Genome-wide association study reveals cytochrome P450 associated with post- emergence metribuzin tolerance in soybean (Glycine max)

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Schroeder, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7303889/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Soybean ( Glycine max ) is one of the most important crops in the world. However, due to multiple herbicide resistance in weeds, few effective postemergence herbicide options remain to control weeds in soybean production systems. Metribuzin is currently one of the most promising herbicides for managing weeds in soybeans due to the low frequency of developing resistant populations. Currently, metribuzin is applied as a pre-emergent herbicide because post-emergence applications are thought to excessively damage most soybean varieties. In this study, a panel of 196 genetically diverse accessions in maturity groups 4, 5, and 6 was used to identify genomic regions associated with soybean response to metribuzin as well as metribuzin-tolerant accessions. Two different concentrations (150 g ai ha − 1 and 300 g ai ha − 1 ) of metribuzin were sprayed across two experimental replications, each with three replicates. A total of nine SNP markers were identified as significantly associated with soybean response to metribuzin. These SNPs are linked to Cytochrome P450 superfamily proteins (CYP), exocyst complex components, and ethylene-responsive transcription factor 3. These candidate genes indicate metabolism is likely the dominant mechanism of tolerance with the management of oxidative stress as a secondary mechanism. Together, these findings reveal novel candidate genes and pathways associated with non-target site resistance and provide valuable markers for breeding soybean cultivars with improved post-emergence metribuzin tolerance. Biological sciences/Biotechnology Biological sciences/Genetics Biological sciences/Molecular biology Biological sciences/Plant sciences Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction With a total of 28% of global agricultural production, soybean (Glycine max ) is one of the most important crops in the United States 1 . Based on regional production estimates, the Southern United States is a major soybean procuding region 2 . However, weed pressure poses significant challenges in crop management and limits the cultivation strategies that can be used by soybean producers 3 . Furthermore, poorly managed weeds in soybean production fields compete with the crop for essential resources such as light, nutrients, and water 4,5 , thus causing yield losses of up to 48% 6,7 . Among the weeds present in soybean production in the Southern US, waterhemp ( Amaranthus tuberculatus ), which causes yield loss of up to 43% 8 , and palmer amaranth ( Amaranthus palmeri ) are particularly devastating species in soybean production 9 . Due to widespread herbicide resistance against glyphosate, glufosinate, synthetic auxins, and PPO in both of these pigweed species, metribuzin and paraquat are often the only effective herbicides for managing Amaranthus species in soybeans being grown in the Southern US 10,11 . However, neither paraquat nor metribuzin can be applied over an established soybean crop because it would greatly damage the plants. While developing either post-emergent metribuzin or paraquat-tolerant soybeans could help soybean producers by adding post-emergent options, metribuzin is a more desirable herbicide to use than paraquat due to its lower toxicity in humans 12,13 . Metribuzin is a triazinone herbicide that disrupts photosynthesis by blocking electron transport in the photosystem II complex from the D1 subunit to the remaining segments of the electron transport chain 14 . It is effective against broad-leaf weed species and provides soil residual activity for 4 to 6 weeks depending on weather conditions 15 . However, in early planted cropping systems, which have become predominant in the Southern US 16 , key weed species often emerge later in the season after the residual activity of pre-emergent metribuzin has declined. Up to 80% of metribuzin can dissipate in the soil within 30 days of application 17,18 , thus limiting its efficacy as a pre-emergent herbicide. One potential strategy to address this issue is the use of a post-emergence metribuzin application. Yet, because metribuzin interferes with photosynthesis, it can cause significant phytotoxicity in sensitive soybean varieties 19 . Symptoms include leaf chlorosis and necrosis, stunted growth, and reduced biomass 20–22 , with intensity influenced by environmental factors, application rates, and the genetic background of individual soybean varieties 23 . One study highlighting variation of pre-emergent metribuzin tolerance in soybean based on physiological processes found that a tolerant variety ‘Essex’ metabolized metribuzin rapidly compared to ‘Coker 102’, a susceptible cultivar. The rapid metabolism of metribuzin led to limited accumulation of unmetabolized herbicide in plant tissue which enhanced tolerance in Essex 24 . On a genetic level, linkage between Rps1-k locus and improved pre-emergent metribuzin tolerance. Cultivars carrying Rps1-k showed significantly higher survival and biomass under metribuzin treatment ​ 25 . However, it is unclear if the Rps-k locus or another locus in close linkage is responsible for tolerance. While the search for improved response to metribuzin has continued, no natural soybean genetic variants responsible for metribuzin tolerance have been identified. 26–28 . Therefore, the objective of this study was to explore novel genetic variations conferring non-target site herbicide resistance (NTSR) against post-emergence metribuzin application in soybean. In addition, this study aimed to identify genetic resources that could be used to further characterize the genetic architecture through QTL mapping, as well as develop improved breeding populations for post-emergence metribuzin tolerance. 2. Materials and methods 1. Plant materials A total of 196 genetically diverse soybean accessions (Supplementary Table 1, Supplementary Figs. 2 & 4) from the Germplasm Resource Information Network (GRIN) were included in this study. These represent a subset of the 382 accessions utilized for a GWAS study for off-target dicamba tolerance in soybean 29 . The panel assembled consisted of maturity groups (MGs) MG-III(49), MG-IV(89) and MG-V(36). All of the accessions used in this study were sourced by submitting a request to GRIN. No accessions were sourced from the wild, limiting the ethical considerations required to obtain the accessions used in this trial. 2. Growing conditions Plants were grown in 2-inch diameter SC10 black “Conetainers” placed into 7 × 14 well cavity trays (Stuewe and Sons, Tangent, Oregon). The containers were filled up to the brim with PROMIX BX BIOFUNGICUDE + MYCORRHIZAE horticultural mix. Fourteen seeds of each variety were sown by hand one inch deep into the horticultural mix; all containers were promptly watered to initiate germination. Seedlings were transplanted into new 98-well cavity trays following experimental design plans. The experiment was set up using a factorial design between dose and variety in randomized complete blocks with 3 replicates per treatment. The entire experiment was repeated twice in two trials separated by time. Plants were grown at the University of Kentucky Greenhouse Facility in Lexington, Kentucky. Supplemental lighting was used to ensure that plants received at least 15 hours of light a day. The first trial was installed on July 29, 2024, and the second trial on September 9, 2024. 3. Phenotyping and data processing In each trial, metribuzin was applied at a rate of 150 g ai ha − 1 and 300 g ai ha − 1 in a DeVries (Hollandale, MN) research spray chamber when the soybean plants reached the V2 growth stage 30 . The metribuzin was applied with 0.5% “chemsurf” non-ionic surfactant added to the solution. Injury ratings were collected 21 days after treatment for both 150 and 300 g ai ha − 1 trials. Spray trials were performed indoors following best practices for limiting exposure to the environment and to personnel carrying out the experiments such as collecting waste from the spray chamber into a tank and requiring personnel to wear gloves when applying the metribuzin treatments. The herbicide injury was reported following a 1–5 scale (Supplementary Fig. 5) defined as: 1) no damage; 2) at least two trifoliate leaves left undamaged on the plant; 3) at least one set of full trifoliate leaf left undamaged on the plant; 4) all leaves are heavily damaged but growing point is still alive; 5) complete plant death. The phenotypic data was processed using a linear-mixed effect model with the ‘ lmer’ of the R package ‘ lme4 ’ 31 . Models were fitted with the full data set for the combined trial GWAS and by trial and dose for the within trials dataset where all terms were included as a fixed effect, except for the flat which was included as random effect to account for the positioning of the flat in the green house or other correlated conditions. The flat plants were randomized into was included as a random effect because there are locations that the flats could be placed in and the effect of the flat is not of experimental interest. The linear model for within trial is as follows: [1] \(\:{y}_{ijk}=\:{g}_{i}+\:{d}_{j}+\:{gd}_{ij}+{\left(f\right)d}_{jk}+{e}_{ijk}\) While the linear model for the full dataset was as follows: [2] \(\:{y}_{ijkl}=\:{g}_{i}+\:{d}_{j}+{t}_{k}+\:{gd}_{ij}+{gt}_{ik}+{dt}_{jk}+{gdt}_{ijk}+{\left(f\right)dt}_{jkl}+{e}_{ijk}\) Where g is the accession, d is the herbicide rate, t is the trial, f is the 98-well cavity tray that the plant was placed in during randomization and e is the normally distributed error term. Best Linear Unbiased Estimates (BLUEs) for genotypes at each rate were estimated by using the ‘ emmeans ’ function in the ‘emmeans’ R package (v.10.6) 32 . Generalized heritability was estimated using the Cullis method 33 from the linear models but the term for accession was converted to random effect in the model (along with any interactions containing accession). [3] \(\:{H}^{2}=1-\frac{{\sigma\:}_{BLUP\:}^{2}}{2{\sigma\:}_{g}^{2}}\:\) Where \(\:{H}^{2}\) is the generalized heritability, \(\:{\sigma\:}_{BLUP\:}^{2}\) is the variation of the BLUPs, \(\:{\sigma\:}_{g}^{2}\) is the variation associated with the genetic effect from the mixed model. 5. Population structure and LD To account for genetic substructure and reduce confounding of genetic subgroups in association analysis, we conducted the population structure and ancestral analysis. Population structure was assessed using the ‘ snmf ’ function in R package ‘LEA’ ( v3.16.0 ) 34 . Three ancestral groups were identified based on the lowest cross-entropy value. Principal component analysis (PCA) was then performed using the ‘ pca ’ function of the R package ‘LEA’, after converting the genotype matrix to the lfmm format. Data were scaled and centered, and PCA results were visualized using the ‘ geom_mark_ellipse function’ in the ggforce R package (V0.4.2) 35 , with pie charts representing individual ancestry proportions. 