted breeding for ABTS improvement.
Two markers were observed to be associated with more than one antioxidant trait analyzed by both the models at <1% error and p < 0.01. Marker RM3701 showed associations with antioxidant traits, GO, TFC, and ABTS present in the germplasm lines. Additionally, RM235 was associated with traits, TAC, TFC, and ABTS by both models (Table 5). These observations indicated the close location of the candidate genes and simultaneous inheritance of these QTLs are expected in the progenies. Hence, simultaneous improvement of both these antioxidant traits will be effective. These genomic locations are considered as chromosome hot spots and are very useful in improvement programs. Recent publications have also suggested easy improvement of the co-localized genes controlling various traits in rice [6,45,76]. Results of the present investigation showed that association mapping is an effective method to detect potential loci for antioxidant traits in rice. The detected loci will further be fine-mapped for application in maker-assisted breeding for improvement of antioxidant traits in rice.

5. Conclusions

Consumption of rice containing a higher content of antioxidants has many health benefits. Donor lines rich in more than three antioxidant traits were identified from the population. The germplasm lines, namely, Ac. 44592, Ac. 44646, Ac. 44595, Ac. 43660, Ac. 43738, Ac. 43660, and Ac. 43669, presented high results for three antioxidant traits. Antioxidant traits such as superoxide dismutase, flavonoids, anthocyanins, γ-oryzanol, and ABTS were mapped in a representative panel population using 136 SSR markers through association mapping. Wide genetic variations were observed for the studied six antioxidant traits in the population. The population was classified into four genetic structure groups by the structure analysis. The existence of linkage disequilibrium for the antioxidant traits was established based on the population’s fixation indices. The population was classified into four subpopulations which showed a fair degree of correspondence with the antioxidant traits present in each subpopulation. A total of 14 significant marker–trait associations for the antioxidant traits were detected of which 3 QTLs namely qANC3, qPAC12-2 for anthocyanin content and qAC12 for ABTS activity were validated in the population. These three QTLs are useful in the marker-assisted breeding programs. Eleven putative QTLs, such as qTAC1.1 and qTAC5.1 for anthocyanin content; qSOD1.1, qSOD5.1 and qSOD10.1 for SOD; qTFC6.1, qTFC11.1, and qTFC12.1 for TFC; qOZ8.1 and qOZ11.1 for γ-oryzanol, and qAC11.1 for ABTS, were detected as novel loci. Co-localization of the QTLs detected for OZ11.1, TFC11.1, and AC11.1 regulating γ-oryzanol, flavonoid, and anthocyanin content, respectively, while PAC12.2 for anthocyanin content remained closer to TFC12.1 for flavonoid content. These strongly associated QTLs will be useful in the antioxidant improvement programs in rice.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy12123036/s1, Figure S1: Scree plot and loadings generated by the six antioxidant traits and eigen values % in the 120 rice germplasm lines. Figure S2: (A) Graph of ΔK value, to the rate of change in the log probability structure of the 120 germplasm lines of the panel population based on membership probability fractions of individual genotypes at K of data between successive K values; (B) Population = 2. Figure S3: (A) Graph of ΔK value, to the rate of chan structure of the 120 germplasm lines of the panel population based on membership probability fractions ge in the log probability of data between successive K values; of individual genotypes at K (B) Population = 3. Figure S4: The distribbuuti pattern of alpha value and Fst values in the 4 subpopulations at K = 4. Table S1: Mean vaalues of carotenoids, SOD, TAC, GO, TFC and AABTS antioxidants in 270 rice germplaassm line. Table S2: The inferred ancestry value and population structure of individual member with their antioxidants classification in the panel population at K = 2 & K = 3. Table S3: Marker-trait associations with antioxidant content in the panel population detected by the model GLM at p < 0.01. Table S4: Marker-trait associations with antioxidant content in the panel population detected by the model MLM at p < 0.01. Table S5: Markers information of the selected 136 SSR markers used for antioxidant content in indica rice.

Author Contributions

S.K.P. conceived the study; R.B., E.P., S.R.B., D.K.N., A.S., A.M. and B.K.J. performed the genotyping work; P.S., J.M., N.B., K.C.P., R.R., S.L. and S.K.P. performed the phenotyping work; and P.K.D. and D.L. analyzed the data; S.K.P. and P.K.D. wrote the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable as the study did not involve humans.

Informed Consent Statement

Not applicable as the study did not involve humans.

Data Availability Statement

The data generated or analyzed in this study are included in this article.

Acknowledgments

The authors are highly grateful to the Head, Crop Improvement Division and Director, ICAR-NRRI, Cuttack for encouraging the team and providing all the necessary facilities.

Conflicts of Interest

The authors declare that there is no competing interest and that the article is submitted without any commercial or economic interest that could be generated as a potential conflict of interest.

