Genome-wide association study of antioxidant compounds and antioxidant activity in a panel of Thai rice cultivars

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In this study, we investigated the genetic architecture of total phenolic content (TPC), total flavonoid content (TFC), and antioxidant capacity (AC, measured by ABTS) in a panel of 159 Thai rice cultivars using genome-wide association analysis. A total of 209,594 high-quality SNPs located in promoter and exonic regions were analyzed using a mixed linear model implemented in GEMMA. All three traits exhibited high broad-sense heritability and strong positive correlations. We identified 158 significant SNPs distributed across multiple chromosomes. Among these, 108 SNPs were associated with at least two antioxidant traits and were consolidated into 38 loci, resulting in eight consensus QTLs. These QTLs collectively explained 10.06–35.81% of phenotypic variation. Within these regions, 14 transcription factor genes were prioritized as candidates, including previously characterized regulators of flavonoid biosynthesis, OsRc and OsCHS2 , as well as additional MYB, bHLH, and WD-repeat transcription factors. Notably, a major QTL on chromosome 7 co-localized with OsRc and the structural gene OsCHS2 . Our findings reveal the central role of known regulatory genes and highlight additional transcriptional regulators that may contribute to variation in antioxidant levels in Thai rice germplasm. These results provide genetic resources for marker-assisted selection to improve rice antioxidant properties. Antioxidant Candidate genes GWAS Heritability Quantitative trait locus Rice Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Rice ( Oryza sativa L.) is the main food for the global population, particularly in Asia. While polished white rice is the most widely consumed, it is nutritionally deficient due to the milling process, which removes essential vitamins, minerals, and other functional compounds. This lack of nutrients can cause some chronic diseases (Dipti et al., 2012 ). In contrast, colored rice cultivars, such as those with red, purple, and black pericarps, are substantial in phytochemicals, especially phenolic compounds. These compounds are significantly associated with a reduced risk of chronic diseases such as coronary heart disease, type-2 diabetes, and cancers (Butsat & Siriamornpun, 2010 ; Chaichana, 2019 ; Ravichanthiran et al., 2018 ; Tian et al., 2004 ). The antioxidant compounds in rice are primarily phenolic compounds, which include several flavonoid classes such as chalcones, flavones, flavonols, dihydroflavonols, proanthocyanidins, and anthocyanidins. These compounds are derived from the general phenylpropanoid pathway, followed by the flavonoid pathway. Especially, Malonyl CoA and p -coumaroyl CoA are the substrates of the flavonoid pathway (Shih et al., 2008 ). The flavonoid synthetic pathway involves many enzymes, each encoded by a specific structural gene. Major enzymes in this pathway include chalcone synthase (CHS), chalcone isomerase (CHI), flavanone 3-hydroxylase (F3H), flavonol synthase (FLS), flavonoid 3’-hydroxylase (F3′H), dihydroflavonol 4-reductase (DFR), leucoanthocyanidin dioxygenase (ANS), UDP-glucosyl transferase (UGT), leucoanthocyanidin reductase (LAR), anthocyanidin reductase (ANR), flavone synthase II (FNSII), O-methyltransferase (OMT), flavanone 2-hydroxylase(F2H), C-glucosyl transferase (CGT) and dehydratase (DH) (Park et al., 2016 ). The expression of these structural genes associated with the flavonoid pathway is controlled by regulatory genes, which belong to the MYB, basic helix-loop-helix (bHLH), and WD40 repeat protein (WDR) family transcription factors (Xu et al., 2015 ). Several studies have identified genetic determinants underlying antioxidants in rice for many decades. For example, Reddy et al. ( 1996 ) detected the CHS protein, and the chs locus was mapped to the centromeric region of rice chromosome 11. Another study reported that the OsRd and OsRc genes were involved in proanthocyanidin biosynthesis in rice. The OsRd gene on chromosome 1 encodes the DFR protein, the first enzyme in the proanthocyanidin pathway. In contrast, the OsRc gene on chromosome 7 encodes a bHLH transcription factor, and the non-deleted 14-bp region contributes to the accumulation of proanthocyanidin in red rice (Furukawa et al., 2007 ). Similarly, the OSB1 and OSB2 genes, encoding MYC-type bHLH transcription factors, were identified at the Purple leaf (Pl) locus on rice chromosome 4 (Sakamoto et al., 2001 ). These genes were similar to the maize R/B family, which regulates anthocyanin pigmentation. The OSB1 gene in black rice is functional, resulting in anthocyanin pigmentation. In contrast, an inactive OSB1 gene with a 2-bp insertion in the white and red varieties causes a frameshift mutation at the C-terminus, leading to premature termination of the regulatory protein. As a result, white and red pericarp rice could not synthesize anthocyanin (Lim & Ha, 2013 ; Wang & Shu, 2007 ). Similarly, the expression of OSB2 genes is restricted to black rice varieties such as Thai black rice varieties (Inta et al., 2013 ), and it is known to up-regulate the expression of structural genes involved in anthocyanin biosynthesis in rice (Sakulsingharoj et al., 2014 ). While Zheng et al. ( 2019 ) revealed that regulatory genes such as OsC1 and OsRb are activated in the anthocyanin biosynthetic pathway of rice leaves, these genes are tissue-specific and do not influence the anthocyanin biosynthetic pathway in the rice pericarp. Additionally, OsP1 and the ternary MYB-bHLH-WDR complex regulate some structural genes in anthocyanin biosynthesis. Genome-wide association study (GWAS) has become a powerful tool for detecting candidate genes and developing molecular markers for marker-assisted selection (MAS). This approach, which uses single-nucleotide polymorphism (SNP) markers, provides a high-resolution method for identifying genes associated with traits. Previous studies have used GWAS to investigate the genetic basis and genes for antioxidant traits in rice. For instance, Shao et al. ( 2011 ) conducted the association mapping in a collection of 461 rice accessions, including 361 white, 50 red, and six purple pericarp rice. The results revealed that the OsRc and OSB1 markers, located on chromosomes 4 and 7, respectively, were associated with total phenolic content (TPC), total flavonoid content (TFC), antioxidant capacity (AC), and color parameters. Moreover, Xu et al. ( 2016 ) performed a GWAS on red- and white-pericarp rice. A total of 30 QTLs associated with TPC, TFC, and AC were found on chromosomes 1, 6, 7, 9, and 11. These results highlight that the OsRc gene was detected for all phenolic traits and OsABCF6 ( LOC_Os11g39020 ) was identified as a potential new gene related to ferulic acid in rice grain. Taken together, these studies demonstrate that GWAS is an effective approach for identifying genes underlying antioxidant traits in rice. Furthermore, significant GWAS-derived SNPs provide valuable genetic resources for developing molecular markers to support marker-assisted selection (MAS) for selecting high antioxidant traits. The previous studies of Zhao et al. ( 2018 ) and Li et al. ( 2019 ) showed the validation for the new molecular markers of cadmium accumulation and alkalinity tolerance, respectively. Although pericarp color is commonly used as an indirect indicator of antioxidant potential, antioxidant content in rice grains is a quantitatively inherited trait controlled by multiple structural and regulatory genes. Reliance solely on visible color may overlook underlying genetic variation influencing antioxidant accumulation. Therefore, dissecting the genetic architecture of antioxidant traits is essential for enabling more precise selection strategies in rice breeding. In this study, we applied GWAS to a panel of 159 Thai rice cultivars to identify loci and candidate genes associated with total phenolic content, total flavonoid content, and antioxidant capacity. The identified loci provide a foundation for developing molecular markers to facilitate marker-assisted selection for improved antioxidant properties. MATERIALS AND METHODS Plant material and phenotyping A panel of 159 local Thai rice cultivars was used in this study, including 150 white pericarp rice, four red pericarp rice, and five purple pericarp rice cultivars. These cultivars were employed to measure two total antioxidant compounds (TPC and TFC) and antioxidant activity (AC) from rice grains. All phenotypic data were recorded in our previous study (Thaworn et al., 2021 ). Statistical Analysis Descriptive statistics, correlation analysis, and a one-way analysis of variance (ANOVA) were performed on the phenotypic data using the IBM SPSS Statistics version 22 software (IBM, 2013 ). The ANOVA was conducted with 159 rice cultivars and three replications (r). The general ANOVA output table is exemplified in Table 1 . These values were used to calculate the genetic parameters, including phenotypic variance (σ p 2 ), genotypic variance (σ g 2 ), and environmental variance (σ e 2 ), which were estimated from the ANOVA mean squares. The phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) were calculated following the method of Miller et al. ( 1958 ). Broad-sense heritability ( H 2 ) was calculated using Allard’s method (Allard, 1960 ). The formulas used for these calculations were presented in equations ( 1 ) to ( 5 ). Table 1 General ANOVA for the antioxidant compounds and capacity between 159 rice cultivars Source of variation df Mean Square Between Groups n-1 MSG Within Groups nr-n MSE Total nr-1 $${\sigma}_{g}^{2}=\left(\frac{MSG-MSE}{r}\right)$$ 1 $${\sigma}_{p}^{2}={\sigma}_{g}^{2}+{\sigma}_{e}^{2}$$ 2 $$GVC=\frac{\sqrt{{\sigma}_{g}^{2}}}{\overline{x}}\times100$$ 3 $$PVC=\frac{\sqrt{{\sigma}_{p}^{2}}}{\overline{x}}\times100$$ 4 $${H}^{2}=\frac{{\sigma}_{g}^{2}}{{\sigma}_{p}^{2}}\times100$$ 5 In the equations, MSG and MSE indicate the mean square between genotypes and the residual mean square (within-genotype variance) from the ANOVA table, respectively. r is the number of replications, and \(\overline{\text{x}}\) is the grand mean of the antioxidant trait. SNPs genotyping The SNP maker data for the 159 Thai rice cultivars were kindly provided by the Center of Excellence in Environment and Plant Physiology, Department of Botany, Faculty of Science, Chulalongkorn University. The SNP filtering and quality procedure were conducted following the protocol of Lekklar et al. ( 2019 ). Briefly, high-quality SNP makers were obtained by filtering out SNPs with minor allele frequency (MAF) 40%. After filtering, a total of 209,594 high-quality SNPs —comprising 96,480 promoter-region SNPs and 113,114 exonic SNPs distributed across the 12 rice chromosomes—were retained for GWAS analysis. GWAS for antioxidant traits and candidate gene identification GWAS was carried out using a set of high-quality SNP markers and antioxidant traits, including TPC, TFC, and AC. A genome-wide association mapping was conducted using the mixed linear model (MLM) through GEMMA software, with genotype PCA included as covariates (Zhang et al., 2010 ; Zhou & Stephens, 2012 , 2014 ). Promoter and exonic SNPs were analyzed separately because only these functional regions were retained for GWAS. The Bonferroni multiple testing correction was therefore applied within each SNP category. The thresholds were set at -log 10 ( p ) ≥ 6.29 for promoter regions and -log 10 ( p ) ≥ 6.35 for exonic regions. Manhattan and Q-Q plot of GWAS results were generated using the qqman package in R (Turner, 2014 ). Significant SNPs were annotated in their position on the rice genome using the MSU Rice Genome Annotation Project version V7.0. Significant SNPs associated with at least two antioxidant traits were prioritized. SNPs within the LD-decay distance of 200 kb (mean r² = 0.1) were merged into a single QTL (Lekklar et al., 2019 ). Only loci supported by at least two significant SNPs were considered as QTLs and overlapping LD-based regions across traits were defined as consensus QTLs. Candidate genes were identified using two complementary approaches: (i) genes located within the LD-defined QTL intervals, and (ii) genes located within 200 kb upstream and downstream of selected significant SNPs. Gene annotations were obtained from the MSU Rice Genome Annotation Project (version 7.0). Phenotypic variation ( R 2 ) was calculated from significant SNPs by using single marker analysis in R (R Core Team, 2020 ), and QTLs with R 2 more than 10% were considered as the candidate QTLs. RESULTS Phenotypic data of 159 Thai rice cultivars TPC, TFC, and AC of 159 Thai rice cultivars were summarized in Table 2 . TPC value ranged from 0.86 to 5.79 mg GAE/g, with an average of 1.43 mg GAE/g. TFC had an average of 3.30 mg RE/g and ranged from 2.03 to 11.13 mg RE/g. The AC value ranged from 78.23 to 2301.92 µg AEAC/g, with an average of 268.03 µg AEAC/g. The data distributions of TPC, TFC, and AC did not follow normal distributions using the Kolmogorov-Smirnov test ( p < 0.05). All traits showed skewed distributions and separated into two distinct groups (Fig. 1 ), which may result from this population comprising 150 white- and nine colored-pericarps of rice cultivars. Correlations among antioxidant traits were analyzed by using Pearson’s correlation analysis. The correlation coefficient (r) showed significant, strong positive correlations among TPC, TFC, and AC (r = 0.901‒0.963, p < 0.01; Fig. 2 ), indicating that the antioxidant traits in rice grain are highly related. Table 2 Mean±standard deviation (SD), PCV, GCV, and H 2 for TPC, TFC, and AC of 159 Thai rice cultivars. Trait Mean ± SD Range PCV GCV H 2 (%) TPC 1.43 ± 0.78 0.86–5.79 58.31 52.43 80.90 TFC 3.30 ± 1.46 2.03–11.13 48.11 42.29 77.23 AC 268.03 ± 342.20 78.32-2301.92 134.48 124.73 86.02 ANOVA analysis indicated that the effects of rice cultivars on TPC, TFC, and AC were highly significant ( p < 0.001) (Table 3 ). The genetic parameters were estimated based on the values in Table 2 . The results revealed high broad-sense heritability ( H 2 ) for TPC (80.90%), TFC (77.23%), and AC (86.02%), indicating that these traits were more heritable. In addition, all antioxidant traits showed high GCV values, ranging from 42.29% to 124.73%. The small differences between the PCV and GCV for all traits are < 10%, suggesting that trait variation is predominantly genetic, with negligible environmental influence. Table 3 One-way ANOVA results for TPC, TFC, and AC testing, showing the difference between 159 rice cultivars Traits Sum of Squares df Mean Square F Sig. TPC Between Groups 285.753 158 1.809 13.688 .000 Within Groups 42.017 318 .132 Total 327.770 476 TFC Between Groups 1015.934 158 6.430 11.200 .000 Within Groups 182.568 318 .574 Total 1198.502 476 AC Between Groups 55846017.978 158 353455.810 19.457 .000 Within Groups 5776740.131 318 18165.849 Total 61622758.108 476 GWAS for antioxidant traits To dissect the genetic basis of antioxidant traits, we performed GWAS using the MLM model. The analyses were conducted on the 159 rice cultivars with 209,594 high-quality SNPs in promoter and exonic regions. GWAS identified a total of 158 significant SNPs across 10 chromosomes. Significant SNPs were located in promoter regions were detected on chromosomes 1–9 and 12 (Fig. 3 ). In contrast, significant SNPs within exonic regions were identified on chromosomes 1, 2, 4, 6, 7, and 8 (Fig. 4 ). As antioxidant traits showed strong positive correlations (Fig. 2 ); the QTL or underlying genes controlling these traits are likely shared or closely linked in the genome. Accordingly, we prioritized the 108 SNPs associated with at least two antioxidant traits. To detect the major QTLs linked to antioxidant traits, we focused on loci supported by multiple identified SNPs. Using this criterion, 38 QTLs associated with antioxidants traits were detected on chromosomes 1, 2, 3, 4, 5, 6, 7, and 8 (Fig. 5 ). Chromosomes 2 and 3 each harbored only a single associated SNP and were therefore excluded from QTL designation (Fig. 5 ). In addition, QTLs located with the same LD-based region were defined as a single locus. Therefore, eight consensus QTLs for antioxidant traits were identified, including QTAN1_1, QTAN4_1, QTAN5_1, QTAN5_2, QTAN6_1, QTAN7_1, QTAN7_2, and QTAN8_1 on chromosomes 1, 4, 5, 6, 7 and 8, respectively (Fig. 5 ). Collectively, these QTLs explained 10.06 to 35.81% of phenotypic variations ( R 2 ) of the antioxidant traits (Table 4 ). Table 4 QTL and candidate gene identified by GWAS for TPC, TFC, and AC. QTL Chr. SNP Locus Trait P -value R 2 Candidate gene QTAN1_1 1 5 LOC_Os01g32890.1 TPC TFC AC 1.73E-09 6.50E-09 9.31E-10 31.21 33.23 25.40 helix-loop-helix DNA-binding domain (LOC_Os01g33400.1) QTAN4_1 4 44 LOC_Os04g39600.1 TPC AC 5.45E-08 4.73E-08 11.41 13.70 MYB family transcription factor (LOC_Os04g39470.1) QTAN5_1 5 7 LOC_Os05g44190.1 TPC TFC 1.92E-07 3.72E-07 12.25 13.03 WD domain, G-beta repeat domain containing protein (LOC_Os05g44320.1) QTAN5_2 5 22 LOC_Os05g51119.1 TPC AC 1.11E-09 7.17E-09 28.87 29.80 helix-loop-helix DNA-binding protein (LOC_Os05g50900.1) Myb transcription factor (LOC_Os05g51160.1) QTAN6_1 6 7 LOC_Os06g08550.1 TPC TFC AC 2.17E-08 6.62E-09 4.48E-07 11.93 13.79 10.06 MYB family transcription factor (LOC_Os06g08290.1) BHLH transcription factor (LOC_Os06g08500.1) QTAN7_1 7 10 LOC_Os07g11100.1 TPC TFC AC 1.87E-11 2.45E-11 1.88E-10 23.34 22.19 24.61 OsRc (LOC_Os07g11020.1) OsCHS2 chalcone synthase (LOC_Os07g11440.1) QTAN7_2 7 2 LOC_Os07g44440.1 TPC AC 5.63E-08 7.24E-08 20.86 23.48 myb-related protein Hv33 (LOC_Os07g44090.1) WD40-like Beta Propeller Repeat family protein (LOC_Os07g44410.1) QTAN8_1 8 8 LOC_Os08g33200.1 TPC TFC AC 4.23E-07 2.37E-07 4.07E-07 34.47 35.81 32.17 MYB family transcription factor (LOC_Os08g33050.1) MYB family transcription factor (LOC_Os08g33150.1) MYB family transcription factor (LOC_Os08g33660.1) DISCUSSION The frequency distribution of 159 Thai rice panels was skewed. It was separated into two distinct groups for all antioxidant traits (Fig. 1 ), which may result from this population comprising 150 white- and nine colored-pericarp rice cultivars. Multiple structural and regulatory genes control TPC, TFC, and AC (Park et al., 2016 ; Tian et al., 2004 ; Xu et al., 2015 ). Theoretically, the data is explained by multiple genes and should demonstrate a continuous frequency distribution. Additionally, in this study, we suggested that TPC, TFC, and AC were associated with the color of the pericarp of rice grains (Shen et al., 2009 ). Thus, the distributions showed the two different groups of white and red pericarp rice. All antioxidant traits showed strong positive correlations. The results are in agreement with Pramai and Jiamyangyuen ( 2016 ) and Shen et al. ( 2009 )who reported similar correlations among TPC, TFC, and AC in white, red, and purple pericarp rice. These results suggest that the antioxidant traits in rice grain are highly interrelated. Therefore, selecting for one antioxidant trait may simultaneously improve other antioxidant traits. This is important information for improving and breeding rice grain quality. In this study, we estimated high heritability ( H 2 ) and GCV for TPC, TFC, and AC, suggesting great heritability and substantial genetic diversity in this population. The minimal gap between PCV and GCV indicates that genetic factors are the main effects in all traits, with slight environmental effects (Mehboob et al., 2016 ). Our results were