Genetic Analysis for Mapping of Physiological Traits Related to Seed Vigour in Rice Through Association Mapping

Good quality seed is an important factor for a good crop. Vigor is the key performing trait of quality seed. Genomic regions controlling the physiological traits related to seed vigour are not fully reported. Results: A panel was prepared by including genotypes from all the groups of the fteen physiological traits representing a population of 250 germplasm lines. Wide variations were observed for the 15 physiological traits in the population. The population was classied into 6 genetic groups. Presence of linkage disequilibrium (LD) was detected in the panel population based on the xation indices of the subpopulations. Moderate values of gene diversity, polymorphic information content (PIC) and other diversity parameters were estimated from the population by genotyping with 109 SSR markers. The population was classied into subpopulations and sub-clusters showed relation with the genotypes for their physiological traits. Ten physiological traits were detected to be signicantly associated with SSR markers analyzed by both the General linear model (GLM) and Mixed linear model (MLM). A total of 19 novel QTLs controlling different physiological traits namely qGP 6.2 and qGP 8.2 for germination % (GP); qGR 9.1 for germination rate (GR); qGI 1.2, qGI 6.1, qGI 8.1, qGI 11.1 and qGI 12.1 for germination index (GI); qRPE1.1 and qRPE8.1 for rate of plumule elongation (RPE); qAGR 8.1 for AGR; qRSG1.2 and qRSG12.2 for rate of shoot growth (RSG); qRGR3.1 and qRGR11.1 for relative growth rate (RGR); qSVI 1.1 for seed vigour index I (SVI I); qSVII 1.1, qSVII3.1, and qSVII12.1 for seed vigour index II (SVI II), and qRRG8.1 for relative root growth rate were identied. The reported QTL for root length, qRL11.1 was validated in this mapping population. Additionally, QTLs, qRPE8.1 and qAGR8.1 of traits, RPE and AGR; qGI 6.1 and qGP6.2 of GI and GP; qGI 11.1 and qRL11.1 of GI and RL; qAGR8.1 and qRRG8.1 of AGR and RRG, and qRSG3.1 and qRGR3.1of traits RSG and RGR were detected for co-localization or co-inheritance. Conclusion: The traits identied and markers detected in the association analysis will be useful for improvement of seed vigour trait through marker-assisted selection in rice. MNP-AC9035, MNP-AC9038, MNP-AC9043, MNP-AC9044A, JBS-AC20282 JBS-AC20770. Very high electrical conductivity genotypes, MNP-AC9030, JBS-AC20282, JBS-AC20845, JBS-AC20907 AC-7008. Germplasm MNP-AC9038, MNP-AC9043, JBS-AC20282 and BS-AC20845 for very high Rate of imbibitions. In seedling root germplam lines MNP-AC9005, MNP-AC9076A, of of lines MNP-AC9038, MNP-AC9043, MNP-AC9044A, JBS-AC20282 had seedling shoot length germplasm lines possessing high values of multiple physiological traits for in RM249 on chromosome 5 and RM225 on chromosome 6 controlled the physiological parameter. As per report of Diwan et al. 2013, qVI on chromosome 5 was linked with the trait. Anandan et al. 2016 found association of seed vigour index with marker, RM341 on chromosome 2. In our investigation, markers RM5638 located on chromosome 1 showed signicant associations with SVI and SVII by both the models. No reports are available for these two traits at this location. Theses novel QTLs are designated as qSVI1.1 and qSVII1.1. The other two markers, RM14723 on chromosome 3 and RM7003 on chromosome 12 associated with SVII are not reported in earlier studies and are novel QTLs. These two QTLs are designated as qSVII3.1 and qSVII12.1. The QTL for relative root growth, qRRL-7 was mapped by Masuda et al. 2018 on chromosome 7. In our study, we detected the QTL on chromosome 8 at 97 cM position. No reports are available for RRG at this location and hence this novel QTL is designated as qRRG8.1. Presence of linkage disequilibrium (LD) was detected in the panel population based on the xation indices of the subpopulations. Moderate values of gene diversity, polymorphic information content (PIC) and other diversity parameters were estimated from the population by genotyping with 109 SSR markers. The population was classied into subpopulations and sub-clusters showed relation with the genotypes for their physiological traits. A total of 19 novel QTLs controlling different physiological parameters namely qGP 6.2 and qGP 8.2 for germination %; qGR 9.1 for germination rate; qGI 1.2, qGI 6.1, qGI 8.1, qGI 11.1 and qGI 12.1 for germination index; qRPE1.1 and qRPE8.1 for rate of plumule elongation (RPE); qAGR 8.1 for AGR; qRSG1.2 and qRSG12.2 for RSG; qRGR3.1 and qRGR11.1 for RGR; qSVI 1.1 for SVI I; qSVII 1.1, qSVII3.1, and qSVII12.1 for SVI II, and qRRG8.1 were identied. The reported QTL for root length, qRL11.1 was validated in this mapping population. Additionally, QTLs, qRPE8.1 and qAGR8.1 of traits, RPE and AGR; qGI 6.1 and qGP6.2 of GI and GP; qGI 11.1 and qRL11.1 of GI and RL; qAGR8.1 and qRRG8.1 of AGR and RRG, and qRSG3.1 and qRGR3.1of traits RSG and RGR were detected for co-localization or co-inheritance. The traits identied and markers detected in the association analysis improvement

