Dissecting Genetic Architecture of Flowering and Maturity Traits in Soybean Using GWAS in Indian Environment

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These traits are regulated by genetic networks controlling flowering time and environmental adaptation, making their genetic basis as an essential knowledge for breeders aiming to improve yield and adaptability. In this study, a Genome-Wide Association Study (GWAS) was conducted for Days to flowering (DTF), days to maturity (DTM) by using FarmCPU, BLINK and MLM model on 254 diverse soybean genotypes over four consecutive years (2019–2022) to dissect genetic architecture for flowering and maturity traits in an Indian Environment. Results In this study, GWAS identified 20 significant loci for days to flowering and maturity, among them 12 are new and 8 were previously reported loci. Among the 12 newly identified loci, a significant locus, Lee.Gm03-3 on chromosome 03, is associated with days to flowering and linked with SNP markers S3_46108324 and S3_46108342. We also identified key candidate genes for Lee.Gm03-3, include Glyma.03G227300 (circadian rhythm and photomorphogenesis, Phytochrome region) Glyma.03G225000 (circadian rhythm, gibberellic acid signaling, red/far-red light signaling), Glyma.03G219100 (cytokinin signaling, embryo sac development), and Glyma.03G226000 (meristem initiation). These genes are vital for light-response and developmental pathways. In addition, we also validated eight previously known genes E2, E4 , E9 , E11 , E10/FT4 , PRR7/Tof12 , Dt1 , and Dt2 that influence flowering and maturity in Indian environment. Conclusions This study advances understanding of the genetic basis underlying photoperiod sensitivity related genes for circadian rhythm and photomorphogenesis, gibberellic acid signaling, red/far-red light signaling in soybean and highlights potential targets for genetic improvement of flowering maturity duration and adaptability of soybean under Indian environment. Molecular Genetics GWAS Days to flowering Maturity Soybean Adaptation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Soybean ( Glycine max ) is an important global crop, valued for its high protein content in animal feed and as a source of vegetable oil. Soybean is a facultative short-day crop and has originated in higher latitudes of China, [ 1 , 2 , 3 , 4 , 5 , 6 ]. Soybeans flowers readily when the day length falls below the critical day length of a genotype. Genotypes adapted to higher latitudes (long days) enter into reproductive phase with little biomass when they are introduced into lower latitudes (short days). Conversely soybeans of lower latitude either do not enter into reproductive phase or delay flowering in higher latitudes. In spite of photosensitive response, the soybean is now cultivated widely throughout the globe. Although the adaptation of soybean ranges from 50°N to 35°S [ 7 ], its individual genotypes adapt to a narrow latitudinal band. Genotypes adapted to higher latitudes have evolved through null or hypo- mutations in genes responsible for sensing photoperiod (photo insensitivity) and helped genotypes flower under long day conditions of these latitudes. Genotypes adapted to lower latitudes have evolved through mutations in which delay in flowering occurs even under short day conditions (long juvenility) [ 8 ]. Varying combinations of photoperiodic and maturity alleles have evolved such a latitude specific genotype. To date, a number of major genetic loci, namely E1 [ 9 , 10 , 11 ] ), E2 [ 11 ], E3[ 12 ], E4 [13]), E5[14]), E6[15]), E7[ 16 ]), E8[17]), E9[18,19,20], E10 [18,19,20] ), E11[21], J[22] and several QTLs, such as Time of flowering 5 (Tof5) [ 23 ]), Tof8[ 24 ]), Tof9[ 25 ], Tof11/Gp11 and Tof12/Gp1/qFT121 [ 25 ], Tof13[ 26 ] ,Tof16[ 27 ], LJ16.1 and LJ16.2 [ 28 ] and Tof18[ 29 ] have been identified to be involved in the control of flowering and maturity in soybean. Among these genes E1 & E2[ 30 ], E3[ 31 ], E4[ 32 ], E5[ 33 ], E6[ 34 ], E7[35] and E8[17] have been identified through classical studies while E9, E10, E11, Tof5, Tof8, Tof9, Tof11, Tof12, Tof13 and Tof16 have been identified through genotypic studies. Soybean E1 locus is known to have the greatest impact on flowering and maturity periods by suppressing GmFT2a and GmFT5a [ 10 ]. Dominant alleles of E1, E2, E3, E4, E7, E8, and E10 inhibit flowering, whereas dominant alleles at E6, E9, E11 and J promote flowering [ 25 ]. E1, E2, E3, E4, E7 and E8 are involved in photoperiod sensitivity, especially to different light qualities under artificially induced LDs. E4 and E3 are phytochrome A (PHYA) genes, GmPHYA2 and GmPHYA3, respectively, acting as photoreceptors that perceive light signals to influence downstream genes. The dominant E9 gene confers early flowering, whereas the recessive e9 gene confers late flowering [ 19 , 36 ]. Dt1 and Dt2 play important role in flowering initiation and stem termination [ 37 , 38 ]. India lies between 8° 4’ N to 37° 6’N latitudes and soybean cultivation in India ranges from 15.31° N to 27.02° N Latitudes. In contrast to most of the countries where soybean is grown as the full season crop, it is a short season crop in India with maturity duration ranging from 90 days in lower latitudes to 125 days in higher latitudes. It is probable that hitherto unknown genes are present in gene pool for imparting adaptation to Indian conditions. In the present studies, a diverse set of germplasm with varying flowering and maturity durations was constituted and evaluated for four years at Indore, India (22.71° N). This set was genotyped Genotyping by sequencing (GBS) and an association study was conducted to decipher the presence of new flowering / maturity genes. Results Phenotypic Evaluation Phenotypic evaluations for flowering and maturity traits, including DTF, and DTM, revealed continuous and significant variation across the 254 soybean accessions (Table 1 , Fig. 1 ). The mean values for DTF, and DTM were 46.0 and 109.6 days, respectively, with observed ranges of 28.8–55.1 days for DTF, and 87.4 -120.1 days for DTM (Table 1 , Fig. 1 A, 1 B) indicating a substantial variability in the GWAS panel. The correlation analysis reveals a moderate positive correlation exists between DTF and DTM (r = 0.77), suggesting that early flowering contributes to shorter times to full maturity (Fig. 1 C). To analyze the interactions and associations between genotypes and varying environments, ANOVA tests, and principal component analysis (PCA) were conducted. In addition, a two-way ANOVA revealed significant differences in the interaction of genotype and environment (years) for both the traits (Table 1 ). Table 1 Phenotypic evaluation for flowering and maturity related traits DTF Mean and Std. Deviation 46.0 ± 5.4 Range (Min-Max) 26.33 (28.82 to 55.1) ANOVA table SS DF MS F (DFn, DFd) P value Genotypes x Years 2888 756 3.82 F (756, 1518) = 1.698 **** Genotypes 87361 252 346.7 F (252, 506) = 18.65 **** Years 7502 3 2501 F (1.000, 506.0) = 1111 **** Subject 9403 506 18.58 F (506, 1518) = 8.259 **** Residual 3415 1518 2.25 DTM Mean and Std. Deviation 109.6 ± 5.6 Range 32.68 (87.42–120.1) ANOVA table SS DF MS F (DFn, DFd) P value Genotypes x Years 4021 756 5.319 F (756, 1518) = 119196082660 **** Genotypes 95557 252 379.2 F (252, 506) = 14.97 **** Years 17522 3 5841 F (1.000, 506.0) = 1.309e + 014 **** Subject 12819 506 25.33 F (506, 1518) = 567737524145 **** Residual 6.77E-08 1518 4.46E-11 Footnotes: Two way repeated measured ANOVA was conducted for Days to Flowering (DTF) and Days to Maturity (DTM) for four consecutive years (2019–2022). Scatter plot for DTF and DTM explained distinct patterns among the genotypes, which indicates a significant amount of genetic diversity (Fig. 1 F). Further we identified some groups based on flowering and maturity which include early flowering and early maturity (EC-538828, CAT-47, CAT-146, NRC-12), early flowering late maturity (NRC-2, M-1052, EC-572136, EC-287457), late flowering late maturity (AGS-143, B-1667, AGS-110, B-471), late flowering early maturity (JS-75-46, CAT-290, DS-97-12, EC-251416). This analysis reveals that early flowering does not always lead to early maturity, late flowering does not consistently indicate late maturity. These results emphasize the independent genetic regulation of flowering and maturity traits in soybean. Analysis of Genetic Diversity and Linkage Disequilibrium After filtration, a total of 66,300 polymorphic SNPs (MAF < 0.05) were retained for analysis (Supplementary figure S1). Population structure analysis of 254 soybean accessions revealed that ΔK peaked when K was set to six (Fig. 2 ), indicating the division of the 254 germplasm accessions into six distinct subpopulations. This stratification was also supported by the neighbor-joining phylogenetic tree, which displayed six clades (Fig. 2 A), and was consistent with the clustering observed in the principal component analysis (PCA) (Fig. 2 B, (Supplementary figure S2). Additionally, linkage disequilibrium (LD) analysis showed that the average genome-wide LD for the diversity panel was r² = 0.471. Common population genetic tests were computed to assess natural diversity and selection pressure in the GWAS panel. The average level of silent-site nucleotide diversities per site (π) [ 44 ] and population mutation parameter (θ) [ 45 ] were observed 0.0000209 and 482.718, respectively. Statistical tests of neutrality, which include Tajima’s D [ 46 ], Fu and Li's D* and Fu and Li's F*, yielded values of 3.55, 4.22, and 4.36, respectively (Supplementary Table S1). GWAS analysis The analysis using different association models identified associations between specific loci and DTM and DTF traits across different years (Fig. 3 A & 3 B, Supplementary figure S3). We identified 20 significant loci for days to flowering and maturity traits, among them 12 are new and 8 were previously known loci (Table 2 & Table 3 ). The 12 newly identified loci, were distributed across different chromosomes 3, 4, 8, 10, 12, 14, 15, and 18 (Table 2 ). Among these, chr 03 emerged as particularly significant, revealing three important loci i.e. Lee.Gm03-1, Lee.Gm03-2, and Lee.Gm03-3. Of these, Lee.Gm03-3 stood out as a key finding for DTF across different environments (2019, 2020, 2021), among various models applied (BLINK, FarmCPU and MLM). For DTF in year 2021, the SNP S3_46108324 showed a highly significant p-value of 1.22E-08 in the BLINK model and 2.20E-06 in the FarmCPU model. In 2020, the SNP S3_46108342 also exhibits strong significance, with p-values of 6.16E-09 in BLINK and 1.76E-07 in FarmCPU for DTF. Identification of significantly associated markers in narrow genomic region across years enhances its reliability and significance as a locus for flowering time across multiple environments (Fig. 3 A & 3 B). Regarding Locus Lee.Gm03-1, significant associations were observed with DTM in multiple years, specifically in 2020 and 2022 using the FarmCPU and MLM models, and in 2021 with the MLM model. Locus Lee.Gm03-2 demonstrated associations with DTF, showing significant results in 2020 with both the BLINK and FarmCPU models, and in 2019 with the MLM model, highlighting additional loci of interest for maturation and flowering characteristics. Locus Lee.Gm03-3 was further investigated to study the effects of alleles and haplotypes on days to flowering (Fig. 4 A to 4 D). The results identified five major haplotypes: Hap1, Hap2, Hap3, Hap4, and Hap6 (Fig. 4 C & 4 D). Hap1 is associated with early flowering in both environments (2020 and 2021) (Fig. 4 D), while Hap3 and Hap6 are associated with comparatively delayed flowering. The allelic effects of two significant markers, S3_46108324 and S3_46108342, showed significant differences between the alleles (Fig. 4 E). Table 2 Significant SNPs identified through GWAS Study for flowering and maturity traits in soybean Position S.no. Locus Chr Trait & Environment Model SNP Glyma Lee Wm82.a2 P.value Effect 1 Lee.Gm03-1 3 DTM_2020 FarmCPU S3_24549807 24549807 21559727 1.92E-11 -3.84 DTM_2021 MLM 1.11E-04 -2.34 DTM_2020 FarmCPU S3_25992215 25992215 22964587 8.18E-07 -2.18 2 Lee.Gm03-2 DTF_2020 BLINK S3_40798304 40798304 37356467 1.72E-05 -1.67 DTF_2019 MLM S3_40903281 40903281 37356467 9.31E-05 -1.8 DTF_2020 FarmCPU S3_41286372 41286372 37836244 8.13E-07 1.44 DTF_2019 MLM S3_41452378 41452378 37902820 5.26E-04 -2.19 3 Lee.Gm03-3 DTF_2021 BLINK S3_46108324 46108324 42575015 1.22E-08 0.15 FarmCPU 2.20E-06 1.69 DTF_2020 BLINK S3_46108342 46108342 42575033 6.16E-09 0.15 FarmCPU 1.76E-07 -1.67 DTF_2019 MLM 1.37E-04 -3.71 4 Lee.Gm04-1 4 DTF_2021 BLINK S4_6912661 6912661 6809687 1.09E-07 0.35 DTF_2022 BLINK 1.00E-07 0.35 DTF_2022 FarmCPU 4.88E-07 -1.62 5 Lee.Gm04-2 4 DTF_2021 BLINK S4_49785758 49785758 47293738 6.57E-06 0.28 DTF_2022 BLINK 4.88E-06 0.28 FarmCPU 3.77E-07 -1.69 6 Lee.Gm07-1 7 DTF_2020 BLINK S7_5538630 5538630 5471070 1.39E-05 0.23 FarmCPU 2.02E-08 1.52 7 Lee.Gm08-1 8 DTF_2021 BLINK S8_20849488 20849488 20430185 2.49E-07 0.26 DTF_2022 BLINK 1.70E-07 0.26 FarmCPU 1.85E-05 1.09 8 Lee.Gm10-1 10 DTF_2021 BLINK S10_40473330 40473330 37138990 9.79E-05 0.07 DTM_2021 FarmCPU 5.40E-08 -2.88 DTM_2019 MLM 1.02E-05 1.8 DTM_2020 1.05E-05 1.9 9 Lee.Gm12-1 12 DTF_2021 BLINK S12_36598172 36598172 13171182 9.43E-06 0.07 DTF_2022 7.94E-06 0.07 DTF_2021 FarmCPU 3.52E-09 3.11 DTF_2022 1.15E-07 2.64 10 Lee.Gm14-1 14 DTF_2020 FarmCPU S14_52397292 52397292 38307979 1.91E-08 2.16 11 Lee.Gm15-1 15 DTM_2020 FarmCPU S15_7613524 7613524 7538344 8.12E-08 -2.03 DTM_2021 S15_8057946 8057946 7982564 9.96E-05 1.61 12 Lee.Gm18-1 18 DTF_2020 BLINK S18_58159788 58159788 54849471 1.24E-05 0.07 DTM_2020 FarmCPU 2.88E-09 -2.95 DTF_2019 MLM 2.5E-05 -2.34 Table 3 Validation of known genes for flowering and maturity traits in soybean Position S.N. Reported Genes Chr Gene position (bp) SNP LEE WM82 p.value Effect Trait & Environment Model References 1 E11 7 4,102,968–4,114,174 S7_42711643 42711643 38883887 3.49E-05 0.17519685 DTF_2021 BLINK [ 11 , 30 ] 3.95E-05 0.17519685 DTF_2022 S7_42766230 42766230 38883887 3.88E-05 0.116141732 DTF_2021 4.85E-05 0.116141732 DTF_2022 2 E10/FT4 8 47,458,142 − 47,459,829 S8_46399480 46399480 43830824 4.67E-06 -1.081631623 DTM_2020 FarmCPU [ 60 , 61 ] S8_47131522 47131522 44564154 3.46E-07 2.062596903 DTF_2020 3 E2 10 45,294,735 − 45,316,121 S10_47942176 47942176 44538772 9.21E-05 0.236220472 DTF_2021 BLINK [ 11 , 30 ] S10_47942176 5.76E-05 0.236220472 DTF_2022 S10_47927282 47927282 44523878 7.99E-05 2.981309437 DTF_2019 MLM 4 PRR7 12 5,508,365-5,522,772 S12_5898961 5898961 5811502 4.57E-05 1.063003583 DTF_2020 FarmCPU [ 62 , 63 , 64 ] 5 E9 16 31,109,999 − 31,114,963 S16_29195077 29195077 27706239 9.60E-06 4.982219442 DTF_2021 MLM [19,20] 9.62E-06 4.98234427 DTF_2020 S16_33016027 33016027 31527189 1.25E-05 2.260087133 DTF_2019 S16_26811600 26811600 25322762 9.02E-07 2.318625057 DTM_2021 FarmCPU S16_29635283 29635283 28079689 1.69E-05 1.162794485 DTF_2021 S16_30814558 30814558 29250204 1.96E-07 1.788084178 DTF_2020 S16_32881071 32881071 31266178 1.48E-05 0.204724409 DTF_2020 BLINK S16_33917645 33917645 32279539 1.05E-09 1.629785415 DTF_2020 FarmCPU S16_33931035 2.19E-05 0.38976378 DTF_2021 BLINK S16_33931035 2.00E-05 0.38976378 DTF_2022 6 DT2 18 55,638,209 − 55,646,547 S18_53487286 53487286 50490755 8.74E-05 0.352362205 DTF_2020 BLINK [ 38 , 65 ] 7.74E-07 0.352362205 DTF_2021 2.53E-06 1.182516985 DTM_2020 FarmCPU S18_54676785 54676785 51651567 1.23E-05 0.06496063 DTF_2020 BLINK S18_54958192 54958192 51929135 1.22E-05 -1.369215003 DTF_2020 FarmCPU S18_53487286 53487286 53487286 50458229 3.74E-05 -2.592311118 DTM_2020 MLM S18_54677671 54677671 54677671 51648614 8.16E-05 -3.868177112 DTF_2019 7 DT1 19 45,183,357 − 45,185,175 S19_42283700 42283700 39914927 1.92E-05 0.200787402 DTF_2021 BLINK [ 65 , 66 ] 2.83E-07 -2.248564364 DTM_2021 FarmCPU 8 E4 20 33,236,018–33,241,692 S20_36021806 36021806 33958681 7.84E-05 0.265748031 DTF_2021 BLINK [13,32] 8.61E-05 0.265748031 DTF_2022 BLINK S20_36824277 36824277 34746065 6.46E-06 1.024440493 DTF_2020 FarmCPU S20_36946413 36946413 34866334 2.51E-05 0.281496063 DTF_2021 BLINK 2.44E-05 0.281496063 DTF_2022 In this study, we validated nine previously reported genes through our GWAS panel (Table 3 ), including E11 on Chr 7 (DTF in 2021 and 2022), E10/FT4 on Chr 8 (DTM and DTF in 2020), and E2 on Chr 10 (DTF in 2021 and 2022). PRR7/Tof12 on Chr 12 was associated with DTF in 2020, while E9 on Chr 16 was linked to both DTM in 2021 and DTF in 2020 and 2021. DT2 on Chr 18 and DT1 on Chr 19 showed associations with both DTF and DTM across years, and