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Agronomy 12 03036 g001 550
Figure 1. Frequency distribution of germplasm lines for each of the studied antioxidant traits, namely, carotenoids, superoxide dismutase, anthocyanins, γ-oryzanol, flavonoids, and ABTS present in the shortlisted 270 germplasm lines.
Figure 1. Frequency distribution of germplasm lines for each of the studied antioxidant traits, namely, carotenoids, superoxide dismutase, anthocyanins, γ-oryzanol, flavonoids, and ABTS present in the shortlisted 270 germplasm lines.
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Figure 2. Estimate of 6 antioxidant traits in the 120 genotypes and their frequency distribution in the panel population. (A) Spider graph showing TFC content and ABTS activity. (B) TAC andγ-oryzanol content; (C) Carotenoid and SOD content; (D) Frequency distribution of germplasm lines for carotenoids, superoxide dismutase, anthocyanins, γ-oryzanol, flavonoids, and ABTS in the panel population.
Figure 2. Estimate of 6 antioxidant traits in the 120 genotypes and their frequency distribution in the panel population. (A) Spider graph showing TFC content and ABTS activity. (B) TAC andγ-oryzanol content; (C) Carotenoid and SOD content; (D) Frequency distribution of germplasm lines for carotenoids, superoxide dismutase, anthocyanins, γ-oryzanol, flavonoids, and ABTS in the panel population.
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Figure 3. Genotype-by-trait biplot diagram showing 120 germplasm lines in two PCs for 6 antioxidant traits.
Figure 3. Genotype-by-trait biplot diagram showing 120 germplasm lines in two PCs for 6 antioxidant traits.
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Figure 4. Heat map showing Pearson’s correlation coefficients for 6 antioxidant traits. Significant correlations are colored either in red (negative) or blue (positive). Shades of blue indicate increasing positive correlation coefficient; shades of red indicate increasing negative correlation coefficient. CART: Carotenoids (mg g −1); SOD: Super oxide dismutase (unit g−1); TAC: Total anthocyanin content (mg 100 g−1); GO: γ-oryzanol (mg 100 g−1); TFC: Total flavonoids content (mg catechin or CEt 100 g−1); ABTS: 2, 2′-azino-bis 3-ethylbenzothiazoline-6-sulfonic acid (% inhibition).
Figure 4. Heat map showing Pearson’s correlation coefficients for 6 antioxidant traits. Significant correlations are colored either in red (negative) or blue (positive). Shades of blue indicate increasing positive correlation coefficient; shades of red indicate increasing negative correlation coefficient. CART: Carotenoids (mg g −1); SOD: Super oxide dismutase (unit g−1); TAC: Total anthocyanin content (mg 100 g−1); GO: γ-oryzanol (mg 100 g−1); TFC: Total flavonoids content (mg catechin or CEt 100 g−1); ABTS: 2, 2′-azino-bis 3-ethylbenzothiazoline-6-sulfonic acid (% inhibition).
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Figure 5. (A) Graph of ∆K value, to the rate of change in the log probability of data between successive K values. (B) Population structure of the panel population based on membership probability fractions of individual genotypes at K = 4. The genotypes with the probability of ≥80% membership proportions were assigned as subgroups, while others were grouped as admixture group. The numbers in the diagram depict the serial number of the germplasm lines listed in the Table 1.
Figure 5. (A) Graph of ∆K value, to the rate of change in the log probability of data between successive K values. (B) Population structure of the panel population based on membership probability fractions of individual genotypes at K = 4. The genotypes with the probability of ≥80% membership proportions were assigned as subgroups, while others were grouped as admixture group. The numbers in the diagram depict the serial number of the germplasm lines listed in the Table 1.
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Figure 6. (A) The physical distance (base pairs, bp) between pairs of loci on chromosomes against linkage disequilibrium (LD) decay (r2) curve plotted in rice. The decay started in million bp estimated by taking the 95th percentile of the distribution of r2R2 for all unlinked loci. (B) The marker ‘P’ versus marker ‘F’ and marker R2.