disequilibrium (LD) mapping analyses (Yu et al. 2006;Pandit et al. 2017). Thus, association estimate based on both the models of General linear model (GLM) and Mixed linear model (MLM) is considered appropriate for mapping complex traits that have shown to perform better than other model analysis.
However, limited information is available on genetic analysis of physiological parameters related to seed vigour in rice utilizing SSR markers in a variable natural population. Therefore, in the present investigation, association mapping of physiological parameters with 109 SSR markers was performed in a representative population shortlisted through phenotyping of 15 physiological traits namely electrical conductivity (EC), RI, GP, GI, SDW, RL, SL, SVI I, SVI II, rate of root growth (RRG), RSG, RPE, relative growth rate relative growth rate (RGR), AGR and MGR from 250 germplasm lines. The study will reveal the population genetic structure, diversity and candidate genes/QTLs involved in the 15 physiological traits associated with seed vigour in rice.

Seed material
The freshly harvested seeds of 250 diverse germplasm lines collected from ve states viz., Assam, MP, Kerala, Odisha and Manipur of India were used for association mapping of physiological growth parameters. The germplasm lines of Odisha state were from the Jeypore tract, the secondary center of origin of rice and known for availability of rich diversity of rice. All the germplasm lines were collected from Gene bank, ICAR-NRRI, Cuttack and grown during wet season, 2017-18 (Supplemental Table 1). The harvested seeds were used for estimation of 15 physiological growth parameters after a storage period of three months to overcome the seed dormancy.  Table 1 were used for mapping of physiological traits ( Table 1).

Phenotyping of physiological traits
Seed physiological characteristics such as electrical conductivity, rate of imbibition, germination %, germination index, root length, shoot length, seedling dry weight, seed vigour index, seed vigour index, rate of root growth, rate of shoot growth, rate of plumule elongation, relative growth rate, absolute growth rate and germination rate were estimated for the mapping study.
Seed physiological traits were estimated by sowing 50 seeds in three replications following top of paper method by incubating at 30 0 C. The EC was determined by measuring the conductivity of the seed leachate of 50 seeds soaked in 150 ml distilled water for 24 hrs at 30 0 C and expressed as µmhos cm -1 . Rate of imbibition of the germplasm lines was determined as increase in volume of seed after soaking the seed in water for 24 hours. The percentage of germinated seeds at 10 th day was referred to as the nal germination percentage. GI was calculated by adopting the method of Maguire, 1962. RL and SL were measured at 10 th day of germination and expressed in cm. The increase in plumule length per day were considered as rate of plumule elongation and expressed in cm day -1 . The increase in root and shoot length per day recorded at 7 th day and 10 th day of germination were considered as RRG and RSG and expressed in cm day -1 . AGR was calculated as per procedure of Reford (1967) and RGR was determined following the procedure of Fisher (1921). MGR was computed by adopting the procedure of Zuo et al. 2018. The seedlings used for recording rate of growth were subsequently oven dried at 70 0 C for 48 hours after removing the cotyledon and seedling dry weight was expressed in gram per seedling. Seed vigour indices (SVI I and II) were calculated using the formula suggested by Abdul-Baki and Anderson (1973). For estimating all the physiological traits except EC, RI and SG (50 seeds in each three replication) observations were recorded on ve seedlings selected from each replication and averaged to get the value of each replication.
Analysis of variance (ANOVA) for individual character including the estimation of mean, range, and coe cient of variation (CV %) were estimated by using Cropstat software 7.0. Pearson's correlation coe cients were analyzed to nd out the relationship among the various physiological traits, based on the mean values of the 96 genotypes and presented in correlation matrix heatmap. The mean estimates of the 15 physiological parameters were classi ed into 4 groups as very high, high, medium and low value containing germplasm lines for this study.
Genomic DNA isolation, PCR analysis and selection of SSR markers Genomic DNA of the germplasm lines was extracted from 15 days-old plant by adopting CTAB method (Murray and Thompson, 1980). The 109 SSR (simple sequence repeat) markers were taken from the data base available in the public domain (Supplementary Table 2). The isolated DNA was quanti ed by resolving the DNA fragments in gel electrophoresis. PCR analysis was done using the markers selected based on position covering all the chromosomes to illustrate the diversity and to identify the polymorphic loci among the 96 rice germplasm lines ( Table 1). Conditions of PCR reaction was 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, 72ºC), nal extension (5 min, 72ºC) and store at 4ºC (in nity). The PCR products were electrophoresed using 2.5% agarose gel containing 0.80g ml -1 ethidium bromide. To determine the size of amplicons, 50 bp DNA ladder was used. The gel was run at 2.5V cm -1 for 4 hrs and photographed using a Gel Documentation System (SynGene).