E4 on Chr 20 was linked to DTF in 2021. Candidate Gene Identification All three significant loci on chromosome 03 were explored for candidate gene identification. The three loci located on chromosome 03 revealed candidate genes related to reproduction, photoperiod, and circadian rhythm. Lee.Gm03-1 carries a single gene, Glyma.03G080600, which plays a critical role in determining bilateral symmetry, meristem initiation, and polarity specification of the adaxial/abaxial axis. Lee.Gm03-2 stands out as a primary locus for genes involved in reproductive functions (Table 4 ). It contains multiple genes associated with flower development, floral organ identity and senescence, as well as pollen tube growth and guidance. For example, Glyma.03g160000 and Glyma.03g160700 are annotated for their role in flower development, while Glyma.03g164200 is annotated with floral organ senescence. Additionally, Glyma.03g161000, Glyma.03g164900, and Glyma.03g165700 contribute to pollen tube growth and guidance, while Glyma.03g166000 is linked to male meiosis. Together, these genes make Lee.Gm03-2 a hub for reproduction-related genes. On the other hand, locus Lee.Gm03-3 predominantly houses genes related to circadian rhythm, photoreception and light responsiveness. This locus consists of several genes that are highly responsive to light, especially red and far-red light signaling pathways, which are crucial for plant adaptation to the environment. For example, genes such as Glyma.03G227300 and Glyma.03G227800 are involved in phototropism, photomorphogenesis, and red/far-red light signaling. Glyma.03G221600 participates in gibberellin catabolic processes responsive to red or far-red light, while Glyma.03G225000 and Glyma.03G227800 are involved in red and far-red light signaling pathways. Additionally, locus Lee.Gm03-3 contains genes controlling circadian rhythm, including Glyma.03G225000, Glyma.03G227300, and Glyma.03G227800, which regulate processes related to circadian timing, light detection, and photo-morphogenesis. Table 4 Identification of putative candidate genes in soybean Locus Gene ID Start End Description PfamID Lee.Gm03-1 Glyma.03G080600 21508100 21512393 determination of bilateral symmetry; meristem initiation; polarity specification of adaxial/abaxial axis; regulation of meristem growth; response to light stimulus FAMILY NOT NAMED Lee.Gm03-2 Glyma.03g160000 37508220 37513007 positive regulation of flower development; KH_1; Glyma.03g160700 37584972 37586022 flower development; specification of floral organ identity zf-C2H2_6; Glyma.03g161000 37612023 37615718 pollen tube growth; protein transport; vesicle-mediated transport; NA Glyma.03g163400 37813139 37813951 seed maturation; Cupin_1; Glyma.03g164000 37866656 37868942 post-embryonic plant morphogenesis; DUF640; Glyma.03g164100 37892273 37895682 photoperiodism, flowering; negative regulation of long-day photoperiodism JmjN; Glyma.03g164200 37902284 37909534 floral organ senescence; NAM; Glyma.03g164900 37980836 37982905 pollen tube growth; PBD; Glyma.03g165700 38045521 38050084 pollen tube guidance; LRR_8; Pkinase; Glyma.03g166000 38068482 38070661 male meiosis II; NA Lee.Gm03-3 Glyma.03G219100 42265891 42275563 cytokinin mediated signaling pathway; embryo sac development Histidine kinase-, DNA Glyma.03G219300 42285599 42286853 positive regulation of seed maturation bZIP transcription factor Glyma.03G219800 42312343 42319568 DNA-dependent; vegetative phase change; vernalization response SET domain Glyma.03G219900 42325550 42327910 floral organ morphogenesis; gibberellic acid mediated signaling pathway GRAS domain family Glyma.03G220100 42334845 42336847 embryo development ending in seed dormancy;pollen development WRKY DNA -binding domain Glyma.03G221200 42433366 42437270 embryo sac egg cell differentiation Helicase conserved C-terminal domain; Glyma.03G221600 42466464 42471412 gibberellin catabolic process; response to red or far red light 2OG-Fe(II) oxygenase superfamily Glyma.03G223300 42601145 42604438 photoperiodism, flowering; protein folding DnaJ domain Glyma.03G224300 42656642 42665584 vegetative phase change; vernalization response SET domain Glyma.03G225000 42726377 42729203 circadian rhythm; gibberellic acid mediated signaling pathway;red or far-red light signaling pathway Helix-loop-helix DNA-binding domain Glyma.03G226000 42818553 42823054 determination of bilateral symmetry; meristem initiation Cellulase (glycosyl hydrolase family 5) Glyma.03G226500 42874585 42876167 embryo development ending in seed dormancy; vegetative to reproductive phase transition of meristem NA Glyma.03G226600 42877084 42882874 embryo development ending in seed dormancy GUCT (NUC152) domain; Glyma.03G227300 42918771 42923401 circadian rhythm; detection of visible light; photomorphogenesis; phototropism;response to continuous far red light stimulus by the high-irradiance response system; response to far red light; response to very low fluence red light stimulus; signal transduction Phytochrome region; GAF domain; His Kinase A (phospho-acceptor) domain; PAS fold; Glyma.03G227800 42980227 42984347 circadian rhythm; de-etiolation; gibberellic acid mediated signaling pathway; gravitropism; positive regulation of anthocyanin metabolic process; red or far-red light signaling pathway; regulation of transcription, DNA-dependent; response to red or far red light; signal transduction Helix-loop-helix DNA-binding domain Glyma.03G228700 43069814 43073090 floral organ formation; regulation of flower development; sepal formation; spindle assembly Domain of unknown function (DUF3635) Glyma.03g224400 42667804 42673022 pollen exine formation; anther wall tapetum development zf-Sec23_Sec24; Sec23_helical; Sec23_trunk; Glyma.03g225000 42726377 42729203 red, far-red light phototransduction; response to red or far red light; de-etiolation HLH; In summary, Lee.Gm03-2 is primarily associated with genes governing reproductive processes, while Lee.Gm03-3 has a greater concentration of genes involved in light responsiveness, photoreception, and circadian rhythm, highlighting each locus's unique contributions to plant development and environmental adaptation. Expression study for putative candidate genes We analyzed the tissue specific expression data available at Phytozome database for all identified putative candidate genes (Fig. 5 A & 5 B) and compared them based on expression data derived from different tissues directly linked to flowering and maturity pathway, including open flowers, unopened flowers, shoot tips, and leaves. The gene expression data of Lee.Gm03-2 and Lee.Gm03-3 loci suggests its roles in plant growth, reproductive development, and environmental responses (Fig. 5 A, 5 B & 5 C). In the Lee.Gm03-2 locus, Glyma.03G164000 showed higher expression in the shoot tip (5.307) and low expression in the leaf (0.052), likely aiding growth in the shoot tip and reproductive transition. Glyma.03G164200, with high expression in open flowers (1.242) and moderate in unopened flowers (0.417), may contribute to floral organ senescence, potentially regulating flower aging. Glyma.03G166000 displays expression across tissues, particularly in the shoot tip (5.01), suggesting involvement in reproductive processes, possibly playing a role in male meiosis II. In the Lee.Gm03-3 locus, Glyma.03G219100 is highly expressed in open flowers (5.2) and moderately in unopened flowers (0.806), possibly influencing reproductive development through cytokinin-mediated signaling pathways. Glyma.03G225000, with elevated expression in open flowers (4.2) and unopened flowers (2.9), may be involved in circadian rhythm and light response pathways, potentially regulating flowering time in response to light. Strong expression of Glyma.03G226000 in open flowers (10.8) suggests it could support flower structure formation, possibly through meristem initiation and symmetry determination. Lastly, Glyma.03G227300 having Phytochrome region; GAF domain; His Kinase A (phospho-acceptor) showing moderate expression across tissues and particularly in unopened flowers (1.078), may contribute to photo-morphogenesis and light response, helping the plant adapt to light conditions through circadian and phototropic responses. This gene expression pattern thus provides insights into how each gene may function in flowering, growth, and environmental adaptation. These genes were further studied with the orthologs from different species (Supplementary figure S4a-S4g). These key genes are also present in important legumes and other species suggesting its crucial role in regulation of flowering and maturity. Validation of Major Locus Lee.Gm03-3 Using KASP Analysis The stable locus Lee.Gm03-3, containing SNPs S3_46108324 and S3_46108342, was validated using KASP marker analysis in a separate population consisting of 157 Indian soybean cultivars (Fig. 6 A & 6 B). For SNP S3_46108324, significant associations were found with DTM, exhibiting p-value of 3.23E-04, which explained 8.34% of phenotypic variation ( R² ) in 2023 (Fig. 6 C). Similarly, SNP S3_46108342 demonstrated significant associations with DTF, and DTM, with p -values of 0.017 and 0.016, respectively, accounting for 4.79% and 4.90%, of phenotypic variation in 2022 (Fig. 6 C). Further allelic effect for DTM-2023 and DTM-2022 phenotype also showed significance difference (Fig. 6 D & 6 E). These findings validate Lee.Gm03-3 and its SNPs as important genetic markers for flowering and maturity traits. Discussion The genetic architectures pertaining to the flowering time and maturity can be key factors in achieving the designer soybean plants that are adaptable and of a desired type for enhanced productivity in specific environments. Several genetic studies revealed critical role of many single genes such as E1, E2, E3, E4, E9 FT2a, FT5a, J, Tof4, Tof5, Tof8, Tof9, Tof11, Tof12, Tof13, Tof16 and Tof18 in photoperiod mediated flowering for adaptation of soybean [ 54 , 25 , 55 ]. In our investigation, for the first time in Indian condition, we dissected the underlying genetic architecture of flowering time, and maturity duration in an Indian environment, along with the key genetic loci and candidate genes that regulate these traits in the soybean. Our GWAS panel consisting diverse 254 germplasm lines, divided into six distinct subpopulations, indicated by population structure analysis, supports the significance of genetic diversity within the dataset, which is essential for accurately mapping these traits. This stratification, confirmed by both phylogenetic clustering and PCA, indicates a robust genetic basis for the identified loci. In GWAS study, we identified a locus, Lee.Gm03-3 as a key locus for flowering time with markers S3_46108324 and S3_46108342, the two markers linked to the locus in both environments i.e. 2020 and 2021, respectively. Furthermore, we converted the two significant SNPs of Lee.Gm03-3 into KASP assays and validated them in a separate soybean population consisting of diverse cultivar, enhancing utility of this locus in breeding. These associations enable breeders to use this consistency to their advantage, further underscoring the importance of Lee.Gm03-3 in the design of such soybean varieties that can grow under different photoperiods. Several genome wide association studies in soybean, revealed several loci controlling flowering time and maturity duration, such as J [ 25 ], Tof5[ 23 ], Tof11 and Tof12[ 25 ], Tof13[ 26 ], Tof16[ 27 ]), and Tof18 [ 29 ]. E2 also detected through GWAS in the identification of Tof5, Tof13, and Tof18 [ 23 , 26 , 29 ]. Our GWAS study not only identified several new loci but also validated the presence of previously known loci associated with flowering time and maturity duration, confirming the robustness of our findings. These known loci include E2, E4, E11, E10/FT4, PRR7/Tof12, E9, Dt1, and Dt2. Functions of these loci is well studied and their role in regulation of flowering time and maturity in different photoperiod regimes is reported in several studies [ 55 ]. For instance, E1 is considered as the main contributor of photoperiod sensitivity and the other E gene interactions affecting the flowering and maturity response making it crucial in enhancing soybean adaptability across latitudes [ 56 ]. E1 by interacting with the FUL transcription suppresses E9 activities through binding the promoter, while FUL interacts with FT2a (E9) and FT5a to enhance their expression with the end result of promoting flowering in long day conditions [ 27 ]. In our GWAS, according to p-value and effect size, E9 was revealed as the major locus determining the variation of DTF and DTM traits in an Indian environment. The two candidate genes of Lee.Gm03-3, viz. Glyma.03G227300, and Glyma.03G225000, may be involved in regulating flowering time in soybean by modulating circadian rhythms and light-responsive pathways. Glyma.03G227300 genes also reported earlier as GmPHYA4 , contains the Phytochrome region; GAF domain; and His Kinase A (phospho-acceptor). This functional gene could involve maintaining the circadian rhythm, detecting light sources, the switching on of a plant’s development processes and the directional growth towards or away from the center of light, red light they are thought may practice light hierarch and assist in opening timing of the flowering phase in accordance with the duration of the day. The function of this gene in photoreceptor activity is consistent with its genetic architecture since it is also a photoperiod control gene, along with other genes studied in Arabidopsis thaliana and Oryza sativa in which the role of phytochromes was also essential for flowering with respect the day-length [ 57 , 58 ]. Moreover, Glyma.03G225000 is associated with the circadian clock and with the GA-mediated signaling pathways and responses to red light and far-red light. This gene is also associated with flowering time and other GA enhancing growth attributes. GA signaling has been known to have functional crosstalk with photoperiod pathways. Such hypothesis is applicable in this scenario because the gene in question is considered to be responsible for coordinating GA signaling and circadian rhythms that ultimately determine flowering time in response to light. This regulatory pathway has already been reported in other plants whereby flowering is dependent on GA and photoreceptors under different light treated conditions [ 6 , 59 ]. In combination, Glyma.03G227300 and Glyma.03G225000 also represent as key gene candidates in breeding programs that alter flowering and maturity time of soybean plants grown under different photoperiods. Ultimately, the detected loci might provide practical markers for improvement of the timing of flowering and maturity traits of soybean that may lead to increase adoption of new variety in India. The findings of present study are not only consistent with previously identified loci but also show the potential of using GWAS as a tool to discover new and stable markers for important traits like DTF and DTM [ 7 ]. The mapping of these loci or genes opens new avenues for crop improvement with genetic engineering that could be initiated for altering genotype and phenotype of soybean genotypes. Additionally, understanding photoperiod sensitivity and genetic control of flowering time will help in developing breeding strategies to balance early flowering with adequate plant growth. This study will provide a valuable foundation for future genetic and molecular research aimed at increasing soybean yield and adaptation. For better insight, functional validation and chacterization of these genes will be helpful for improvement of soybean varieties. Similar studies need to be conducted at different altitude to get robust marker and genes for regulation of flowering and maturity time. Conclusions This study enhances the current understanding of the genetic mechanisms influencing photoperiod sensitivity and flowering in soybean. By identifying 20 significant loci, including 12 novel loci, and validating 8 previously known reported genes, it provides a comprehensive framework for genetic improvement. The discovery of the 4 key candidate genes Glyma.03G227300, Glyma.03G225000, Glyma.03G219100 and Glyma.03G226000 associated with the significant locus Lee.Gm03-3 on chromosome 03 offers valuable insights into the regulatory pathways of flowering and maturity, including circadian rhythm, hormone signaling, and light-response pathways. SNP markers identified in this study will help in the molecular breeding programme for developing early maturing soybean cultivars. These findings open new avenues for