Donor Identification, Genetic Diversity, Population Structure and Marker–Trait Association Analyses for Iron Toxicity Tolerance Using Rice Landraces

Abstract

Uptake of excess iron by lowland rice plants causes iron toxicity, which is a major problem in the affected areas. This study investigated molecular diversity, genetic structure, and marker–trait associations for tolerance to iron toxicity in a panel of germplasm lines using microsatellite markers. The studied population showed a moderate to high degree of genetic diversity, as revealed by the estimated molecular diversity parameters and principal component, cluster and box plot analyses. The landraces Mahipal, Dhusura, Dhabalabhuta, Champa, Sunapani and Kusuma were identified as suitable for cultivation in the areas affected by high iron levels. The landraces Dhusura, Kusuma, Kendrajhali, Ranisaheba, Panjabaniswarna, Mahipal, Dhinkisiali, Champa, Kalamara and Ratanmali, which showed low scores for tolerance, were considered good donors for iron toxicity tolerance improvement programs. Utilizing STRUCTURE software, a total of four genetic structure groups were detected in the panel germplasm of lines. These structural subgroups exhibited good correlations among their members for iron toxicity tolerance and other yield-related traits. Marker–trait association analysis validated the reported iron toxicity tolerance QTLs qFeTox 4.2 and qFeTox 4.3, which are useful for marker-assisted improvement. A new QTL, qFeTox 7.1, located on chromosome 7, was detected as controlling iron toxicity tolerance in rice.

1. Introduction

Rice (Oryza sativa L.) is the world’s most important cereal grain and provides a primary source of nutrition for more than half of the global population. Recently, it has been shown that the adverse effects of climate change have posed challenges for higher rice productivity [1,2]. Iron (Fe) is an important micronutrient in plants that regulates a wide range of biological activities, such as the biosynthesis of chlorophyll, the development of chloroplasts, cellular respiration, the metabolism of nitrogen, and the activity of redox enzymes [3]. Despite this, excessive iron deposition causes iron toxicity in lowland rice-growing areas, which is a major problem in the world [2,3,4]. Excessive iron produces reactive oxygen species and hydroxyl ions that target the lipids and proteins of membrane and nucleic acids, resulting in considerable chlorophyll oxidation and decreased photosynthesis [4]. The success of developing an iron toxicity-tolerant genotype is dependent on the existence of genetic variability for the trait in the breeding population. A primary goal for efficient genetic improvement in agricultural production is to characterize and quantify genetic variation across distinct genotypes. The development of tolerant varieties has been difficult owing to the intricate genetic framework of iron toxicity tolerance and significant genotype-by-environment interactions. The leaf bronzing score (LBS) is the predominant phenotyping technique for visually evaluating the proportion of damaged leaf area under iron toxicity circumstances [5]. A high LBS characteristic has been demonstrated to have a high correlation with yield reduction in field situations, but traditional phenotyping approaches, like hotspot and pot screening via visible symptoms of stress, are frequently unreliable, unpredictable and difficult to replicate [6]. Molecular markers have been developed as a result of improvements in plant genetics and molecular biology [7,8]. Genetic diversity in many crop species has been assessed using DNA markers [9,10]. Among the various molecular markers, simple sequence repeat (SSR) markers, though low throughput, have more advantages, such as high reproducibility, polymorphism and accuracy. SSR markers are widely used to analyze rice genetic variation and have proven to be extremely effective [11,12,13]. Previous research has addressed genetic diversity by determining the relatedness of genotypes using cluster analysis. STRUCTURE is a software that uses Hardy–Weinberg disequilibrium and linkage disequilibrium (LD) induced by mixing between populations to produce meaningful information about the population [14]. The genetics of iron toxicity tolerance is complex in nature and governed by many genes. A few genes and QTLs controlling iron toxicity have been reported in earlier mapping studies [1,15,16,17,18,19,20]. However, robust markers are rarely reported, validated and used in marker-assisted improvement of iron toxicity tolerance in rice. Marker–trait association analyses may facilitate the identification of a larger number of markers linked to iron toxicity tolerance in rice.