Molecular data analysis
Data scoring was carried out from the presence or absence of ampli ed products obtained on the basis genotype-primer combination. A binary data matrix was used as discrete variables for the entry of our result data. Software, 'Power Marker Ver3.25' was used to analyze the parameters namely polymorphic information content (PIC), observed heterozygosity (H), number of alleles (N), major allele frequency (A) and gene diversity (GD) for each SSR locus (Liu and Muse 2005). A Bayesian model based clustering approach STRUCTURE 2.3.6 software was used to analysis genetic data and obtain population structure (Pritchard et al. 2000). To derive the ideal number of groups (K), STRUCTURE software was run with K varying from 1 to 10, with 10 iterations for each K value. running period. Highest value of ΔK was pick up from Evanno table used to detect the subpopulation groups from the panel of populations in the next step.
The maximal value of L(K) was identi ed 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 (Evanno et al. 2005). Structure Harvester was used for estimation of the ΔK-value as function of K showing a clear peak as the optimal K-value (Earl and Vonholdt, 2012). The principal coordinate analysis of all the genotypes and unweighted neighbor joining unrooted tree for NEI coe cient dissimilarity index (Nei, 1972) with bootstrap value of 1,000 were obtained by using DARwin5 software (Perrier and Jacquemoud-Collet, 2006). The presence of molecular variance across the whole population, within a population and between the sub-population structures (F IT , F IS , F ST ) was calculated by the deviation from Hardy-Weinberg expectation and estimated through Analysis of molecular variance (AMOVA) using GenAlEx 6.5 software. All the detailed protocols of the above mentioned softwares were described in earlier publications Pandit et al. 2017;Pandit et al. 2020;).
Software, "TASSEL 5.0" was used to analyze the marker-trait association for mapping study of the seed vigor traits in rice. General linear model and Mixed linear model in TASSEL 5.0 were used to perform the genetic association between the phenotypic traits and molecular markers (Bradbury et al. 2007). By considering the signi cant p-value and r 2 value convincing associated markers were identi ed. The associations of markers were further con rmed by the Q-Q plot generated by the software. Linkage disequilibrium plot was obtained using LD measured r 2 , between pair of markers is plotted against the distance between the pair. Also, the accuracy of the marker-trait association by estimating the FDR adjusted p-values (q-values) using R software as described in the earlier publications (Pandit et al. 2017;).

Results
Phenotyping of the population for physiological traits in rice The mean values of 250 genotypes for 15 physiological traits viz., EC, RI, G,GI, RL, SL, SDW, SVI-I, SVI-II, RRG, RSG, RPLE, RGR, AGR, and MGR related to seed vigour were estimated during wet seasons of 2019 and 2020 (Supplementary Table 1). Signi cant differences were noticed among the germplasm lines for these 15 traits. The frequency distribution of the 250 germplasm lines were broadly classi ed into 4 groups each for the 15 physiological parameters ( Fig. 1A-C). The distribution of germplasm lines into various groups were categorized into groups or subpopulations (Fig. 1). A representative panel population containing 96 genotypes was developed from the original population by shortlisting germplasm lines from all the phenotypic groups of each parameter (Table  1; Fig. 2). The mean values of the 15 physiological traits estimated from the studied panel population also showed signi cant variation among the genotypes for each trait (Table 1) (Table 1).