breeding soybean varieties with optimized flowering duration and better adaptability to Indian environmental conditions, contributing to improved agricultural productivity. Material and methods Plant Materials and Phenotypic evaluation Phenotyping was conducted on 254 diverse soybean germplasm accessions during the summer season (Mid-June to Mid-October) over four consecutive years, from 2019 to 2022, using an augmented design at the ICAR-Indian Institute of Soybean Research (22.7196° N, 75.8577° E), Indore, India. Morphological traits related to flowering and maturity, such as Days to Flowering (DTF) recorded at the R1 stage (the day when 50% of the plants in a plot have an open flower on one of the top four nodes that bears a fully expanded leaf) and Days to Maturity (DTM) were measured in the field for all four years. Phenotypic analysis included normality distribution and descriptive statistics such as mean, standard deviation (SD), maximum and minimum trait values, and the coefficient of variation (CV%). In addition, correlation, PCA analysis and two-way Analysis of Variance (ANOVA) were performed using GraphPad Prism version 9 ( www.graphpad.com ) to evaluate the effects of genotype (G), environment (E), and genotype-by-environment interaction (G × E) on both the traits. Genotyping The genomic DNA from the leaves derived form 254 soybean diverse germplasm accessions was extracted using CTAB method [ 39 ]. Genotyped by sequencing (GBS) was conducted following the methods and recommendations outlined by [ 40 , 41 ]and [ 42 , 43 ]). The GBS library was created with Ape KI restriction enzyme digestion. A 158 million single-end reads were generated with an Ion Torrent Proton System (Thermo Fisher Scientific Inc., USA) by ICRISAT (International Crops Research Institute for the Semi-Arid ‘Tropics), Hyderabad, India. These were processed using the Fast-GBS.v2 pipeline [ 43 ]. FASTQ files were demultiplexed, trimmed, and then mapped against the soybean reference genome (Glyma.Lee_v2.0). Imputations were performed in TASSEL software to fill missing data and further SNP data were filtered by minor allele frequency (MAF) 10% and finally a total of 66300 SNPs distributed all over 20 chromosomes were used for further study. Genetic Diversity analysis Level of silent-site nucleotide diversities per site (π) [ 44 ]) and population mutation n parameter (θ) [ 45 ] was estimated. Statistical tests of neutrality such as Tajima’s D [ 46 ]), Fu and Li’s D* and F were also calculated to examine the selection pressure at SNPs in our GWAS panel by using DnaSP software version 5.10. ( http://www.ub.edu/dnasp/index_v5.html ). A Neighbour-joining tree was constructed using the TASSEL software ( https://www.maizegenetics.net/tassel . Principal Component Analysis (PCA) and LD decay plot were generated using the GAPIT package ( https://www.maizegenetics.net/gapit ) implemented in R. Population structure was developed using STRUCTURE software ( https://web.stanford.edu/group/pritchardlab/structure.html ). Genome-wide association study and candidate gene identification The analysis involved 254 diverse soybean accessions to study SNPs associated with flowering and maturity across four years (2019–2022). The association analysis was performed using three analysis models. Mixed Linear Model (MLM, using PCA (fixed-effect factor) + K (random-effect factor)) was implemented in TASSEL v5.0[ 47 ]). The Fixed and Random Model Circulating Probability Unification (FarmCPU) [ 48 ] and Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) [ 49 ] models were applied through GAPIT package [ 50 ] in R. The first two principal components were included as covariates in both models. A Bonferroni correction (α/N) was used to set the significance threshold, where N represents the number of tested SNPs and α = 1. Manhattan plots illustrated significant markers, while quantile-quantile (Q-Q) plots compared expected versus observed p-value distributions (on a -log10 scale). For candidate gene identification, genomic regions 250 kb upstream and downstream of significant SNPs (totaling 500 kb) were analyzed, based on the average linkage disequilibrium decay in soybean [ 51 , 48 , 52 ]. Genes within these regions were identified using the Lee reference genome and Wm82.a2 genome assembly, with data obtained from SoyBase ( www.soybase.org ). Further haplotypes were analyzed with in the LD region by using DnaSP software version 5.10 ( http://www.ub.edu/dnasp/index_v5.html ). Expression analysis of putative candidate genes Expression data for the putative candidate genes identified in various loci were obtained from the Phytozome database ( https://phytozome-next.jgi.doe.gov/ ). The mRNA expression data covered different growth stages related to flowering and maturity, including flower opening, unopened flowers, shoot tip, and leaf tissues. To effectively visualize the expression patterns and relationships among these candidate genes, the expression data were converted into a heatmap using the TBtools software [ 53 ]. Validation of SNPs with KASP marker The two SNPs of major locus Lee.Gm03-3 were converted to Kompetitive allele specific PCR (KASP) assays and validated using KASP analysis in a new set of 157 soybean genotypes. These 157 soybean genotypes consisting of released soybean cultivars of India, were phenotyped for DTF and DTM in 2022 and 2023. Generalized linear model (GLM) was used for trait association in TASSEL v5.0 [ 47 ]. Abbreviations BLINK Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway DTF Days to Flowering DTM Days to Maturity FarmCPU Fixed and Random Model Circulating Probability Unification GBS Genotyping-by-sequencing GWAS Genome-Wide Association Study KASP Kompetitive Allele-Specific PCR LD Linkage Disequilibrium MAF Minor Allele Frequencies MLM Mixed Linear Model PCA Principal Component Analysis QTL Quantitative Trait Loci SNP Single Nucleotide Polymorphisms Declarations Acknowledgements This research was funded and supported by the National Agriculture Science Fund (NASF) of Indian Council of Agricultural Research, New Delhi. Availability of data and materials All the data and statistics about the current study has been included in the current manuscript in the form of figure and tables. Raw data are available on request. References Broich SL, Palmer RG. 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Liu B, Watanabe S, Uchiyama T, Kong F, Kanazawa A, Xia Z, Nagamatsu A, Arai M, Yamada T, Kitamura K, Masuta C. The soybean stem growth habit gene Dt1 is an ortholog of Arabidopsis TERMINAL FLOWER1. Plant Physiology 2010;153(1):198-210. Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryFigures.pptx Supplementary figure S1: Distribution of Markers, MAF for 2 soybean germplasm lines. (A) Distribution of 66300 SNP across 20 chromosome of soybean data, (B) Distribution of minor allele frequencies (MAF), (C) Distribution of Markers Supplementary figure S2. Principal component analysis (PCA) for the entire GWAS panel derived from SNP data. (A) Three different PCA plot derived from PC 1, 2 and 3, (B) Eigenvectors for PCA analysis Supplementary figure S3: Association of SNPs with DTF and DTM in diverse soybean germplasm line. A total of 66300 SNPs was used for association study. GWAS was conducted by employing Mixed Linear Model (MLM) in TASSEL software (https://www.maizegenetics.net/tassel). Manhattan plot showing significant SNPs associated with DTF and DTM derived from four consecutive years 2019 to 2022. (B&D) Quantile- quantile (QQ) Plots to show the distribution of SNPs with respect to traits. Supplementary Figure S4a-S4g: Phylogenetic tree of key genes from different species. SupplementaryTableS1.xlsx Table S1: Genetic Diversity Analysis for our GWAS panal Cite Share Download PDF Status: Posted Version 1 posted 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-6076609","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":418909401,"identity":"e257d7da-5986-4376-aa9b-966ebc2479ea","order_by":0,"name":"Rishiraj Raghuvanshi","email":"","orcid":"https://orcid.org/0000-0001-5977-0980","institution":"ICAR-Indian Institute of Soybean Research, Indore, India","correspondingAuthor":false,"prefix":"","firstName":"Rishiraj","middleName":"","lastName":"Raghuvanshi","suffix":""},{"id":418909402,"identity":"1ada2c5c-2150-4e04-bc9b-3ac1c19125d7","order_by":1,"name":"Giriraj Kumawat","email":"","orcid":"","institution":"ICAR-Indian Institute of Soybean Research, Indore, India","correspondingAuthor":false,"prefix":"","firstName":"Giriraj","middleName":"","lastName":"Kumawat","suffix":""},{"id":418910180,"identity":"92a93e2b-1420-495f-9ba6-97e21671b419","order_by":2,"name":"Rucha Kavishwar","email":"","orcid":"","institution":"ICAR-Indian Institute of Soybean Research, Indore, India","correspondingAuthor":false,"prefix":"","firstName":"Rucha","middleName":"","lastName":"Kavishwar","suffix":""},{"id":418910181,"identity":"0adda4ac-659f-4543-b558-ce25283f6d57","order_by":3,"name":"Sanjay Gupta","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sanjay","middleName":"","lastName":"Gupta","suffix":""},{"id":418910182,"identity":"c5ceb7da-0d8a-4a45-883c-a029be31d46f","order_by":4,"name":"Annapurna Chitikineni","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Annapurna","middleName":"","lastName":"Chitikineni","suffix":""},{"id":418910183,"identity":"15ffb688-e054-448b-b7ce-211bb7c9b485","order_by":5,"name":"Subash Chandra","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Subash","middleName":"","lastName":"Chandra","suffix":""},{"id":418910184,"identity":"457809c0-e685-4035-b5da-f3cbb9af673f","order_by":6,"name":"Gyanesh K. Satpute","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Gyanesh","middleName":"K.","lastName":"Satpute","suffix":""},{"id":418910185,"identity":"86205498-c223-48e6-b49b-85adea5abb15","order_by":7,"name":"Vennampally Nataraj","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Vennampally","middleName":"","lastName":"Nataraj","suffix":""},{"id":418910186,"identity":"18973d07-b5ea-44dd-9023-a9c0208283f9","order_by":8,"name":"Rajeev K Varshney","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Rajeev","middleName":"K","lastName":"Varshney","suffix":""},{"id":418915124,"identity":"eef5ae23-82ee-4ffe-b64c-6cab4267a32f","order_by":9,"name":"Henry Nguyen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Henry","middleName":"","lastName":"Nguyen","suffix":""},{"id":418915125,"identity":"95d98409-be57-47bb-afa3-c74388533957","order_by":10,"name":"Vangala Rajesh","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Vangala","middleName":"","lastName":"Rajesh","suffix":""},{"id":418915126,"identity":"9e9c68d8-0e7b-4e5d-95f5-99e073af476b","order_by":11,"name":"Shivakumar Maranna","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Shivakumar","middleName":"","lastName":"Maranna","suffix":""},{"id":418915127,"identity":"d68586df-30c2-4d61-8146-b6b898bd3fa1","order_by":12,"name":"Mrinal K. Kuchlan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mrinal","middleName":"K.","lastName":"Kuchlan","suffix":""},{"id":418915128,"identity":"28a001e4-03b1-48ed-b983-23b70fa34e2c","order_by":13,"name":"Punam Kuchlan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Punam","middleName":"","lastName":"Kuchlan","suffix":""},{"id":418915129,"identity":"1103d117-30d2-4dee-b9a0-a869694701cf","order_by":14,"name":"Ajay K. Singh","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ajay","middleName":"K.","lastName":"Singh","suffix":""},{"id":418915130,"identity":"ad7c63e5-1d69-42f9-ac18-e71e04d83bd3","order_by":15,"name":"K.H. Singh","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"K.H.","middleName":"","lastName":"Singh","suffix":""},{"id":418915131,"identity":"27ef1a2e-7748-4972-90ab-f812e3a3ef18","order_by":16,"name":"Milind B. Ratnaparkhe","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABK0lEQVRIie3RMWuDQBTA8SdpM2ldhdD2K7wQEAot+SoRwSldi0MhDYXLou3cpZ/BKbPywOmKq1tahE4dhA5VCtI7sZPaucP9BzmEH/ruAahU/zDM+OTTqGAzPf9AqrBxogNRKQ5g7gLUatYjF6QDhQE4j5OTeaH75GI+XSWBT2BxjuCkPbKU5IWB83RvLhY6Jw9zHemIE6C1FuS9R+xfEpFpzwxGa0FK0pgk3mqUXDPYIBn7Wc0af3kII0GajuxHyW1LNFb5mBuSVDDPhwlKcsXAE0N9fWsNlz/2SuIAmLsxOM/D5JSBKw434iupHB/omKUdeRghgSXGb0l7yZiEjHSLJ3fxEMmyQqzyUhJbErnKoqwZnZm77fZtYJVdVv+VLh/xGFCpVCrVn/0A6XSVCIxUMD0AAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Milind","middleName":"B.","lastName":"Ratnaparkhe","suffix":""}],"badges":[],"createdAt":"2025-02-21 06:30:44","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6076609/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6076609/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77005303,"identity":"dca5b312-8852-415d-bcd1-2b3461ea7d42","added_by":"auto","created_at":"2025-02-24 08:30:35","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":123304,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhenotypic evaluation of flowering and maturity-related traits in diverse soybean lines. \u003c/strong\u003ePhenotypic evaluations were conducted for flowering and maturity traits. (A) \u0026amp; (B) showing distribution pattern for four consecutive years (2021-2022) for Days to Flowering (DTF) and Days to Maturity (DTM). (C), correlation between DTF and DTM. (D \u0026amp; E) showing frequency distribution for average DTF and DTM, across 254 soybean accessions. (F), The scattered plot depicting genotypic distribution based on their flowering and maturity groups.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6076609/v1/6ea7f77c8de0959fb7f48dc0.jpeg"},{"id":77005863,"identity":"95963451-cc2c-4aed-a5c9-9ceff9c7bdf7","added_by":"auto","created_at":"2025-02-24 08:38:35","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":159652,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiversity analysis using SNPs for the GWAS panel of soybean.\u003c/strong\u003e A total of 66,300 SNPs derived from a GWAS panel of 254 diverse lines were used to assess genetic diversity. A Neighbor-joining tree (A), constructed using the TASSEL software (https://www.maizegenetics.net/tassel), shows six distinct clades in the GWAS panel. Principal Component Analysis (B) reveals the extent of diversity in the panel. (C) Population structure developed using STRUCTURE software (https://web.stanford.edu/group/pritchardlab/structure.html), indicates six distinct subpopulations coded by different colors.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6076609/v1/60081e5207a9c67cfd168e82.jpeg"},{"id":77005310,"identity":"f841fb94-f642-4193-8f2b-64ab60a4fbe5","added_by":"auto","created_at":"2025-02-24 08:30:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":336653,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA: Association of SNPs with flowering and maturity related traits in soybean. \u003c/strong\u003eThe GWAS analysis involved 254 diverse soybean germplasm lines to study traits associated with flowering and maturity across four years (2019–2022). The analysis was performed using the GAPIT package in R, applying the Fixed and Random Model Circulating Probability Unification (FarmCPU). Manhattan plot and QQ plot (Quantile-Quantile) showing marker associated with for DTF (A) \u0026amp; DTM (A) derived from four consecutive years 2019 to 2022.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB: A: Association of SNPs with DTF in diverse soybean germplasm line. \u003c/strong\u003eA total of 66300 SNPs was used for association study. GWAS was conducted by employing Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK). Manhattan plot and QQ plot showing marker associated with different traits derived from four consecutive years 2019 to 2022.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6076609/v1/332c98e66545684fa0c50df7.png"},{"id":77005308,"identity":"5ac9c234-f0b6-4383-bddd-2fe080e43f9c","added_by":"auto","created_at":"2025-02-24 08:30:36","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":263004,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of Allele and Haplotype Effects on Days to Flowering in Soybean. \u003c/strong\u003e(A) Manhattan and QQ plots showing significant markers associated with Days to Flowering (DTF) in 2020 and 2021 on chromosome 3. (B) Linkage Disequilibrium (LD) region containing significant SNPs on chromosome 3. (C) Haplotypes identified within the LD region. (D) Association of haplotypes with DTF across two different environments. (E) Allele effects on associated phenotypic traits across two environments (2020 and 2021).