Figure 6. (A) The physical distance (base pairs, bp) between pairs of loci on chromosomes against linkage disequilibrium (LD) decay (r2) curve plotted in rice. The decay started in million bp estimated by taking the 95th percentile of the distribution of r2R2 for all unlinked loci. (B) The marker ‘P’ versus marker ‘F’ and marker R2.
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Figure 7. Principal coordinate analysis (PCoA) of 120 genotypes in the panel population for the 6 antioxidant traits using 136 molecular markers. The dot numbers in the figure represent the serial number of the genotypes enlisted in the Table 1. The numbers are colored on the basis of sub-populations obtained from structure analysis (SP1: Pink; SP2; Blue; SP3: Red; SP4: Green, and Admix: Black).
Figure 7. Principal coordinate analysis (PCoA) of 120 genotypes in the panel population for the 6 antioxidant traits using 136 molecular markers. The dot numbers in the figure represent the serial number of the genotypes enlisted in the Table 1. The numbers are colored on the basis of sub-populations obtained from structure analysis (SP1: Pink; SP2; Blue; SP3: Red; SP4: Green, and Admix: Black).
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Figure 8. (A) Grouping of the panel germplasm lines A. Ward’s clustering based on the antioxidant content (B) Unrooted tree using unweighted-neighbor joining method depicting clustering pattern of 120 germplasm lines with respect to 136 molecular markers colored on the basis of subpopulations obtained from structure analysis (SP1: Pink; SP2; Blue; SP3: Red; SP4: Green, and Admix: Black).
Figure 8. (A) Grouping of the panel germplasm lines A. Ward’s clustering based on the antioxidant content (B) Unrooted tree using unweighted-neighbor joining method depicting clustering pattern of 120 germplasm lines with respect to 136 molecular markers colored on the basis of subpopulations obtained from structure analysis (SP1: Pink; SP2; Blue; SP3: Red; SP4: Green, and Admix: Black).
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Figure 9. (A) Positions of the QTLs on the chromosomes for antioxidant content detected by association mapping in rice. (B) Distribution of marker–trait association and quantile–quantile (Q-Q) plot generated from Generalized Linear Model analysis for six antioxidant traits at (A) p < 0.05 and (B) at p < 0.01.
Figure 9. (A) Positions of the QTLs on the chromosomes for antioxidant content detected by association mapping in rice. (B) Distribution of marker–trait association and quantile–quantile (Q-Q) plot generated from Generalized Linear Model analysis for six antioxidant traits at (A) p < 0.05 and (B) at p < 0.01.
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Table
Table 1. Mean values of carotenoids, SOD, TAC, GO, TFC, and ABTS antioxidants estimated from 120 genotypes present in the panel population.
Table 1. Mean values of carotenoids, SOD, TAC, GO, TFC, and ABTS antioxidants estimated from 120 genotypes present in the panel population.
Sl. No.Kernel ColorGenotype/Vernacular Name/Accession No.CarotenoidsSODTACGOTFCABTS
1WhiteAc. 59930.1150.2390.20943.75012.3338.853
2WhiteAc. 62210.4230.1010.15947.37513.3338.952
3WhiteAc. 61830.1820.0150.10247.12513.88914.119
4WhiteAc. 61701.1650.1760.09052.25013.33311.063
5WhiteAc. 60230.1120.2800.14333.31313.00010.522
6WhiteAc. 61720.2970.1810.22534.12513.4447.569
7WhiteAc. 60270.1330.1750.14138.18812.3337.983
8WhiteAc. 60070.2870.1920.02732.12513.1117.983
9WhiteAc. 90061.0140.2840.06470.43817.88911.412
10WhiteAc. 90210.4440.1990.08376.31322.00011.769
11RedAc. 90280.7760.2160.25087.50045.55636.976
12WhiteAc. 90300.6860.1500.12339.56318.66711.555
13WhiteAc. 90350.2620.1960.11749.50017.66711.698
14RedAc. 90380.3710.2410.45928.43847.00041.341
15WhiteAc. 90430.3080.1750.06139.68818.4448.131
16WhiteAc. 9044A0.7130.2210.04849.93817.55615.906
17RedAc. 209201.2640.3120.32554.12543.88926.061
18RedAc. 209070.9190.3080.55164.75052.44426.501
19WhiteAc. 208451.2570.2650.10261.25018.8896.442