This investigation involved a study of 154 germplasm lines, from which 59 rice germplasm lines were shortlisted to determine the population genetic structure, genetic diversity parameters and molecular marker–trait associations for iron toxicity tolerance using 45 SSR markers.

2. Materials and Methods

2.1. Plant Materials, Phenotyping for Iron Toxicity Tolerance and DNA Extraction

A total of 156 rice landraces were screened in the high-iron plot at Orissa University of Agriculture and Technology (OUAT), Bhubaneswar, to constitute a panel population for studying marker–trait association for iron toxicity tolerance in rice. The germplasm lines were collected from ICAR-National Rice Research Institute (NRRI), Cuttack, and OUAT, Bhubaneswar (Table S1). The germplasm lines were planted in the iron toxicity plot of OUAT using an augmented block design consisting of 7 blocks, each containing 24 entries, including two check varieties, during the wet seasons of 2019 and 2020. The check varieties, Dhusura and Sebati, were used as tolerant and susceptible checks, respectively, in the experiment. A panel population which represented the initial population was shortlisted for the phenotyping and genotyping purposes. The panel population was prepared by taking germplasm lines from all the phenotypic classes of the population based on tolerance response to iron toxicity. During the 2019 and 2020 rainy seasons, the panel was phenotyped using an iron toxicity plot at OUAT, Bhubaneswar. The germplasm lines were transplanted in normal and Fe-toxic plots following randomized block design. The soil Fe status was estimated by sampling soils from five different locations of the high-iron plot and observed to be between 220 and 250 ppm. Phenotyping of germplasm lines was performed for days to 50% flowering, plant height, number of grains per panicle, 1000-grain weight, numbers of tillers/hill, grain yield, and leaf bronzing index (LBI). These observations were recorded by following Standard Evaluation System of Rice [5]. Genomic DNA was extracted from the young leaves using the CTAB [20] DNA extraction method with very minimal modifications.

Based on the genotyping results of 45 microsatellite markers (SSR) across the chromosomes, the molecular diversity parameters were estimated for the 59 representative rice germplasm accessions (Table S2). Standard techniques adopted in the previous studies were followed here for the polymerase chain reactions (PCRs), electrophoresis and gel documentation [21,22,23].

2.2. Data Analysis

The NTSYS-pc application version 2.11a was used to examine genetic linkages with the help of the SSR scores. The dendrogram was constructed using Jaccard’s similarity coefficient and marker data from all rice germplasms, using unweighted pair group method analysis (UPGMA) [22]. Binary data were employed to assess genetic distance and similarity coefficients for 45 SSR marker alleles. Version 3.25 of PowerMarker was employed to assess allele frequency, allele count, polymorphic information content (PIC), genetic diversity, and heterozygosity in the experiment. Cluster analysis was conducted with the methodology outlined in prior publications [24,25,26].

2.3. Population Structure Analysis

Gene structure was estimated by utilizing Structure 2.3.6 software and the Bayesian cluster technique with a likely population (K) and greater delta K value. Using a 15,000 burn in period followed by a 15,000 MCMC replication, we estimated the population structure by executing each ‘K’ value 10 times with a different value of ‘delta K’ between 1 and 10. Estimating the greatest value of delta K from the Evanno table and taking that into account as a number of possible sub-populations present in the population allows for the detection of sub-population values in the panel.

2.4. Marker–Trait Association Analysis

The TASSEL5 software facilitated the investigation of marker–trait associations. Two methodologies, specifically the General Linear Model (GLM) and the Mixed Linear Model (MLM), were employed to investigate the genetic connection between phenotypic traits and the utilized SSR markers [27]. The procedure for the analysis was adopted from earlier publications [28,29,30,31].