\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6076609/v1/4b611ecce48dfe0e5732c7d2.jpeg"},{"id":77005314,"identity":"6c027347-4085-4ff5-af90-d0cd80b5b7d4","added_by":"auto","created_at":"2025-02-24 08:30:43","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":110778,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression data analysis for putative candidate genes in Soybean. \u003c/strong\u003eExpression data for the putative candidate genes identified in Lee.Gm03-2 (A) and Lee.Gm03-3 (B) loci were obtained from the Phytozome database (https://phytozome-next.jgi.doe.gov/) for different stages inludes: flower opening, unopened flowers, shoot tip, and leaf tissues. Tthe expression data were converted into a heatmap using the TBtools software. (C) Illustrating the genes from both the loci showing differential expression among different tissue.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6076609/v1/2a7c8ccc06fc23a85b277543.jpeg"},{"id":77005879,"identity":"f79bff84-4a35-442f-ad32-088609053f18","added_by":"auto","created_at":"2025-02-24 08:38:36","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":148355,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of Lee.Gm03-3 loci in soybean cultivar genotypes by using KASP marker. \u003c/strong\u003e157 genotypes consisting of released soybean cultivars of India were phenotyped for DTF and DTM in 2022 to 2023 and further used for association analysis by using GLM (General Liner Model) implemented in TASSEL software (https://www.maizegenetics.net/tassel). (A and B) showing phenotypic data distribution, (C) result obtained from GLM model, (D) Allelic effect for DTM-2023, (E) Allelic effect for DTM-2022 phenotype.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6076609/v1/e476a4ec9bc72d06d75e7779.jpeg"},{"id":77006336,"identity":"9aeaf5b2-e402-4ed6-9427-a82e723023d5","added_by":"auto","created_at":"2025-02-24 08:39:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2722646,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6076609/v1/734ecebe-5aa4-491d-8ee3-19d1eb3e4cfb.pdf"},{"id":77005865,"identity":"0dfbc584-7b4b-4e4e-81d4-e1e12617d4b6","added_by":"auto","created_at":"2025-02-24 08:38:36","extension":"pptx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3736891,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary figure S1: \u003c/strong\u003eDistribution of Markers, MAF for 2 soybean germplasm lines. (A) Distribution of 66300 SNP across 20 chromosome of soybean data, (B) Distribution of minor allele frequencies (MAF), (C) Distribution of Markers\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary figure S2. \u003c/strong\u003ePrincipal component analysis (PCA) for the entire GWAS panel derived from SNP data. (A) Three different PCA plot derived from PC 1, 2 and 3, (B) Eigenvectors for PCA analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary figure S3: \u003c/strong\u003eAssociation of SNPs with DTF and DTM in diverse soybean germplasm line. A total of 66300 SNPs was used for association study. GWAS was conducted by employing Mixed Linear Model (MLM) in TASSEL software (https://www.maizegenetics.net/tassel). \u0026nbsp;Manhattan plot showing significant SNPs associated with DTF and DTM derived from four consecutive years 2019 to 2022. (B\u0026amp;D) Quantile- quantile (QQ) Plots to show the distribution of SNPs with respect to traits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Figure S4a-S4g: \u003c/strong\u003ePhylogenetic tree of key genes from different species.\u003c/p\u003e","description":"","filename":"SupplementaryFigures.pptx","url":"https://assets-eu.researchsquare.com/files/rs-6076609/v1/b3b3f19c4e4ca3b1412a14c9.pptx"},{"id":77005311,"identity":"a469e04e-a890-4bb7-b8ae-d122313e6a85","added_by":"auto","created_at":"2025-02-24 08:30:37","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":13204,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1: Genetic Diversity Analysis for our GWAS panal \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"SupplementaryTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6076609/v1/0ec54f9f50b247eaa3461433.xlsx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDissecting Genetic Architecture of Flowering and Maturity Traits in Soybean Using GWAS in Indian Environment\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSoybean (\u003cem\u003eGlycine max\u003c/em\u003e) is an important global crop, valued for its high protein content in animal feed and as a source of vegetable oil. Soybean is a facultative short-day crop and has originated in higher latitudes of China, [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Soybeans flowers readily when the day length falls below the critical day length of a genotype. Genotypes adapted to higher latitudes (long days) enter into reproductive phase with little biomass when they are introduced into lower latitudes (short days). Conversely soybeans of lower latitude either do not enter into reproductive phase or delay flowering in higher latitudes. In spite of photosensitive response, the soybean is now cultivated widely throughout the globe. Although the adaptation of soybean ranges from 50\u0026deg;N to 35\u0026deg;S [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], its individual genotypes adapt to a narrow latitudinal band.\u003c/p\u003e \u003cp\u003eGenotypes adapted to higher latitudes have evolved through null or hypo- mutations in genes responsible for sensing photoperiod (photo insensitivity) and helped genotypes flower under long day conditions of these latitudes. Genotypes adapted to lower latitudes have evolved through mutations in which delay in flowering occurs even under short day conditions (long juvenility) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Varying combinations of photoperiodic and maturity alleles have evolved such a latitude specific genotype. To date, a number of major genetic loci, namely \u003cem\u003eE1\u003c/em\u003e [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] ), E2 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], E3[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], E4 [13]), E5[14]), E6[15]), E7[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e16\u003c/span\u003e]), E8[17]), E9[18,19,20], E10 [18,19,20] ), E11[21], J[22] and several QTLs, such as Time of flowering 5 (Tof5) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e23\u003c/span\u003e]), Tof8[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e24\u003c/span\u003e]), Tof9[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e25\u003c/span\u003e], Tof11/Gp11 and Tof12/Gp1/qFT121 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e25\u003c/span\u003e], Tof13[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e26\u003c/span\u003e] ,Tof16[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e27\u003c/span\u003e], LJ16.1 and LJ16.2 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and Tof18[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e29\u003c/span\u003e] have been identified to be involved in the control of flowering and maturity in soybean. Among these genes E1 \u0026amp; E2[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e30\u003c/span\u003e], E3[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e31\u003c/span\u003e], E4[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e32\u003c/span\u003e], E5[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e33\u003c/span\u003e], E6[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e34\u003c/span\u003e], E7[35] and E8[17] have been identified through classical studies while E9, E10, E11, Tof5, Tof8, Tof9, Tof11, Tof12, Tof13 and Tof16 have been identified through genotypic studies. Soybean E1 locus is known to have the greatest impact on flowering and maturity periods by suppressing \u003cem\u003eGmFT2a\u003c/em\u003e and \u003cem\u003eGmFT5a\u003c/em\u003e [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Dominant alleles of E1, E2, E3, E4, E7, E8, and E10 inhibit flowering, whereas dominant alleles at E6, E9, E11 and J promote flowering [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. E1, E2, E3, E4, E7 and E8 are involved in photoperiod sensitivity, especially to different light qualities under artificially induced LDs. E4 and E3 are phytochrome A (PHYA) genes, GmPHYA2 and GmPHYA3, respectively, acting as photoreceptors that perceive light signals to influence downstream genes. The dominant E9 gene confers early flowering, whereas the recessive e9 gene confers late flowering [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Dt1 and Dt2 play important role in flowering initiation and stem termination [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIndia lies between 8\u0026deg; 4\u0026rsquo; N to 37\u0026deg; 6\u0026rsquo;N latitudes and soybean cultivation in India ranges from 15.31\u0026deg; N to 27.02\u0026deg; N Latitudes. In contrast to most of the countries where soybean is grown as the full season crop, it is a short season crop in India with maturity duration ranging from 90 days in lower latitudes to 125 days in higher latitudes. It is probable that hitherto unknown genes are present in gene pool for imparting adaptation to Indian conditions. In the present studies, a diverse set of germplasm with varying flowering and maturity durations was constituted and evaluated for four years at Indore, India (22.71\u0026deg; N). This set was genotyped Genotyping by sequencing (GBS) and an association study was conducted to decipher the presence of new flowering / maturity genes.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePhenotypic Evaluation\u003c/h2\u003e \u003cp\u003ePhenotypic evaluations for flowering and maturity traits, including DTF, and DTM, revealed continuous and significant variation across the 254 soybean accessions (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean values for DTF, and DTM were 46.0 and 109.6 days, respectively, with observed ranges of 28.8\u0026ndash;55.1 days for DTF, and 87.4 -120.1 days for DTM (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) indicating a substantial variability in the GWAS panel. The correlation analysis reveals a moderate positive correlation exists between DTF and DTM (r\u0026thinsp;=\u0026thinsp;0.77), suggesting that early flowering contributes to shorter times to full maturity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). To analyze the interactions and associations between genotypes and varying environments, ANOVA tests, and principal component analysis (PCA) were conducted. In addition, a two-way ANOVA revealed significant differences in the interaction of genotype and environment (years) for both the traits (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\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\u003ePhenotypic evaluation for flowering and maturity related traits\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=\"left\" 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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eDTF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean and Std. Deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e46.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange (Min-Max)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e26.33 (28.82 to 55.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eANOVA table\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eDF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eF (DFn, DFd)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenotypes x Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF (756, 1518)\u0026thinsp;=\u0026thinsp;1.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e346.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF (252, 506)\u0026thinsp;=\u0026thinsp;18.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYears\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF (1.000, 506.0)\u0026thinsp;=\u0026thinsp;1111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubject\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF (506, 1518)\u0026thinsp;=\u0026thinsp;8.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.25\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=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cb\u003eDTM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean and Std. Deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e109.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e32.68 (87.42\u0026ndash;120.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eANOVA table\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eDF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eF (DFn, DFd)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenotypes x Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF (756, 1518)\u0026thinsp;=\u0026thinsp;119196082660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e379.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF (252, 506)\u0026thinsp;=\u0026thinsp;14.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYears\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF (1.000, 506.0)\u0026thinsp;=\u0026thinsp;1.309e\u0026thinsp;+\u0026thinsp;014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubject\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF (506, 1518)\u0026thinsp;=\u0026thinsp;567737524145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.77E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.46E-11\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 \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eFootnotes: Two way repeated measured ANOVA was conducted for Days to Flowering (DTF) and Days to Maturity (DTM) for four consecutive years (2019\u0026ndash;2022).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eScatter plot for DTF and DTM explained distinct patterns among the genotypes, which indicates a significant amount of genetic diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). Further we identified some groups based on flowering and maturity which include early flowering and early maturity (EC-538828, CAT-47, CAT-146, NRC-12), early flowering late maturity (NRC-2, M-1052, EC-572136, EC-287457), late flowering late maturity (AGS-143, B-1667, AGS-110, B-471), late flowering early maturity (JS-75-46, CAT-290, DS-97-12, EC-251416). This analysis reveals that early flowering does not always lead to early maturity, late flowering does not consistently indicate late maturity. These results emphasize the independent genetic regulation of flowering and maturity traits in soybean.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnalysis of Genetic Diversity and Linkage Disequilibrium\u003c/h3\u003e\n\u003cp\u003eAfter filtration, a total of 66,300 polymorphic SNPs (MAF\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were retained for analysis (Supplementary figure S1). Population structure analysis of 254 soybean accessions revealed that ΔK peaked when K was set to six (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), indicating the division of the 254 germplasm accessions into six distinct subpopulations. This stratification was also supported by the neighbor-joining phylogenetic tree, which displayed six clades (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), and was consistent with the clustering observed in the principal component analysis (PCA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, (Supplementary figure S2). Additionally, linkage disequilibrium (LD) analysis showed that the average genome-wide LD for the diversity panel was r\u0026sup2; = 0.471. Common population genetic tests were computed to assess natural diversity and selection pressure in the GWAS panel. The average level of silent-site nucleotide diversities per site (π) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and population mutation parameter (θ) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e45\u003c/span\u003e] were observed 0.0000209 and 482.718, respectively. Statistical tests of neutrality, which include Tajima\u0026rsquo;s D [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e46\u003c/span\u003e], Fu and Li's D* and Fu and Li's F*, yielded values of 3.55, 4.22, and 4.36, respectively (Supplementary Table S1).