20RedAc. 207701.3790.3130.56862.68862.33335.959
21RedAc. 206271.1640.2450.45193.37545.33322.694
22WhiteAc. 206860.9680.2900.07343.18821.7784.539
23WhiteAc. 206640.8280.2560.07051.93819.7787.028
24RedAc. 206140.7270.2730.60985.25062.84138.448
25WhiteJhagrikartik0.0800.2090.16739.68815.22210.623
26WhiteDadghani0.4110.2060.13051.87516.00012.606
27WhiteShayam0.4550.1960.17058.31319.33312.677
28WhiteBasumati-B0.0910.1770.12455.75020.66715.935
29RedBharati0.1080.2350.44241.25035.66733.669
30WhiteJoha0.0940.2480.15541.68817.00011.402
31RedAdira-10.3500.1370.94346.750114.22239.115
32RedAdira-20.5110.0940.90154.31380.11138.316
33RedAdira-30.4720.0392.99648.75079.66738.099
34RedPK60.2170.1121.16846.12562.22233.091
35RedVachaw0.3880.0781.56847.56354.11139.317
36RedKozhivalan0.4760.0070.68451.50067.66727.279
37RedMarathondi0.4790.0590.50145.18845.55635.626
38RedEzhoml-20.2340.0350.80146.68885.66733.512
39RedJyothi0.4370.0620.90156.75058.88931.916
40RedKantakapura0.9470.0680.41739.00062.33336.994
41RedKantakaamala1.2020.1160.45134.87560.11131.503
42RedKapanthi0.9890.1770.45110.81341.44441.757
43WhiteKarpurkanti1.0520.1550.07944.62518.33312.645
44RedKathidhan0.0870.1430.60125.75035.22228.107
45RedKundadhan0.4890.0080.87630.06356.55639.595
46RedChampaeisiali0.3600.2220.53420.68831.44430.275
47WhiteLatamahu0.4930.1890.14123.37521.44413.584
48RedLatachaunri0.5070.2111.01819.87550.44430.925
49WhiteAc. 106080.4270.0870.10843.12512.33310.414
50WhiteAc. 101870.3950.1590.08530.06337.11112.981
51RedAc. 101620.2590.1920.52645.00081.33332.397
52WhiteAc. 72820.1360.0470.08437.06320.66710.335
53WhiteAc. 72690.1190.0050.20443.93814.11110.189
54WhiteAc. 71340.4180.1440.11846.43817.3336.841
55WhiteAc. 70080.9130.0110.07842.43822.4449.534
56WhiteAc. 90930.3570.2150.06145.75017.00011.270
57WhiteAc. 90900.2550.2210.07948.43816.66710.556
58WhiteAc. 9076A0.8990.1590.04843.68822.88912.126
59RedAc. 90650.3530.1760.35944.87561.77830.485
60RedAc. 90630.8600.2350.375110.56352.22223.538
61WhiteAc. 90580.5730.1260.0555.31323.22211.698
62WhiteAc. 9053A0.1540.1590.05342.00017.3339.415
63RedAc. 90500.3950.1910.38828.31354.88932.411
64WhiteAc. 90051.6120.2680.12647.37524.33314.622
65WhiteAc. 203891.2470.2790.03566.25019.33310.102
66WhiteAc. 203710.8390.2840.083110.00032.0006.149
67RedAc. 204230.7130.1820.43446.62553.00033.031
68WhiteAc. 203620.8110.3120.07768.75019.22210.688
69WhiteAc. 203281.3310.3120.07867.50022.0006.076
70WhiteAc. 203170.8700.3320.10279.06323.44410.542
71RedAc. 202821.1180.2011.04384.50076.88942.167
72RedAc. 20246 1.0830.2792.84667.87569.33341.947
73RedAc. 203471.1880.2920.27257.31323.77827.906
74WhitePalinadhan-10.0940.3420.15038.31321.00014.589
75WhiteChatuimuchi0.5250.3220.12049.87518.77816.714
76WhiteUttarbangalocal-90.0980.2960.06051.50018.33315.439
77WhiteGochi0.0980.3230.11842.00022.00014.731
78WhiteSugandha-2 0.2730.2780.12757.12519.44411.615
79WhiteJhingesal0.4230.2090.16339.75019.00013.456
80RedCheruvirippu0.3150.1140.67637.31389.66737.205
81RedMahamaga0.3430.1870.58438.00040.77833.861
82WhiteJaya0.0910.0790.09343.68814.77816.255
83RedD10.1640.1530.45181.93873.11137.997
84RedPk-210.2690.1690.56840.00044.22232.964
85WhiteGandhakasala0.0660.2500.12966.75017.00013.353
86RedSreyas0.2170.1480.61857.375119.88931.495
87RedGondiachampeisiali0.7620.2130.62624.75054.55624.855
88WhiteChinamal0.7480.3000.11118.31322.2229.104
89WhiteMagra0.1460.3110.11919.87517.1119.971
90RedLandi1.3800.1420.91828.00063.11129.480
91WhiteLalgundi0.3530.2890.12410.56322.22211.272
92WhiteBalisaralaktimachi0.2340.2530.11618.75039.11111.922
93WhiteLaxmibilash0.2890.1910.21140.81318.66712.139
94RedKaniar1.0270.2140.65139.00016.77821.532
95WhiteKanakchampa0.1290.2720.15939.31316.44415.795
96WhiteMagura-s0.2100.2950.13443.06316.00013.512
97WhiteAc. 446031.0980.2270.11060.87543.88913.088
98RedAc. 445850.6930.1880.91861.00080.11138.705
99WhiteAc. 445981.9380.1240.22459.31328.88911.618
100RedAc. 445921.0320.1182.32064.938242.00050.515
101RedAc. 446461.0250.25110.40763.938316.88958.750
102WhiteAc. 446041.2590.2030.14960.31328.88913.015
103WhiteAc. 445971.7350.0750.11654.87540.65413.015
104WhiteAc. 446380.8010.1610.10477.25055.6679.559