3. Results

3.1. Rice Germplasm Diversity Based on Morphology Under Iron Toxicity Condition

The high-iron-toxicity plot was used to screen 156 germplasm lines for tolerance to iron toxicity stress. A small panel population was developed by taking the germplasm lines from all the phenotypic groups obtained from the screening results (Supplementary Table S1). Based on the IRRI leaf bronzing index (LBI) scoring, 59 germplasm lines were shortlisted out of 156 to create the panel population for phenotyping and genotyping. The phenotyping results of the 59 shortlisted germplasm lines for LBI were observed to be 33 tolerant (1–3 score), 20 moderately tolerant (4–5 score) and 6 moderately susceptible (5–7 score) types (Table 1). The genotypes which produced higher yield in normal conditions and also comparable yield under Fe-toxic fields are desirable genotypes for iron-rich fields for rice cultivation. From the evaluation results of the genotypes under normal and iron toxicity conditions, landraces such as Mahipal, Dhusura, Dhabalabhuta, Champa, Sunapani and Kusuma showed higher yield under both normal and iron toxicity stress conditions (Table 1). Consequently, these genotypes can be recommended for cultivation in areas subjected to iron toxicity.

The landraces Dhusura, Kusuma, Kendrajhali, Ranisaheba, Panjabaniswarna, Mahipal, Dhinkisiali, Champa, Kalamara and Ratanmali showed <3 score for iron toxicity tolerance, so they may be useful as good donors for breeding programs.

The population was classified into subgroups by Wards clustering approach. The subgroups accommodated genotypes based on the nine studied traits. The genotype–trait biplot diagram was constructed to represent the germplasm lines in the two dimensions based on the nine studied traits. The biplot diagram showed the distribution of the germplasm lines in all of the quadrants (Figure 1). In addition, from the box plot diagram (Figure 2) and biplot, adequate diversity was found in the study population for the marker–trait analysis.

Using PCA (principal component analysis) according to LBI and other parameters, the members of the population were distributed in the four quadrants. The first quadrant (right top) accommodated the majority of the good germplasm lines showing higher yield and also tolerance to iron toxicity stress. The toxicity tolerance scores of low to medium containing donor lines are mostly present in the second quadrant (Figure 1). From the studied population, three phenotypic groups were identified. Population was reclassified into four subpopulation groups based on the structure analysis from the genotyping results using marker data of 45 SSR markers. The panel population thus used in this study for genetic variation of iron toxicity tolerance showed considerable genetic variation.

3.2. Existence of Molecular Diversity in the Population

The panel containing 59 germplasm lines was used to estimate genetic diversity parameters using 45 SSR markers. The marker loci utilized for genetic analysis of the genotypes are depicted in Supplementary Table S2. The 45 microsatellite markers amplified 178 alleles with an average of 3.95 alleles per locus (Supplementary Figure S1). Marker RM335 had the highest of six alleles, while the range was from two to six different copies of each allele. With a mean of 0.5127, the main allele frequency was found at a minimum value of 0.2627 (RM80) and maximum of 0.7627 (RM26212). The PIC value ranged from 0.2965 (RM26212) to 0.7867 (RM335), with a mean of 0.5416. Maximum heterozygosity (Ho) was detected by the marker, RM 1337, with a value of 0.6949, while 14 markers showed zero heterozygosity. The highest gene diversity was shown by RM335 (0.8133) and the lowest value recorded from RM26212 (0.3620), with a mean value of 0.5877 (Supplementary Table S3).

3.3. Population Genetic Structure Analysis

In the present research, 59 genotypes were studied utilizing structure analysis software for distribution of the 178 SSR alleles relying on genetic relatedness and subpopulation (K). Utilizing the STRUCTURE analysis software, the panel population was distributed into four genetic groups. Based on the K and ΔK peak value, the peak value was obtained at K = 4 (Figure 3) (Supplementary Table S3). In subpopulations 1, 2, 3, and 4, membership proportion (overall) in each cluster was 0.270, 0.293, 0.191 and 0.246, respectively. For subpopulations 1, 2, 3, and 4, the fixation index (Fst) values were 0.3609, 0.2443, 0.2703, and 0.3315, respectively (Supplementary Table S4). Individuals in subpopulations 1, 2, 3 and 4 had expected heterozygosity (average distances) of 0.3925, 0.4769, 0.5180, and 0.4259, respectively. Out four subpopulations, subpopulation 1 (SP1) comprised fifteen, subpopulation 2 had fifteen, subpopulation 3 accommodated eight and subpopulation 4 showed twelve germplasm lines (Supplementary Table S4). Subpopulation 1 had a majority of the members of the germplasm lines showing tolerance to iron toxicity, while subpopulation 2 had most of the moderately susceptible types. Subpopulation 3 accommodated a majority of the moderately tolerant germplasm lines, while subpopulation 4 had mainly resistant and moderately resistant germplasm lines. The F_st values obtained for the four subpopulations and their distribution patterns provided a clue that the subpopulations were different from each other (Figure 4).