34 . LD analysis was performed using the LD decay function from the ‘sommer’ R package (v4.3.6) 36 in R. The analysis utilized a marker matrix and a map with distances between markers in base pairs. 6. GWAS Multiple GWAS were performed using each genotype’s BLUEs as the response variable for the following scenarios: i) each rate in each trial, and ii) each rate with the trials combined. The MLMM 37 and BLINK 38 methods were implemented using ‘GAPIT’ function from the ‘GAPIT’ package in R 39 . The MLMM method has lower power than the BLINK method, however the MLMM method results are more stable and conservative than BLINK because it uses a kinship matrix with a mixed model to control for relatedness between accessions. The BLINK method was also used because it has greater power to detect SNPs than the MLMM as it uses significant markers found in previous iterations to control population stratification without the loss of power due to the inclusion of a random effect using genomic based kinship matrix. Both models are multi-marker methods that include additional QTLs in a stepwise fashion. Principal components were sequentially added to each model until the QQ-plots demonstrated that the population structure was controlled for not overly reducing statistical power. SNPs were considered significant when they were below Bonferonni where alpha is 0.05. 7. Candidate genes and phylogenetic tree of CYP450s Candidate genes were identified within 50 kb (upstream and downstream, Supplementary Figs. 1 & 3) of each significant SNP using the Soybase Genome Browser tool 40 . Following the identification of Cytochrome P450 superfamily proteins(CYP) candidate genes, their coding sequences (CDS) and protein sequences were retrieved from SoyBase. Basic Local Alignment Search Tool (BLAST) was used to establish functional homology and evolutionary conservation between identified candidate genes and reference genomes of Arabidopsis thaliana and Glycine max. BLASTp and BLASTn were used for protein and nucleotide sequences comparison, respectively. Ensuring high-confidence alignments for downstream evolutionary analyses, thresholds for sequence homology were set as E-value ≤ 1 − 10 , identity ≥ 80%, and coverage ≥ 70%. To highlight the relationship of candidate genes across Arabidopsis thaliana and Glycine max , multiple sequence alignment was performed using ClustalW within the MEGA (Molecular Evolutionary Genetics Analysis) software (version 11) 41 . To eliminate the ambiguities, alignment was visually inspected and manually curated. Phylogenetic tree was constructed by using the Maximum Likelihood (ML) method. Reliability of the inferred phylogenetic relationships was made sure by using bootstrap analysis with 1,000 replicates in MEGA11. Subsequently, branch colors, labels, and additional metadata were incorporated using iTOL (Interactive Tree of Life) to improve interpretation 42 . 3. Results 3.1 Phenotypic analysis A total of 196 soybean genotypes were evaluated under two concentrations of the herbicide metribuzin: 150 g ai ha -1 and 300 g ai ha -1 . Each experiment was conducted with two experimental replications and three biological replications. Generalized heritability, the mean, range (min and max values), standard deviation, and correlations between Best Linear Unbiased Estimates (BLUEs) at each dose and trial combination were calculated to characterize the variation in traits and obtain a measure of the effect of each trial, dosage and genotype (Table 1). The mean of BLUEs across lines for injury rating (IR) are significantly higher (Supplementary Table 2.) in the 300 g ai ha -1 dose (3.09 vs 2.64 in trial 1; 2.94 vs 2.7 in trial 2 and 3.02 vs 2.67 in combined trails; Table 1). Generalized heritability was high for all the doses and trials ranging from 0.7 to 0.84 (Table 1). There were significant differences among soybean lines (p < 2.2E-16, Supplemental table 1). The effect of herbicide dose on IR was significant in both trials and combined trial (p = 7.75E-05, 0.0009, 1.2E-07, Supplemental Table 2). While dose was found to be a relevant factor, the trial effect was found not to be a significant factor (p = 0.2985, supplemental Table 1). In Fig. 1b, the AMMI biplot illustrates genotype × environment (G×E) interactions by plotting genotypic responses across varying trial and herbicide dose conditions. Genotypes located near the origin of the plot—such as Hopei E602 , Pi xian , and Macoupin —demonstrate high phenotypic stability and lower injury ratings, indicating both tolerance to metribuzin and minimal interaction with specific environments, thus reflecting broad tolerance. 3.2. Marker-trait associations To identify genomic loci underlying variation in metribuzin tolerance, we conducted a genome-wide association study (GWAS) across two herbicide concentrations (150 and 300 g ai ha⁻¹) using mixed linear models (Figs 2 & 3). At the 150 g ai ha⁻¹ dosage, four significant SNPs were identified on chromosomes 3, 4, and 16 (Table 2). The strongest signal was observed at ss715585023 on chromosome 3 (position 2, 918, 336), which exhibited a minor allele frequency (MAF) of 0.13, a p-value of 5.94 × 10⁻⁷, and explained 39.4% of the phenotypic variance (Fig. 4). Additional SNPs— ss715624298 (Chr16), ss715585223 (Chr3), and ss715587370 (Chr4)—each explained 12.4% to 34.1% of the variance, further supporting a polygenic model for post-emergence metribuzin response under low dosage (Table 2). At the 300 g ai ha⁻¹ dosage, six SNPs reached genome-wide significance, four of which mapped to a narrow interval on chromosome 3, suggesting the presence of a major-effect locus (Table 2). The most significant SNP, ss715585023 , with a p-value of 2.19 × 10⁻⁹ and explaining up to 54.98% of the variance in Trial 1 (Fig. 4). This SNP also showed consistent effects in the combined dataset, accounting for 27.77% of variation (Fig. 4). Nearby SNPs ( ss715585205 , ss715585199 , ss715585228 ) explained 4.2–28.6% of the variance, reinforcing the importance of this genomic region. Two additional SNP, ss715629879 and ss715632132 , located on chromosome 18, explained 23.32% and 10.02% of variation, indicating potential secondary loci. 3.3. Candidate genes In chromosome 3, four of five SNPs were close to each other (ss715585199, ss715585205, ss715585223, ss715585228; Table 3). The position distance among these four SNPs was 61 kb (chromosome 3: 3323487-3384900; Table 3). Also, they distribute into two linkage blocks in block 9 and block 10 (Supplemental Fig. 3). In this region, we found four candidate genes ( Glyma03g03550, Glyma03g03560, Glyma03g03580, Glyma03g03590 ) using Wm82.a1 as a reference genome (Table 3). The Wm82.a1 reference genome was selected because the VCF file available on Soybase (soybase.org) for all of the GRIN varieties was generated using a 50k SNP chip based on the Wm82.a1 genome thus the SNPs correspond to positions in that genome. Three of them were annotated as Cytochrome P450 superfamily protein (CYP) (Table 3). Interestingly, another five CYP candidate genes were found in the upstream and downstream sequence of this region (Table 3). The eight CYP candidate genes identified in herbicide treatment trials were clustered into the CYP83A1 group (Fig. 5). The SNP marker ss715585023 is found within one candidate gene Glyma03g03120 using Wm82.a1 as a reference genome (Table 3). Glyma03g03120 has been annotated as one of the exocyst complex, component SEC6. In all eukaryotic cells, extracellular matrix components, cytoplasmic membrane lipids, and proteins are predominantly transported to the cell surface via exocytotic vesicles. Additionally, exocyst complex component sec10 ( Glyma18g19370 ) and exocyst subunit exo70 family protein E1 ( Glyma18g50160 ) in ss715629879 and ss715632132, respectively (Table 3). 4. Discussion Soybean is one of the most important crops in the world. However, the weed pressure poses significant challenges in crop management and limits the cultivation for soybean producers to use. One potential strategy is using s post-emergence herbicide to control the weed growth in soybean field. In this study, we conducted a GWAS to explore the genetic variation against post-emergence metribuzin in soybean cultivars. The phenotype results demonstrate that genetic factors play a crucial role in IR response to metribuzin treatment. Also, the dose-dependent effect and high heritability values suggest that breeding for metribuzin tolerance can be effectively achieved through phenotypic and genotypic-based selection (marker-assisted or genomic selection). However, the interaction between variety and dose or trial indicates that some varieties have more stable tolerance to metribuzin than other varieties. The significant SNPs results reveal a concentration-independent, high-confidence locus on chromosome 3 that contributes substantially to post-emergence metribuzin tolerance, with ss715585023 emerging as a robust marker for marker-assisted selection. In this study, we found several CYP genes located in chromosome 3. CYP is one of the major superfamily enzymes involving in herbicide metabolic resistance 43,44 . Typically, P450 enzymes incorporate molecular oxygen into herbicide molecules, increasing their reactivity or solubility by utilizing an electron from Nicotinamide Adenine Dinucleotide Phosphate (NADPH P450) reductase. Consequently, these herbicide molecules are metabolized into products with reduced or altered phytotoxicity in weeds that possess metabolism-based resistance mechanisms 45 . Cytochrome P450 monooxygenases mediated the phase I herbicide detoxification through oxidation, reduction, or hydrolysis 45 . CYP83A1 is required for Arabidopsis adaptation to the powdery mildew fungus Erysiphe cruciferarum 46 . Additionally, CYP83A1 is involved in phenylpropanoid and glucosinolate biosynthesis 47,48 . These pathways are secondary metabolic pathways essential for plant defense mechanisms. CYP83A1 plays a direct role in the biosynthesis of glucosinolates by catalyzing the conversion of aldoximes to thiohydroximates. Furthermore, CYP83A1 has been found to be responsible for the bioactivation of clomazone into 5-ketoclomazone. T-DNA insertion of CYP83A1 in Arabidopsis confer increased resistance to clomazone herbicide, as the altered enzyme is unable to metabolize the herbicide for detoxification 49 . Phylogenetically, CYP83A1 proteins belong to the CYP71 clade 50,51 . A total of 30 plant CYP proteins from clan 71 and clan 72 are associated with resistance or tolerance to herbicides from diverse chemical classes. Among these, the majority (25 out of 30) are involved in the metabolism of phenylureas or sulfonylureas 52–65 . Another candidate genes belong to exocyst complex. Exocytosis represents the final stage of the secretory pathway, involving the tethering and docking of secretory vesicles to the cytoplasmic membrane (PM). This process then fuses with the membrane and thereby releases their cargos 66 . It has been indicated that specific exocyst subcomplexes are involved in autophagy and plant pathogen defense