105RedAc. 445951.0140.1456.61866.500334.11169.412
106RedAc. 445880.9100.2231.30259.750227.77850.368
107RedAc. 445911.1580.2060.81847.188124.11135.147
108RedAc. 445940.9860.1913.38860.563183.22235.735
109RedAc. 437370.1360.29511.93437.375230.22248.544
110WhiteAc. 436601.1970.2920.22041.25026.77812.955
111WhiteAc. 437320.6650.2570.07931.06333.77835.239
112WhiteAc. 436610.1640.2810.10743.00050.77824.600
113RedAc. 437380.1640.27411.27447.500246.00053.566
114WhiteAc. 436691.0280.2430.11555.06331.50540.175
115WhiteAc. 436630.1540.2690.21740.62562.66715.429
116RedAc. 436580.3250.26919.79638.68879.77852.475
117WhiteAc. 436620.1120.2580.07936.37566.22213.028
118RedAc. 436700.1150.28228.37556.813358.44481.441
119WhiteAc. 436750.1680.2380.11540.87524.44432.678
120RedAc. 436760.1610.18610.28034.188226.33346.288
Mean 0.5860.2001.92448.20961.05920.678
CV 12.253.10012.8001.8106.7006.200
LSD5% 0.1740.05820.3893.5237.8332.421
Carotenoids (mg g−1); SOD: super oxide dismutase (unit g−11); TAC: total anthocyanin content (mg 100 g−11); GO: ga γ-oryzanol (mg 100 g−11); TFC: Total flavonoids content (mg catechin or CEt 100 g−11) and ABTS: 2,2′-azino-bis 3-ethylbenzothiazoline-6-sulfonic acid (% inhibition).
Table
Table 2. Estimation of genetic diversity parameters based on 136 SSR marker loci in a panel containing 120 rice germplasm lines.
Table 2. Estimation of genetic diversity parameters based on 136 SSR marker loci in a panel containing 120 rice germplasm lines.
Sl. NoMarkerNo. of
Alleles		Range of
Amplicon (bp)		Major Allele FrequencyGene
Diversity		HeterozygosityPICInbreeding Coefficient
(f)
1RM53104140–1900.7830.3670.0330.3430.910
2RM5824210–2450.7080.4660.0330.4330.929
3RM133354160–1800.5630.5320.0080.4350.984
4RM62754140–1600.7210.4470.0580.4110.870
5RM504190–2050.4000.6890.0250.6300.964
6RM85480–1100.4130.6750.1250.6150.816
7RM2224210–2500.6290.5570.0250.5190.956
8RM2475140–2000.5000.5970.0670.5190.889
9RM3283185–2000.5670.5800.0000.5131.000
10RM3376155–4000.4460.6680.1170.6120.827
11RM3405100–2200.7130.4540.1000.4150.781
12RM470560–1400.4630.6900.8330.644−0.203
13RM4723290–4100.5130.5080.0920.3870.821
14RM5063120–1300.6830.4590.1330.3900.712
15RM18123130–1400.4420.6070.0000.5231.000
16RM37014160–2600.6750.4840.4920.428−0.012
17RM69473150–1600.8830.2120.0000.1991.000
18RM149783240–2500.4170.6390.0000.5631.000
19RM187763175–2000.8460.2670.0250.2420.907
20RM22034375–850.9170.1550.0000.1471.000
21RM241614270–2900.5420.6120.1170.5520.811
22RM2235110–1700.6540.5360.0580.5040.892
23RM4405160–2100.4080.6890.2580.6340.628
24RM2014150–1600.4670.6450.2170.5810.666
25RM2164145–1600.5130.6390.1250.5830.806
26RM2583140–1500.3830.6520.0000.5761.000
27RM2864100–1300.4710.6320.1000.5620.843
28RM37354135–5000.3330.7250.9580.674−0.318
29RM13473100–1100.5170.5660.0000.4751.000
30RM75713130–1400.7130.4330.0080.3730.981
31RM147234220–2500.4920.6430.2000.5810.691
32RM1033255–3300.4920.5590.7670.461−0.369
33RM3153135–1400.8670.2350.0000.2141.000
34RM2253135–1500.5250.5470.1830.4490.667
35RM4863130–1400.6540.4690.1080.3800.770
36RM2563110–1500.7210.4110.0580.3390.859
37RM11133150–1800.6710.4570.0580.3730.873
38RM34233125–1400.5000.5750.0000.4841.000
39RM61003170–1800.4420.6430.0330.5690.949
40RM5903140–1500.7250.4310.0670.3840.846
41RM57933115–1300.6330.5250.0170.4640.969
42RM4053100–1100.6750.4910.0000.4411.000
43RM5475190–3000.4710.5730.1670.4810.711
44RM73645180–2500.6210.5730.1670.5410.711
45RM2053130–1800.6210.5320.0250.4670.953
46RM1674130–1800.7040.4630.1000.4210.786
47RM2295120–1400.3580.7100.1330.6570.814
48RM20A3230–2400.6250.5330.0170.4720.969
49RM2355100–1450.3960.7190.1750.6710.758
50RM70034100–1100.6670.5020.0830.4530.835
51RM54364155–1900.4420.6210.0580.5450.907
52RM251815130–1600.3790.7100.1670.6600.767
53RM4693100–1100.6210.5240.0420.4520.921
54RM65473155–1650.8670.2400.0170.2260.931
55RM1524145–1550.5080.6280.0170.5650.974
56RM1482140–1500.6750.4390.0830.3420.812
57RM4213250–2600.4580.6310.0000.5551.000
58RM26343100–1200.3790.6580.0250.5840.962
59RM248475–1150.3460.7320.1170.6840.842
60RM7179550–2500.3250.7650.3580.7270.535
61RM2153155–1650.6170.4910.0170.3920.966
62RM3244220–2600.5420.6350.1580.5900.753