consistent with Sanghamitra et al. ( 2018 ), who also reported that the high H 2 and small differences between the PCV and GCV of antioxidant traits in rice. These findings highlight that these antioxidant traits can be effectively targeted for selection to improve rice antioxidant quality in breeding programs. GWAS detected eight consensus QTLs, which annotated 14 potential candidate genes, including seven MYB family transcription factors, three helix-loop-helix (HLH) family transcriptional regulatory proteins, two WD-repeat domain proteins, and two genes ( OsRc and OsCHS2 ). The helix-loop-helix DNA-binding domains, MYB family transcription factors, and WD domain are regulatory genes (Massari & Murre, 2000 ). However, these genes remain understudied to regulate the structural genes of the flavonoid biosynthetic pathway in rice (Yang et al., 2018 ). In this study, we prioritized a major QTL on chromosome 7, which linked to the regulatory gene OsRc and the structural gene OsCHS2 genes (Table 4 and Fig. 5 ). The OsRc gene encodes the bHLH transcription factor that regulates proanthocyanidin biosynthesis and is responsible for red pericarp color in rice (Furukawa et al., 2007 ). As demonstrated by Furukawa et al. ( 2007 ), the Rc allele in red rice led to the accumulation of proanthocyanidin, whereas the rc allele in white rice did not. Additionally, in this region, we identified OsCHS2— the key gene of the flavonoid biosynthesis pathway— encoding chalcone synthase, which is the first enzyme of the flavonoid biosynthesis pathway (Shih et al., 2008 ). The co-localization of these two genes indicates genetic evidence for direct functional association in controlling antioxidant synthesis. Shao et al. ( 2011 ) revealed that OsRc and OSB1 markers were associated with TPC, TFC, AC, and color parameters in a diverse panel of pigmented rice. GWAS results from Xu et al. ( 2016 ) demonstrated that 30 significant QTLs on chromosomes 1, 6, 7, 9, and 11 associated with TPC, TFC, and AC traits in whole rice grains of red and white pericarp rice. A locus on chromosome 7 was associated with TPC and antioxidant capacities and located within the OsRc gene (Xu et al., 2016 ). Our findings also identified the QTAN7_1 locus on chromosome 7—containing OsRc and OsCHS2 —as a major locus for all antioxidant traits. This region was also detected consistently in previous studies (Purnama et al., 2025 ; Shao et al., 2011 ; Xu et al., 2016 ). In another Thai rice panel, Purnama et al. ( 2025 ) identified the OsRc gene significantly associated with antioxidant traits, including TPC, TFC, DDPH, FRAP, and ABTS, using GWAS. The alleles of marker OsRc , which are linked to the OsRc gene, can distinguish between red rice with high TPC and ABTS (allele Hom4) and white rice with low antioxidant traits (allele Hom3) (Purnama et al., 2025 ). In addition, a marker linked to the promoter of the OsRc gene showed a high association with TPC ( R 2 = 52.2%) and FRAP ( R 2 = 43.0%) in the F 2 population derived from the cross of red and white Thai rice cultivars (Tomjai et al., 2025 ). The consistent detection of this gene across independent studies underscores its important role in controlling the accumulation of phenolic compounds in colored rice. Furthermore, the marker linked to this gene serves as an effective genetic tool for MAS in rice breeding programs aimed at improving nutritional quality. In this study, the candidate genes for antioxidants were identified through GWAS, especially 14 transcription factor genes. Among these, two genes, OsRc and OsCHS2 , have been previously studied for their expression levels and functions. Consequently, both genes are promising candidate genes for developing molecular markers. The specific function of the other 12 transcription factor genes remains unexplored, although they may be involved in antioxidant biosynthesis. Further research is required to elucidate their functions. CONCLUSION GWAS is a powerful tool for identifying candidate genes for antioxidant traits in local Thai rice cultivars. We identified eight major QTLs and a set of 14 potential candidate genes, including the previously tested regulatory genes OsRc and structural gene OsCHS2 . Moreover, 12 regulatory transcription factor genes remain understudied in their functions related to antioxidant properties. The co-localization of OsRc and OsCHS2 on chromosomes indicates molecular targets for breeding. The high heritability of all antioxidant traits, along with the identified genes, provides a strong basis for developing molecular markers. These markers could be used to select high-antioxidant rice varieties at the seedling stage precisely. Consequently, it is beneficial to accelerate the breeding process for improving superior rice cultivars with high nutritional quality. Declarations Acknowledgements This work was supported by The 100 th Anniversary Chulalongkorn University Fund for Doctoral Scholarship, The 90 th Anniversary of Chulalongkorn University Fund (Ratchadaphiseksomphot Endowment Fund), the Agricultural Research Development Agency (a public organization) (PRP6105020640), and the Program in Biotechnology of the Faculty of Science, Chulalongkorn University, Thailand and the Department of Botany Faculty of Science Chulalongkorn University, Thailand. The authors thank the Center of Excellence in Environment and Plant Physiology (Chulalongkorn University) for providing the rice grains and SNP genotyping data in this study. Author contributions ST: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization. PT: Writing - Review & Editing, Visualization. TB: Methodology, Formal analysis, Resources, Supervision. SC: Resources, Writing - Review & Editing, Supervision. MP: Methodology, Writing - Original Draft, Writing - Review & Editing, Supervision. CP: Conceptualization, Methodology, Formal analysis, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization, Supervision. WK: Conceptualization, Methodology, Formal analysis, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization, Supervision, Project administration. All authors have read and approved the final manuscript. Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials All data generated or analyzed during this study are provided within the article. Additional inquiries can be addressed to the corresponding author upon reasonable request. Competing interest The authors declare no competing interests. References Allard, R. (1960). Principles of Plant Breeding. John Wiley and Sons, New York, USA. 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K., Zhang, Y., Jin, Y., & Pan, B. (2018). Phytochemical profile of brown rice and its nutrigenomic implications. Antioxidants 7:71. https://doi.org/10.3390/antiox7060071 Reddy, A. R., Scheffler, B., Madhuri, G., Srivastava, M. N., Kumar, A., Sathyanarayanan, P. V., Nair, S., & Mohan, M. (1996). Chalcone synthase in rice ( Oryza sativa L.): detection of the CHS protein in seedlings and molecular mapping of the chs locus. Plant Mol Biol 32:735-743. https://doi.org/10.1007/bf00020214 Sakamoto, W., Ohmori, T., Kageyama, K., Miyazaki, C., Saito, A., Murata, M., Noda, K., & Maekawa, M. (2001). The Purple leaf (Pl) locus of rice: the Pl(w) allele has a complex organization and includes two genes encoding basic helix-loop-helix proteins involved in anthocyanin biosynthesis. Plant Cell Physiol 42:982-991. https://doi.org/10.1093/pcp/pce128 Sakulsingharoj, C., Inta, P., Sukkasem, R., Pongjaroenkit, S., Chowpongpang, S., & Sangtong, V. (2014). Overexpression of OSB2 gene in transgenic rice up-regulated expression of structural genes in anthocyanin biosynthesis pathway. Thai J Genet:173–182. https://doi.org/10.14456/tjg.2014.24 Sanghamitra, P., Sah, R. P., Bagchi, T. B., Sharma, S. G., Kumar, A., Munda, S., & Sahu, R. K. (2018). Evaluation of variability and environmental stability of grain quality and agronomic parameters of pigmented rice ( O. sativa L.). J Food Sci Technol 55:879-890. https://doi.org/10.1007/s13197-017-2978-9 Shao, Y., Jin, L., Zhang, G., Lu, Y., Shen, Y., & Bao, J. (2011). Association mapping of grain color, phenolic content, flavonoid content and antioxidant capacity in dehulled rice. Theor Appl Genet 122:1005-1016. https://doi.org/10.1007/s00122-010-1505-4 Shen, Y., Jin, L., Xiao, P., Lu, Y., & Bao, J. (2009). Total phenolics, flavonoids, antioxidant capacity in rice grain and their relations to grain color, size and weight. J Cereal Sci 49:106-111. Shih, C. H., Chu, H., Tang, L. K., Sakamoto, W., Maekawa, M., Chu, I. K., Wang, M., & Lo, C. (2008). Functional characterization of key structural genes in rice flavonoid biosynthesis. Planta 228:1043-1054. https://doi.org/10.1007/s00425-008-0806-1 Thaworn, S., Chadchawan, S., Paliyavuth, C., & Kasettranan, W. (2021). Clustering of white, red and purple rice cultivars according to their total phenolic content, total flavonoid content and antioxidant capacity in their grains. Agr Nat Resour 55:89-97. ttps://doi.org/10.34044/j.anres.2021.55.1.12 Tian, S., Nakamura, K., & Kayahara, H. (2004). Analysis of phenolic compounds in white rice, brown rice, and germinated brown rice. J Agric Food Chem 52:4808-4813. https://doi.org/10.1021/jf049446f Tomjai, P., Paliyavuth, C., Chadchawan, S., Tiyayon, P., Anantasri, P., & Kasettranan, W. (2025). Validation of molecular markers for breeding high antioxidant traits in Thai rice. Plant Breed Biotech 13:176-195. https://doi.org/10.9787/PBB.2025.13.176 Turner, S. D. (2014). qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. bioRxiv:005165. https://doi.org/10.1101/005165 Wang, C., & Shu, Q. (2007). Fine mapping and candidate gene analysis of purple pericarp gene Pb in rice ( Oryza sativa L.). Chin Sci Bull 52:3097-3104. https://doi.org/10.1007/s11434-007-0472-x Xu, F., Bao, J., Kim, T.