process 67,68 . In Arabidopsis , the Sec6 has been transported into the vacuole and accumulated protein in the vacuole during autophagy process 69 . In the non-target site herbicide resistance process, herbicides are secreted and transported away from sensitive sites within the plant, thereby minimizing their toxicity and effectiveness. ABC transporters (ATP-binding cassette transporters) play a critical role in this process. After CYP450 enzymes metabolize herbicides, the resulting less-toxic metabolites may be sequestered in vacuoles by ABC transporters or other sequestration mechanisms. This reduces the concentration of herbicides in the cytoplasm and prevents them from interfering with vital cellular functions 70,71 . In glyphosate-resistance horseweed species, it evolves a rapid vacuolar sequestration strategy to reduce less glyphosate available for translocation from source to sink tissue and plants are less toxic 72 . Phytohormones are important regulators in plant response to biotic and abiotic stress. In SNP marker ss715587370, we found a candidate gene ethylene-responsive transcription factor 3 ( Glyma04g19650 , Table 3). Ethylene can inhibit ABA accumulation in response to synthetic auxin herbicide 73 . Interestingly, Syngenta holds two patents for pre-emergent metribuzin tolerance in soybean nearby/overlapping regions of chromosome 3 where CYP83s are present. A patent in the United States, US10667478B1 highlighted a QTL for metribuzin tolerance on chromosome 3 defined by SNP interval SY0670AQ (~ 41.15 Mb) to SY0903AQ (~ 43.50 Mb). This haplotype accounts for up to 35% phenotypic variance in metribuzin tolerance 74 . Moreover, the worldwide patent WO2014036231A2 reported a major QTL detected on chromosoem3 by linkage map of a biparental mapping population. On the bases of Glyma.Wm82.a2 reference genome, they defined the QTL between two SSR markers namely NGMAX006077640 and NS0138011 spanning around 1.1Mb region 75 . Being able to identify the same loci that Syngenta found through pre-emergent screening QTL-mapping and our post-emergence GWAS study, indicates that there may be similar genetic mechanisms underlying pre and post-emergence metribuzin tolerance in soybeans. Based on the identified SNPs on chromosome 3, our GWAS results emphasize the critical role of Cytochrome P450 enzymes, exocyst complex components, and phytohormonal signaling in soybean response to herbicide resistance. The proximity of SNPs (ss715585199, ss715585205, ss715585223, ss715585228) to several CYP genes indicates a robust genetic basis for herbicide detoxification, with these enzymes facilitating the oxidative metabolism of herbicides to less toxic forms, thus contributing to cross-resistance against various herbicide classes. Additionally, the involvement of exocyst components, such as Glyma03g03120 , indicates that exocytosis plays a vital role in transporting detoxified products or signaling molecules, potentially allowing for vacuolar sequestration of herbicides, reducing their harmful translocation within the plant. Phytohormonal regulation, highlighted by the ethylene-responsive transcription factor Glyma04g19650 , further integrates this response by modulating stress pathways and influencing the plant's reaction to synthetic auxin herbicides. Together, these elements form a complex network that governs soybean's adaptive strategies to herbicide exposure, offering insights for enhancing herbicide resistance in future cultivars. Declarations Funding: This work was supported by the Southern Soybean Research Program and the Mid-South Soybean Board (Grant No. 55955), under the project titled 'Characterizing and Utilizing Metribuzin Tolerance in Soybeans to Improve Weed Management Strategies in Early-Planted Soybeans. Ethics Declarations: Competing interests The authors declare no competing interests. Ethics approval and consent to participate The methods carried out in our manuscript were in accordance with the local, national, and international guidelines and regulations. Data and Code Availability: The datasets generated and/or analysed and the code used for analysis during the current study are available in the Figshare repository, https://doi.org/10.6084/m9.figshare.29897075. 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RalB and the exocyst mediate the cellular starvation response by direct activation of autophagosome assembly. Cell 144 , 253-67 (2011). Kulich, I. et al. Arabidopsis exocyst subcomplex containing subunit EXO70B1 is involved in autophagy-related transport to the vacuole. Traffic 14 , 1155-65 (2013). Brillada, C. et al. Exocyst subunit Exo70B2 is linked to immune signaling and autophagy. Plant Cell 33 , 404-419 (2021). Owen, M.D. & Zelaya, I.A. Herbicide‐resistant crops and weed resistance to herbicides. Pest Management Science: formerly Pesticide Science 61 , 301-311 (2005). Yuan, J.S., Tranel, P.J. & Stewart, C.N., Jr. Non-target-site herbicide resistance: a family business. Trends Plant Sci 12 , 6-13 (2007). Ge, X., d'Avignon, D.A., Ackerman, J.J. & Sammons, R.D. Rapid vacuolar sequestration: the horseweed glyphosate resistance mechanism. Pest Manag Sci 66 , 345-8 (2010). Kraft, M., Kuglitsch, R., Kwiatkowski, J., Frank, M. & Grossmann, K. Indole-3-acetic acid and auxin herbicides up-regulate 9-cis-epoxycarotenoid dioxygenase gene expression and abscisic acid accumulation in cleavers (Galium aparine): interaction with ethylene. J Exp Bot 58 , 1497-503 (2007). Zhanyou Xu, J.P., Gregory Lynn Doonan. Metribuzin tolerance alleles in soybean. (2018). Jesse Gilsinger, B.L. Molecular markers and phenotypic screening for metribuzin tolerance. (2020). Tables Table 1. Summary of soybean tolerance response to post-emergence metribuzin treatment across two field trials. Trail Dose (g ai ha -1 ) Mean Min Max SD H 2 Trial 1 150 2.64 1.33 4.33 0.58 0.78 300 3.09 1.67 5 0.68 0.84 Trial 2 150 2.7 1 4 0.48 0.7 300 2.94 1.48 4.48 0.51 0.76 Trial 1 + Trial 2 150 2.67 1.5 4 0.42 0.71 300 3.02 1.82 4.67 0.47 0.7 Table 2. SNP significantly associated with metribuzin tolerance in soybeans identified by the GWAS analysis. SNP[1] Chr[2] Position P.value MAF[3] Effect[4] PVE[5] Rate[6] Model Data[7] ss715585023 3 2918336 5.94E-07 0.13 0.31 39.38 150 MLM, BLINK TRAIL 1 ss715585023 3 2918336 2.19E-09 0.13 0.45 54.98 300 MLM TRAIL 1 ss715629879 18 21144784 2.53E-07 0.22 -0.22 23.32 300 BLINK TRAIL 1 ss715585205 3 3334303 9.13E-08 0.40 0.19 21.61 300 BLINK TRAIL 2 ss715624298 16 30228382 2.43E-06 0.15 0.21 34.09 150 BLINK FULL ss715585223 3 3373279 8.00E-07 0.22 0.15 16.30 150 BLINK FULL ss715587370 4 21252334 6.24E-07 0.32 0.13 12.40 150 BLINK FULL ss715585023 3 2918336 1.02E-06 0.13 0.00 27.77 300 MLM FULL ss715585199 3 3323487 1.05E-06 0.38 0.00 4.24 300 MLM FULL ss715585205 3 3334303 1.68E-08 0.40 0.00 12.56 300 MLM FULL ss715585228 3 3384900 4.74E-07 0.28 0.15 28.56 300 BLINK FULL ss715632132 18 59352206 8.65E-08 0.35 -0.15 10.02 300 BLINK FULL [1] Single nucleotide polymorphism [2] Chromosome [3] Minor Allele Frequency [4] Effect size of SNP in the GWAS model [5] Percent variation explained by SNP [6] Rate of metribuzin applied in g ai ha -1 [7] The data set used to find the marker trait association, “FULL” is the data from both trials pooled Table 3. Candidate genes identified through the GWAS analysis. SNP Chr position candidate gene gene name ss715585199 3 3323487-3384900 Glyma03g03550 CYP450 1 ss715585205 Glyma03g03560 CYP450 ss715585223 Glyma03g03580 ruBisCO-associated protein-like ss715585228 Glyma03g03590 CYP450 ss715585184-ss715585288 3 glyma.Wm82.gnm1.Gm03:3,287,723:3499047 Glyma03g03630 CYP450 Glyma03g03640 CYP450 Glyma03g03670 CYP450 Glyma03g03700 CYP450 Glyma03g03721 CYP450 ss715585023 3 2918336 Glyma03g03120 Exocyst complex component Sec6 ss715629879 18 21144784 Glyma18g19370 exocyst complex component sec10 ss715587370 4 21252334 Glyma04g19650 ethylene-responsive transcription factor 3 ss715632132 18 59352206 Glyma18g50160 exocyst subunit exo70 family protein E1 1 Cytochrome P450 superfamily protein Additional Declarations No competing interests reported. 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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-7303889","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":513960994,"identity":"8295e372-9022-46b2-baf1-866d7a161d79","order_by":0,"name":"Lichun Zhou","email":"","orcid":"","institution":"University of Kentucky","correspondingAuthor":false,"prefix":"","firstName":"Lichun","middleName":"","lastName":"Zhou","suffix":""},{"id":513960995,"identity":"7f1e576a-905b-448a-991c-129d6ccf23da","order_by":1,"name":"Abdaal Ali","email":"","orcid":"","institution":"University of Kentucky","correspondingAuthor":false,"prefix":"","firstName":"Abdaal","middleName":"","lastName":"Ali","suffix":""},{"id":513960996,"identity":"79f62185-57cc-4b61-9c91-e7a872a246ff","order_by":2,"name":"Mohammad Jan Shamim","email":"","orcid":"","institution":"University of Kentucky","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Jan","lastName":"Shamim","suffix":""},{"id":513960997,"identity":"2b6cb53b-f465-41a4-97be-facfc7822c68","order_by":3,"name":"Zoe V. Schroeder","email":"","orcid":"","institution":"University of Kentucky","correspondingAuthor":false,"prefix":"","firstName":"Zoe","middleName":"V.","lastName":"Schroeder","suffix":""},{"id":513960998,"identity":"52d658df-43bf-4bb1-ac85-a76acb68abee","order_by":4,"name":"Caio Canella Vieira","email":"","orcid":"","institution":"University of Arkansas","correspondingAuthor":false,"prefix":"","firstName":"Caio","middleName":"Canella","lastName":"Vieira","suffix":""},{"id":513960999,"identity":"87fc7f56-5a4c-40c2-b9b3-8946cc6e4549","order_by":5,"name":"Samuel Revolinski","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYBAC+fkH2D8AaRk2BgZmhoQKBgOQqAQ+LQY3EthACnggWs4Qo0UCqoUBpIWxjVgtH9tsePik2x8bPJx32NjgAPPB2zwE/PJxZlsaD5vMGeOExG2HzQwOsCVb49PCcJ+B/TDPmcM8bBI5zAeAWmwMDvCYSePVcrOBLfkPWEv64wOJc0Ba+L/h13LjAJsxQwVISwLQYQ0gh/Gw4dVicCOxTbKnIg3kMGODhGPpxpKH2Ywt5+Dz/ozkYxI/DGzk5GekP5b8UWNt2He8+eGNN/gcxsDYgMpXOIxXOVZ7GwgqGQWjYBSMghEGAJ8XSti3qPjHAAAAAElFTkSuQmCC","orcid":"","institution":"University of Kentucky","correspondingAuthor":true,"prefix":"","firstName":"Samuel","middleName":"","lastName":"Revolinski","suffix":""}],"badges":[],"createdAt":"2025-08-05 20:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7303889/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7303889/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91269516,"identity":"0674380a-2f93-49d0-ae6c-1cdc856602ad","added_by":"auto","created_at":"2025-09-14 09:46:13","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":136111,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Correlations between metribuzin response among trials and compared to dicamba injury ratings\u003csup\u003e29\u003c/sup\u003e. (b) Stability analysis of the metribuzin response across lines and trials where PC1 is the first principal component from an AMMI analysis and mean is the average injury rating across all doses and trials.