63RM3173150–1600.7250.4030.0000.3281.000
64RM1743230–2700.5080.6210.0670.5510.893
65RM5563190–2100.8420.2790.0330.2600.881
66RM2574130–1550.4080.6630.2330.5950.651
67RM5023260–2650.8080.3180.0000.2811.000
68RM331495–1150.4830.6640.0580.6110.913
69RM4034110–1300.5960.5700.0830.5150.855
70RM3093180–1900.6960.4600.0250.4050.946
71RM66413140–1450.5670.5830.0000.5171.000
72RM33110–1200.3830.6630.0330.5890.950
73RM5943300–3200.5880.5580.0080.4880.985
74RM33924160–1800.5040.6150.1080.5450.825
75RM12783135–1500.7830.3610.0670.3290.817
76RM168395–1250.6250.5100.1500.4310.708
77RM33753190–2000.5670.5760.0330.5060.943
78RM2823140–1500.7250.4360.0000.3951.000
79RM266324450–5500.3630.7010.1580.6440.776
80RM13413170–1900.6130.5290.0250.4550.953
81RM41123160–1700.4880.6230.1580.5490.748
82RM203774300–3800.7710.3690.0670.3260.821
83RM2105130–1800.3630.7340.7000.6870.051
84RM2184130–1600.5830.5850.0330.5310.943
85RM4945130–1800.3830.7170.0250.6700.965
86RM3365105–1600.3830.7110.0920.6610.872
87RM34754135–1600.4500.6560.0420.5910.937
88RM4804190–2100.5380.6180.0250.5610.960
89RM5664150–2000.4330.6560.0170.5910.975
90RM117013210–2300.6420.4710.0000.3751.000
91RM220685–1300.3580.7450.1830.7030.756
92RM4886155–2000.3210.7500.1920.7080.746
93RM63746130–1600.3380.7710.0750.7370.904
94RM2335130–1600.3500.7270.2330.6800.681
95RM1123130–1350.8750.2220.0000.2041.000
96RM136004105–1300.4790.6620.1000.6070.850
97RM4953145–1650.6000.5600.0330.4990.941
98RM4937180–2500.2830.8130.5580.7870.317
99RM4445180–2400.3210.7730.1580.7370.797
100RM4683210–2200.7710.3790.0250.3460.935
101RM60543120–1300.9250.1420.0170.1370.883
102RM5093165–1700.7580.3950.0000.3601.000
103RM56386190–2400.6130.5870.1330.5580.775
104RM80446240–3000.2790.7610.2330.7210.695
105RM82715180–2500.4040.7230.1330.6790.817
106RM1714380–4200.5170.6330.0580.5750.909
107RM16686390–1000.4170.6550.0000.5811.000
108RM4344250–2800.5670.5950.0250.5370.958
109RM6091470–800.8170.3180.0000.2991.000
110RM2094145–1750.5420.6120.0000.5521.000
111RM2454145–1550.5830.5770.0000.5181.000
112RM10894210–2600.4170.6370.0670.5650.896
113RM2284110–1700.6250.5440.1920.4910.650
114RM4013250–3000.7540.3980.0580.3600.855
115RM113140–1600.4630.5900.0080.5020.986
116RM33513170–1900.5830.5170.0000.4201.000
117RM57493130–1600.5880.5040.0250.4000.951
118RM3352100–1100.7210.4020.0750.3210.815
119RM1443200–2100.5880.5160.1580.4190.695
120RM3003125–1450.8670.2380.0170.2210.930
121RM1132490–1250.3580.7240.0330.6740.954
122RM4004210–2600.3670.7170.4670.6650.353
123RM4713100–1200.8000.3380.0000.3091.000
124RM2433120–1400.5750.5540.0170.4750.970
125RM4673200–2100.5580.5750.0000.5021.000
126RM5644250–3000.4500.5990.1000.5150.834
127RM80073130–1500.7670.3850.0000.3521.000
128RM4414160–2000.4750.6270.5670.5570.100
129RM5183150–1700.5420.5370.0000.4371.000
130RM2534130–1700.5540.5940.0830.5300.861
131RM274375–800.6670.4770.0000.4061.000
132RM2424200–2400.5750.5910.0170.5360.972
133RM32314170–5500.3460.7030.6500.6450.080
134RM56874160–5000.4170.6870.6500.6300.059
135RM56263165–1800.5830.5120.7330.411−0.430
136RM4523240–2500.4750.6180.0000.5411.000
Mean3.74––0.5610.5550.1160.4960.793
Table
Table 3. The inferred ancestry value and population structure of individual member in the panel population with their antioxidant classification.
Table 3. The inferred ancestry value and population structure of individual member in the panel population with their antioxidant classification.
Sl. No.Accession No./
Vernacular Name of Germplasm Line		Inferred Ancestry Value at K = 4Antioxidants Content in Each Germplasm Line
Q1Q2Q3Q4Group
1Ac. 59930.9860.0090.0030.003SP1high SOD
2Ac. 62210.9840.0060.0030.007SP1Low
3Ac. 61830.9450.0030.0030.049SP1Low
4Ac. 61700.9940.0020.0020.002SP1high Carotenoid
5Ac. 60230.9780.0090.0020.012SP1high SOD
6Ac. 61720.9630.0050.0020.03SP1Low
7Ac. 60270.0120.0020.9830.002SP3Low
8Ac. 60070.9940.0020.0020.003SP1Low
9Ac. 90060.9730.0060.0090.012SP1high
10Ac. 90210.9270.0530.0050.015SP1Low
11Ac. 90280.9240.0060.0030.066SP1high GO& SOD
12Ac. 90300.9890.0050.0010.005SP1Low
13Ac. 90350.9590.0210.0170.003SP1Low
14Ac. 90380.9820.0150.0010.002SP1high SOD
15Ac. 90430.950.0460.0020.002SP1Low