-S., & Park, Y.-J. (2016). Genome-wide association mapping of polyphenol contents and antioxidant capacity in whole-grain rice. J Agric Food Chem 64:4695-4703. https://doi.org/10.1021/acs.jafc.6b01289 Xu, W., Dubos, C., & Lepiniec, L. (2015). Transcriptional control of flavonoid biosynthesis by MYB-bHLH-WDR complexes. Trends Plant Sci 20:176-185. https://doi.org/10.1016/j.tplants.2014.12.001 Yang, X., Xia, X., Zeng, Y., Nong, B., Zhang, Z., Wu, Y., Xiong, F., Zhang, Y., Liang, H., Deng, G., & Li, D. (2018). Identification of candidate genes for gelatinization temperature, gel consistency and pericarp color by GWAS in rice based on SLAF-sequencing. PLOS ONE 13:e0196690. https://doi.org/10.1371/journal.pone.0196690 Zhang, Z., Ersoz, E., Lai, C.-Q., Todhunter, R. J., Tiwari, H. K., Gore, M. A., Bradbury, P. J., Yu, J., Arnett, D. K., Ordovas, J. M., & Buckler, E. S. (2010). Mixed linear model approach adapted for genome-wide association studies. Nat Genet 42:355-360. https://doi.org/10.1038/ng.546 Zhao, J., Yang, W., Zhang, S., Yang, T., Liu, Q., Dong, J., Fu, H., Mao, X., & Liu, B. (2018). Genome-wide association study and candidate gene analysis of rice cadmium accumulation in grain in a diverse rice collection. Rice 11:61. https://doi.org/10.1186/s12284-018-0254-x Zheng, J., Wu, H., Zhu, H., Huang, C., Liu, C., Chang, Y., Kong, Z., Zhou, Z., Wang, G., Lin, Y., & Chen, H. (2019). Determining factors, regulation system, and domestication of anthocyanin biosynthesis in rice leaves. New Phytol 223:705-721. https://doi.org/10.1111/nph.15807 Zhou, X., & Stephens, M. (2012). Genome-wide efficient mixed-model analysis for association studies. Nat Genet 44:821-824. https://doi.org/10.1038/ng.2310 Zhou, X., & Stephens, M. (2014). Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nat Methods 11:407-409. https://doi.org/10.1038/nmeth.2848 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 11 May, 2026 Reviews received at journal 11 May, 2026 Reviews received at journal 09 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers agreed at journal 31 Mar, 2026 Reviewers invited by journal 23 Mar, 2026 Editor assigned by journal 17 Mar, 2026 Submission checks completed at journal 17 Mar, 2026 First submitted to journal 16 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9139863","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":610595778,"identity":"389838df-c500-407f-af91-0a82d6741c70","order_by":0,"name":"Saranyu Thaworn","email":"","orcid":"","institution":"Chulalongkorn University","correspondingAuthor":false,"prefix":"","firstName":"Saranyu","middleName":"","lastName":"Thaworn","suffix":""},{"id":610595779,"identity":"e89523bd-5eaa-4b75-8378-557c3e71f499","order_by":1,"name":"Phanomsak Tomjai","email":"","orcid":"","institution":"Chulalongkorn University","correspondingAuthor":false,"prefix":"","firstName":"Phanomsak","middleName":"","lastName":"Tomjai","suffix":""},{"id":610595780,"identity":"6dee1d81-00d2-4d2c-bd3a-5a1decebb260","order_by":2,"name":"Teerapong Buaboocha","email":"","orcid":"","institution":"Chulalongkorn University","correspondingAuthor":false,"prefix":"","firstName":"Teerapong","middleName":"","lastName":"Buaboocha","suffix":""},{"id":610595781,"identity":"0ebfe312-90bc-4595-884e-5047f8fa60c2","order_by":3,"name":"Supachitra Chadchawan","email":"","orcid":"","institution":"Chulalongkorn University","correspondingAuthor":false,"prefix":"","firstName":"Supachitra","middleName":"","lastName":"Chadchawan","suffix":""},{"id":610595782,"identity":"ed58a774-9e48-49eb-86b0-14dccb793aa7","order_by":4,"name":"Monnat Pongpanich","email":"","orcid":"","institution":"Chulalongkorn University","correspondingAuthor":false,"prefix":"","firstName":"Monnat","middleName":"","lastName":"Pongpanich","suffix":""},{"id":610595783,"identity":"12d57bb2-867c-4fcf-b5bb-528fbdfccff1","order_by":5,"name":"Chanita Paliyavuth","email":"","orcid":"","institution":"Chulalongkorn University","correspondingAuthor":false,"prefix":"","firstName":"Chanita","middleName":"","lastName":"Paliyavuth","suffix":""},{"id":610595784,"identity":"3ecf6d03-a1ea-4928-bf37-e01459ca3b2b","order_by":6,"name":"Waraluk Kasettranan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIie3PsQrCMBCA4SuFugRcU4T6ClcKVUH0VVKEuDg4ieCgIujiAwj6MIEOWaquHZVCJwfBxdFUEZzSjg75AyEcfHABMJn+MBTqugADlAsHYPyZ2qWEqeNvhSL4HlqrSiSgrCqRx+s9WnMI3SzPCPagvhG1bKwhYTIMaLQeQbfBWwHBAdCEWaudjggOikwVGTkNgurjqVqM6Mg5t58FCd2kIHNolpF+yp33YgElBYkBy0gnzZ02O3Hib3noHlASP4mWex3BM7fT+2TgoYxzepvOPE/G4qEj38jPw1pUACaTyWTS9QJ0BUa3OFtjjAAAAABJRU5ErkJggg==","orcid":"","institution":"Chulalongkorn University","correspondingAuthor":true,"prefix":"","firstName":"Waraluk","middleName":"","lastName":"Kasettranan","suffix":""}],"badges":[],"createdAt":"2026-03-16 15:40:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9139863/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9139863/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105403156,"identity":"3e17b480-3ed4-4286-8a1a-feb616a1779f","added_by":"auto","created_at":"2026-03-25 15:42:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91065,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency distributions of total phenolic content (A), total flavonoid content (B), and antioxidant capacity (C) of 159 Thai rice cultivars\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9139863/v1/f512ed8ee448fbc0cef52633.png"},{"id":105403152,"identity":"8cb21d3e-b363-48f4-8225-d661aa9cdecf","added_by":"auto","created_at":"2026-03-25 15:42:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":96425,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of antioxidant compound and activity. (A) correlation coefficient (\u003cem\u003er\u003c/em\u003e) between TPC and TFC (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01), (B) correlation between TPC and AC (\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01), (C) correlation between TFC and AC (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9139863/v1/37538a93b933d785d6ea9eab.png"},{"id":105403161,"identity":"22766614-46b4-4826-86cc-59f022a2e5bd","added_by":"auto","created_at":"2026-03-25 15:42:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":230199,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan and Q-Q plots of the association between promoter regions and total phenolic content (A), total flavonoid content (B), and antioxidant capacity (C). Horizontal dashed line indicates the significant threshold at -log\u003csub\u003e10\u003c/sub\u003e (\u003cem\u003ep\u003c/em\u003e) ≥ 6.29\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9139863/v1/52ca48463fa6d26a7772f3bd.png"},{"id":105403158,"identity":"e80d2eea-71ac-4cdd-8636-2f84333a3df2","added_by":"auto","created_at":"2026-03-25 15:42:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":215489,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan and Q-Q plot of the association between exonic regions and total phenolic content (A), total flavonoid content (B) antioxidant capacity (C). Horizontal dashed line indicates the significant threshold at -log\u003csub\u003e10\u003c/sub\u003e (\u003cem\u003ep\u003c/em\u003e) ≥ 6.35.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9139863/v1/3cce3f05c81c2875fbed6044.png"},{"id":105403155,"identity":"608d496d-5f42-443c-96e8-e5746cf1c8d4","added_by":"auto","created_at":"2026-03-25 15:42:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":183053,"visible":true,"origin":"","legend":"\u003cp\u003eDistributions of 38 loci for two or three antioxidant traits with significant SNPs in promoter and exonic regions on chromosomes 1, 2, 3, 4, 5, 6, 7, and 8. Green lines on the right side indicate consensus QTLs. Red lines denote the position of candidate genes within prioritized QTL\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9139863/v1/d5e2966bb404974a289ab850.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome-wide association study of antioxidant compounds and antioxidant activity in a panel of Thai rice cultivars","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eRice (\u003cem\u003eOryza sativa\u003c/em\u003e L.) is the main food for the global population, particularly in Asia. While polished white rice is the most widely consumed, it is nutritionally deficient due to the milling process, which removes essential vitamins, minerals, and other functional compounds. This lack of nutrients can cause some chronic diseases (Dipti et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In contrast, colored rice cultivars, such as those with red, purple, and black pericarps, are substantial in phytochemicals, especially phenolic compounds. These compounds are significantly associated with a reduced risk of chronic diseases such as coronary heart disease, type-2 diabetes, and cancers (Butsat \u0026amp; Siriamornpun, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Chaichana, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ravichanthiran et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tian et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The antioxidant compounds in rice are primarily phenolic compounds, which include several flavonoid classes such as chalcones, flavones, flavonols, dihydroflavonols, proanthocyanidins, and anthocyanidins. These compounds are derived from the general phenylpropanoid pathway, followed by the flavonoid pathway. Especially, Malonyl CoA and \u003cem\u003ep\u003c/em\u003e-coumaroyl CoA are the substrates of the flavonoid pathway (Shih et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The flavonoid synthetic pathway involves many enzymes, each encoded by a specific structural gene. Major enzymes in this pathway include chalcone synthase (CHS), chalcone isomerase (CHI), flavanone 3-hydroxylase (F3H), flavonol synthase (FLS), flavonoid 3\u0026rsquo;-hydroxylase (F3\u0026prime;H), dihydroflavonol 4-reductase (DFR), leucoanthocyanidin dioxygenase (ANS), UDP-glucosyl transferase (UGT), leucoanthocyanidin reductase (LAR), anthocyanidin reductase (ANR), flavone synthase II (FNSII), O-methyltransferase (OMT), flavanone 2-hydroxylase(F2H), C-glucosyl transferase (CGT) and dehydratase (DH) (Park et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The expression of these structural genes associated with the flavonoid pathway is controlled by regulatory genes, which belong to the MYB, basic helix-loop-helix (bHLH), and WD40 repeat protein (WDR) family transcription factors (Xu et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral studies have identified genetic determinants underlying antioxidants in rice for many decades. For example, Reddy et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) detected the CHS protein, and the \u003cem\u003echs\u003c/em\u003e locus was mapped to the centromeric region of rice chromosome 11. Another study reported that the \u003cem\u003eOsRd\u003c/em\u003e and \u003cem\u003eOsRc\u003c/em\u003e genes were involved in proanthocyanidin biosynthesis in rice. The \u003cem\u003eOsRd\u003c/em\u003e gene on chromosome 1 encodes the DFR protein, the first enzyme in the proanthocyanidin pathway. In contrast, the \u003cem\u003eOsRc\u003c/em\u003e gene on chromosome 7 encodes a bHLH transcription factor, and the non-deleted 14-bp region contributes to the accumulation of proanthocyanidin in red rice (Furukawa et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Similarly, the \u003cem\u003eOSB1\u003c/em\u003e and \u003cem\u003eOSB2\u003c/em\u003e genes, encoding MYC-type bHLH transcription factors, were identified at the \u003cem\u003ePurple leaf (Pl)\u003c/em\u003e locus on rice chromosome 4 (Sakamoto et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). These genes were similar to the maize \u003cem\u003eR/B\u003c/em\u003e family, which regulates anthocyanin pigmentation. The \u003cem\u003eOSB1\u003c/em\u003e gene in black rice is functional, resulting in anthocyanin pigmentation. In contrast, an inactive \u003cem\u003eOSB1\u003c/em\u003e gene with a 2-bp insertion in the white and red varieties causes a frameshift mutation at the C-terminus, leading to premature termination of the regulatory protein. As a result, white and red pericarp rice could not synthesize anthocyanin (Lim \u0026amp; Ha, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wang \u0026amp; Shu, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Similarly, the expression of \u003cem\u003eOSB2\u003c/em\u003e genes is restricted to black rice varieties such as Thai black rice varieties (Inta et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and it is known to up-regulate the expression of structural genes involved in anthocyanin biosynthesis in rice (Sakulsingharoj et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). While Zheng et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) revealed that regulatory genes such as \u003cem\u003eOsC1\u003c/em\u003e and \u003cem\u003eOsRb\u003c/em\u003e are activated in the anthocyanin biosynthetic pathway of rice leaves, these genes are tissue-specific and do not influence the anthocyanin biosynthetic pathway in the rice pericarp. Additionally, \u003cem\u003eOsP1\u003c/em\u003e and the ternary MYB-bHLH-WDR complex regulate some structural genes in anthocyanin biosynthesis.\u003c/p\u003e \u003cp\u003eGenome-wide association study (GWAS) has become a powerful tool for detecting candidate genes and developing molecular markers for marker-assisted selection (MAS). This approach, which uses single-nucleotide polymorphism (SNP) markers, provides a high-resolution method for identifying genes associated with traits. Previous studies have used GWAS to investigate the genetic basis and genes for antioxidant traits in rice. For instance, Shao et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) conducted the association mapping in a collection of 461 rice accessions, including 361 white, 50 red, and six purple pericarp rice. The results revealed that the \u003cem\u003eOsRc\u003c/em\u003e and \u003cem\u003eOSB1\u003c/em\u003e markers, located on chromosomes 4 and 7, respectively, were associated with total phenolic content (TPC), total flavonoid content (TFC), antioxidant capacity (AC), and color parameters. Moreover, Xu et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) performed a GWAS on red- and white-pericarp rice. A total of 30 QTLs associated with TPC, TFC, and AC were found on chromosomes 1, 6, 7, 9, and 11. These results highlight that the \u003cem\u003eOsRc\u003c/em\u003e gene was detected for all phenolic traits and \u003cem\u003eOsABCF6\u003c/em\u003e (\u003cem\u003eLOC_Os11g39020\u003c/em\u003e) was identified as a potential new gene related to ferulic acid in rice grain. Taken together, these studies demonstrate that GWAS is an effective approach for identifying genes underlying antioxidant traits in rice. Furthermore, significant GWAS-derived SNPs provide valuable genetic resources for developing molecular markers to support marker-assisted selection (MAS) for selecting high antioxidant traits. The previous studies of Zhao et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Li et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) showed the validation for the new molecular markers of cadmium accumulation and alkalinity tolerance, respectively.\u003c/p\u003e \u003cp\u003eAlthough pericarp color is commonly used as an indirect indicator of antioxidant potential, antioxidant content in rice grains is a quantitatively inherited trait controlled by multiple structural and regulatory genes. Reliance solely on visible color may overlook underlying genetic variation influencing antioxidant accumulation. Therefore, dissecting the genetic architecture of antioxidant traits is essential for enabling more precise selection strategies in rice breeding. In this study, we applied GWAS to a panel of 159 Thai rice cultivars to identify loci and candidate genes associated with total phenolic content, total flavonoid content, and antioxidant capacity. The identified loci provide a foundation for developing molecular markers to facilitate marker-assisted selection for improved antioxidant properties.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant material and phenotyping\u003c/h2\u003e \u003cp\u003eA panel of 159 local Thai rice cultivars was used in this study, including 150 white pericarp rice, four red pericarp rice, and five purple pericarp rice cultivars. These cultivars were employed to measure two total antioxidant compounds (TPC and TFC) and antioxidant activity (AC) from rice grains. All phenotypic data were recorded in our previous study (Thaworn et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics, correlation analysis, and a one-way analysis of variance (ANOVA) were performed on the phenotypic data using the IBM SPSS Statistics version 22 software (IBM, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The ANOVA was conducted with 159 rice cultivars and three replications (r). The general ANOVA output table is exemplified in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These values were used to calculate the genetic parameters, including phenotypic variance (σ\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e), genotypic variance (σ\u003csub\u003eg\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e), and environmental variance (σ\u003csub\u003ee\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e), which were estimated from the ANOVA mean squares. The phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) were calculated following the method of Miller et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1958\u003c/span\u003e). Broad-sense heritability (\u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) was calculated using Allard\u0026rsquo;s method (Allard, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1960\u003c/span\u003e). The formulas used for these calculations were presented in equations (\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) to (\u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral ANOVA for the antioxidant compounds and capacity between 159 rice cultivars\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSource of variation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Square\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBetween Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMSG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithin Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enr-n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMSE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enr-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${\\sigma}_{g}^{2}=\\left(\\frac{MSG-MSE}{r}\\right)$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${\\sigma}_{p}^{2}={\\sigma}_{g}^{2}+{\\sigma}_{e}^{2}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e \u003cdiv id=\"Equ3\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$GVC=\\frac{\\sqrt{{\\sigma}_{g}^{2}}}{\\overline{x}}\\times100$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e \u003cdiv id=\"Equ4\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$PVC=\\frac{\\sqrt{{\\sigma}_{p}^{2}}}{\\overline{x}}\\times100$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e \u003cdiv id=\"Equ5\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$${H}^{2}=\\frac{{\\sigma}_{g}^{2}}{{\\sigma}_{p}^{2}}\\times100$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the equations, MSG and MSE indicate the mean square between genotypes and the residual mean square (within-genotype variance) from the ANOVA table, respectively. r is the number of replications, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\overline{\\text{x}}\\)\u003c/span\u003e\u003c/span\u003e is the grand mean of the antioxidant trait.