3.4. Analysis of Molecular Variance (AMOVA)

At K = 4, the analysis of molecular variance (AMOVA) revealed genetic differences among and within subpopulations. The genetic variance was 4% among individuals of populations and 95% among individuals in the four sub-populations at K = 4. The population exhibited a difference, as expected from Hardy–Weinberg’s prediction using Wright’s F statistic. The analysis of the four sub-populations showed average Fis and Fit values of 0.044 and 0.050 by using all 45 loci, respectively (Table 2). The Fst values obtained for the four subpopulations were different from each other (Figure 4). The alpha value obtained from the panel population was also very low. The mean Fst value was found to be 0.030. By using Fst values at K = 4, it was possible to distinguish between different subpopulations, showing differences among them.

3.5. Tolerance to Fe-Toxicity and Other Parameters Linked to Molecular Markers

Using TASSEL5.0 software, a strong correlation was detected for a few molecular markers with the iron toxicity tolerance trait of rice, as well as other relevant attributes. At p < 0.05, all significant correlations were considered and compared using the GLM and MLM methodologies. Marker r² values for LBI ranged from 0.07402 to 0.14599 in MLM analysis and from 0.06815 to 0.17279 by the GLM analyses when compared at p < 0.05. Considering both models, GLM and MLM at p < 0.05 detected an association of five markers with days to flowering; two markers with number of tillers; three markers with panicle length; two markers for plant height; one marker each for grain weight and grain number; and three markers for yield (Table 3). The most important trait to estimate iron toxicity tolerance was LBI. This was associated with two markers (RM335 and RM346) analyzed by both models.

4. Discussion

The evaluation results for the 59 germplasm lines under normal and iron toxicity conditions revealed that the landraces Mahipal, Dhusura, Dhabalabhuta, Champa, Sunapani and Kusuma showed higher yield under both normal and iron toxicity stress conditions (Table 1). These genotypes may be recommended for cultivation as immediate measures in the iron toxicity-affected areas. The landraces Dhusura, Kusuma, Kendrajhali, Ranisaheba, Panjabaniswarna, Mahipal, Dhinkisiali, Champa, Kalamara and Ratanmali showed <3 score for iron toxicity tolerance, and thus were considered as good donors for breeding programs. The constituted panel population used in this study for marker–trait association for iron toxicity tolerance and grain yield contributing traits showed considerable genetic variation for these traits. Many researchers also confirmed the presence of genetic variation for iron toxicity tolerance in rice [15,16,17,18,19,32]. The biplot diagram showed the placement of germplasm lines based on traits including toxicity tolerance and grain yield, along with related traits in the germplasm lines. All of the germplasm lines were distributed into all four quadrants as per the nine studied traits using the panel population. The distribution of the germplasm lines into the four quadrants indicated that the studied traits, namely yield contributing traits, had adequate variation. Therefore, the developed panel population used for the marker–trait study may be useful for sources of germplasm for the improvement of yield and for tolerance to iron toxicity. Only six landraces were detected to combine both for improved grain yield and tolerance to iron toxicity. This provides indirect indication for the inheritance of QTLs controlling iron toxicity tolerance and a few grain yield contributing traits together. So, there is potential to enhance both iron toxicity tolerance and grain yield in rice simultaneously. Previous publications also demonstrated the enhancement of high grain yield along with protein, Fe, and Zn content in rice [33,34]. However, future study for multi-environment trials for germplasm testing will confirm the iron toxicity tolerance in the germplasm lines. Moderate to high molecular genetic diversity parameters were found in the panel population according to the analysis by the power marker software. The study on trait-based genetic diversity is closely aligning with the earlier findings reported in the earlier publications [26,28,29,35,36,37,38,39,40].