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7303889/v1/1964b9e88be0a87af0200d2c.jpg"},{"id":91269518,"identity":"d172a6c0-ae3a-4fcc-af66-282c5070bf1b","added_by":"auto","created_at":"2025-09-14 09:46:13","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":199366,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCircular Manhattan plot of each trait where the Y axis is the -log10 (p-value) from the BLINK model. Where (a) is combined dataset at 150 g ai ha\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-1 \u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e, (b) is the combined dataset at 300 g ai ha\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-1 \u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e, (c) is the data from trial 1 at 150 g ai ha\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-1 \u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e, (d) is the data from trial 1 at 300 g ai ha\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-1 \u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e, (e) is the data from trial 1 at 150 g ai ha\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-1 \u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e, (f) is the data from trial 1 at 300 g ai ha\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-1 \u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7303889/v1/a73f13c7f2fcda4798a51c16.jpg"},{"id":91269517,"identity":"b9aa0f12-7fd0-47a8-8074-d944eb82f89c","added_by":"auto","created_at":"2025-09-14 09:46:13","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":209616,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCircular Manhattan plot of each trait where the Y axis is the -log10 (p-value) from the MLMM model. Where (a) is combined dataset at 150 g ai ha\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-1 \u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e, (b) is the combined dataset at 300 g ai ha\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-1 \u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e, (c) is the data from trial 1 at 150 g ai ha\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-1 \u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e, (d) is the data from trial 1 at 300 g ai ha\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-1 \u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e, (d) is the data from trial 1 at 150 g ai ha\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-1 \u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e, (e) is the data from trial 1 at 300 g ai ha\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-1 \u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7303889/v1/8d6aa733abc47df740dcf078.jpg"},{"id":91269531,"identity":"92dc274d-cd0e-41e4-ad85-419e3bf2e427","added_by":"auto","created_at":"2025-09-14 09:46:13","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":140695,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypic distribution with each allelic state of significant SNPs.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7303889/v1/db24a13fd4bdd433f9bf3689.jpg"},{"id":91269522,"identity":"1ab697f5-bedb-4e7b-a7d7-b4e61c9ae1bd","added_by":"auto","created_at":"2025-09-14 09:46:13","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":135284,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhylogenetic tree of Soybean CYP450 candidate genes. Different color represents different species, and candidate genes highlight with green color\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7303889/v1/5f86ceec2807da9210cc7b45.jpg"},{"id":91623981,"identity":"4d7b2118-f94b-4acb-b3c4-484631d64669","added_by":"auto","created_at":"2025-09-18 11:54:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2104330,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7303889/v1/3130031e-fd96-44f3-a316-24e8a6995f31.pdf"},{"id":91269520,"identity":"1e756533-40fb-43b6-a2d6-74cf3cece2f1","added_by":"auto","created_at":"2025-09-14 09:46:13","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1479290,"visible":true,"origin":"","legend":"","description":"","filename":"supMaterialsFinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-7303889/v1/838fbcb9d971064ea1e3f133.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome-wide association study reveals cytochrome P450 associated with post- emergence metribuzin tolerance in soybean (Glycine max)","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWith a total of 28% of global agricultural production, soybean \u003cem\u003e(Glycine max\u003c/em\u003e) is one of the most important crops in the United States \u003csup\u003e1\u003c/sup\u003e. Based on regional production estimates, the Southern United States is a major soybean procuding region \u003csup\u003e2\u003c/sup\u003e. However, weed pressure poses significant challenges in crop management and limits the cultivation strategies that can be used by soybean producers\u003csup\u003e3\u003c/sup\u003e. Furthermore, poorly managed weeds in soybean production fields compete with the crop for essential resources such as light, nutrients, and water \u003csup\u003e4,5\u003c/sup\u003e, thus causing yield losses of up to 48% \u003csup\u003e6,7\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAmong the weeds present in soybean production in the Southern US, waterhemp (\u003cem\u003eAmaranthus tuberculatus\u003c/em\u003e), which causes yield loss of up to 43% \u003csup\u003e8\u003c/sup\u003e, and palmer amaranth (\u003cem\u003eAmaranthus palmeri\u003c/em\u003e) are particularly devastating species in soybean production\u003csup\u003e9\u003c/sup\u003e. Due to widespread herbicide resistance against glyphosate, glufosinate, synthetic auxins, and PPO in both of these pigweed species, metribuzin and paraquat are often the only effective herbicides for managing \u003cem\u003eAmaranthus\u003c/em\u003e species in soybeans being grown in the Southern US\u003csup\u003e10,11\u003c/sup\u003e. However, neither paraquat nor metribuzin can be applied over an established soybean crop because it would greatly damage the plants. While developing either post-emergent metribuzin or paraquat-tolerant soybeans could help soybean producers by adding post-emergent options, metribuzin is a more desirable herbicide to use than paraquat due to its lower toxicity in humans \u003csup\u003e12,13\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMetribuzin is a triazinone herbicide that disrupts photosynthesis by blocking electron transport in the photosystem II complex from the D1 subunit to the remaining segments of the electron transport chain \u003csup\u003e14\u003c/sup\u003e. It is effective against broad-leaf weed species and provides soil residual activity for 4 to 6 weeks depending on weather conditions \u003csup\u003e15\u003c/sup\u003e. However, in early planted cropping systems, which have become predominant in the Southern US \u003csup\u003e16\u003c/sup\u003e, key weed species often emerge later in the season after the residual activity of pre-emergent metribuzin has declined. Up to 80% of metribuzin can dissipate in the soil within 30 days of application\u003csup\u003e17,18\u003c/sup\u003e, thus limiting its efficacy as a pre-emergent herbicide.\u003c/p\u003e\u003cp\u003eOne potential strategy to address this issue is the use of a post-emergence metribuzin application. Yet, because metribuzin interferes with photosynthesis, it can cause significant phytotoxicity in sensitive soybean varieties\u003csup\u003e19\u003c/sup\u003e. Symptoms include leaf chlorosis and necrosis, stunted growth, and reduced biomass \u003csup\u003e20\u0026ndash;22\u003c/sup\u003e, with intensity influenced by environmental factors, application rates, and the genetic background of individual soybean varieties \u003csup\u003e23\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOne study highlighting variation of pre-emergent metribuzin tolerance in soybean based on physiological processes found that a tolerant variety \u0026lsquo;Essex\u0026rsquo; metabolized metribuzin rapidly compared to \u0026lsquo;Coker 102\u0026rsquo;, a susceptible cultivar. The rapid metabolism of metribuzin led to limited accumulation of unmetabolized herbicide in plant tissue which enhanced tolerance in Essex \u003csup\u003e24\u003c/sup\u003e. On a genetic level, linkage between \u003cem\u003eRps1-k\u003c/em\u003e locus and improved pre-emergent metribuzin tolerance. Cultivars carrying \u003cem\u003eRps1-k\u003c/em\u003e showed significantly higher survival and biomass under metribuzin treatment ​\u003csup\u003e25\u003c/sup\u003e. However, it is unclear if the \u003cem\u003eRps-k\u003c/em\u003e locus or another locus in close linkage is responsible for tolerance. While the search for improved response to metribuzin has continued, no natural soybean genetic variants responsible for metribuzin tolerance have been identified.\u003csup\u003e26\u0026ndash;28\u003c/sup\u003e. Therefore, the objective of this study was to explore novel genetic variations conferring non-target site herbicide resistance (NTSR) against post-emergence metribuzin application in soybean. In addition, this study aimed to identify genetic resources that could be used to further characterize the genetic architecture through QTL mapping, as well as develop improved breeding populations for post-emergence metribuzin tolerance.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003e1. Plant materials\u003c/p\u003e\u003cp\u003eA total of 196 genetically diverse soybean accessions (Supplementary Table\u0026nbsp;1, Supplementary Figs.\u0026nbsp;2 \u0026amp; 4) from the Germplasm Resource Information Network (GRIN) were included in this study. These represent a subset of the 382 accessions utilized for a GWAS study for off-target dicamba tolerance in soybean \u003csup\u003e29\u003c/sup\u003e. The panel assembled consisted of maturity groups (MGs) MG-III(49), MG-IV(89) and MG-V(36). All of the accessions used in this study were sourced by submitting a request to GRIN. No accessions were sourced from the wild, limiting the ethical considerations required to obtain the accessions used in this trial.\u003c/p\u003e\n\u003ch3\u003e2. Growing conditions\u003c/h3\u003e\n\u003cp\u003ePlants were grown in 2-inch diameter SC10 black “Conetainers” placed into 7 × 14 well cavity trays (Stuewe and Sons, Tangent, Oregon). The containers were filled up to the brim with PROMIX BX BIOFUNGICUDE + MYCORRHIZAE horticultural mix. Fourteen seeds of each variety were sown by hand one inch deep into the horticultural mix; all containers were promptly watered to initiate germination. Seedlings were transplanted into new 98-well cavity trays following experimental design plans. The experiment was set up using a factorial design between dose and variety in randomized complete blocks with 3 replicates per treatment. The entire experiment was repeated twice in two trials separated by time. Plants were grown at the University of Kentucky Greenhouse Facility in Lexington, Kentucky. Supplemental lighting was used to ensure that plants received at least 15 hours of light a day. The first trial was installed on July 29, 2024, and the second trial on September 9, 2024.