16Ac. 90440.9870.0060.0040.003SP1high SOD
17Ac. 209200.510.480.0070.004Admixhigh SOD & Carotenoid
18Ac. 209070.8660.1310.0010.002SP1high SOD
19Ac. 208450.0870.9070.0010.005SP2high Carotenoid
20Ac. 207700.9660.0250.0080.002SP1high SOD & Carotenoid
21Ac. 206270.3780.6190.0010.002Admixhigh Carotenoid & SOD
22Ac. 206860.4320.5640.0020.002Admixhigh SOD
23Ac. 206640.0060.990.0010.003SP2Medium
24Ac. 206140.1090.8870.0030.001SP2high SOD
25Jhagrikarti0.970.020.0020.008SP1high GO
26Dadghani0.9630.030.0030.004SP1high SOD
27Shayam0.0040.0020.9930.002SP3Very low
28Basumati0.1280.0050.8620.005SP3Very low
29Bharati0.5510.4440.0040.001Admixhigh SOD
30Joha0.9730.0230.0020.002SP1high SOD
31Adira-10.5860.020.3640.03AdmixMedium
32Adira-20.9920.0040.0020.002SP1Medium
33Adira-30.2560.3270.4130.004AdmixMedium
34PK60.9850.0020.010.003SP1Low
35Vachaw0.8030.1540.0410.002SP1Medium
36Kozhivalan0.9880.0080.0010.002SP1Low
37Marathondi0.0170.4860.4640.033AdmixMedium
38Ezhoml-20.8620.1350.0020.001SP1Medium
39Jyothi0.9730.0250.0010.001SP1Medium
40Kantakopura0.5210.4760.0020.001AdmixMedium
41Kantakaamal0.0550.5850.2070.153AdmixMedium
42Kapanthi0.0320.2960.3330.339AdmixLow
43Karpurkanti0.0010.0420.9560.001SP3Very low
44Kathidhan0.4260.4750.0050.094AdmixMedium
45Kundadhan0.0050.9920.0010.002SP2Low
46Champaeisia0.0050.9910.0020.002SP2high SOD
47Latamahu0.0160.9770.0020.005SP2Medium
48Latachaunri0.0280.9660.0020.005SP2high SOD
49Ac. 106080.9810.0130.0010.005SP1Low
50Ac. 101870.9440.0050.0020.049SP1Low
51Ac. 101620.9410.0120.0210.026SP1Low
52Ac. 72820.0030.0020.9950.001SP3Very low
53Ac. 72690.9940.0030.0010.002SP1Very low
54Ac. 71340.7490.0320.210.009AdmixLow
55Ac. 70080.940.0570.0010.002SP1Low
56Ac. 90930.990.0050.0040.001SP1high SOD
57Ac. 90900.9580.0220.0160.004SP1high SOD
58Ac. 9076A0.8440.1480.0010.007SP1Low
59Ac. 90650.9230.0120.0610.004SP1Low
60Ac. 90630.6670.3240.0010.008AdmixGO & SOD
61Ac. 90580.9920.0050.0010.001SP1Low
62Ac. 9053A0.8520.0070.0140.127SP1Low
63Ac. 90500.8940.0970.0070.002SP1Low
64Ac. 90050.9850.0090.0030.004SP1high SOD
65Ac. 203890.9630.0040.0080.026SP1high Carotenoid & SOD
66Ac. 203710.9760.0190.0010.004SP1high GO & SOD
67Ac. 204230.9750.0190.0010.005SP1Medium
68Ac. 203620.9680.0130.0060.013SP1high SOD
69Ac. 203280.8040.1720.0140.009SP1high SOD
70Ac. 203170.8820.0890.0270.003SP1high SOD
71Ac. 202820.5360.3390.0090.116Admixhigh GO & SOD
72Ac. 202460.6390.2620.0690.03Admixhigh SOD & Carotenoid
73Ac. 203470.9270.0290.0020.042SP1high SOD & Carotenoid
74Palinadhan-0.3210.0380.3810.26Admixhigh SOD
75Chatuimuchi0.0010.0010.9960.001SP3high SOD
76Uttarbangal0.7430.1550.0020.101Admixhigh SOD
77Gochi0.9430.0070.0070.043SP1high SOD
78Sugandha-20.0030.0020.9950.001SP3high SOD
79Jhingesal0.3650.6310.0010.002Admixhigh SOD
80Cheruviripp0.8520.1420.0020.004SP1Low
81Mahamaga0.5480.3990.0020.051AdmixVery low
82Jaya0.9280.0640.0010.007SP1Low
83D10.890.0420.0190.049SP1Low
84PK210.7050.270.0020.023AdmixLow
85Gandhakasal0.0020.0860.9080.004SP3high SOD
86Sreyas0.9090.0850.0030.002SP1Medium
87Gondiachampeisiali0.0110.9860.0020.002SP2high SOD
88Chinamal0.2290.7610.0080.002Admixhigh SOD
89Magra0.2670.7260.0050.003Admixhigh SOD
90Landi0.0110.9860.0020.002SP2Low
91Lalgundi0.0050.9880.0040.003SP2high SOD
92Balisaralak0.0040.990.0020.003SP2VL, L, SOD
93Laxmibilash0.0050.4650.5270.003AdmixVery low
94Kaniar0.030.9580.0060.007SP2 high Carotenoid & SOD
95Kanakchampa0.0370.950.0090.004SP2high SOD
96Magura-S0.0030.9840.0120.001SP2high SOD
97Ac. 446030.0140.0170.0010.967SP4 high Carotenoid & SOD
98Ac. 445850.0050.0030.0120.981SP4Low
99Ac. 445980.020.0030.010.968SP4high Carotenoid
100Ac. 445920.0010.0010.0140.984SP4high Carotenoid, TFC, ABTS
101Ac. 446460.0020.0010.0010.996SP4High Carotenoid, TAC, TFC, SOD, ABTS
102Ac. 446040.0280.0040.0120.956SP4high Carotenoid & SOD
103Ac. 445970.0020.0030.0010.994SP4high TFC & Carotenoid
104Ac. 446380.0010.0010.7010.297AdmixLow
105Ac. 445950.0070.0030.0110.978SP4high SOD, Carotenoid, ABTS
106Ac. 445880.0020.0010.0010.995SP4High ABTS
107Ac. 445910.0020.0020.0010.995SP4high Carotenoid & SOD
108Ac. 445940.0110.0060.0020.981SP4high SOD
109Ac. 437370.0030.0020.0020.993SP4high TAC & SOD