Relatedness among germplasm lines for physiological traits through genotype-by-trait biplot analysis
The scatter diagram was plotted taking the rst two principal components to generate genotype-by-trait biplot graph for the 15 physiological traits estimated from the 96 genotypes present in the panel (Fig. 3). The rst and second principal components showed 99.651 and 0.2204 of the total variability with eigen value of 102641 and 227.016, respectively (Supplementary Fig. 1). SVI-I contributed maximum diversity among the 15 physiological parameters followed by GI and SVI-II for the panel population based on the principal component analysis (Fig. 3). The scattering pattern of genotypes in the 4 quadrants indicated that genotypes containing high estimates of parameters are placed in opposite direction of the quadrant 1 and II. Higher estimates of physiological parameters containing genotypes have been encircled in the gure (Fig. 3

Nature of association among seed vigour related traits
The association among 15 physiological traits revealed a strong positive correlation (r≥0.7) of SVI I with GI and GP; SVI II with SDW; GP with GI, and AGR with RSG with SL; AGR with RL, SL; MGR with GI, SDW, SVI II were observed. Weak positive correlation (r < 0.5) was noticed for SVI I with RI, RRG, RSG, AGR and MGR; SVI II with RI, GP, RL and SL; RRG with SL, RSG and AGR; RRG with SL; RSG with GI, RL, SDW and RRG; RPE with RL and SL; AGR with RI, SDW; MGR with Germ, RL, SL, RSG. However, weak negative correlation was estimated for RGR with GI, GP and SVI I (Fig. 4).

Genetic diversity parameters analysis
The constituted panel containing 96 genotypes from the original population which exhibited wide variation for the physiological parameters was genotyped using 109 molecular markers. The gene diversity, loci used for genetic diversity and other diversity related parameters are presented in Table 2. A total of four hundred four markers alleles were obtained with average value of 3.07 alleles per locus. The range of alleles per locus varied from 2 to 7 per marker showing the highest number of alleles by RM220, RM448 and RM493 in the studied panel for the physiological parameters. The average value of the major allele frequency of the parameters linked to the polymorphic markers was observed to be 0.578 which varied from 0.292 (RM488 and 493) and 0.958 (RM22034) ( Table 2). The range for PIC value was estimated to be from 0.141 (RM315 and 6054) to 0.771 (RM493) with mean value of 0.477. The observed average heterozygosity (Ho) in the population was 0.117 which varied from 0.00 to 0.958. The gene diversity (He) in the panel ranged from 0.061 (RM556) to 0.799 (RM493) showing a mean value of 0.533.

Population Genetic Structure Analysis
The genotypes in the panel exhibiting variation for the studied physiological parameters were evaluated for genetic structure adopting probable subpopulations (K) and selecting higher delta K-value estimated by STRUCTURE 2.3.6 software. The delta K value is related to the rate of change in the log probability of data between successive K values. It categorized the genotypes into two sub-populations ( Fig. 3A; Fig.3B) with a high ∆K peak value of 264.2 at K = 2 among the assumed K (Fig. 5). The proportions of genotypes in the inferred clusters were 0.875 and 0.125 in subpopulation 1 and subpopulation 2, respectively. However, the two subpopulations did not show correspondence well with the studied physiological parameters. Hence, next peak at the ∆K peak was considered and the population was categorized into 6 subpopulations. The proportions of genotypes in the inferred clusters were 0.179, 0.211, 0.258, 0.081, 0.181 and 0.091 for the sub-population 1, 2, 3, 4, 5 and 6, respectively. The xation index (Fst) values were 0.278, 0.254, 0.201, 0.332, 0.206 and 0.507 for the sub-population 1, 2, 3, 4, 5 and 6, respectively. The expected average distances or heterozygosity were 0.342, 0.348, 0.366, 0.390, 0.373 and 0.331 in the sub-population 1, 2, 3, 4, 5 and 6, respectively. The genotypes with ≥80% ancestry value were categorized for that subpopulation (Table 3; Fig. 5).
The physiological parameters showed a relatively fair correspondence at K=6 with the structure subpopulations present in the panel population. Majority of the moderate to high seed vigour showing germplasm lines present in the subpopulations SP2 and SP6 while poor vigour containing lines were in subpopulation SP4 and SP5. The panel also showed a low alpha value (alpha = 0.0591) by the structure analysis at K=6. Positively skewed leptokurtic distributions were observed for mean alpha-value, Fst3, Fst4 and Fst5 while mesokurtic distributions detected for Fst1, Fst2 and Fst6 for the panel population showing a distinct variation in the distribution among the Fst values ( Supplementary Fig. 2).