\u003c/p\u003e\n\u003ch3\u003e3. Phenotyping and data processing\u003c/h3\u003e\n\u003cp\u003eIn each trial, metribuzin was applied at a rate of 150 g ai ha\u003csup\u003e− 1\u003c/sup\u003e and 300 g ai ha\u003csup\u003e− 1\u003c/sup\u003e in a DeVries (Hollandale, MN) research spray chamber when the soybean plants reached the V2 growth stage\u003csup\u003e30\u003c/sup\u003e. The metribuzin was applied with 0.5% “chemsurf” non-ionic surfactant added to the solution. Injury ratings were collected 21 days after treatment for both 150 and 300 g ai ha\u003csup\u003e− 1\u003c/sup\u003e trials. Spray trials were performed indoors following best practices for limiting exposure to the environment and to personnel carrying out the experiments such as collecting waste from the spray chamber into a tank and requiring personnel to wear gloves when applying the metribuzin treatments.\u003c/p\u003e\u003cp\u003eThe herbicide injury was reported following a 1–5 scale (Supplementary Fig.\u0026nbsp;5) defined as: 1) no damage; 2) at least two trifoliate leaves left undamaged on the plant; 3) at least one set of full trifoliate leaf left undamaged on the plant; 4) all leaves are heavily damaged but growing point is still alive; 5) complete plant death. The phenotypic data was processed using a linear-mixed effect model with the ‘\u003cem\u003elmer’\u003c/em\u003e of the R package ‘\u003cem\u003elme4\u003c/em\u003e’\u003csup\u003e31\u003c/sup\u003e. Models were fitted with the full data set for the combined trial GWAS and by trial and dose for the within trials dataset where all terms were included as a fixed effect, except for the flat which was included as random effect to account for the positioning of the flat in the green house or other correlated conditions. The flat plants were randomized into was included as a random effect because there are locations that the flats could be placed in and the effect of the flat is not of experimental interest. The linear model for within trial is as follows:\u003c/p\u003e\u003cp\u003e[1] \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{ijk}=\\:{g}_{i}+\\:{d}_{j}+\\:{gd}_{ij}+{\\left(f\\right)d}_{jk}+{e}_{ijk}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eWhile the linear model for the full dataset was as follows:\u003c/p\u003e\u003cp\u003e[2] \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{ijkl}=\\:{g}_{i}+\\:{d}_{j}+{t}_{k}+\\:{gd}_{ij}+{gt}_{ik}+{dt}_{jk}+{gdt}_{ijk}+{\\left(f\\right)dt}_{jkl}+{e}_{ijk}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cem\u003eg\u003c/em\u003e is the accession, \u003cem\u003ed\u003c/em\u003e is the herbicide rate, \u003cem\u003et\u003c/em\u003e is the trial, \u003cem\u003ef\u003c/em\u003e is the 98-well cavity tray that the plant was placed in during randomization and \u003cem\u003ee\u003c/em\u003e is the normally distributed error term.\u003c/p\u003e\u003cp\u003eBest Linear Unbiased Estimates (BLUEs) for genotypes at each rate were estimated by using the ‘\u003cem\u003eemmeans\u003c/em\u003e’ function in the ‘emmeans’ R package (v.10.6)\u003csup\u003e32\u003c/sup\u003e. Generalized heritability was estimated using the Cullis method \u003csup\u003e33\u003c/sup\u003e from the linear models but the term for accession was converted to random effect in the model (along with any interactions containing accession).\u003c/p\u003e\u003cp\u003e[3] \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{H}^{2}=1-\\frac{{\\sigma\\:}_{BLUP\\:}^{2}}{2{\\sigma\\:}_{g}^{2}}\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{H}^{2}\\)\u003c/span\u003e\u003c/span\u003e is the generalized heritability, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{BLUP\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003eis the variation of the BLUPs, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{g}^{2}\\)\u003c/span\u003e\u003c/span\u003e is the variation associated with the genetic effect from the mixed model.\u003c/p\u003e\n\u003ch3\u003e5. Population structure and LD\u003c/h3\u003e\n\u003cp\u003eTo account for genetic substructure and reduce confounding of genetic subgroups in association analysis, we conducted the population structure and ancestral analysis. Population structure was assessed using the ‘\u003cem\u003esnmf\u003c/em\u003e’ function in R package ‘LEA’ (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ev3.16.0\u003c/span\u003e) \u003csup\u003e34\u003c/sup\u003e. Three ancestral groups were identified based on the lowest cross-entropy value. Principal component analysis (PCA) was then performed using the ‘\u003cem\u003epca\u003c/em\u003e’ function of the R package ‘LEA’, after converting the genotype matrix to the lfmm format. Data were scaled and centered, and PCA results were visualized using the ‘\u003cem\u003egeom_mark_ellipse function’\u003c/em\u003e in the ggforce R package (V0.4.2)\u003csup\u003e35\u003c/sup\u003e, with pie charts representing individual ancestry proportions.\u003csup\u003e34\u003c/sup\u003e. LD analysis was performed using the LD decay function from the ‘sommer’ R package (v4.3.6)\u003csup\u003e36\u003c/sup\u003e in R. The analysis utilized a marker matrix and a map with distances between markers in base pairs.\u003c/p\u003e\n\u003ch3\u003e6. GWAS\u003c/h3\u003e\n\u003cp\u003eMultiple GWAS were performed using each genotype’s BLUEs as the response variable for the following scenarios: i) each rate in each trial, and ii) each rate with the trials combined. The MLMM \u003csup\u003e37\u003c/sup\u003e and BLINK \u003csup\u003e38\u003c/sup\u003e methods were implemented using \u003cem\u003e‘GAPIT’\u003c/em\u003e function from the ‘GAPIT’ package in R \u003csup\u003e39\u003c/sup\u003e. The MLMM method has lower power than the BLINK method, however the MLMM method results are more stable and conservative than BLINK because it uses a kinship matrix with a mixed model to control for relatedness between accessions. The BLINK method was also used because it has greater power to detect SNPs than the MLMM as it uses significant markers found in previous iterations to control population stratification without the loss of power due to the inclusion of a random effect using genomic based kinship matrix. Both models are multi-marker methods that include additional QTLs in a stepwise fashion. Principal components were sequentially added to each model until the QQ-plots demonstrated that the population structure was controlled for not overly reducing statistical power. SNPs were considered significant when they were below Bonferonni where alpha is 0.05.\u003c/p\u003e\n\u003ch3\u003e7. Candidate genes and phylogenetic tree of CYP450s\u003c/h3\u003e\n\u003cp\u003eCandidate genes were identified within 50 kb (upstream and downstream, Supplementary Figs.\u0026nbsp;1 \u0026amp; 3) of each significant SNP using the Soybase Genome Browser tool \u003csup\u003e40\u003c/sup\u003e. Following the identification of Cytochrome P450 superfamily proteins(CYP) candidate genes, their coding sequences (CDS) and protein sequences were retrieved from SoyBase. Basic Local Alignment Search Tool (BLAST) was used to establish functional homology and evolutionary conservation between identified candidate genes and reference genomes of \u003cem\u003eArabidopsis thaliana\u003c/em\u003e and \u003cem\u003eGlycine max.\u003c/em\u003e BLASTp and BLASTn were used for protein and nucleotide sequences comparison, respectively. Ensuring high-confidence alignments for downstream evolutionary analyses, thresholds for sequence homology were set as E-value ≤ 1\u003csup\u003e− 10\u003c/sup\u003e, identity ≥ 80%, and coverage ≥ 70%.\u003c/p\u003e\u003cp\u003eTo highlight the relationship of candidate genes across \u003cem\u003eArabidopsis thaliana\u003c/em\u003e and \u003cem\u003eGlycine max\u003c/em\u003e, multiple sequence alignment was performed using ClustalW within the MEGA (Molecular Evolutionary Genetics Analysis) software (version 11) \u003csup\u003e41\u003c/sup\u003e. To eliminate the ambiguities, alignment was visually inspected and manually curated. Phylogenetic tree was constructed by using the Maximum Likelihood (ML) method. Reliability of the inferred phylogenetic relationships was made sure by using bootstrap analysis with 1,000 replicates in MEGA11. Subsequently, branch colors, labels, and additional metadata were incorporated using iTOL (Interactive Tree of Life) to improve interpretation \u003csup\u003e42\u003c/sup\u003e.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Phenotypic analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 196 soybean genotypes were evaluated under two concentrations of the herbicide metribuzin: 150 g ai ha\u003csup\u003e-1\u003c/sup\u003e and 300 g\u0026nbsp;ai ha\u003csup\u003e-1\u003c/sup\u003e. Each experiment was conducted with two experimental replications and three biological replications. Generalized heritability, the mean, range (min and max values), standard deviation, and correlations between Best Linear Unbiased Estimates (BLUEs) at each dose and trial combination were calculated to characterize the variation in traits and obtain a measure of the effect of each trial, dosage and genotype\u0026nbsp;(Table 1).\u0026nbsp;The mean of BLUEs across lines for injury rating (IR) are significantly higher (Supplementary Table 2.) in the 300 g\u0026nbsp;ai ha\u003csup\u003e-1\u003c/sup\u003e dose\u0026nbsp;(3.09 vs 2.64 in trial 1; 2.94 vs 2.7 in trial 2 and 3.02 vs 2.67 in combined trails; Table 1). Generalized heritability was high for all the doses and trials ranging from 0.7 to 0.84 (Table 1). There were significant differences among soybean lines (p \u0026lt; 2.2E-16, Supplemental table 1). The effect of herbicide dose on IR was significant in both trials and combined trial (p = 7.75E-05, 0.0009, 1.2E-07, Supplemental Table 2). While dose was found to be a relevant factor, the trial effect was found not to be a significant factor\u0026nbsp;(p =\u0026nbsp;0.2985, supplemental Table 1). In Fig. 1b, the AMMI biplot illustrates genotype \u0026times; environment (G\u0026times;E) interactions by plotting genotypic responses across varying trial and herbicide dose conditions. Genotypes located near the origin of the plot\u0026mdash;such as \u003cem\u003eHopei E602\u003c/em\u003e, \u003cem\u003ePi xian\u003c/em\u003e, and \u003cem\u003eMacoupin\u003c/em\u003e\u0026mdash;demonstrate high phenotypic stability and lower injury ratings, indicating both tolerance to metribuzin and minimal interaction with specific environments, thus reflecting broad tolerance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Marker-trait associations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify genomic loci underlying variation in metribuzin tolerance, we conducted a genome-wide association study (GWAS) across two herbicide concentrations (150 and 300 g ai ha⁻\u0026sup1;) using mixed linear models (Figs 2 \u0026amp; 3). At the 150 g ai ha⁻\u0026sup1; dosage, four significant SNPs were identified on chromosomes 3, 4, and 16 (Table 2). The strongest signal was observed at \u003cem\u003ess715585023\u003c/em\u003e on chromosome 3 (position 2, 918, 336), which exhibited a minor allele frequency (MAF) of 0.13, a p-value of 5.94 \u0026times; 10⁻⁷, and explained 39.4% of the phenotypic variance (Fig. 4). Additional SNPs\u0026mdash;\u003cem\u003ess715624298\u003c/em\u003e (Chr16), \u003cem\u003ess715585223\u003c/em\u003e (Chr3), and \u003cem\u003ess715587370\u003c/em\u003e (Chr4)\u0026mdash;each explained 12.4% to 34.1% of the variance, further supporting a polygenic model for post-emergence metribuzin response under low dosage (Table 2).