110Ac. 436600.0030.0030.0010.993SP4high Caro, TAC, TFC, SOD, ABTS
111Ac. 437320.0020.0010.0010.995SP4high SOD & ABTS
112Ac. 436610.0060.0040.0010.989SP4high SOD
113Ac. 437380.0020.0040.0020.992SP4high SOD, ABTS, TAC
114Ac. 436690.0060.0040.0030.987SP4high Caro, TAC, TFC, SOD
115Ac. 436630.0020.0020.0020.994SP4high SOD
116Ac. 436580.0010.0010.0010.997SP4High TAC & SOD
117Ac. 436620.0040.0020.0270.967SP4High SOD
118Ac. 436700.0030.0030.180.815SP4High SOD, ABTS, TAC
119Ac. 436750.0030.0020.0140.98SP4High TAC, SOD
120Ac. 436760.0070.0150.0430.935SP4High SOD
Table
Table 4. Analysis of molecular variance (AMOVA) of the sub-populations of the panel population for the antioxidant traits in the 120 rice genotypes.
Table 4. Analysis of molecular variance (AMOVA) of the sub-populations of the panel population for the antioxidant traits in the 120 rice genotypes.
Source of VariationAMOVA for the Four Subpopulations at K = 4
Df.Mean Sum of SquaresVariance
Components		Percentage
Variation
Among populations4551.6342.5756%
Among individuals (accessions) within population1152983.7210.0000%
Within individuals (accessions)1205027.00041.89294%
Total2398562.35444.467100%
F-StatisticsValuep-Value
Fst0.0710.001
FIS−0.2351.000
FIT−0.1481.000
FST max.0.501
F′ST0.141
Table
Table 5. Marker–trait associations with antioxidant content in the panel population detected by both the models of GLM and MLM at p < 0.01.
Table 5. Marker–trait associations with antioxidant content in the panel population detected by both the models of GLM and MLM at p < 0.01.
Sl. NoAntioxidant CompoundsMarkerPosition
(cM)		GLMMLM
Marker_FMarker_pMarker_R2q-ValueMarkerMarker_FMarker_pMarker_R2q-Value
1SODRM58266.4–66.4 cM7.513260.007130.06170.0617RM58210.357240.001690.091910.005571
2SODRM40528.6–28.6 cM8.283450.004790.067590.06759RM40512.01287.52 × 10−40.106610.005571
3SODRM46746.8–46.8 cM9.708310.002330.078290.07829RM4679.703770.002340.086120.005571
5TACRM44092.7–92.7 cM10.077640.001940.066460.06646RM4409.060640.003230.080130.005726
6TACRM563886–86 cM12.020367.47 × 10−40.078030.07803RM563811.045730.00120.097680.005571
7TACRM25337–37 cM11.306770.001060.074430.07443RM25310.512610.001570.092970.005571
8TACRM562699–99 cM9.368750.002760.062150.06215RM56269.358220.002780.082760.005571
9GORM370145.3–45.3 cM14.944331.87 × 10−40.117290.11729RM37019.333360.002820.081550.005571
10GORM502121.8–121.8 cM21.524939.54 × 10−60.159350.15935RM5028.354070.004630.0730.006936
11TFCRM370145.3–45.3 cM11.628419.06 × 10−40.066130.06613RM37018.956290.003410.072790.005782
12TFCRM235101.8–103.8 cM16.060181.11 × 10−40.087460.08746RM2359.208850.0030.074840.005571
13TFCRM494124.4–124.4 cM9.851640.002170.056380.05638RM4949.644810.002410.078390.005571
14ABTSRM370145.3–45.3 cM12.554635.79 × 10−40.083460.08346RM370110.974790.001250.096990.005571
15ABTSRM235101.8–103.8 cM8.088680.00530.055330.05533RM2357.064570.009020.062430.009257
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Bastia, R.; Pandit, E.; Sanghamitra, P.; Barik, S.R.; Nayak, D.K.; Sahoo, A.; Moharana, A.; Meher, J.; Dash, P.K.; Raj, R.; et al. Association Mapping for Quantitative Trait Loci Controlling Superoxide Dismutase, Flavonoids, Anthocyanins, Carotenoids, γ-Oryzanol and Antioxidant Activity in Rice. Agronomy 2022, 12, 3036. https://doi.org/10.3390/agronomy12123036

AMA Style

Bastia R, Pandit E, Sanghamitra P, Barik SR, Nayak DK, Sahoo A, Moharana A, Meher J, Dash PK, Raj R, et al. Association Mapping for Quantitative Trait Loci Controlling Superoxide Dismutase, Flavonoids, Anthocyanins, Carotenoids, γ-Oryzanol and Antioxidant Activity in Rice. Agronomy. 2022; 12(12):3036. https://doi.org/10.3390/agronomy12123036

Chicago/Turabian Style

Bastia, Ramakrushna, Elssa Pandit, Priyadarsini Sanghamitra, Saumya Ranjan Barik, Deepak Kumar Nayak, Auromira Sahoo, Arpita Moharana, Jitendriya Meher, Prasanta K. Dash, Reshmi Raj, and et al. 2022. "Association Mapping for Quantitative Trait Loci Controlling Superoxide Dismutase, Flavonoids, Anthocyanins, Carotenoids, γ-Oryzanol and Antioxidant Activity in Rice" Agronomy 12, no. 12: 3036. https://doi.org/10.3390/agronomy12123036

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