Breeders are better able to select specific genotypes that will more efficiently combine different sources of genetic diversity when they understand the genetic structure of potential source genotypes. This analysis helps in obtaining targeted desirable progenies from individuals present in a population. The improvement of a particular trait will be quicker by combining the germplasm from a population with known genetic structure. The panel population for iron toxicity tolerance was grouped into four genetic structure groups. The panel population was grouped into four subpopulations based on iron toxicity tolerance. We detected a low alpha value (α = 0.1955) for the tolerance, providing a clue for a common primary ancestor for the iron toxicity tolerance. The existence of more subpopulations in the panel indicates more genes/QTLs involved for the trait expression. Similar findings were also reported in earlier studies [41,42,43]. The evolutionary forces created the subpopulations with the generation of admix genotypes. These admix genotypes might have evolved through natural hybridization. The inferred ancestry revealed from the structure analysis provided clues for the presence of QTLs for minor effects. These effects may be pooled in a superior background through a molecular breeding approach. Previous publications on LD studies also give similar views about QTLs/gene(s) pyramiding for improvement of the traits [8,10,43,44].

To improve iron toxicity tolerance in the superior offspring, recombinant breeding needs to be used by selecting the parental lines which exhibit higher Fst values. In order to increase tolerance for this toxicity stress, the superior offspring should carry multiple QTLs regulating iron toxicity tolerance from different subpopulations. Similar suggestions have previously been provided in the earlier publications for iron toxicity tolerance in rice [37,39]. The panel population’s alpha value was 0.1735, which indicates a lower alpha value at K = 4. The two markers, RM335 and RM346, were significantly associated with leaf bronzing index tolerance, which is detected by both GLM and MLM models, showing high F and r² values. Three distinct regions on chromosome 2 (25.86–26.66 Mb, 31.49–35.13 Mb and 2.76 Mb) were reported to be linked with tolerance to iron toxicity in the earlier publications [16,32,43]. We detected the toxicity tolerance with RM335, which is located on this chromosome at the 21.5 cM position. This is in between the QTL reported by power et al. [43] (18.8 Mb) and Nawaz et al. [44] (25–28 Mb). Hence, the detected QTL may be qFeTox 4.2 or qFeTox 4.3. Therefore, this QTL holds potential for application in future marker-assisted breeding efforts for iron toxicity tolerance in rice crops. The region associated with other marker, RM346, on chromosome 7 is not reported in earlier studies for iron toxicity tolerance. The QTL is a new QTL controlling iron toxicity tolerance in rice and designated as qFeTox 7.1. Further, functional validation of the QTLs using an alternate population will be performed before use in the marker-assisted breeding program.

5. Conclusions

The studied population showed moderate to high levels of genetic diversity for iron toxicity tolerance. The study identified six germplasm lines for cultivation under both normal and iron toxicity stress conditions suitable for cultivation as immediate measures in the iron toxicity-affected areas. However, 10 landraces, namely Dhusura, Kusuma, Kendrajhali, Ranisaheba, Panjabaniswarna, Mahipal, Dhinkisiali, Champa, Kalamara and Ratanmali, showed low LBI scores for iron tolerance and are considered as good donors for iron toxicity tolerance breeding programs. Using the STRUCTURE software tool, the studied population was grouped into four genetic structure groups. LBI showed significant associations with markers RM335 and RM346 by both the GLM and MLM models. The detected QTL on chromosome 4 may be qFeTox 4.2 or qFeTox 4.3, while the other one is a new QTL present on chromosome 7 and designated as qFeTox 7.1. These markers will be useful in marker-assisted breeding for enhancing the iron toxicity tolerance in rice.