\u003c/p\u003e\n\u003cp\u003eAt the 300 g ai ha⁻\u0026sup1; dosage, six SNPs reached genome-wide significance, four of which mapped to a narrow interval on chromosome 3, suggesting the presence of a major-effect locus (Table 2). The most significant SNP, \u003cem\u003ess715585023\u003c/em\u003e, with a\u0026nbsp;p-value of 2.19 \u0026times; 10⁻⁹\u0026nbsp;and explaining up to\u0026nbsp;54.98%\u0026nbsp;of the variance in Trial 1 (Fig. 4). This SNP also showed consistent effects in the combined dataset, accounting for\u0026nbsp;27.77%\u0026nbsp;of variation (Fig. 4). Nearby SNPs (\u003cem\u003ess715585205\u003c/em\u003e, \u003cem\u003ess715585199\u003c/em\u003e, \u003cem\u003ess715585228\u003c/em\u003e) explained 4.2\u0026ndash;28.6% of the variance, reinforcing the importance of this genomic region. Two additional SNP, \u003cem\u003ess715629879 and ss715632132\u003c/em\u003e, located on chromosome 18, explained 23.32% \u0026nbsp;and 10.02% of variation, indicating potential secondary loci.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. Candidate genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn chromosome 3, four of five SNPs were close to each other (ss715585199, ss715585205, ss715585223, ss715585228; Table 3). The position distance among these four SNPs was 61 kb (chromosome 3: 3323487-3384900; Table 3). Also, they distribute into two linkage blocks in block 9 and block 10 (Supplemental Fig. 3). In this region, we found four candidate genes (\u003cem\u003eGlyma03g03550, Glyma03g03560, Glyma03g03580, Glyma03g03590\u003c/em\u003e) using \u003cem\u003eWm82.a1\u003c/em\u003e as a reference genome (Table 3). The \u003cem\u003eWm82.a1\u003c/em\u003e reference genome was selected because the VCF file available on Soybase (soybase.org) for all of the GRIN varieties was generated using a 50k SNP chip based on the \u003cem\u003eWm82.a1\u003c/em\u003e genome thus the SNPs correspond to positions in that genome. Three of them were annotated as Cytochrome P450 superfamily protein (CYP) (Table 3). \u0026nbsp;Interestingly, another five CYP candidate genes were found in the upstream and downstream sequence of this region (Table 3). The eight CYP candidate genes identified in herbicide treatment trials were clustered into the CYP83A1 group (Fig. 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe SNP marker ss715585023 is found within one candidate gene \u003cem\u003eGlyma03g03120\u003c/em\u003e using \u003cem\u003eWm82.a1\u003c/em\u003e as a reference genome (Table 3). \u003cem\u003eGlyma03g03120\u0026nbsp;\u003c/em\u003ehas been annotated as one of the exocyst complex, component SEC6. In all eukaryotic cells, extracellular matrix components, cytoplasmic membrane lipids, and proteins are predominantly transported to the cell surface via exocytotic vesicles. Additionally, exocyst complex component sec10 (\u003cem\u003eGlyma18g19370\u003c/em\u003e) and exocyst subunit exo70 family protein E1 (\u003cem\u003eGlyma18g50160\u003c/em\u003e) in ss715629879 and ss715632132, respectively (Table 3).\u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eSoybean is one of the most important crops in the world. However, the weed pressure poses significant challenges in crop management and limits the cultivation for soybean producers to use. One potential strategy is using s post-emergence herbicide to control the weed growth in soybean field. In this study, we conducted a GWAS to explore the genetic variation against post-emergence metribuzin in soybean cultivars.\u003c/p\u003e\u003cp\u003eThe phenotype results demonstrate that genetic factors play a crucial role in IR response to metribuzin treatment. Also, the dose-dependent effect and high heritability values suggest that breeding for metribuzin tolerance can be effectively achieved through phenotypic and genotypic-based selection (marker-assisted or genomic selection). However, the interaction between variety and dose or trial indicates that some varieties have more stable tolerance to metribuzin than other varieties.\u003c/p\u003e\u003cp\u003eThe significant SNPs results reveal a concentration-independent, high-confidence locus on chromosome 3 that contributes substantially to post-emergence metribuzin tolerance, with \u003cem\u003ess715585023\u003c/em\u003e emerging as a robust marker for marker-assisted selection.\u003c/p\u003e\u003cp\u003eIn this study, we found several CYP genes located in chromosome 3. CYP is one of the major superfamily enzymes involving in herbicide metabolic resistance \u003csup\u003e43,44\u003c/sup\u003e. Typically, P450 enzymes incorporate molecular oxygen into herbicide molecules, increasing their reactivity or solubility by utilizing an electron from Nicotinamide Adenine Dinucleotide Phosphate (NADPH P450) reductase. Consequently, these herbicide molecules are metabolized into products with reduced or altered phytotoxicity in weeds that possess metabolism-based resistance mechanisms \u003csup\u003e45\u003c/sup\u003e. Cytochrome P450 monooxygenases mediated the phase I herbicide detoxification through oxidation, reduction, or hydrolysis \u003csup\u003e45\u003c/sup\u003e. CYP83A1 is required for \u003cem\u003eArabidopsis\u003c/em\u003e adaptation to the powdery mildew fungus \u003cem\u003eErysiphe cruciferarum\u003c/em\u003e\u003csup\u003e\u003cem\u003e46\u003c/em\u003e\u003c/sup\u003e. Additionally, CYP83A1 is involved in phenylpropanoid and glucosinolate biosynthesis\u003csup\u003e47,48\u003c/sup\u003e. These pathways are secondary metabolic pathways essential for plant defense mechanisms. CYP83A1 plays a direct role in the biosynthesis of glucosinolates by catalyzing the conversion of aldoximes to thiohydroximates. Furthermore, CYP83A1 has been found to be responsible for the bioactivation of clomazone into 5-ketoclomazone. T-DNA insertion of CYP83A1 in \u003cem\u003eArabidopsis\u003c/em\u003e confer increased resistance to clomazone herbicide, as the altered enzyme is unable to metabolize the herbicide for detoxification\u003csup\u003e49\u003c/sup\u003e. Phylogenetically, CYP83A1 proteins belong to the CYP71 clade\u003csup\u003e50,51\u003c/sup\u003e. A total of 30 plant CYP proteins from clan 71 and clan 72 are associated with resistance or tolerance to herbicides from diverse chemical classes. Among these, the majority (25 out of 30) are involved in the metabolism of phenylureas or sulfonylureas\u003csup\u003e52\u0026ndash;65\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAnother candidate genes belong to exocyst complex. Exocytosis represents the final stage of the secretory pathway, involving the tethering and docking of secretory vesicles to the cytoplasmic membrane (PM). This process then fuses with the membrane and thereby releases their cargos \u003csup\u003e66\u003c/sup\u003e. It has been indicated that specific exocyst subcomplexes are involved in autophagy and plant pathogen defense process\u003csup\u003e67,68\u003c/sup\u003e. In \u003cem\u003eArabidopsis\u003c/em\u003e, the Sec6 has been transported into the vacuole and accumulated protein in the vacuole during autophagy process \u003csup\u003e69\u003c/sup\u003e. In the non-target site herbicide resistance process, herbicides are secreted and transported away from sensitive sites within the plant, thereby minimizing their toxicity and effectiveness. ABC transporters (ATP-binding cassette transporters) play a critical role in this process. After CYP450 enzymes metabolize herbicides, the resulting less-toxic metabolites may be sequestered in vacuoles by ABC transporters or other sequestration mechanisms. This reduces the concentration of herbicides in the cytoplasm and prevents them from interfering with vital cellular functions\u003csup\u003e70,71\u003c/sup\u003e. In glyphosate-resistance horseweed species, it evolves a rapid vacuolar sequestration strategy to reduce less glyphosate available for translocation from source to sink tissue and plants are less toxic \u003csup\u003e72\u003c/sup\u003e. Phytohormones are important regulators in plant response to biotic and abiotic stress. In SNP marker ss715587370, we found a candidate gene ethylene-responsive transcription factor 3 (\u003cem\u003eGlyma04g19650\u003c/em\u003e, Table\u0026nbsp;3). Ethylene can inhibit ABA accumulation in response to synthetic auxin herbicide \u003csup\u003e73\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eInterestingly, Syngenta holds two patents for pre-emergent metribuzin tolerance in soybean nearby/overlapping regions of chromosome 3 where CYP83s are present. A patent in the United States, US10667478B1 highlighted a QTL for metribuzin tolerance on chromosome 3 defined by SNP interval SY0670AQ (~\u0026thinsp;41.15 Mb) to SY0903AQ (~\u0026thinsp;43.50 Mb). This haplotype accounts for up to 35% phenotypic variance in metribuzin tolerance \u003csup\u003e74\u003c/sup\u003e. Moreover, the worldwide patent WO2014036231A2 reported a major QTL detected on chromosoem3 by linkage map of a biparental mapping population. On the bases of \u003cem\u003eGlyma.Wm82.a2\u003c/em\u003e reference genome, they defined the QTL between two SSR markers namely NGMAX006077640 and NS0138011 spanning around 1.1Mb region\u003csup\u003e75\u003c/sup\u003e. Being able to identify the same loci that Syngenta found through pre-emergent screening QTL-mapping and our post-emergence GWAS study, indicates that there may be similar genetic mechanisms underlying pre and post-emergence metribuzin tolerance in soybeans.\u003c/p\u003e\u003cp\u003eBased on the identified SNPs on chromosome 3, our GWAS results emphasize the critical role of Cytochrome P450 enzymes, exocyst complex components, and phytohormonal signaling in soybean response to herbicide resistance. The proximity of SNPs (ss715585199, ss715585205, ss715585223, ss715585228) to several CYP genes indicates a robust genetic basis for herbicide detoxification, with these enzymes facilitating the oxidative metabolism of herbicides to less toxic forms, thus contributing to cross-resistance against various herbicide classes. Additionally, the involvement of exocyst components, such as \u003cem\u003eGlyma03g03120\u003c/em\u003e, indicates that exocytosis plays a vital role in transporting detoxified products or signaling molecules, potentially allowing for vacuolar sequestration of herbicides, reducing their harmful translocation within the plant. Phytohormonal regulation, highlighted by the ethylene-responsive transcription factor \u003cem\u003eGlyma04g19650\u003c/em\u003e, further integrates this response by modulating stress pathways and influencing the plant's reaction to synthetic auxin herbicides. Together, these elements form a complex network that governs soybean's adaptive strategies to herbicide exposure, offering insights for enhancing herbicide resistance in future cultivars.