\u003c/p\u003e\n\u003ch3\u003eGWAS analysis\u003c/h3\u003e\n\u003cp\u003eThe analysis using different association models identified associations between specific loci and DTM and DTF traits across different years (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA \u0026amp; \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, Supplementary figure S3). We identified 20 significant loci for days to flowering and maturity traits, among them 12 are new and 8 were previously known loci (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u0026amp; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The 12 newly identified loci, were distributed across different chromosomes 3, 4, 8, 10, 12, 14, 15, and 18 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among these, chr 03 emerged as particularly significant, revealing three important loci i.e. Lee.Gm03-1, Lee.Gm03-2, and Lee.Gm03-3. Of these, Lee.Gm03-3 stood out as a key finding for DTF across different environments (2019, 2020, 2021), among various models applied (BLINK, FarmCPU and MLM). For DTF in year 2021, the SNP S3_46108324 showed a highly significant p-value of 1.22E-08 in the BLINK model and 2.20E-06 in the FarmCPU model. In 2020, the SNP S3_46108342 also exhibits strong significance, with p-values of 6.16E-09 in BLINK and 1.76E-07 in FarmCPU for DTF. Identification of significantly associated markers in narrow genomic region across years enhances its reliability and significance as a locus for flowering time across multiple environments (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA \u0026amp; \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Regarding Locus Lee.Gm03-1, significant associations were observed with DTM in multiple years, specifically in 2020 and 2022 using the FarmCPU and MLM models, and in 2021 with the MLM model. Locus Lee.Gm03-2 demonstrated associations with DTF, showing significant results in 2020 with both the BLINK and FarmCPU models, and in 2019 with the MLM model, highlighting additional loci of interest for maturation and flowering characteristics. Locus Lee.Gm03-3 was further investigated to study the effects of alleles and haplotypes on days to flowering (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eA to \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). The results identified five major haplotypes: Hap1, Hap2, Hap3, Hap4, and Hap6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eC \u0026amp; \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Hap1 is associated with early flowering in both environments (2020 and 2021) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eD), while Hap3 and Hap6 are associated with comparatively delayed flowering. The allelic effects of two significant markers, S3_46108324 and S3_46108342, showed significant differences between the alleles (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eE).\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\u003eSignificant SNPs identified through GWAS Study for flowering and maturity traits in soybean\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003ePosition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.no.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTrait \u0026amp; Environment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGlyma Lee\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWm82.a2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP.value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEffect\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLee.Gm03-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTM_2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eS3_24549807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e24549807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e21559727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.92E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-3.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTM_2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.11E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-2.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTM_2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS3_25992215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25992215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e22964587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.18E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-2.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eLee.Gm03-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTF_2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLINK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS3_40798304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40798304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e37356467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.72E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-1.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTF_2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS3_40903281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40903281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e37356467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.31E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-1.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTF_2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS3_41286372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e41286372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e37836244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.13E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTF_2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS3_41452378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e41452378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e37902820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.26E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-2.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eLee.Gm03-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDTF_2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLINK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eS3_46108324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e46108324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e42575015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.22E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.20E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDTF_2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLINK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eS3_46108342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e46108342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e42575033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.16E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.76E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-1.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTF_2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.37E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-3.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLee.Gm04-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTF_2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLINK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eS4_6912661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e6912661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e6809687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.09E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTF_2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLINK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTF_2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.88E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-1.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLee.Gm04-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTF_2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLINK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eS4_49785758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e49785758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e47293738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.57E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDTF_2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLINK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.88E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.77E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-1.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLee.Gm07-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDTF_2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLINK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eS7_5538630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5538630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5471070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.39E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.02E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLee.Gm08-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTF_2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLINK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eS8_20849488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e20849488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e20430185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.49E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDTF_2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLINK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.70E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.85E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eLee.Gm10-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTF_2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLINK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eS10_40473330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e40473330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e37138990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.79E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTM_2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.40E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-2.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTM_2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.02E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTM_2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.05E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eLee.Gm12-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTF_2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBLINK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eS12_36598172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e36598172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e13171182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.43E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTF_2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.94E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTF_2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.52E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTF_2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.15E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLee.Gm14-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTF_2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS14_52397292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e52397292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e38307979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.91E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLee.Gm15-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTM_2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS15_7613524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7613524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7538344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.12E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-2.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTM_2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS15_8057946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8057946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7982564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.96E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLee.Gm18-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTF_2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLINK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eS18_58159788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e58159788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e54849471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.24E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTM_2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.88E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-2.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTF_2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.5E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-2.34\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 \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\u003eValidation of known genes for flowering and maturity traits in soybean\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\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 \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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ePosition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.N.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReported Genes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGene position (bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLEE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWM82\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep.value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eTrait \u0026amp; Environment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eE11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e4,102,968\u0026ndash;4,114,174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eS7_42711643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e42711643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e38883887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.49E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.17519685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDTF_2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"3\" nameend=\"c12\" namest=\"c11\" rowspan=\"4\"\u003e \u003cp\u003eBLINK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.95E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.17519685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDTF_2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eS7_42766230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e42766230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e38883887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.88E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.116141732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDTF_2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.85E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.116141732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDTF_2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eE10/FT4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e47,458,142\u0026thinsp;\u0026minus;\u0026thinsp;47,459,829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS8_46399480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46399480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e43830824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.67E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.081631623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDTM_2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c12\" namest=\"c11\" rowspan=\"2\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e61\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS8_47131522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47131522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44564154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.46E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.062596903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDTF_2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eE2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e45,294,735\u0026thinsp;\u0026minus;\u0026thinsp;45,316,121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS10_47942176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e47942176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e44538772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.21E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.236220472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDTF_2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c12\" namest=\"c11\" rowspan=\"2\"\u003e \u003cp\u003eBLINK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS10_47942176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.76E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.236220472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDTF_2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS10_47927282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47927282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44523878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.99E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.981309437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDTF_2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eMLM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e 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align=\"left\" colname=\"c13\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e64\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e\u003cb\u003eE9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e31,109,999\u0026thinsp;\u0026minus;\u0026thinsp;31,114,963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eS16_29195077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e29195077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e27706239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.60E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.982219442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDTF_2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c12\" namest=\"c11\" rowspan=\"3\"\u003e \u003cp\u003eMLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e[19,20]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e 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align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.69E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.162794485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDTF_2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS16_30814558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30814558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29250204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.96E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.788084178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDTF_2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e 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rowspan=\"2\"\u003e \u003cp\u003eBLINK\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS16_33931035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.00E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.38976378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDTF_2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003eDT2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e55,638,209\u0026thinsp;\u0026minus;\u0026thinsp;55,646,547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eS18_53487286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e53487286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e50490755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.74E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.352362205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDTF_2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c12\" namest=\"c11\" rowspan=\"2\"\u003e \u003cp\u003eBLINK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e65\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.74E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.352362205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDTF_2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.53E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.182516985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDTM_2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e 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align=\"left\" colname=\"c7\"\u003e \u003cp\u003e51929135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.22E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.369215003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDTF_2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS18_53487286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53487286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53487286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50458229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.74E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-2.592311118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDTM_2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c12\" namest=\"c11\" rowspan=\"2\"\u003e \u003cp\u003eMLM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS18_54677671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54677671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54677671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e51648614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.16E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-3.868177112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDTF_2019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eDT1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e45,183,357\u0026thinsp;\u0026minus;\u0026thinsp;45,185,175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eS19_42283700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e42283700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e39914927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.92E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.200787402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDTF_2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eBLINK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e66\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.83E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-2.248564364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDTM_2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eE4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e33,236,018\u0026ndash;33,241,692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eS20_36021806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e36021806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e33958681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.84E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.265748031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDTF_2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eBLINK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e[13,32]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.61E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.265748031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDTF_2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eBLINK\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS20_36824277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36824277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34746065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.46E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.024440493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDTF_2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eS20_36946413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e36946413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e34866334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.51E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.281496063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDTF_2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c12\" namest=\"c11\" rowspan=\"2\"\u003e \u003cp\u003eBLINK\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.44E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.281496063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDTF_2022\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\u003eIn this study, we validated nine previously reported genes through our GWAS panel (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), including E11 on Chr 7 (DTF in 2021 and 2022), E10/FT4 on Chr 8 (DTM and DTF in 2020), and E2 on Chr 10 (DTF in 2021 and 2022). PRR7/Tof12 on Chr 12 was associated with DTF in 2020, while E9 on Chr 16 was linked to both DTM in 2021 and DTF in 2020 and 2021. DT2 on Chr 18 and DT1 on Chr 19 showed associations with both DTF and DTM across years, and E4 on Chr 20 was linked to DTF in 2021.\u003c/p\u003e\n\u003ch3\u003eCandidate Gene Identification\u003c/h3\u003e\n\u003cp\u003eAll three significant loci on chromosome 03 were explored for candidate gene identification. The three loci located on chromosome 03 revealed candidate genes related to reproduction, photoperiod, and circadian rhythm. Lee.Gm03-1 carries a single gene, Glyma.03G080600, which plays a critical role in determining bilateral symmetry, meristem initiation, and polarity specification of the adaxial/abaxial axis. Lee.Gm03-2 stands out as a primary locus for genes involved in reproductive functions (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). It contains multiple genes associated with flower development, floral organ identity and senescence, as well as pollen tube growth and guidance. For example, Glyma.03g160000 and Glyma.03g160700 are annotated for their role in flower development, while Glyma.03g164200 is annotated with floral organ senescence. Additionally, Glyma.03g161000, Glyma.03g164900, and Glyma.03g165700 contribute to pollen tube growth and guidance, while Glyma.03g166000 is linked to male meiosis. Together, these genes make Lee.Gm03-2 a hub for reproduction-related genes. On the other hand, locus Lee.Gm03-3 predominantly houses genes related to circadian rhythm, photoreception and light responsiveness. This locus consists of several genes that are highly responsive to light, especially red and far-red light signaling pathways, which are crucial for plant adaptation to the environment. For example, genes such as Glyma.03G227300 and Glyma.03G227800 are involved in phototropism, photomorphogenesis, and red/far-red light signaling. Glyma.03G221600 participates in gibberellin catabolic processes responsive to red or far-red light, while Glyma.03G225000 and Glyma.03G227800 are involved in red and far-red light signaling pathways. Additionally, locus Lee.Gm03-3 contains genes controlling circadian rhythm, including Glyma.03G225000, Glyma.03G227300, and Glyma.03G227800, which regulate processes related to circadian timing, light detection, and photo-morphogenesis.\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\u003eIdentification of putative candidate genes in soybean\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=\"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 \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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStart\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePfamID\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLee.Gm03-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03G080600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21508100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21512393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edetermination of bilateral symmetry; meristem initiation; polarity specification of adaxial/abaxial axis; regulation of meristem growth; response to light stimulus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFAMILY NOT NAMED\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eLee.Gm03-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03g160000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37508220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37513007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003epositive regulation of flower development;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKH_1;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03g160700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37584972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37586022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eflower development; specification of floral organ identity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ezf-C2H2_6;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03g161000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37612023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37615718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003epollen tube growth; protein transport; vesicle-mediated transport;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03g163400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37813139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37813951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eseed maturation;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCupin_1;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03g164000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37866656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37868942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003epost-embryonic plant morphogenesis;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDUF640;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03g164100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37892273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37895682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ephotoperiodism, flowering; negative regulation of long-day photoperiodism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJmjN;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03g164200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37902284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37909534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003efloral organ senescence;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNAM;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03g164900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37980836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37982905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003epollen tube growth;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePBD;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03g165700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38045521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38050084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003epollen tube guidance;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLRR_8; Pkinase;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03g166000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38068482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38070661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003emale meiosis II;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"17\" rowspan=\"18\"\u003e \u003cp\u003eLee.Gm03-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03G219100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42265891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42275563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ecytokinin mediated signaling pathway; embryo sac development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHistidine kinase-, DNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03G219300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42285599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42286853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003epositive regulation of seed maturation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ebZIP transcription factor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03G219800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42312343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42319568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDNA-dependent; vegetative phase change; vernalization response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSET domain\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03G219900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42325550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42327910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003efloral organ morphogenesis; gibberellic acid mediated signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGRAS domain family\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03G220100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42334845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42336847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eembryo development ending in seed dormancy;pollen development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWRKY DNA -binding domain\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03G221200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42433366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42437270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eembryo sac egg cell differentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHelicase conserved C-terminal domain;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03G221600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42466464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42471412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003egibberellin catabolic process; response to red or far red light\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2OG-Fe(II) oxygenase superfamily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03G223300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42601145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42604438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ephotoperiodism, flowering; protein folding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDnaJ domain\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03G224300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42656642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42665584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003evegetative phase change; vernalization response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSET domain\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03G225000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42726377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42729203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ecircadian rhythm; gibberellic acid mediated signaling pathway;red or far-red light signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHelix-loop-helix DNA-binding domain\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03G226000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42818553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42823054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edetermination of bilateral symmetry; meristem initiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCellulase (glycosyl hydrolase family 5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03G226500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42874585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42876167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eembryo development ending in seed dormancy; vegetative to reproductive phase transition of meristem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03G226600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42877084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42882874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eembryo development ending in seed dormancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGUCT (NUC152) domain;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03G227300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42918771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42923401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ecircadian rhythm; detection of visible light; photomorphogenesis; phototropism;response to continuous far red light stimulus by the high-irradiance response system; response to far red light; response to very low fluence red light stimulus; signal transduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePhytochrome region; GAF domain; His Kinase A (phospho-acceptor) domain; PAS fold;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03G227800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42980227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42984347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ecircadian rhythm; de-etiolation; gibberellic acid mediated signaling pathway; gravitropism; positive regulation of anthocyanin metabolic process; red or far-red light signaling pathway; regulation of transcription, DNA-dependent; response to red or far red light; signal transduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHelix-loop-helix DNA-binding domain\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03G228700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43069814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43073090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003efloral organ formation; regulation of flower development; sepal formation; spindle assembly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDomain of unknown function (DUF3635)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03g224400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42667804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42673022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003epollen exine formation; anther wall tapetum development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ezf-Sec23_Sec24; Sec23_helical; Sec23_trunk;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlyma.03g225000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42726377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42729203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ered, far-red light phototransduction; response to red or far red light; de-etiolation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHLH;\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\u003eIn summary, Lee.Gm03-2 is primarily associated with genes governing reproductive processes, while Lee.Gm03-3 has a greater concentration of genes involved in light responsiveness, photoreception, and circadian rhythm, highlighting each locus's unique contributions to plant development and environmental adaptation.\u003c/p\u003e\n\u003ch3\u003eExpression study for putative candidate genes\u003c/h3\u003e\n\u003cp\u003eWe analyzed the tissue specific expression data available at Phytozome database for all identified putative candidate genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eA \u0026amp; \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) and compared them based on expression data derived from different tissues directly linked to flowering and maturity pathway, including open flowers, unopened flowers, shoot tips, and leaves.\u003c/p\u003e \u003cp\u003eThe gene expression data of Lee.Gm03-2 and Lee.Gm03-3 loci suggests its roles in plant growth, reproductive development, and environmental responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eB \u0026amp; \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). In the Lee.Gm03-2 locus, Glyma.03G164000 showed higher expression in the shoot tip (5.307) and low expression in the leaf (0.052), likely aiding growth in the shoot tip and reproductive transition. Glyma.03G164200, with high expression in open flowers (1.242) and moderate in unopened flowers (0.417), may contribute to floral organ senescence, potentially regulating flower aging. Glyma.03G166000 displays expression across tissues, particularly in the shoot tip (5.01), suggesting involvement in reproductive processes, possibly playing a role in male meiosis II.\u003c/p\u003e \u003cp\u003eIn the Lee.Gm03-3 locus, Glyma.03G219100 is highly expressed in open flowers (5.2) and moderately in unopened flowers (0.806), possibly influencing reproductive development through cytokinin-mediated signaling pathways. Glyma.03G225000, with elevated expression in open flowers (4.2) and unopened flowers (2.9), may be involved in circadian rhythm and light response pathways, potentially regulating flowering time in response to light. Strong expression of Glyma.03G226000 in open flowers (10.8) suggests it could support flower structure formation, possibly through meristem initiation and symmetry determination. Lastly, Glyma.03G227300 having Phytochrome region; GAF domain; His Kinase A (phospho-acceptor) showing moderate expression across tissues and particularly in unopened flowers (1.078), may contribute to photo-morphogenesis and light response, helping the plant adapt to light conditions through circadian and phototropic responses. This gene expression pattern thus provides insights into how each gene may function in flowering, growth, and environmental adaptation. These genes were further studied with the orthologs from different species (Supplementary figure S4a-S4g). These key genes are also present in important legumes and other species suggesting its crucial role in regulation of flowering and maturity.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eValidation of Major Locus Lee.Gm03-3 Using KASP Analysis\u003c/h2\u003e \u003cp\u003eThe stable locus Lee.Gm03-3, containing SNPs S3_46108324 and S3_46108342, was validated using KASP marker analysis in a separate population consisting of 157 Indian soybean cultivars (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eA \u0026amp; \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). For SNP S3_46108324, significant associations were found with DTM, exhibiting p-value of 3.23E-04, which explained 8.34% of phenotypic variation (\u003cem\u003eR\u0026sup2;\u003c/em\u003e) in 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Similarly, SNP S3_46108342 demonstrated significant associations with DTF, and DTM, with \u003cem\u003ep\u003c/em\u003e-values of 0.017 and 0.016, respectively, accounting for 4.79% and 4.90%, of phenotypic variation in 2022 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Further allelic effect for DTM-2023 and DTM-2022 phenotype also showed significance difference (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eD \u0026amp; \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). These findings validate Lee.Gm03-3 and its SNPs as important genetic markers for flowering and maturity traits.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe genetic architectures pertaining to the flowering time and maturity can be key factors in achieving the designer soybean plants that are adaptable and of a desired type for enhanced productivity in specific environments. Several genetic studies revealed critical role of many single genes such as E1, E2, E3, E4, E9 FT2a, FT5a, J, Tof4, Tof5, Tof8, Tof9, Tof11, Tof12, Tof13, Tof16 and Tof18 in photoperiod mediated flowering for adaptation of soybean [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. In our investigation, for the first time in Indian condition, we dissected the underlying genetic architecture of flowering time, and maturity duration in an Indian environment, along with the key genetic loci and candidate genes that regulate these traits in the soybean. Our GWAS panel consisting diverse 254 germplasm lines, divided into six distinct subpopulations, indicated by population structure analysis, supports the significance of genetic diversity within the dataset, which is essential for accurately mapping these traits. This stratification, confirmed by both phylogenetic clustering and PCA, indicates a robust genetic basis for the identified loci. In GWAS study, we identified a locus, Lee.Gm03-3 as a key locus for flowering time with markers S3_46108324 and S3_46108342, the two markers linked to the locus in both environments i.e. 2020 and 2021, respectively. Furthermore, we converted the two significant SNPs of Lee.Gm03-3 into KASP assays and validated them in a separate soybean population consisting of diverse cultivar, enhancing utility of this locus in breeding. These associations enable breeders to use this consistency to their advantage, further underscoring the importance of Lee.Gm03-3 in the design of such soybean varieties that can grow under different photoperiods. Several genome wide association studies in soybean, revealed several loci controlling flowering time and maturity duration, such as J [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e25\u003c/span\u003e], Tof5[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e23\u003c/span\u003e], Tof11 and Tof12[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e25\u003c/span\u003e], Tof13[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e26\u003c/span\u003e], Tof16[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e27\u003c/span\u003e]), and Tof18 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. E2 also detected through GWAS in the identification of Tof5, Tof13, and Tof18 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur GWAS study not only identified several new loci but also validated the presence of previously known loci associated with flowering time and maturity duration, confirming the robustness of our findings. These known loci include E2, E4, E11, E10/FT4, PRR7/Tof12, E9, Dt1, and Dt2. Functions of these loci is well studied and their role in regulation of flowering time and maturity in different photoperiod regimes is reported in several studies [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. For instance, E1 is considered as the main contributor of photoperiod sensitivity and the other E gene interactions affecting the flowering and maturity response making it crucial in enhancing soybean adaptability across latitudes [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. E1 by interacting with the FUL transcription suppresses E9 activities through binding the promoter, while FUL interacts with FT2a (E9) and FT5a to enhance their expression with the end result of promoting flowering in long day conditions [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In our GWAS, according to p-value and effect size, E9 was revealed as the major locus determining the variation of DTF and DTM traits in an Indian environment.\u003c/p\u003e \u003cp\u003eThe two candidate genes of Lee.Gm03-3, viz. Glyma.03G227300, and Glyma.03G225000, may be involved in regulating flowering time in soybean by modulating circadian rhythms and light-responsive pathways. Glyma.03G227300 genes also reported earlier as \u003cem\u003eGmPHYA4\u003c/em\u003e, contains the Phytochrome region; GAF domain; and His Kinase A (phospho-acceptor). This functional gene could involve maintaining the circadian rhythm, detecting light sources, the switching on of a plant\u0026rsquo;s development processes and the directional growth towards or away from the center of light, red light they are thought may practice light hierarch and assist in opening timing of the flowering phase in accordance with the duration of the day. The function of this gene in photoreceptor activity is consistent with its genetic architecture since it is also a photoperiod control gene, along with other genes studied in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e and \u003cem\u003eOryza sativa\u003c/em\u003e in which the role of phytochromes was also essential for flowering with respect the day-length [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, Glyma.03G225000 is associated with the circadian clock and with the GA-mediated signaling pathways and responses to red light and far-red light. This gene is also associated with flowering time and other GA enhancing growth attributes. GA signaling has been known to have functional crosstalk with photoperiod pathways. Such hypothesis is applicable in this scenario because the gene in question is considered to be responsible for coordinating GA signaling and circadian rhythms that ultimately determine flowering time in response to light. This regulatory pathway has already been reported in other plants whereby flowering is dependent on GA and photoreceptors under different light treated conditions [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. In combination, Glyma.03G227300 and Glyma.03G225000 also represent as key gene candidates in breeding programs that alter flowering and maturity time of soybean plants grown under different photoperiods.\u003c/p\u003e \u003cp\u003eUltimately, the detected loci might provide practical markers for improvement of the timing of flowering and maturity traits of soybean that may lead to increase adoption of new variety in India. The findings of present study are not only consistent with previously identified loci but also show the potential of using GWAS as a tool to discover new and stable markers for important traits like DTF and DTM [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The mapping of these loci or genes opens new avenues for crop improvement with genetic engineering that could be initiated for altering genotype and phenotype of soybean genotypes.\u003c/p\u003e \u003cp\u003eAdditionally, understanding photoperiod sensitivity and genetic control of flowering time will help in developing breeding strategies to balance early flowering with adequate plant growth. This study will provide a valuable foundation for future genetic and molecular research aimed at increasing soybean yield and adaptation. For better insight, functional validation and chacterization of these genes will be helpful for improvement of soybean varieties. Similar studies need to be conducted at different altitude to get robust marker and genes for regulation of flowering and maturity time.