Author Contributions

Conceptualization: S.K.P. and I.C.M.; methodology: D.S. and S.D. (Shiv Datt); investigation: D.S., C.S. (Chittaranjan Sahoo), S.T., U.K.B., S.D. (Susmita Das), S.P.M. and S.R.B.; Resources: S.K.P. and I.C.M.; formal analyses: M.K.R., C.S. (Chandrasekhar Sahu), T.L.M. and S.R.B.; Writing—Original Draft Preparation: D.S., T.L.M., S.K.P. and U.N.M.; writing—review and editing: D.S., S.D. (Shiv Datt) S.K.P. and C.S. (Chittaranjan Sahoo); Funding Acquisition, S.K.P. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Included in the article and Supplementary Materials.

Acknowledgments

The authors are grateful to ICAR-National Rice Research Institute, Cuttack for providing necessary help in carrying out the present study.

Conflicts of Interest

The authors declare that they have no conflicts of interest regarding the manuscript’s content and study undertaken.

Footnotes

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Figures and Tables

Figure 1. Trait biplot diagram obtained using 9 studied traits and the panel population.

Figure 2. Box plot diagram showing the variation in the nine traits in the panel population used for marker–trait analysis. DFF: days to 50% flowering, NETN: Number of Effective Tiller, PH: plant height, LBI: leaf bronzing score, PL: panicle length, NGPP: number of grains/panicle, GW: grain weight, YLD (Fe): yield under iron toxicity, YLD (C): yield under control.

Figure 3. (A) Graph generated by plotting delta K and K for determination of peak value and (B) the genetic structure groups obtained for the studied panel population and sorted as per the group.

Figure 4. Fst values obtained for the 4 subpopulations and alpha values obtained from the panel population.

Table 1

Agro-morphologic traits and LBI of the panel germplasm lines estimated under iron toxicity condition.