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThis work was supported by the Southern Soybean Research Program and the Mid-South Soybean Board (Grant No. 55955), under the project titled\u003c/em\u003e\u003cem\u003e\u0026apos;Characterizing and Utilizing Metribuzin Tolerance in Soybeans to Improve Weed Management Strategies in Early-Planted Soybeans.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Declarations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe methods carried out in our manuscript were in accordance with the local, national, and international guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and Code Availability:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed and the code used for analysis during the current study are available in the Figshare repository, https://doi.org/10.6084/m9.figshare.29897075.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eL.Z. wrote the Results and Discussion section of the main manuscript. A.A. wrote the Introduction section, and A.A. and S.M. provided supplementary figures. Z.S. helped set up the experiment. C.V. and S.R. reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eUSDA. Production-Soybeans. (2024).\u003c/li\u003e\n\u003cli\u003eService, N.A.S. Crop Production 2024 Summary. (ed. Agriculture, U.S.D.o.) (2025).\u003c/li\u003e\n\u003cli\u003eMiller, L.R., Landau, C.A., Williams II, M.M. \u0026amp; Hager, A.G. Weed Management in Early Planted Soybean Early Planted Soybean Weed Management as Effected by Herbicide Application Rate and Timing. \u003cem\u003eWeed Technology\u003c/em\u003e, 1-20 (2025).\u003c/li\u003e\n\u003cli\u003eVan Acker, R.C., Swanton, C.J. \u0026amp; Weise, S.F. The critical period of weed control in soybean [Glycine max (L.) Merr.]. \u003cem\u003eWeed Science\u003c/em\u003e \u003cstrong\u003e41\u003c/strong\u003e, 194-200 (1993).\u003c/li\u003e\n\u003cli\u003eKumar, S. \u0026amp; Rana, S. 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Herbicide‐resistant crops and weed resistance to herbicides. \u003cem\u003ePest Management Science: formerly Pesticide Science\u003c/em\u003e \u003cstrong\u003e61\u003c/strong\u003e, 301-311 (2005).\u003c/li\u003e\n\u003cli\u003eYuan, J.S., Tranel, P.J. \u0026amp; Stewart, C.N., Jr. Non-target-site herbicide resistance: a family business. \u003cem\u003eTrends Plant Sci\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 6-13 (2007).\u003c/li\u003e\n\u003cli\u003eGe, X., d\u0026apos;Avignon, D.A., Ackerman, J.J. \u0026amp; Sammons, R.D. Rapid vacuolar sequestration: the horseweed glyphosate resistance mechanism. \u003cem\u003ePest Manag Sci\u003c/em\u003e \u003cstrong\u003e66\u003c/strong\u003e, 345-8 (2010).\u003c/li\u003e\n\u003cli\u003eKraft, M., Kuglitsch, R., Kwiatkowski, J., Frank, M. \u0026amp; Grossmann, K. Indole-3-acetic acid and auxin herbicides up-regulate 9-cis-epoxycarotenoid dioxygenase gene expression and abscisic acid accumulation in cleavers (Galium aparine): interaction with ethylene. \u003cem\u003eJ Exp Bot\u003c/em\u003e \u003cstrong\u003e58\u003c/strong\u003e, 1497-503 (2007).\u003c/li\u003e\n\u003cli\u003eZhanyou Xu, J.P., Gregory Lynn Doonan. Metribuzin tolerance alleles in soybean. (2018).\u003c/li\u003e\n\u003cli\u003eJesse Gilsinger, B.L. Molecular markers and phenotypic screening for metribuzin tolerance. (2020).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Summary of soybean tolerance response to post-emergence metribuzin treatment across two field trials.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"679\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.8791%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrail\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.6991%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDose (g ai ha\u003csup\u003e-1\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.8791%;\"\u003e\n \u003cp\u003eTrial 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.6991%;\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e4.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.8791%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.6991%;\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.8791%;\"\u003e\n \u003cp\u003eTrial 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.6991%;\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.8791%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.6991%;\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e4.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.8791%;\"\u003e\n \u003cp\u003eTrial 1 + Trial 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.6991%;\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.8791%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.6991%;\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e4.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eSNP significantly associated with metribuzin tolerance in soybeans identified by the GWAS analysis.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"699\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.5908%;\"\u003e\n \u003cp\u003eSNP[1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.72246%;\"\u003e\n \u003cp\u003eChr[2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.7296%;\"\u003e\n \u003cp\u003ePosition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5866%;\"\u003e\n \u003cp\u003eP.value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.29614%;\"\u003e\n \u003cp\u003eMAF[3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.01288%;\"\u003e\n \u003cp\u003eEffect[4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.1574%;\"\u003e\n \u003cp\u003ePVE[5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.86695%;\"\u003e\n \u003cp\u003eRate[6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.5937%;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.4435%;\"\u003e\n \u003cp\u003eData[7]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.5908%;\"\u003e\n \u003cp\u003ess715585023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.72246%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.7296%;\"\u003e\n \u003cp\u003e2918336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5866%;\"\u003e\n \u003cp\u003e5.94E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.29614%;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.01288%;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.1574%;\"\u003e\n \u003cp\u003e39.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.86695%;\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.5937%;\"\u003e\n \u003cp\u003eMLM, BLINK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.4435%;\"\u003e\n \u003cp\u003eTRAIL 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.5908%;\"\u003e\n \u003cp\u003ess715585023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.72246%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.7296%;\"\u003e\n \u003cp\u003e2918336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5866%;\"\u003e\n \u003cp\u003e2.19E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.29614%;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.01288%;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.1574%;\"\u003e\n \u003cp\u003e54.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.86695%;\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.5937%;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.4435%;\"\u003e\n \u003cp\u003eTRAIL 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.5908%;\"\u003e\n \u003cp\u003ess715629879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.72246%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.7296%;\"\u003e\n \u003cp\u003e21144784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5866%;\"\u003e\n \u003cp\u003e2.53E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.29614%;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.01288%;\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.1574%;\"\u003e\n \u003cp\u003e23.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.86695%;\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.5937%;\"\u003e\n \u003cp\u003eBLINK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.4435%;\"\u003e\n \u003cp\u003eTRAIL 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.5908%;\"\u003e\n \u003cp\u003ess715585205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.72246%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.7296%;\"\u003e\n \u003cp\u003e3334303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5866%;\"\u003e\n \u003cp\u003e9.13E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.29614%;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.01288%;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.1574%;\"\u003e\n \u003cp\u003e21.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.86695%;\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.5937%;\"\u003e\n \u003cp\u003eBLINK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.4435%;\"\u003e\n \u003cp\u003eTRAIL 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.5908%;\"\u003e\n \u003cp\u003ess715624298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.72246%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.7296%;\"\u003e\n \u003cp\u003e30228382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5866%;\"\u003e\n \u003cp\u003e2.43E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.29614%;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.01288%;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.1574%;\"\u003e\n \u003cp\u003e34.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.86695%;\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.5937%;\"\u003e\n \u003cp\u003eBLINK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.4435%;\"\u003e\n \u003cp\u003eFULL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.5908%;\"\u003e\n \u003cp\u003ess715585223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.72246%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.7296%;\"\u003e\n \u003cp\u003e3373279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5866%;\"\u003e\n \u003cp\u003e8.00E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.29614%;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.01288%;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.1574%;\"\u003e\n \u003cp\u003e16.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.86695%;\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.5937%;\"\u003e\n \u003cp\u003eBLINK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.4435%;\"\u003e\n \u003cp\u003eFULL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.5908%;\"\u003e\n \u003cp\u003ess715587370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.72246%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.7296%;\"\u003e\n \u003cp\u003e21252334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5866%;\"\u003e\n \u003cp\u003e6.24E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.29614%;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.01288%;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.1574%;\"\u003e\n \u003cp\u003e12.