Molecular variance (AMOVA) and LD decay plot analysis
The closely related plants in a population are clustered into isolated groups and form various subpopulations. Genetic variations between and within the subpopulations at K=6 were detected through analysis of molecular variance (AMOVA) ( Table 4). The genetic variations obtained between and within at K=6 was computed to be 12% among the populations, 67% among individuals and 21% variation within individuals of the panel population. Deviation from Hardy-Weinberg's prediction was calculated from Wright's F statistics estimates. Different parameters like uniformity of individual within the subpopulation (F IS ) and individual within the total population (F IT ) were estimated for differentiation of population. The F IT and F IS values of total population and within population based on 109 loci were 0.791 and 0.763, whereas F ST was 0.118 between the two subpopulations. Fst is estimated to measure the population differentiation or the subpopulations within the total population. The Fst values of each sub-population and their distribution pattern showed a clear differentiation between the six sub-populations from each other (Supplemental Fig. 2).
The nonrandom association of alleles at different loci is successfully utilized for marker-trait association study. The LD decay rate is important factor for getting marker-trait association. The decay rate will facilitate the discovery of reliable markers associated with the physiological parameters and will facilitate the discovery of new genes or allelic variants controlling these traits. Syntenicr2 was used to plot the LD decay of the population against the physical distance in million base pair (Fig. 6). Tightly linked markers have the highest r 2 and average r 2 rapidly decreases as linkage distance increases. There was a sharp decline in LD decay for the linked markers at 1-2 mega base pair and thereafter a very slow and gradual decay was noticed. Overall, it is clear that LD decay occur for the physiological parameters.

Genetic relatedness among genotypes by principal coordinates and cluster analyses
The two dimensions diagram for principal coordinate analysis (PCoA) is drawn based on 109 markers which grouped the genotypes as per the genetic relatedness among them (Fig. 7). The component 1 accounted for 11.04% inertia and component 2 for 6.71% of total inertia. The panel genotypes were placed in various spots of the 4 quadrants which formed three major groups (Fig. 7). A total of 30, 46, 11 and 9 number of germplasm lines were distributed in the 1 st , 2 nd , 3 rd and 4 th quadrant, respectively. The genotypes belonging to the 6 different sub-populations are grouped in different quadrants. The 1 st quadrant genotypes are divided into 3 groups whereas the 2 nd quadrant genotypes are divided into two groups of which one group is closer to axis1 and another is to axis 2. This 2 nd group genotypes closer to axis2 are admix type depicted in black colour (Fig. 7).
The majority of the germplasm lines containing high to very high mean values of physiological traits were placed in the 1st (top right) and 2nd (bottom right) quadrants of the PCoA. The PCoA distributed the genotypes in the four quadrants forming 7 clusters including the admix type subpopulation. The subpopulations clustered by PCoA are encircled in the gure and showed correspondence with population structure (Fig. 7). Germplasm lines having high to very high mean values of physiological traits placed in the quadrant II were ARS-AC-6221, MP-Joha, KE-Adira-2-Pallakad-R3, KE-PK-18-Ezhoml-2, KE-PK-14-Vachaw and KE-PK-24-Jyothi. Genotypes namely OD-Landi, OD-Balisaralaktimachi, OD-Kaniar, OD-Kanakchampa, ARS-AC-6023, ARS-AC-6172, MNP-AC-9030, KE-PK-19-Cheruvirippu, JBS-AC-20614, MNP-AC-9005, JBS-AC-20371 and JBS-AC-20423 were observed in the quadrant I. Quadrant III consisted of genotypes mostly from SP4 subpopulation. Majority of the germplasm lines in quadrant IV were from SP6 subpopulation. Majority of the admix genotypes were found in quadrant I and II.
Six sub groups were observed in the dendrogram based on the mean values of studied physiological parameters (Fig. 8A). A total of 15, 4, 11, 21, 19 and 26 genotypes were distributed in the cluster I to VI, respectively. Cluster VI was biggest cluster which accommodated 26 germplasm lines while cluster II was smallest with only 4 genotypes. The germplasm lines present in sub-population 2 of genetic structure were observed in group 6 of the dendrogram. Similarly, four genotypes of structure subpopulation 1 were in the group 4 of the dendrogram. Admix genotypes obtained from the structure were found in all the groups of this phenotype cluster except group 4.
The cluster analysis discriminated the germplasm lines on the basis of genotyping of 109 SSR markers and placed the genotypes into different clusters which corresponded with the studied physiological parameters. The unweighted-neighbour joining tree differentiated the genotypes into 6 different clusters (Fig. 8).
Clusters SP 1 was differentiated from SP 3 by the presence of high estimates of root length and rate of plumule in it whereas Seedling dry weight & Seed vigour index II were rich in SP 3 . SP 2 and SP 6 had accommodated majority of germplasm lines containing high values for the studied parameters except Seedling dry weight in SP 2 and germination % in SP 6 . Sp4 was discriminated from others based on absence of germplasm lines containing high estimates for germination rate and seed vigour index II while root length and seedling dry weight were absent in SP 5 .