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSNPs genotyping\u003c/h3\u003e\n\u003cp\u003eThe SNP maker data for the 159 Thai rice cultivars were kindly provided by the Center of Excellence in Environment and Plant Physiology, Department of Botany, Faculty of Science, Chulalongkorn University. The SNP filtering and quality procedure were conducted following the protocol of Lekklar et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Briefly, high-quality SNP makers were obtained by filtering out SNPs with minor allele frequency (MAF)\u0026thinsp;\u0026lt;\u0026thinsp;5% and missingness\u0026thinsp;\u0026gt;\u0026thinsp;40%. After filtering, a total of 209,594 high-quality SNPs \u0026mdash;comprising 96,480 promoter-region SNPs and 113,114 exonic SNPs distributed across the 12 rice chromosomes\u0026mdash;were retained for GWAS analysis.\u003c/p\u003e\n\u003ch3\u003eGWAS for antioxidant traits and candidate gene identification\u003c/h3\u003e\n\u003cp\u003eGWAS was carried out using a set of high-quality SNP markers and antioxidant traits, including TPC, TFC, and AC. A genome-wide association mapping was conducted using the mixed linear model (MLM) through GEMMA software, with genotype PCA included as covariates (Zhang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zhou \u0026amp; Stephens, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Promoter and exonic SNPs were analyzed separately because only these functional regions were retained for GWAS. The Bonferroni multiple testing correction was therefore applied within each SNP category. The thresholds were set at -log\u003csub\u003e10\u003c/sub\u003e (\u003cem\u003ep\u003c/em\u003e)\u0026thinsp;\u0026ge;\u0026thinsp;6.29 for promoter regions and -log\u003csub\u003e10\u003c/sub\u003e (\u003cem\u003ep\u003c/em\u003e)\u0026thinsp;\u0026ge;\u0026thinsp;6.35 for exonic regions. Manhattan and Q-Q plot of GWAS results were generated using the qqman package in R (Turner, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSignificant SNPs were annotated in their position on the rice genome using the MSU Rice Genome Annotation Project version V7.0. Significant SNPs associated with at least two antioxidant traits were prioritized. SNPs within the LD-decay distance of 200 kb (mean \u003cem\u003er\u0026sup2;\u003c/em\u003e = 0.1) were merged into a single QTL (Lekklar et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Only loci supported by at least two significant SNPs were considered as QTLs and overlapping LD-based regions across traits were defined as consensus QTLs. Candidate genes were identified using two complementary approaches: (i) genes located within the LD-defined QTL intervals, and (ii) genes located within 200 kb upstream and downstream of selected significant SNPs. Gene annotations were obtained from the MSU Rice Genome Annotation Project (version 7.0). Phenotypic variation (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e) was calculated from significant SNPs by using single marker analysis in R (R Core Team, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and QTLs with \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e more than 10% were considered as the candidate QTLs.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePhenotypic data of 159 Thai rice cultivars\u003c/h2\u003e \u003cp\u003eTPC, TFC, and AC of 159 Thai rice cultivars were summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. TPC value ranged from 0.86 to 5.79 mg GAE/g, with an average of 1.43 mg GAE/g. TFC had an average of 3.30 mg RE/g and ranged from 2.03 to 11.13 mg RE/g. The AC value ranged from 78.23 to 2301.92 \u0026micro;g AEAC/g, with an average of 268.03 \u0026micro;g AEAC/g. The data distributions of TPC, TFC, and AC did not follow normal distributions using the Kolmogorov-Smirnov test (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). All traits showed skewed distributions and separated into two distinct groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which may result from this population comprising 150 white- and nine colored-pericarps of rice cultivars.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCorrelations among antioxidant traits were analyzed by using Pearson\u0026rsquo;s correlation analysis. The correlation coefficient (r) showed significant, strong positive correlations among TPC, TFC, and AC (r\u0026thinsp;=\u0026thinsp;0.901‒0.963, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), indicating that the antioxidant traits in rice grain are highly related.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean\u0026plusmn;standard deviation (SD), PCV, GCV, and H\u003csup\u003e2\u003c/sup\u003e for TPC, TFC, and AC of 159 Thai rice cultivars.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePCV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGCV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.43 \u0026plusmn; 0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86\u0026ndash;5.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.30 \u0026plusmn; 1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.03\u0026ndash;11.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e77.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e268.03 \u0026plusmn; 342.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.32-2301.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e134.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e124.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e86.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eANOVA analysis indicated that the effects of rice cultivars on TPC, TFC, and AC were highly significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The genetic parameters were estimated based on the values in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The results revealed high broad-sense heritability (\u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) for TPC (80.90%), TFC (77.23%), and AC (86.02%), indicating that these traits were more heritable. In addition, all antioxidant traits showed high GCV values, ranging from 42.29% to 124.73%. The small differences between the PCV and GCV for all traits are \u0026lt;\u0026thinsp;10%, suggesting that trait variation is predominantly genetic, with negligible environmental influence.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOne-way ANOVA results for TPC, TFC, and AC testing, showing the difference between 159 rice cultivars\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSum of Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBetween Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e285.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithin Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e327.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBetween Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1015.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithin Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e182.568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1198.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBetween Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55846017.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e353455.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithin Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5776740.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18165.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61622758.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eGWAS for antioxidant traits\u003c/h2\u003e \u003cp\u003eTo dissect the genetic basis of antioxidant traits, we performed GWAS using the MLM model. The analyses were conducted on the 159 rice cultivars with 209,594 high-quality SNPs in promoter and exonic regions. GWAS identified a total of 158 significant SNPs across 10 chromosomes. Significant SNPs were located in promoter regions were detected on chromosomes 1\u0026ndash;9 and 12 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In contrast, significant SNPs within exonic regions were identified on chromosomes 1, 2, 4, 6, 7, and 8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). As antioxidant traits showed strong positive correlations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e); the QTL or underlying genes controlling these traits are likely shared or closely linked in the genome. Accordingly, we prioritized the 108 SNPs associated with at least two antioxidant traits. To detect the major QTLs linked to antioxidant traits, we focused on loci supported by multiple identified SNPs. Using this criterion, 38 QTLs associated with antioxidants traits were detected on chromosomes 1, 2, 3, 4, 5, 6, 7, and 8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Chromosomes 2 and 3 each harbored only a single associated SNP and were therefore excluded from QTL designation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In addition, QTLs located with the same LD-based region were defined as a single locus. Therefore, eight consensus QTLs for antioxidant traits were identified, including QTAN1_1, QTAN4_1, QTAN5_1, QTAN5_2, QTAN6_1, QTAN7_1, QTAN7_2, and QTAN8_1 on chromosomes 1, 4, 5, 6, 7 and 8, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Collectively, these QTLs explained 10.06 to 35.81% of phenotypic variations (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) of the antioxidant traits (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQTL and candidate gene identified by GWAS for TPC, TFC, and AC.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQTL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChr.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLocus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCandidate gene\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQTAN1_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOC_Os01g32890.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTPC\u003c/p\u003e \u003cp\u003eTFC\u003c/p\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.73E-09\u003c/p\u003e \u003cp\u003e6.50E-09\u003c/p\u003e \u003cp\u003e9.31E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.21\u003c/p\u003e \u003cp\u003e33.23\u003c/p\u003e \u003cp\u003e25.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ehelix-loop-helix DNA-binding domain (LOC_Os01g33400.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQTAN4_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOC_Os04g39600.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTPC\u003c/p\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.45E-08\u003c/p\u003e \u003cp\u003e4.73E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.41\u003c/p\u003e \u003cp\u003e13.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMYB family transcription factor (LOC_Os04g39470.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQTAN5_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOC_Os05g44190.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTPC\u003c/p\u003e \u003cp\u003eTFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.92E-07\u003c/p\u003e \u003cp\u003e3.72E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.25\u003c/p\u003e \u003cp\u003e13.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWD domain, G-beta repeat domain containing protein (LOC_Os05g44320.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQTAN5_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOC_Os05g51119.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTPC\u003c/p\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.11E-09\u003c/p\u003e \u003cp\u003e7.17E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.87\u003c/p\u003e \u003cp\u003e29.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ehelix-loop-helix DNA-binding protein (LOC_Os05g50900.1)\u003c/p\u003e \u003cp\u003eMyb transcription factor (LOC_Os05g51160.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQTAN6_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOC_Os06g08550.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTPC\u003c/p\u003e \u003cp\u003eTFC\u003c/p\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.17E-08\u003c/p\u003e \u003cp\u003e6.62E-09\u003c/p\u003e \u003cp\u003e4.48E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.93\u003c/p\u003e \u003cp\u003e13.79\u003c/p\u003e \u003cp\u003e10.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMYB family transcription factor (LOC_Os06g08290.1)\u003c/p\u003e \u003cp\u003eBHLH transcription factor (LOC_Os06g08500.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQTAN7_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOC_Os07g11100.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTPC\u003c/p\u003e \u003cp\u003eTFC\u003c/p\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.87E-11\u003c/p\u003e \u003cp\u003e2.45E-11\u003c/p\u003e \u003cp\u003e1.88E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.34\u003c/p\u003e \u003cp\u003e22.19\u003c/p\u003e \u003cp\u003e24.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eOsRc\u003c/em\u003e (LOC_Os07g11020.1)\u003c/p\u003e \u003cp\u003e\u003cem\u003eOsCHS2\u003c/em\u003e chalcone synthase (LOC_Os07g11440.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQTAN7_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOC_Os07g44440.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTPC\u003c/p\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.63E-08\u003c/p\u003e \u003cp\u003e7.24E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.86\u003c/p\u003e \u003cp\u003e23.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003emyb-related protein Hv33 (LOC_Os07g44090.1)\u003c/p\u003e \u003cp\u003eWD40-like Beta Propeller Repeat family protein (LOC_Os07g44410.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQTAN8_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOC_Os08g33200.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTPC\u003c/p\u003e \u003cp\u003eTFC\u003c/p\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.23E-07\u003c/p\u003e \u003cp\u003e2.37E-07\u003c/p\u003e \u003cp\u003e4.07E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.47\u003c/p\u003e \u003cp\u003e35.81\u003c/p\u003e \u003cp\u003e32.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMYB family transcription factor (LOC_Os08g33050.1)\u003c/p\u003e \u003cp\u003eMYB family transcription factor (LOC_Os08g33150.1)\u003c/p\u003e \u003cp\u003eMYB family transcription factor (LOC_Os08g33660.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe frequency distribution of 159 Thai rice panels was skewed. It was separated into two distinct groups for all antioxidant traits (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which may result from this population comprising 150 white- and nine colored-pericarp rice cultivars. Multiple structural and regulatory genes control TPC, TFC, and AC (Park et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tian et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Theoretically, the data is explained by multiple genes and should demonstrate a continuous frequency distribution. Additionally, in this study, we suggested that TPC, TFC, and AC were associated with the color of the pericarp of rice grains (Shen et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Thus, the distributions showed the two different groups of white and red pericarp rice.\u003c/p\u003e \u003cp\u003eAll antioxidant traits showed strong positive correlations. The results are in agreement with Pramai and Jiamyangyuen (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Shen et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e)who reported similar correlations among TPC, TFC, and AC in white, red, and purple pericarp rice. These results suggest that the antioxidant traits in rice grain are highly interrelated. Therefore, selecting for one antioxidant trait may simultaneously improve other antioxidant traits. This is important information for improving and breeding rice grain quality.\u003c/p\u003e \u003cp\u003eIn this study, we estimated high heritability (\u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) and GCV for TPC, TFC, and AC, suggesting great heritability and substantial genetic diversity in this population. The minimal gap between PCV and GCV indicates that genetic factors are the main effects in all traits, with slight environmental effects (Mehboob et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Our results were consistent with Sanghamitra et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), who also reported that the high \u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e and small differences between the PCV and GCV of antioxidant traits in rice. These findings highlight that these antioxidant traits can be effectively targeted for selection to improve rice antioxidant quality in breeding programs.\u003c/p\u003e \u003cp\u003eGWAS detected eight consensus QTLs, which annotated 14 potential candidate genes, including seven MYB family transcription factors, three helix-loop-helix (HLH) family transcriptional regulatory proteins, two WD-repeat domain proteins, and two genes (\u003cem\u003eOsRc\u003c/em\u003e and \u003cem\u003eOsCHS2\u003c/em\u003e). The helix-loop-helix DNA-binding domains, MYB family transcription factors, and WD domain are regulatory genes (Massari \u0026amp; Murre, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). However, these genes remain understudied to regulate the structural genes of the flavonoid biosynthetic pathway in rice (Yang et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In this study, we prioritized a major QTL on chromosome 7, which linked to the regulatory gene \u003cem\u003eOsRc\u003c/em\u003e and the structural gene \u003cem\u003eOsCHS2\u003c/em\u003e genes (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The \u003cem\u003eOsRc\u003c/em\u003e gene encodes the bHLH transcription factor that regulates proanthocyanidin biosynthesis and is responsible for red pericarp color in rice (Furukawa et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). As demonstrated by Furukawa et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), the \u003cem\u003eRc\u003c/em\u003e allele in red rice led to the accumulation of proanthocyanidin, whereas the \u003cem\u003erc\u003c/em\u003e allele in white rice did not. Additionally, in this region, we identified \u003cem\u003eOsCHS2\u0026mdash;\u003c/em\u003ethe key gene of the flavonoid biosynthesis pathway\u0026mdash; encoding chalcone synthase, which is the first enzyme of the flavonoid biosynthesis pathway (Shih et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The co-localization of these two genes indicates genetic evidence for direct functional association in controlling antioxidant synthesis. Shao et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) revealed that \u003cem\u003eOsRc\u003c/em\u003e and \u003cem\u003eOSB1\u003c/em\u003e markers were associated with TPC, TFC, AC, and color parameters in a diverse panel of pigmented rice. GWAS results from Xu et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) demonstrated that 30 significant QTLs on chromosomes 1, 6, 7, 9, and 11 associated with TPC, TFC, and AC traits in whole rice grains of red and white pericarp rice. A locus on chromosome 7 was associated with TPC and antioxidant capacities and located within the \u003cem\u003eOsRc\u003c/em\u003e gene (Xu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Our findings also identified the