Genotypes	DF	Tillers	PH	PL	GN	GW	Yield (t/ha)		LBI	Response
							Fe-Plot	NormalPlot
Sankaribako	105.73	6.65	111.83	21.11	77.81	25.16	11.40	7.33	4.67	MR
Kalakrushna	100.75	6.37	133.98	25.06	144.57	14.06	16.58	18.855	3.83	MR
Assamchudi	100.05	5.16	117.63	22.90	102.80	21.70	11.09	8.485	4.67	MR
Gelei	97.70	7.20	106.93	22.10	120.05	16.04	15.40	14.935	3.50	R
Kalamara	99.08	4.61	131.43	21.80	79.43	14.63	9.78	10.28	2.33	MR
Nini	96.60	6.68	125.34	24.51	97.96	21.09	9.68	8.995	3.67	R
Gurumukhi	104.75	5.57	119.04	21.27	86.53	24.55	16.74	20.53	4.00	MR
Jubaraj	105.00	6.15	116.16	24.13	84.35	18.99	12.51	11.19	5.17	MS
Champa	105.33	6.42	119.46	20.81	124.10	22.76	24.22	24.025	2.67	R
Veleri	110.25	5.72	126.42	24.75	87.85	21.90	12.05	19.73	4.00	MS
Dhinkisiali	107.58	7.51	123.44	21.45	94.09	18.16	12.34	12.425	2.33	R
Dhabalabhuta	106.17	6.58	129.95	21.78	86.17	20.44	23.22	23.3	3.00	R
Bayabhanda	108.25	6.96	129.83	23.29	85.87	18.85	17.17	19.425	3.17	R
Latamahu	102.83	6.29	118.90	20.46	89.79	18.94	20.47	20.565	3.67	R
Hatipanjara	105.50	8.80	128.44	22.77	87.92	19.04	22.38	19.54	3.00	R
Mugei	103.34	5.17	119.21	21.67	76.71	19.94	12.13	15.39	3.67	R
Sagiri	102.00	4.99	138.59	22.33	125.97	24.24	15.70	21.095	5.00	MR
Kakiri	102.67	5.79	113.64	22.31	102.22	23.30	18.27	18.63	4.83	MR
Madia	101.75	5.71	129.12	24.47	106.22	19.86	19.86	15.07	5.67	MS
Dhusura	102.50	6.54	122.82	25.70	82.58	21.18	21.18	21.17	1.83	R
Bangali	100.83	5.32	125.18	22.84	99.77	24.11	24.11	20.55	3.67	R
Banda	107.22	4.77	149.83	25.79	108.51	13.85	13.85	16.45	3.00	R
Jalpaya	103.42	5.77	123.60	23.25	99.39	19.56	19.56	19.355	3.67	R
Chudi	107.08	5.85	120.95	26.29	127.39	22.97	22.97	21.81	4.33	MR
Nilarpati	104.83	5.13	121.85	22.34	103.19	24.65	14.65	21.49	3.50	MS
Gelei	106.42	5.96	117.49	20.82	134.07	25.43	25.43	26.775	4.33	MR
Ratanmali	105.25	6.77	109.45	25.04	123.58	19.17	14.17	19.53	2.50	MS
Umarcudi	103.42	6.14	113.86	26.21	125.99	17.14	17.14	14.59	4.50	MR
Juiphula	103.58	6.32	120.87	21.40	140.80	18.44	18.44	18.175	5.50	MS
Karpurakranti	104.17	6.51	123.97	22.69	104.81	16.64	16.64	16.73	4.00	MR
Ramakrushnabilash	102.67	7.45	121.47	23.44	135.25	25.72	25.72	21.04	3.33	R
Sunapani	110.58	6.89	102.60	25.90	123.62	45.03	45.03	37.095	3.17	R
Anu	100.50	6.53	125.53	22.64	148.89	18.21	18.21	16.44	3.17	R
Mayurkantha	100.67	5.48	132.31	22.44	101.52	26.14	26.14	20.825	3.33	R
Champeisiali	107.50	6.14	118.30	23.89	95.17	15.66	18.66	19.17	4.33	R
Nalijagannath	106.42	5.60	121.50	19.88	120.45	46.92	35.92	37.925	4.67	R
Mahipal	111.25	7.75	119.67	26.62	143.37	42.32	42.32	33.085	3.00	R
Ranisaheba	104.17	6.79	117.14	23.07	129.63	25.14	25.14	22.84	2.50	R
Punjabniswarna	104.92	5.24	124.55	26.30	93.20	17.38	17.38	16.87	2.67	R
Kusuma	102.17	5.19	120.35	23.22	119.48	23.38	23.38	26.04	1.67	R
Kenrdajhali	103.17	6.18	113.39	23.84	122.56	12.89	12.89	12.985	2.67	R
Jaiphula	100.17	6.48	127.70	24.79	98.49	10.12	10.12	15.26	2.50	R
Jabaphula	104.62	5.72	115.40	24.50	116.91	10.83	10.83	9.59	3.50	MR
Khandasagar	101.33	5.37	117.95	22.67	74.25	11.96	11.96	10.755	5.83	MS
Pipalbasa	102.67	6.03	128.92	24.90	67.66	11.19	11.19	11.84	5.00	MR
Budidhan	105.08	6.29	122.70	27.24	120.65	13.91	13.91	16.335	3.33	MS
Karpuragundi	107.67	5.79	116.57	22.65	126.80	17.83	15.83	18.39	5.33	MS
Basapatri	100.67	6.48	122.08	21.61	100.36	14.31	14.31	15.555	4.67	MR
Bagadachinamala	104.00	6.54	115.70	22.63	98.51	23.90	23.90	17.585	4.00	MR
Kalaheera	105.33	6.56	124.13	23.71	118.39	33.23	33.23	26.77	4.33	MR
Rasapanjari	106.42	4.81	113.87	23.38	126.93	23.27	23.27	22.63	4.17	MR
Biridibankoj	109.67	6.37	123.72	23.27	108.16	24.95	24.95	19.53	3.00	R
Jagabalia	113.25	6.36	115.67	21.85	141.56	38.40	27.05	25.07	3.67	R
Dhoiamadhoi	109.75	5.97	115.66	25.75	107.71	23.56	33.95	33.815	3.50	R
Kaniara	104.50	5.56	98.61	20.68	100.63	17.89	20.45	18.05	3.17	R
Bishnupriya	106.75	6.06	111.91	21.54	121.40	17.90	21.26	19.025	5.67	MS
Madhabi	108.17	5.12	113.98	22.07	126.24	22.79	23.53	25.265	4.00	MR
Jungajhata	104.83	6.75	120.45	25.64	106.07	23.59	21.68	23.65	4.00	MR
Rangasiuli	107.00	6.19	125.65	25.34	123.16	18.93	21.06	21.68	3.33	R
Mean	104.57	6.12	120.96	23.34	108.94	19.83	19.29	18.61	3.75	-
CV	2.5	10.2	6.6	7.8	10.8	12.02	14.7	13.34	17.1	-
CD	4.272	1.8	12.959	2.957	29.584	7.061	7.709		2.248	-

Table 2

Analysis of molecular variance (AMOVA).