Association of marker alleles with physiological parameters in rice
Association of molecular markers with 15 physiological parameters was computed using Mixed Linear Model (MLM/ K+Q model) and Generalized Linear Model (GLM) by TASSEL 5 software. The marker-trait comparisons were subjected to ltration at less than 1% error i.e. 99% con dence (p<0.01). Twelve parameters showed signi cant associations with markers using both the models at p<0.01. A total of 112 and 93 signi cant marker-trait associations were detected by GLM and MLM, respectively at p<0.01. The marker R 2 values computed by GLM approach was from 0.565 to 22.7 while the range was 0.7009 to 0.175 by Mixed Linear Model (Supplementary Table 3). Signi cant marker-trait associations were detected for G1 with 5 markers; SVI-II, RSG and MGR with 3 markers; GP, RGR and RP with 2 markers, and RI, RL, SVI, AGR and RRG with 1 marker by both GML and MLM models at p<0.01. Considering trimming at r 2 >0.10 and p<0.01, 6 markers exhibited associations with 4 physiological parameters namely GI with RM225 and RM502; GP with RM225 and RM502; SVI with RM5638 and RP with RM220 (Table 5; Supplemental table 4). The Q-Q plot also con rmed the association of these markers with the associated physiological traits in rice (Fig…).
Four markers showed signi cant association with GI detected by GLM and MLM models at p<0.01. The genomic regions controlling the trait, GI was detected on chromosome 1, 8, 11 and 12 associated by markers RM5638, RMRM502, RM229 and RM20A, respectively. Among the four markers, RM502 showed highest marker R 2 value of 0.227 analyzed by GLM and 0.175 by MLM. Three markers namely RM5638, RM14723 and RM7003 located at 204, 86 and 132 cM positions on chromosome 1, 3 and 12, respectively were associated with the parameter, SVII. RSG was detected to be associated with RM 6547, RM3701 and RM7003 present on chromosome 1, 11 and 12, respectively. MGR was found to be controlled by the QTLs present on the chromosome 1, 8 and 9 which showed associations with markers RM220, RM502 and RM201, respectively. QTLs for Germination % showed signi cant associations with RM225 on chromosome 6 and RM502 on chromosome 8. The parameter, RPE was detected to be located on chromosome 1 showing association with RM220 and RM403. Relative growth rate exhibited association with RM468 and RM3701. Marker RM256 showed signi cant association with RRG and AGR. Signi cant associations of markers RM248, RM229 and RM5638 with RI, RL and SVI, respectively were detected by both the models. Marker RM256 was strongly associated with parameters, RRG and AGR. In addition, RI, RL and SVI showed signi cant associations with RM248, RM229 and RM5638, respectively ( Table  5). The Q-Q plot also con rmed the associations of these markers with the estimated physiological parameters in rice ( Fig. 9).
Common markers were observed to be associated with different physiological parameters in rice. Few markers showed signi cant associations with two physiological parameters namely RM220 with RP and AGR; RM225 with GI and GP; RM229 with GI and RL; RM256 with AGR and RRG; RM3701 with RSG and RGR, and RM7003 with SVII and RSG by both the models at <1% error. Also, marker RM602 showed signi cant associations with 3 traits namely GI, GP and MGR. In addition, RM5638 also associated with 3 parameters namely GI, SVI and SVII by both the models at p<0.01 (Table 5).