QTAN7_1 locus on chromosome 7\u0026mdash;containing \u003cem\u003eOsRc\u003c/em\u003e and \u003cem\u003eOsCHS2\u003c/em\u003e\u0026mdash;as a major locus for all antioxidant traits. This region was also detected consistently in previous studies (Purnama et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Shao et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In another Thai rice panel, Purnama et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) identified the \u003cem\u003eOsRc\u003c/em\u003e gene significantly associated with antioxidant traits, including TPC, TFC, DDPH, FRAP, and ABTS, using GWAS. The alleles of marker \u003cem\u003eOsRc\u003c/em\u003e, which are linked to the \u003cem\u003eOsRc\u003c/em\u003e gene, can distinguish between red rice with high TPC and ABTS (allele Hom4) and white rice with low antioxidant traits (allele Hom3) (Purnama et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In addition, a marker linked to the promoter of the \u003cem\u003eOsRc\u003c/em\u003e gene showed a high association with TPC (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;52.2%) and FRAP (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;43.0%) in the F\u003csub\u003e2\u003c/sub\u003e population derived from the cross of red and white Thai rice cultivars (Tomjai et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The consistent detection of this gene across independent studies underscores its important role in controlling the accumulation of phenolic compounds in colored rice. Furthermore, the marker linked to this gene serves as an effective genetic tool for MAS in rice breeding programs aimed at improving nutritional quality.\u003c/p\u003e \u003cp\u003eIn this study, the candidate genes for antioxidants were identified through GWAS, especially 14 transcription factor genes. Among these, two genes, \u003cem\u003eOsRc\u003c/em\u003e and \u003cem\u003eOsCHS2\u003c/em\u003e, have been previously studied for their expression levels and functions. Consequently, both genes are promising candidate genes for developing molecular markers. The specific function of the other 12 transcription factor genes remains unexplored, although they may be involved in antioxidant biosynthesis. Further research is required to elucidate their functions.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eGWAS is a powerful tool for identifying candidate genes for antioxidant traits in local Thai rice cultivars. We identified eight major QTLs and a set of 14 potential candidate genes, including the previously tested regulatory genes \u003cem\u003eOsRc\u003c/em\u003e and structural gene \u003cem\u003eOsCHS2\u003c/em\u003e. Moreover, 12 regulatory transcription factor genes remain understudied in their functions related to antioxidant properties. The co-localization of \u003cem\u003eOsRc\u003c/em\u003e and \u003cem\u003eOsCHS2\u003c/em\u003e on chromosomes indicates molecular targets for breeding. The high heritability of all antioxidant traits, along with the identified genes, provides a strong basis for developing molecular markers. These markers could be used to select high-antioxidant rice varieties at the seedling stage precisely. Consequently, it is beneficial to accelerate the breeding process for improving superior rice cultivars with high nutritional quality.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by The 100\u003csup\u003eth\u003c/sup\u003e Anniversary Chulalongkorn University Fund for Doctoral Scholarship, The 90\u003csup\u003eth\u003c/sup\u003e Anniversary of Chulalongkorn University Fund (Ratchadaphiseksomphot Endowment Fund), the Agricultural Research Development Agency (a public organization) (PRP6105020640), and the Program in Biotechnology of the Faculty of Science, Chulalongkorn University, Thailand and the Department of Botany Faculty of Science Chulalongkorn University, Thailand. The authors thank the Center of Excellence in Environment and Plant Physiology (Chulalongkorn University) for providing the rice grains and SNP genotyping data in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eST: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Writing - Review \u0026amp; Editing, Visualization. PT: Writing - Review \u0026amp; Editing, Visualization. TB: Methodology, Formal analysis, Resources, Supervision. SC: Resources, Writing - Review \u0026amp; Editing, Supervision. MP: Methodology, Writing - Original Draft, Writing - Review \u0026amp; Editing, Supervision. CP: Conceptualization, Methodology, Formal analysis, Data Curation, Writing - Original Draft, Writing - Review \u0026amp; Editing, Visualization, Supervision. WK: Conceptualization, Methodology, Formal analysis, Data Curation, Writing - Original Draft, Writing - Review \u0026amp; Editing, Visualization, Supervision, Project administration. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are provided within the article. Additional inquiries can be addressed to the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllard, R. (1960). Principles of Plant Breeding. John Wiley and Sons, New York, USA.\u003c/li\u003e\n\u003cli\u003eButsat, S., \u0026amp; Siriamornpun, S. (2010). Antioxidant capacities and phenolic compounds of the husk, bran and endosperm of Thai rice. Food Chem\u003cem\u003e \u003c/em\u003e119:606-613. https://doi.org/10.1016/j.foodchem.2009.07.001\u003c/li\u003e\n\u003cli\u003eChaichana, N. (2019). Analysis of nutritional composition, antioxidant activity, and callus induction of \u003cem\u003eOryza sativa \u003c/em\u003ecultivars Khumthan and Norprae. 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Nat Methods\u003cem\u003e \u003c/em\u003e11:407-409. https://doi.org/10.1038/nmeth.2848\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"euphytica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"euph","sideBox":"Learn more about [Euphytica](https://www.springer.com/journal/10681)","snPcode":"10681","submissionUrl":"https://submission.springernature.com/new-submission/10681/3","title":"Euphytica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Antioxidant, Candidate genes, GWAS, Heritability, Quantitative trait locus, Rice","lastPublishedDoi":"10.21203/rs.3.rs-9139863/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9139863/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAntioxidant traits in rice are quantitatively inherited and play an important role in improving nutritional quality. In this study, we investigated the genetic architecture of total phenolic content (TPC), total flavonoid content (TFC), and antioxidant capacity (AC, measured by ABTS) in a panel of 159 Thai rice cultivars using genome-wide association analysis. A total of 209,594 high-quality SNPs located in promoter and exonic regions were analyzed using a mixed linear model implemented in GEMMA. All three traits exhibited high broad-sense heritability and strong positive correlations. We identified 158 significant SNPs distributed across multiple chromosomes. Among these, 108 SNPs were associated with at least two antioxidant traits and were consolidated into 38 loci, resulting in eight consensus QTLs. These QTLs collectively explained 10.06\u0026ndash;35.81% of phenotypic variation. Within these regions, 14 transcription factor genes were prioritized as candidates, including previously characterized regulators of flavonoid biosynthesis, \u003cem\u003eOsRc\u003c/em\u003e and \u003cem\u003eOsCHS2\u003c/em\u003e, as well as additional MYB, bHLH, and WD-repeat transcription factors. Notably, a major QTL on chromosome 7 co-localized with \u003cem\u003eOsRc\u003c/em\u003e and the structural gene \u003cem\u003eOsCHS2\u003c/em\u003e. Our findings reveal the central role of known regulatory genes and highlight additional transcriptional regulators that may contribute to variation in antioxidant levels in Thai rice germplasm. These results provide genetic resources for marker-assisted selection to improve rice antioxidant properties.\u003c/p\u003e","manuscriptTitle":"Genome-wide association study of antioxidant compounds and antioxidant activity in a panel of Thai rice cultivars","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-25 15:41:17","doi":"10.21203/rs.3.rs-9139863/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-11T23:56:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T15:51:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-09T05:17:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316718638694852931108520398507101835999","date":"2026-04-06T05:12:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179376630517740818461281760934196591074","date":"2026-03-31T04:06:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-23T08:45:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-17T15:09:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-17T15:08:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Euphytica","date":"2026-03-16T15:26:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"euphytica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"euph","sideBox":"Learn more about [Euphytica](https://www.springer.com/journal/10681)","snPcode":"10681","submissionUrl":"https://submission.springernature.com/new-submission/10681/3","title":"Euphytica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"042b45a5-518c-4fb5-93a1-adad5d2e29d2","owner":[],"postedDate":"March 25th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-11T23:56:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T15:51:26+00:00","index":22,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T00:09:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-25 15:41:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9139863","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9139863","identity":"rs-9139863","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}