Source of Variation	Df.	Mean Sum of Squares	Variance Components	Percentage Variation
Among individuals	54	27.464	0.021	4%
Within individuals (accessions)	59	27.500	0.466	95%
Total	63	870.48	13.827	100%
F Statistics	Value	p value
Fst	0.007	0.074
Fis	0.044	0.028
Fit	0.050	0.010
Fst max	0.026
F’st	0.254

Table 3

Marker–trait association for iron toxicity tolerance and other traits using molecular markers.

Trait	MarkerName	MLM				GLM
		F Value	p-Value	q Value	R2	F Value	p-Value	q Value	R2
DF	RM293	9.57037	0.00313	0.010228	0.14502	7.62473	0.00785	0.046149	0.13674
DF	RM249	5.19551	0.02662	0.029752	0.08455	4.0294	0.04973	0.04973	0.07226
DF	RM5638	5.98752	0.01769	0.026736	0.09615	4.33541	0.04208	0.046149	0.07775
DF	RM3686	4.11138	0.04754	0.04754	0.06815	4.5943	0.0366	0.046149	0.08239
DF	RM144	4.27263	0.04354	0.045959	0.07063	5.20392	0.02651	0.046149	0.09333
Tiller no	RM3686	5.22963	0.02615	0.029752	0.08707	4.85618	0.03183	0.046149	0.08707
Tiller no	RM222	5.42038	0.02368	0.029752	0.08996	5.01719	0.02923	0.046149	0.08996
Panicle Length	RM10	6.85167	0.01147	0.023446	0.10605	4.53468	0.03779	0.046149	0.07925
Panicle Length	RM137	5.77697	0.0197	0.026736	0.09103	4.26518	0.04372	0.046149	0.07454
Panicle Length	RM287	12.13193	9.91 × 10−4	0.010228	0.17279	7.78103	0.00728	0.046149	0.13599
PH	RM528	9.76412	0.00286	0.010228	0.13982	6.45774	0.01395	0.046149	0.10934
PH	RM201	10.90051	0.00171	0.010228	0.15336	4.98005	0.02981	0.046149	0.08432
GN	RM5626	6.72307	0.01222	0.023446	0.10479	4.70743	0.03445	0.046149	0.08353
GW	RM416	9.58231	0.00311	0.010228	0.14972	8.38009	0.00546	0.046149	0.14599
Yield	RM416	9.50088	0.00323	0.010228	0.1437	4.69019	0.03477	0.046149	0.08412
Yield	RM258	5.84387	0.01904	0.026736	0.09379	6.29605	0.01513	0.046149	0.11293
Yield	RM258	6.4546	0.01397	0.02413	0.10254	6.11019	0.01662	0.046149	0.10959
LBI	RM335	6.70401	0.01234	0.023446	0.10898	4.32992	0.04221	0.046149	0.07402
LBI	RM346	6.79939	0.01177	0.023446	0.11036	5.87825	0.01871	0.046149	0.10048

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/d17010033/s1, Supplementary Figure S1: Representation of electropherogram of 59 genotypes using microsatellite markers; Supplementary Table S1. Initial rice germplasm lines used for assessment of iron toxicity response evaluated using high iron plot during the wet season, 2019; Supplementary Table S2. SSR markers used for marker-trait association study for iron toxicity tolerance in a panel population; Supplementary Table S3. Estimation of genetic diversity parameters of the panel population using 45 SSR markers; Supplementary Table S4. Population genetic structure group of accessions based on the inferred ancestry values.

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