Discussion
The germplasm lines present in the panel population were signi cantly different from each other with respect to the 15 studied physiological traits (Table 1). Higher magnitude of correlation coe cients was estimated in many studied physiological parameters. This revealed the improvement of seed vigour through the associated traits based on the values of the coe cients of the correlated parameters and availability of higher genetic variations in the population (Table   4). Earlier reports of high variations and genetic advance for many traits were published by many researchers ( (Table 2). A moderate to high PIC value and more alleles in the population revealed about better informative markers and for use in the breeding program. The germplasms used in this study were collections from the states known for existence of rich rice genetic diversity ). Germplasm lines from Jayapur tract of Odisha, the secondary centre of origin were also included in this study. The germplasm lines having high values of multiple physiological traits for ≥ 10 were MNP-AC9005, MNP-AC9006, MNP-AC9038, MNP-AC9043, MNP-AC9044A, JBS-AC20282 and JBS-AC20328. These germplasm lines will be potential donors for seed vigour trait in breeding programs (Table 1). Hence, it is expected that breeding programme with inclusion of parental lines from this population may be effective for seed vigour and its related Twelve physiological parameters in uencing seed vigour were found to be associated with 10 SSR markers analyzed by both GLM and MLM approaches (Table 5). The markers detected to be associated by both the models at p<0.01 and low 'p'value are considered to be very robust and useful for seed vigour improvement program. Hence, markers namely RM5638, RMRM502, RM229 and RM20A for GI; RM14723, RM7003 and RM5638 for SVII; RM 6547, RM3701 and RM7003 for RSG; RM220, RM502 and RM201 for MGR; RM225 and RM502 for germination %; RM220 and RM403 on chromosome 1 for RPE; RM468 and RM3701 for RGR; RM256 for RRG and AGR; RM248, RM229 and RM5638 for RI, RL and SVI, respectively are useful in molecular breeding for improvement of seed vigour in rice (Table 5). Q-Q plot also con rmed the associations of these markers with various seed vigour in uencing traits in rice (Fig. 9). novel QTLs controlling different physiological parameters namely qGP 6.2 and qGP 8.2 for germination %; qGR 9.1 for germination rate; qGI 1.2, qGI 6.1, qGI 8.1, qGI 11.1 and qGI 12.1 for germination index; qRPE1.1 and qRPE8.1 for rate of plumule elongation (RPE); qAGR 8.1 for AGR; qRSG1.2 and qRSG12.2 for RSG; qRGR3.1 and qRGR11.1 for RGR; qSVI 1.1 for SVI I; qSVII 1.1, qSVII3.1, and qSVII12.1 for SVI II, and qRRG8.1 were identi ed. The reported QTL for root length, qRL11.1 was validated in this mapping population. Additionally, QTLs, qRPE8.1 and qAGR8.1 of traits, RPE and AGR; qGI 6.1 and qGP6.2 of GI and GP; qGI 11.1 and qRL11.1 of GI and RL; qAGR8.1 and qRRG8.1 of AGR and RRG, and qRSG3.1 and qRGR3.1of traits RSG and RGR were detected for colocalization or co-inheritance. The traits identi ed and markers detected in the association analysis will be useful for improvement of seed vigour trait through marker-assisted selection in rice.

Declarations
Ethics approval and consent to participate: The authors declare that this study complies with the current laws of the country in which the experiments were performed.

Consent for publication: Not applicable
Availability of data and material: The data generated or analyzed in this study are included in this article.
Competing interest:The authors declare no con icts of interest       Table 1. with the probability of ≥80% membership proportions were assigned as subgroups while others grouped as admixture group. The numbers in the diagram depict the serial number of the germplasm lines listed in Table 1.

Supplementary Files
This is a list of supplementary les associated with this preprint. Click to download.