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study enhances the current understanding of the genetic mechanisms influencing photoperiod sensitivity and flowering in soybean. By identifying 20 significant loci, including 12 novel loci, and validating 8 previously known reported genes, it provides a comprehensive framework for genetic improvement. The discovery of the 4 key candidate genes Glyma.03G227300, Glyma.03G225000, Glyma.03G219100 and Glyma.03G226000 associated with the significant locus Lee.Gm03-3 on chromosome 03 offers valuable insights into the regulatory pathways of flowering and maturity, including circadian rhythm, hormone signaling, and light-response pathways. SNP markers identified in this study will help in the molecular breeding programme for developing early maturing soybean cultivars. These findings open new avenues for breeding soybean varieties with optimized flowering duration and better adaptability to Indian environmental conditions, contributing to improved agricultural productivity.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePlant Materials and Phenotypic evaluation\u003c/h2\u003e \u003cp\u003ePhenotyping was conducted on 254 diverse soybean germplasm accessions during the summer season (Mid-June to Mid-October) over four consecutive years, from 2019 to 2022, using an augmented design at the ICAR-Indian Institute of Soybean Research (22.7196\u0026deg; N, 75.8577\u0026deg; E), Indore, India. Morphological traits related to flowering and maturity, such as Days to Flowering (DTF) recorded at the R1 stage (the day when 50% of the plants in a plot have an open flower on one of the top four nodes that bears a fully expanded leaf) and Days to Maturity (DTM) were measured in the field for all four years.\u003c/p\u003e \u003cp\u003ePhenotypic analysis included normality distribution and descriptive statistics such as mean, standard deviation (SD), maximum and minimum trait values, and the coefficient of variation (CV%). In addition, correlation, PCA analysis and two-way Analysis of Variance (ANOVA) were performed using GraphPad Prism version 9 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.graphpad.com\u003c/span\u003e\u003cspan address=\"http://www.graphpad.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to evaluate the effects of genotype (G), environment (E), and genotype-by-environment interaction (G \u0026times; E) on both the traits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGenotyping\u003c/h2\u003e \u003cp\u003eThe genomic DNA from the leaves derived form 254 soybean diverse germplasm accessions was extracted using CTAB method [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Genotyped by sequencing (GBS) was conducted following the methods and recommendations outlined by [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e41\u003c/span\u003e]and [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e43\u003c/span\u003e]). The GBS library was created with \u003cem\u003eApe\u003c/em\u003eKI restriction enzyme digestion. A 158\u0026nbsp;million single-end reads were generated with an Ion Torrent Proton System (Thermo Fisher Scientific Inc., USA) by ICRISAT (International Crops Research Institute for the Semi-Arid \u0026lsquo;Tropics), Hyderabad, India. These were processed using the Fast-GBS.v2 pipeline [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. FASTQ files were demultiplexed, trimmed, and then mapped against the soybean reference genome (Glyma.Lee_v2.0). Imputations were performed in TASSEL software to fill missing data and further SNP data were filtered by minor allele frequency (MAF)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and missing rate\u0026thinsp;\u0026gt;\u0026thinsp;10% and finally a total of 66300 SNPs distributed all over 20 chromosomes were used for further study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGenetic Diversity analysis\u003c/h2\u003e \u003cp\u003eLevel of silent-site nucleotide diversities per site (π) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e44\u003c/span\u003e]) and population mutation n parameter (θ) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e45\u003c/span\u003e] was estimated. Statistical tests of neutrality such as Tajima\u0026rsquo;s D [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e46\u003c/span\u003e]), Fu and Li\u0026rsquo;s D* and F were also calculated to examine the selection pressure at SNPs in our GWAS panel by using DnaSP software version 5.10. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ub.edu/dnasp/index_v5.html\u003c/span\u003e\u003cspan address=\"http://www.ub.edu/dnasp/index_v5.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A Neighbour-joining tree was constructed using the TASSEL software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.maizegenetics.net/tassel\u003c/span\u003e\u003cspan address=\"https://www.maizegenetics.net/tassel\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Principal Component Analysis (PCA) and LD decay plot were generated using the GAPIT package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.maizegenetics.net/gapit\u003c/span\u003e\u003cspan address=\"https://www.maizegenetics.net/gapit\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) implemented in R. Population structure was developed using STRUCTURE software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://web.stanford.edu/group/pritchardlab/structure.html\u003c/span\u003e\u003cspan address=\"https://web.stanford.edu/group/pritchardlab/structure.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGenome-wide association study and candidate gene identification\u003c/h2\u003e \u003cp\u003eThe analysis involved 254 diverse soybean accessions to study SNPs associated with flowering and maturity across four years (2019\u0026ndash;2022). The association analysis was performed using three analysis models. Mixed Linear Model (MLM, using PCA (fixed-effect factor)\u0026thinsp;+\u0026thinsp;K (random-effect factor)) was implemented in TASSEL v5.0[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e47\u003c/span\u003e]). The Fixed and Random Model Circulating Probability Unification (FarmCPU) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e48\u003c/span\u003e] and Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e49\u003c/span\u003e] models were applied through GAPIT package [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e50\u003c/span\u003e] in R. The first two principal components were included as covariates in both models. A Bonferroni correction (α/N) was used to set the significance threshold, where N represents the number of tested SNPs and α\u0026thinsp;=\u0026thinsp;1. Manhattan plots illustrated significant markers, while quantile-quantile (Q-Q) plots compared expected versus observed p-value distributions (on a -log10 scale).\u003c/p\u003e \u003cp\u003eFor candidate gene identification, genomic regions 250 kb upstream and downstream of significant SNPs (totaling 500 kb) were analyzed, based on the average linkage disequilibrium decay in soybean [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Genes within these regions were identified using the Lee reference genome and Wm82.a2 genome assembly, with data obtained from SoyBase (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.soybase.org\u003c/span\u003e\u003cspan address=\"http://www.soybase.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Further haplotypes were analyzed with in the LD region by using DnaSP software version 5.10 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ub.edu/dnasp/index_v5.html\u003c/span\u003e\u003cspan address=\"http://www.ub.edu/dnasp/index_v5.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eExpression analysis of putative candidate genes\u003c/h2\u003e \u003cp\u003eExpression data for the putative candidate genes identified in various loci were obtained from the Phytozome database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://phytozome-next.jgi.doe.gov/\u003c/span\u003e\u003cspan address=\"https://phytozome-next.jgi.doe.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The mRNA expression data covered different growth stages related to flowering and maturity, including flower opening, unopened flowers, shoot tip, and leaf tissues. To effectively visualize the expression patterns and relationships among these candidate genes, the expression data were converted into a heatmap using the TBtools software [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eValidation of SNPs with KASP marker\u003c/h2\u003e \u003cp\u003eThe two SNPs of major locus Lee.Gm03-3 were converted to Kompetitive allele specific PCR (KASP) assays and validated using KASP analysis in a new set of 157 soybean genotypes. These 157 soybean genotypes consisting of released soybean cultivars of India, were phenotyped for DTF and DTM in 2022 and 2023. Generalized linear model (GLM) was used for trait association in TASSEL v5.0 [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBLINK\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBayesian-information and Linkage-disequilibrium Iteratively Nested Keyway\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDTF\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDays to Flowering\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDTM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDays to Maturity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFarmCPU\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFixed and Random Model Circulating Probability Unification\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGBS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenotyping-by-sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGWAS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenome-Wide Association Study\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eKASP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKompetitive Allele-Specific PCR\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLinkage Disequilibrium\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMAF\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMinor Allele Frequencies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMLM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMixed Linear Model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePCA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrincipal Component Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eQTL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuantitative Trait Loci\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSNP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle Nucleotide Polymorphisms\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis research was funded and supported by the National Agriculture Science Fund (NASF) of Indian Council of Agricultural Research, New Delhi.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eAll the data and statistics about the current study has been included in the current manuscript in the form of figure and tables. Raw data are available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBroich SL, Palmer RG. Evolutionary studies of the soybean: the frequency and distribution of alleles among collections of \u003cem\u003eGlycine max\u003c/em\u003e and \u003cem\u003eG. soja\u003c/em\u003e of Various origin. Euphytica 1981; 30:55-64.\u003c/li\u003e\n \u003cli\u003eHymowitz T, Newell CA. Taxonomy of the genus \u003cem\u003eGlycine\u003c/em\u003e, domestication and uses of soybeans.\u0026nbsp;Economic botany,\u0026nbsp;1981;35:272-288.\u003c/li\u003e\n \u003cli\u003eGuo J, Wang Y, Song C, Zhou J, Qiu L, Huang H, Wang Y. A single origin and moderate bottleneck during domestication of soybean (\u003cem\u003eGlycine max\u003c/em\u003e): implications from microsatellites and nucleotide sequences.\u0026nbsp;Annals of Botany 2010;106(3):505-514.\u003c/li\u003e\n \u003cli\u003eLi YH, Li W, Zhang C, Yang L, Chang RZ, Gaut BS, Qiu LJ. 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Characterization of two growth period QTLs reveals modification of PRR3 genes during soybean domestication. Plant and Cell Physiology 2019;60(2):407-20.\u003c/li\u003e\n \u003cli\u003eLu S, Dong L, Fang C, Liu S, Kong L, Cheng Q, Chen L, Su T, Nan H, Zhang D, Zhang L. Stepwise selection on homeologous PRR genes controlling flowering and maturity during soybean domestication. Nature Genetics 2020;52(4):428-36.\u003c/li\u003e\n \u003cli\u003eBernard RL. Two genes affecting stem termination in soybeans 1. Crop Science 1972;12(2):235-9.\u003c/li\u003e\n \u003cli\u003eLiu B, Watanabe S, Uchiyama T, Kong F, Kanazawa A, Xia Z, Nagamatsu A, Arai M, Yamada T, Kitamura K, Masuta C. The soybean stem growth habit gene Dt1 is an ortholog of \u003cem\u003eArabidopsis\u003c/em\u003e TERMINAL FLOWER1. Plant Physiology 2010;153(1):198-210.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"b9c121af-233a-4838-8164-7ae99d9c61dd","identifier":"10.13039/501100001503","name":"Indian Council of Agricultural Research","awardNumber":"NASF/GTR-6035/2018-19","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"GWAS, Days to flowering, Maturity, Soybean, Adaptation","lastPublishedDoi":"10.21203/rs.3.rs-6076609/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6076609/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSoybean (\u003cem\u003eGlycine max\u003c/em\u003e [L.] Merril) is a photoperiod-sensitive crop, with traits like days to flowering, days to maturity playing crucial roles in its adaptability and yield. These traits are regulated by genetic networks controlling flowering time and environmental adaptation, making their genetic basis as an essential knowledge for breeders aiming to improve yield and adaptability. In this study, a Genome-Wide Association Study (GWAS) was conducted for Days to flowering (DTF), days to maturity (DTM) by using FarmCPU, BLINK and MLM model on 254 diverse soybean genotypes over four consecutive years (2019\u0026ndash;2022) to dissect genetic architecture for flowering and maturity traits in an Indian Environment.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn this study, GWAS identified 20 significant loci for days to flowering and maturity, among them 12 are new and 8 were previously reported loci. Among the 12 newly identified loci, a significant locus, Lee.Gm03-3 on chromosome 03, is associated with days to flowering and linked with SNP markers S3_46108324 and S3_46108342. We also identified key candidate genes for Lee.Gm03-3, include Glyma.03G227300 (circadian rhythm and photomorphogenesis, Phytochrome region) Glyma.03G225000 (circadian rhythm, gibberellic acid signaling, red/far-red light signaling), Glyma.03G219100 (cytokinin signaling, embryo sac development), and Glyma.03G226000 (meristem initiation). These genes are vital for light-response and developmental pathways. In addition, we also validated eight previously known genes \u003cem\u003eE2, E4\u003c/em\u003e, \u003cem\u003eE9\u003c/em\u003e, \u003cem\u003eE11\u003c/em\u003e, \u003cem\u003eE10/FT4\u003c/em\u003e, \u003cem\u003ePRR7/Tof12\u003c/em\u003e, \u003cem\u003eDt1\u003c/em\u003e, and \u003cem\u003eDt2\u003c/em\u003e that influence flowering and maturity in Indian environment.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study advances understanding of the genetic basis underlying photoperiod sensitivity related genes for circadian rhythm and photomorphogenesis, gibberellic acid signaling, red/far-red light signaling in soybean and highlights potential targets for genetic improvement of flowering maturity duration and adaptability of soybean under Indian environment.\u003c/p\u003e","manuscriptTitle":"Dissecting Genetic Architecture of Flowering and Maturity Traits in Soybean Using GWAS in Indian Environment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-24 08:30:24","doi":"10.21203/rs.3.rs-6076609/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d769c630-50f5-4e73-9649-a271ccb10cca","owner":[],"postedDate":"February 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44664469,"name":"Molecular Genetics"}],"tags":[],"updatedAt":"2025-02-24T08:30:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-24 08:30:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6076609","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6076609","identity":"rs-6076609","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}