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.86695%;\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.5937%;\"\u003e\n \u003cp\u003eBLINK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.4435%;\"\u003e\n \u003cp\u003eFULL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.5908%;\"\u003e\n \u003cp\u003ess715585023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.72246%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.7296%;\"\u003e\n \u003cp\u003e2918336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5866%;\"\u003e\n \u003cp\u003e1.02E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.29614%;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.01288%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.1574%;\"\u003e\n \u003cp\u003e27.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.86695%;\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.5937%;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.4435%;\"\u003e\n \u003cp\u003eFULL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.5908%;\"\u003e\n \u003cp\u003ess715585199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.72246%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.7296%;\"\u003e\n \u003cp\u003e3323487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5866%;\"\u003e\n \u003cp\u003e1.05E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.29614%;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.01288%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.1574%;\"\u003e\n \u003cp\u003e4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.86695%;\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.5937%;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.4435%;\"\u003e\n \u003cp\u003eFULL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.5908%;\"\u003e\n \u003cp\u003ess715585205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.72246%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.7296%;\"\u003e\n \u003cp\u003e3334303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5866%;\"\u003e\n \u003cp\u003e1.68E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.29614%;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.01288%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.1574%;\"\u003e\n \u003cp\u003e12.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.86695%;\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.5937%;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.4435%;\"\u003e\n \u003cp\u003eFULL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.5908%;\"\u003e\n \u003cp\u003ess715585228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.72246%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.7296%;\"\u003e\n \u003cp\u003e3384900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5866%;\"\u003e\n \u003cp\u003e4.74E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.29614%;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.01288%;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.1574%;\"\u003e\n \u003cp\u003e28.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.86695%;\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.5937%;\"\u003e\n \u003cp\u003eBLINK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.4435%;\"\u003e\n \u003cp\u003eFULL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.5908%;\"\u003e\n \u003cp\u003ess715632132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.72246%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.7296%;\"\u003e\n \u003cp\u003e59352206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5866%;\"\u003e\n \u003cp\u003e8.65E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.29614%;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.01288%;\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.1574%;\"\u003e\n \u003cp\u003e10.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.86695%;\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.5937%;\"\u003e\n \u003cp\u003eBLINK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.4435%;\"\u003e\n \u003cp\u003eFULL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e[1] Single nucleotide polymorphism\u003c/p\u003e\n\u003cp\u003e[2] Chromosome\u003c/p\u003e\n\u003cp\u003e[3] Minor Allele Frequency\u003c/p\u003e\n\u003cp\u003e[4] Effect size of SNP in the GWAS model\u003c/p\u003e\n\u003cp\u003e[5] Percent variation explained by SNP\u003c/p\u003e\n\u003cp\u003e[6] Rate of metribuzin applied in g ai ha\u003csup\u003e-1\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e[7] The data set used to find the marker trait association, \u0026ldquo;FULL\u0026rdquo; is the data from both trials pooled\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Candidate genes identified through the GWAS analysis.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"618\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003eSNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eChr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 265px;\"\u003e\n \u003cp\u003eposition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ecandidate gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003egene name\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003ess715585199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 34px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 265px;\"\u003e\n \u003cp\u003e3323487-3384900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eGlyma03g03550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eCYP450\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003ess715585205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eGlyma03g03560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eCYP450\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003ess715585223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eGlyma03g03580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eruBisCO-associated protein-like\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003ess715585228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eGlyma03g03590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eCYP450\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 124px;\"\u003e\n \u003cp\u003ess715585184-ss715585288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 34px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 265px;\"\u003e\n \u003cp\u003eglyma.Wm82.gnm1.Gm03:3,287,723:3499047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eGlyma03g03630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eCYP450\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eGlyma03g03640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eCYP450\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eGlyma03g03670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eCYP450\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eGlyma03g03700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eCYP450\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eGlyma03g03721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eCYP450\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003ess715585023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 265px;\"\u003e\n \u003cp\u003e2918336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eGlyma03g03120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eExocyst complex component Sec6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003ess715629879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 265px;\"\u003e\n \u003cp\u003e21144784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eGlyma18g19370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eexocyst complex component sec10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003ess715587370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 265px;\"\u003e\n \u003cp\u003e21252334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eGlyma04g19650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eethylene-responsive transcription factor 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003ess715632132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 265px;\"\u003e\n \u003cp\u003e59352206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eGlyma18g50160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eexocyst subunit exo70 family protein E1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Cytochrome P450 superfamily protein\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-7303889/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7303889/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSoybean (\u003cem\u003eGlycine max\u003c/em\u003e) is one of the most important crops in the world. However, due to multiple herbicide resistance in weeds, few effective postemergence herbicide options remain to control weeds in soybean production systems. Metribuzin is currently one of the most promising herbicides for managing weeds in soybeans due to the low frequency of developing resistant populations. Currently, metribuzin is applied as a pre-emergent herbicide because post-emergence applications are thought to excessively damage most soybean varieties. In this study, a panel of 196 genetically diverse accessions in maturity groups 4, 5, and 6 was used to identify genomic regions associated with soybean response to metribuzin as well as metribuzin-tolerant accessions. Two different concentrations (150 g ai ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 300 g ai ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) of metribuzin were sprayed across two experimental replications, each with three replicates. A total of nine SNP markers were identified as significantly associated with soybean response to metribuzin. These SNPs are linked to Cytochrome P450 superfamily proteins (CYP), exocyst complex components, and ethylene-responsive transcription factor 3. These candidate genes indicate metabolism is likely the dominant mechanism of tolerance with the management of oxidative stress as a secondary mechanism. Together, these findings reveal novel candidate genes and pathways associated with non-target site resistance and provide valuable markers for breeding soybean cultivars with improved post-emergence metribuzin tolerance.\u003c/p\u003e","manuscriptTitle":"Genome-wide association study reveals cytochrome P450 associated with post- emergence metribuzin tolerance in soybean (Glycine max)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-14 09:46:08","doi":"10.21203/rs.3.rs-7303889/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":"402a69d9-0ced-489c-a6d7-a6514a8b9699","owner":[],"postedDate":"September 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54601851,"name":"Biological sciences/Biotechnology"},{"id":54601852,"name":"Biological sciences/Genetics"},{"id":54601853,"name":"Biological sciences/Molecular biology"},{"id":54601854,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2025-09-18T11:53:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-14 09:46:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7303889","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7303889","identity":"rs-7303889","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}